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PubMed2001

Independent Sampling vs Interitem Dependencies in Whole Report Processing: Contributions of Processing Architecture and Variable Attention.

All current models of visual whole report processing assume perceptual independence among the displayed items in which the perceptual processing of individual items is not affected by other items in the display. However, models proposed by Townsend (1981, Acta Psychologica 47, 149-173), Shibuya and Bundesen (1988, Journal of Experimental Psychology: Human Perception and Performance 14, 591-600), and Bundesen (1990, Psychological Review 97, 523-547) contain postperceptual buffers that must predict negative dependencies. The perceptual-independence assumption forms what we term the modal model class. A recent example of a model that assumes perceptual independence is the Independent Sampling Model of Loftus, Busey, and Senders (1993, Perception and Psychophysics 54, 535-554). The fundamental independence assumption has only been directly tested once before, where tests revealed no dependencies except those produced by guessing. The present study tests the independence assumption using several different statistics and, contrary to most extant models of whole report, finds significant positive dependence. Poisson models do predict a positive dependence and we develop a succinctly parameterized version, the Weighted Path Poisson Model, which allows the finishing order to be a weighted probabilistic mechanism. However, it does not predict the data quite as well as a new model, the Variable Attention Model, which allows independence within trials (unlike the Poisson models). This model assumes that attention (or, potentially, other aspects such as signal quality) varies widely across trials, thus predicting an overall positive dependence. Intuitions for and against the competing models are discussed. In addition, we show, through mimicking formulae, that models which contain the proper qualitative type of dependence structure can be cast in either serial or parallel form. Copyright 2001 Academic Press.

Journal Article↗
PubMed2006

Cost-effectiveness of letrozole in the extended adjuvant treatment of women with early breast cancer.

Adjuvant tamoxifen therapy for 5 years reduces recurrence in hormone receptor positive, post-menopausal women with early breast cancer, but offers no advantage when prolonged to another 5 years, during which the risk of recurrence remains high. Treating patients, who remain disease-free after 5 years of tamoxifen, with letrozole significantly reduces recurrence, regardless of nodal status. This study evaluated the life-time cost-utility of extended adjuvant letrozole therapy in 62-year-old patients from a third-party payer perspective. A Markov model incorporated locoregional, contralateral, and metastatic recurrences. The comparator was placebo. Event rates were based on published trials. Utility values were taken from a clinical trial and published literature. Costs were obtained from published literature, provincial payment schedules, cancer agencies, and drug plans formularies. Resource use reflected Canadian treatment patterns. Robustness of the model was tested using deterministic and probabilistic sensitivity analyses. Extended adjuvant letrozole therapy of a cohort consisting of 50% node-negative and 50% node-positive patients prolonged their lives on average by 0.466 years or 0.267 quality-adjusted life years (QALYs) at an additional cost of Can$8,031 per patient, yielding an incremental cost-utility ratio (ICUR) of $34,058 per QALY. Letrozole was more cost-effective in node-positive than in node-negative patients (Can$26,553 vs Can$46,049 per QALY). Results were robust to variations in age, healthcare costs, and utilities. The degree of confidence that the cost per QALY would be below Can$50,000 reached 100% for node-positive and 77% for node-negative patients. Extended adjuvant letrozole is cost-effective in both node-negative and node-positive patients having ICURs below Can$50,000/QALY.

Antineoplastic Agents↗
PubMed2006

The growth and form of tunnelling networks in ants.

Many biological networks grow under strong spatial constraints, where the large-scale structure emerges from the extension, the branching and intersection of growing parts of the network. One example is provided by ant tunnelling networks, which represent the most common nest architecture in ants. Our goal was to understand how these network structures emerge from the tunnel growth dynamics. We used a standardized two-dimensional set-up shaped as a disk and studied the characteristics of tunnel growth in terms of initiation, propagation and termination of new digging sites and found that they can be described with simple probabilistic laws. We show that a model based on these simple laws and for which parameters were measured from the sand disks experiments can account for the emergence of several topological properties that were observed in experimental networks. In particular, the model accurately reproduced an allometric relation between the number of edges and the number of nodes, as well as an invariance of the node degree distribution. The model was then used to make predictions about the resulting networks' topology when the geometry of the sand substrate was shaped as a square. Experiments aimed at testing the model's predictions showed that the predictions were indeed validated. Both in the model and in the experiments, there was a similar trend for the node degree distribution tail to be steeper in the square sand patch than in the disk sand patch, while other characteristics such as the meshedness (i.e. how densely the network is internally connected) remained constant. Because network growth based on branching/fusion events is widespread in biological systems, this general model might provide useful insights for the study of other systems and, more generally, the evolution of spatial networks in biological systems.

Animals↗
PubMed2004

Universal Grammar, statistics or both?

Recent demonstrations of statistical learning in infants have reinvigorated the innateness versus learning debate in language acquisition. This article addresses these issues from both computational and developmental perspectives. First, I argue that statistical learning using transitional probabilities cannot reliably segment words when scaled to a realistic setting (e.g. child-directed English). To be successful, it must be constrained by knowledge of phonological structure. Then, turning to the bona fide theory of innateness--the Principles and Parameters framework--I argue that a full explanation of children's grammar development must abandon the domain-specific learning model of triggering, in favor of probabilistic learning mechanisms that might be domain-general but nevertheless operate in the domain-specific space of syntactic parameters.

Child↗
PubMed2003

Integrated analysis of transcript profiling and protein sequence data.

Transcript profiling can be used to elucidate the molecular and cellular mechanisms involved in ageing and cancer. A recent study of human gastrointestinal stromal tumours (GISTs) with mutations in the KIT gene, Cancer Res. 61 (2001) 8624 exemplifies a common type of investigation. cDNA microarrays were used to generate measurements for 1987 clones in two types of tissues: 13 KIT mutation-positive GISTs and 6 spindle cell tumours from locations outside the gastrointestinal tract. Statistical problems associated with such two-class, high-dimensional profiling data include simultaneous classification and relevant feature identification, probabilistic clustering and protein sequence family modelling. Here, the GIST data were reexamined using specific solutions to these problems, namely sparse hyperplanes, nai;ve Bayes models and profile hidden Markov models respectively. The integrated analysis of molecular profiling and sequence data highlighted 6 clones that may be of clinical and experimental interest. The protein encoded by one of these putative biomarkers defined a novel protein family present in diverse eucarya. The family may be involved in chromosome segregation and/or stability. One family member is a potential biomarker identified recently from a retrospective analysis of transcript profiles for sporadic breast cancer samples from patients with poor and good prognosis, Signal Process. (in press).

Amino Acid Sequence↗
PubMed2002

Quantifying the risk of extreme seasonal precipitation events in a changing climate.

Increasing concentrations of atmospheric carbon dioxide will almost certainly lead to changes in global mean climate. But because--by definition--extreme events are rare, it is significantly more difficult to quantify the risk of extremes. Ensemble-based probabilistic predictions, as used in short- and medium-term forecasts of weather and climate, are more useful than deterministic forecasts using a 'best guess' scenario to address this sort of problem. Here we present a probabilistic analysis of 19 global climate model simulations with a generic binary decision model. We estimate that the probability of total boreal winter precipitation exceeding two standard deviations above normal will increase by a factor of five over parts of the UK over the next 100 years. We find similar increases in probability for the Asian monsoon region in boreal summer, with implications for flooding in Bangladesh. Further practical applications of our techniques would be helped by the use of larger ensembles (for a more complete sampling of model uncertainty) and a wider range of scenarios at a resolution adequate to analyse average-size river basins.

Journal Article↗
PubMed2006

ProfilePSTMM: capturing tree-structure motifs in carbohydrate sugar chains.

MOTIVATION: Carbohydrate sugar chains, or glycans, are considered the third major class of biomolecules after DNA and proteins. They consist of branching monosaccharides, starting from a single monosaccharide. They are extremely vital to the development and functioning of multicellular organisms because they are recognized by various proteins to allow them to perform specific functions. Our motivation is to study this recognition mechanism using informatics techniques from the data available. Previously, we introduced a probabilistic sibling-dependent tree Markov model (PSTMM), which we showed could be efficiently trained on sibling-dependent tree structures and return the most likely state paths. However, it had some limitations in that the extra dependency between siblings caused overfitting problems. The retrieval of the patterns from the trained model also involved manually extracting the patterns from the most likely state paths. Thus we introduce a profilePSTMM model which avoids these problems, incorporating a novel concept of different types of state transitions to handle parent-child and sibling dependencies differently. RESULTS: Our new algorithms are more efficient and able to extract the patterns more easily. We tested the profilePSTMM model on both synthetic (controlled) data as well as glycan data from the KEGG GLYCAN database. Additionally, we tested it on glycans which are known to be recognized and bound to proteins at various binding affinities, and we show that our results correlate with results published in the literature.

Algorithms↗
PubMed2006

Qualitative assessment of human exposure to consumption of injection site residues.

The recent adoption of the guideline on injection site residues in food producing animals by the Committee for Medicinal Products for Veterinary Use (CVMP) raised the question on the risk specifically related to injection site consumption. As noted in this guideline, residues at injection sites commonly deplete erratically. For the calculation of withdrawal time, treating residues at the injection site like those that occur in other tissues is questionable. An assessment of the risk related to the consumption of a whole or part of an injection site during a year was performed using a probabilistic approach. The risk assessment was modelled on the regimen of a 7-day antibiotic treatment, and was calculated on the scale of the European Union (EU) as a whole or of each European country. Our analysis indicated a maximal risk of 4 days of injection site consumption during a year in the EU. It also indicated that 37% of consumers would never eat an injection site in the same period. These results suggest that the risk of injection site consumption belongs more appropriately to an acute exposure risk than to a chronic one which is usually posed by the residues present in other edible tissues.

Animals↗
PubMed2005

Compressed natural gas bus safety: a quantitative risk assessment.

This study assesses the fire safety risks associated with compressed natural gas (CNG) vehicle systems, comprising primarily a typical school bus and supporting fuel infrastructure. The study determines the sensitivity of the results to variations in component failure rates and consequences of fire events. The components and subsystems that contribute most to fire safety risk are determined. Finally, the results are compared to fire risks of the present generation of diesel-fueled school buses. Direct computation of the safety risks associated with diesel-powered vehicles is possible because these are mature technologies for which historical performance data are available. Because of limited experience, fatal accident data for CNG bus fleets are minimal. Therefore, this study uses the probabilistic risk assessment (PRA) approach to model and predict fire safety risk of CNG buses. Generic failure data, engineering judgments, and assumptions are used in this study. This study predicts the mean fire fatality risk for typical CNG buses as approximately 0.23 fatalities per 100-million miles for all people involved, including bus passengers. The study estimates mean values of 0.16 fatalities per 100-million miles for bus passengers only. Based on historical data, diesel school bus mean fire fatality risk is 0.091 and 0.0007 per 100-million miles for all people and bus passengers, respectively. One can therefore conclude that CNG buses are more prone to fire fatality risk by 2.5 times that of diesel buses, with the bus passengers being more at risk by over two orders of magnitude. The study estimates a mean fire risk frequency of 2.2 x 10(-5) fatalities/bus per year. The 5% and 95% uncertainty bounds are 9.1 x 10(-6) and 4.0 x 10(-5), respectively. The risk result was found to be affected most by failure rates of pressure relief valves, CNG cylinders, and fuel piping.

Journal Article↗
PubMed2000

From dangerousness to risk assessment of community violence: taking stock at the turn of the century.

This article focuses on a history of ideas-the history of the last quarter century, in which great advances have occurred in the reshaping of the concept of dangerousness while, at the same time, disappointingly little has happened to improve the abilities of frontline clinical decision-makers to make violence risk assessments. We are now operating in a fundamentally changed environment in the three core areas of research, clinical practice, and law as a result of evolving thinking and new data, shown in the following ways. (1) Risk assessment concepts and associated research models have altered the focus to probabilistic thinking and ideas of graduated interventions as opposed to the Yes-No, In-Out dichotomies formerly associated with the concept of dangerousness. (2) Likewise, it has become clear that dangerousness is a legal and not a medical concept, in which the actual clinical corollary is risk of future violent behavior. (3) The accuracy of psychiatric assessments of future violent behavior is limited, but it may increase for specific subgroups within specified time frames and locations.

Commitment of Persons with Psychiatric Disorders↗
PubMed1998

Accidents and Decision Making under Uncertainty: A Comparison of Four Models.

Heinrich's (1931) classical study implies that most industrial accidents can be characterized as a probabilistic result of human error. The present research quantifies Heinrich's observation and compares four descriptive models of decision making in the abstracted setting. The suggested quantification utilizes signal detection theory (Green & Swets, 1966). It shows that Heinrich's observation can be described as a probabilistic signal detection task. In a controlled experiment, 90 decision makers participated in 600 trials of six safety games. Each safety game was a numerical example of the probabilistic SDT abstraction of Heinrich's proposition. Three games were designed under a frame of gain to represent perception of safe choice as costless, while the other three were designed under a frame of loss to represent perception of safe choice as costly. Probabilistic penalty for Miss was given at three different levels (1, .5, .1). The results showed that decisions tended initially to be risky and that experience led to safer behavior. As the probability of being penalized was lowered decisions became riskier and the learning process was impaired. The results support the cutoff reinforcement learning model suggested by Erev et al. (1995). The hill-climbing learning model (Busemeyer & Myung, 1992) was partially supported. Theoretical and practical implications are discussed. Copyright 1998 Academic Press.

Journal Article↗
PubMed1992

On the use of age-intensity data to detect immunity to parasitic infections, with special reference to Schistosoma mansoni in Kenya.

We consider two phenomena, related to the host age-intensity profiles of parasitic infections, which have been suggested to be indicative of acquired immunity: (i) a lower age of peak intensity among more intensely infected hosts; and (ii) a decline with age in the dispersion of the distribution of parasites between hosts. We demonstrate that these phenomena occur among Kenyan schoolchildren infected with Schistosoma mansoni, although the magnitude of both is small. We also examine the mathematical models underlying these predictions and conclude that both phenomena are possible in the absence of acquired immunity or, indeed, in the absence of any density-dependent effect. In our opinion, insufficient attention has been focused upon mathematical models, describing the null hypothesis, i.e. density-independent models. In particular, we regard the usual assumptions made for the two stochastic components of these models, describing the heterogeneity between hosts and the probabilistic nature of infection and death of parasites, as too rigid and unrealistic. We demonstrate that deviation from these assumptions undermines the qualitative distinctions between models which describe acquired immunity or density dependence and those which are density-independent.

Adolescent↗
PubMed1998

Pretherapy dental decisions in patients with head and neck cancer. A proposed model for dental decision support.

OBJECTIVE: The proposed model was designed to function as a tool for the development and testing of evidence-based clinical guidelines for the pretherapy oral screening and dental management of patients with head and neck cancer. STUDY DESIGN: Methods of clinical decision analysis were used to analyze the decision dilemma and construct a decision algorithm and decision tree. The robustness of the model was tested by means of a probabilistic sensitivity analysis with second-order Monte Carlo simulations (n = 10.000). RESULTS: Clinical criteria for evaluating dental pathologic conditions and malignancy- and patient-related conditions were transformed in probability estimates. The tradeoffs between the benefits and drawbacks of the dental intervention were integrated into the model to identify the optimal option for dental intervention. The calculation process of "folding back and averaging out" the decision tree enabled the identification of the optimal options for dental intervention in four different pretherapy risk conditions. CONCLUSIONS: A priori testing of the proposed model with 95% confidence intervals suggests that it has a great potential for solving clinical dilemmas associated with pretherapy dental decision-making. In addition, it seems a useful tool for the development of evidence-based clinical guidelines. A posteriori clinical testing should further validate the model before its assimilation into clinical practice takes place.

Algorithms↗
PubMed2005

Multitensor approach for analysis and tracking of complex fiber configurations.

A multidiffusion-tensor model (MDT) is presented containing two anisotropic and one isotropic diffusion tensors. This approach has the ability to detect areas of fiber crossings and resolve the direction of crossing fibers. The mean diffusivity and the ratio of the tensor compartments were merged to one independent parameter by fitting MDT to the diffusion-weighted intensities of a two-point data acquisition scheme. By an F-test between the errors of the standard single diffusion tensor and the more complex MDT, fiber crossings were detected and the more accurate model was chosen voxel by voxel. The performance of crossing detection was compared with the spherical harmonics approach in simulations as well as in vivo. Similar results were found in both methods. The MDT model, however, did not only detect crossings but also yielded the single fiber directions. The FACT algorithm and a probabilistic connectivity algorithm were extended to support the MDT model. For example, a mean angular error smaller than 10 degrees was found for the MDT model in a simulated fiber crossing with an SNR of 80. By tracking the corticospinal tract the MDT-based tracks reached a significantly greater area of the gyrus precentralis.

Algorithms↗
PubMed2002

Unsupervised continuous sleep analysis.

One aim of the EU-funded project SIESTA was to develop a new way of describing the human sleep-wake continuum with high temporal resolution, and independent of subjective rules, to serve as an alternative to traditional sleep scoring. Here, we report new findings obtained with a fully automatic, probabilistic sleep-analyzer using Hidden Markov Models (HMMs) based on data from a single electroencephalogram (EEG) channel. HMMs allow the analysis of non-stationary time series by modeling both the probability density functions of locally stationary data and the transition probabilities between these stable states. In the context of sleep analysis, the locally stable states can be thought of as sleep stages. The sleep-wake continuum was modeled as a mixture of three different processes by defining a three-state Gaussian Observation HMM (GOHMM). No class information from human scorers was used to train the model. The probabilities of being in any of the three states at each point in time roughly indicate the amount of wakefulness, deep sleep and rapid-eye-movement (REM) sleep with a one-second time resolution. Although it was not the aim to replicate the traditional Rechtschaffen and Kales (R&K) scoring, pseudo R&K hypnograms were constructed from the probability plots in order to compare the analyzer results with classical sleep stages by human experts. We expected that the analyzer would be able to classify data correctly from the "cornerstones" of human sleep (wakefulness, deep sleep, and REM sleep). Contrary to our previous efforts, we trained the HMMs on data from two different sleep laboratories separately, instead of generalizing data from diverse laboratories. While these stages could be detected with an accuracy of around 80% at the sleep laboratory for which we already had achieved the best results, there was no improvement from previous results by the training of a separate model in the other laboratory. This finding indicates clear laboratory effects in the signal characteristics, probably due to differences in hardware and filter settings. The presented approach, going beyond a mere replication of the traditional R&K standard, offers a continuous description of human sleep which is based on probabilistic principles. It provides a second-by-second quantification of the sleep-wake continuum and captures, although being entirely data-driven instead of rule-based, the three main processes in human sleep: wakefulness, deep sleep and REM sleep.

Adult↗
PubMed2000

Optimal amnesic probabilistic automata or how to learn and classify proteins in linear time and space.

Statistical modeling of sequences is a central paradigm of machine learning that finds multiple uses in computational molecular biology and many other domains. The probabilistic automata typically built in these contexts are subtended by uniform, fixed-memory Markov models. In practice, such automata tend to be unnecessarily bulky and computationally imposing both during their synthesis and use. Recently, D. Ron, Y. Singer, and N. Tishby built much more compact, tree-shaped variants of probabilistic automata under the assumption of an underlying Markov process of variable memory length. These variants, called Probabilistic Suffix Trees (PSTs) were subsequently adapted by G. Bejerano and G. Yona and applied successfully to learning and prediction of protein families. The process of learning the automaton from a given training set S of sequences requires theta(Ln2) worst-case time, where n is the total length of the sequences in S and L is the length of a longest substring of S to be considered for a candidate state in the automaton. Once the automaton is built, predicting the likelihood of a query sequence of m characters may cost time theta(m2) in the worst case. The main contribution of this paper is to introduce automata equivalent to PSTs but having the following properties: Learning the automaton, for any L, takes O (n) time. Prediction of a string of m symbols by the automaton takes O (m) time. Along the way, the paper presents an evolving learning scheme and addresses notions of empirical probability and related efficient computation, which is a by-product possibly of more general interest.

Algorithms↗
PubMed2006

MUMMALS: multiple sequence alignment improved by using hidden Markov models with local structural information.

We have developed MUMMALS, a program to construct multiple protein sequence alignment using probabilistic consistency. MUMMALS improves alignment quality by using pairwise alignment hidden Markov models (HMMs) with multiple match states that describe local structural information without exploiting explicit structure predictions. Parameters for such models have been estimated from a large library of structure-based alignments. We show that (i) on remote homologs, MUMMALS achieves statistically best accuracy among several leading aligners, such as ProbCons, MAFFT and MUSCLE, albeit the average improvement is small, in the order of several percent; (ii) a large collection (>10 000) of automatically computed pairwise structure alignments of divergent protein domains is superior to smaller but carefully curated datasets for estimation of alignment parameters and performance tests; (iii) reference-independent evaluation of alignment quality using sequence alignment-dependent structure superpositions correlates well with reference-dependent evaluation that compares sequence-based alignments to structure-based reference alignments.

Markov Chains↗
PubMed2004

Bayesian representation learning in the cortex regulated by acetylcholine.

A brain needs to detect an environmental change and to quickly learn internal representations necessary in a new environment. This paper presents a theoretical model of cortical representation learning that can adapt to dynamic environments, incorporating the results by previous studies on the functional role of acetylcholine (ACh). We adopt the probabilistic principal component analysis (PPCA) as a functional model of cortical representation learning, and present an on-line learning method for PPCA according to Bayesian inference, including a heuristic criterion for model selection. Our approach is examined in two types of simulations with synthesized and realistic data sets, in which our model is able to re-learn new representation bases after the environment changes. Our model implies the possibility that a higher-level recognition regulates the cortical ACh release in the lower-level, and that the ACh level alters the learning dynamics of a local circuit in order to continuously acquire appropriate representations in a dynamic environment.

Acetylcholine↗