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PubMed2002

A spiking neuron model: applications and learning.

This paper presents a biologically inspired, hardware-realisable spiking neuron model, which we call the Temporal Noisy-Leaky Integrator (TNLI). The dynamic applications of the model as well as its applications in Computational Neuroscience are demonstrated and a learning algorithm based on postsynaptic delays is proposed. The TNLI incorporates temporal dynamics at the neuron level by modelling both the temporal summation of dendritic postsynaptic currents which have controlled delay and duration and the decay of the somatic potential due to its membrane leak. Moreover, the TNLI models the stochastic neurotransmitter release by real neuron synapses (with probabilistic RAMs at each input) and the firing times including the refractory period and action potential repolarisation. The temporal features of the TNLI make it suitable for use in dynamic time-dependent tasks like its application as a motion and velocity detector system presented in this paper. This is done by modelling the experimental velocity selectivity curve of the motion sensitive H1 neuron of the visual system of the fly. This application of the TNLI indicates its potential applications in artificial vision systems for robots. It is also demonstrated that Hebbian-based learning can be applied in the TNLI for postsynaptic delay training based on coincidence detection, in such a way that an arbitrary temporal pattern can be detected and recognised. The paper also demonstrates that the TNLI can be used to control the firing variability through inhibition; with 80% inhibition to concurrent excitation, firing at high rates is nearly consistent with a Poisson-type firing variability observed in cortical neurons. It is also shown with the TNLI, that the gain of the neuron (slope of its transfer function) can be controlled by the balance between inhibition and excitation, the gain being a decreasing function of the proportion of inhibitory inputs. Finally, in the case of perfect balance between inhibition and excitation, i.e. where the average input current is zero, the neuron can still fire as a result of membrane potential fluctuations. The firing rate is then determined by the average input firing rate. Overall this work illustrates how a hardware-realisable neuron model can capitalise on the unique computational capabilities of biological neurons.

Analog-Digital Conversion↗
PubMed2004

Incorporating genotyping uncertainty in haplotype inference for single-nucleotide polymorphisms.

The accuracy of the vast amount of genotypic information generated by high-throughput genotyping technologies is crucial in haplotype analyses and linkage-disequilibrium mapping for complex diseases. To date, most automated programs lack quality measures for the allele calls; therefore, human interventions, which are both labor intensive and error prone, have to be performed. Here, we propose a novel genotype clustering algorithm, GeneScore, based on a bivariate t-mixture model, which assigns a set of probabilities for each data point belonging to the candidate genotype clusters. Furthermore, we describe an expectation-maximization (EM) algorithm for haplotype phasing, GenoSpectrum (GS)-EM, which can use probabilistic multilocus genotype matrices (called "GenoSpectrum") as inputs. Combining these two model-based algorithms, we can perform haplotype inference directly on raw readouts from a genotyping machine, such as the TaqMan assay. By using both simulated and real data sets, we demonstrate the advantages of our probabilistic approach over the current genotype scoring methods, in terms of both the accuracy of haplotype inference and the statistical power of haplotype-based association analyses.

Algorithms↗
PubMed2006

Modeling unidimensional categorization in monkeys.

The categorization performance of monkeys on a unidimensional perceptual categorization task was examined with reference to decision bound and exemplar theories of categorization. Three rhesus monkeys were presented with stimuli varying along a single dimension, the displacement of a target light from a fixation point. Left or right saccade responses were probabilistically reinforced according to one of three functions, two of which were nonmonotonic at one end of the stimulus space. The monkeys all showed a monotonic increase in response probability as a function of target light displacement in this region, consistent with decision bound theory. Fits of a single-boundary model (GRT, Ashby & Gott, 1988) and two exemplar models--one using a probabilistic response function (GCM; Nosofsky, 1986), the other using a deterministic response function (DEM; Ashby & Maddox, 1993)--revealed overall support for the decision bound model. The results suggest that monkeys used a perceptual decision boundary to perform the task.

Animals↗
PubMed1996

[20 years of cardiovascular epidemiology. The epidemiologist's viewpoint].

The author reviews the evolution of cardiovascular epidemiology during the twenty last years. The mortality from cardiovascular diseases with atherosclerosis as their pathological basis grew rapidly in the industrialised countries after World War II. This led to epidemiological research which started in 1950 with respectively the studies of Framingham in the United States and the Seven Countries Study, essentially in Europe. The concept of multifactorial origin of coronary heart disease was proposed during the 60s. Three major coronary risk factors besides age and gender, hypercholesterolemia, hypertension and cigarette smoking emerged systematically in epidemiological studies before 1975. The last twenty years have confirmed the importance of these three modifiable coronary risk factors which are long-term predictors in different ethnic groups, both in males and females. Other risk factors appeared in this period. Some polymorphisms of several genes as well as their phenotypic expression have been found to be related to an increased risk of coronary heart disease. Uni- and multifactorial primary and secondary prevention trials have confirmed the observations of analytical epidemiology. Moreover, the crucial role of nutrition within a metabolic hypothesis has been confirmed by experimental research. The concept of the strategy of prevention in higher risk subjects as well as in the whole population was proposed by international bodies following the publications by the English epidemiologist G. Rose. Both France and Belgium contributed to the development of cardiovascular epidemiology during the last twenty years. France is doing cutting-edge research on genetic predictors of cardiovascular diseases. Although the multifactorial causal model of cardiovascular diseases is robust, it is still a probabilistic one; it predicts however relative risks of about 15 to 1 according to the decile of the multilogistic function distribution. Environmental factors essentially related to lifestyles explain a great part of these differences. At the individual level, genetic factors most probably modulate these environmental influences and one can foresee in the future a more predictive model. In most medical schools, teaching of cardiovascular epidemiology and prevention has a low priority with clinicians putting the accent on the risk factors rather than on the risk profile of the individual. Secondary prevention is more in the realm of clinical medicine due to a large publicity given to the results of the large randomised trials. Consequently, in hospital mortality of acute myocardial infarction is decreasing as well as long-term mortality. Finally, prevention at the population level is connected to political decisions in public health which could have a major impact on the economy at the country level. Consequently, these political decisions are very slow to be taken both at the national and European Union level.

Cardiovascular Diseases↗
PubMed2005

Toward an accurate statistics of gapped alignments.

Sequence alignment has been an invaluable tool for finding homologous sequences. The significance of the homology found is often quantified statistically by p-values. Theory for computing p-values exists for gapless alignments [Karlin, S., Altschul, S.F., 1990. Methods for assessing the statistical significance of molecular sequence features by using general scoring schemes. Proc. Natl. Acad. Sci. USA 87, 2264-2268; Karlin, S., Dembo A., 1992. Limit distributions of maximal segmental score among Markov-dependent partial sums. Adv. Appl. Probab. 24, 13-140], but a full generalization to alignments with gaps is not yet complete. We present a unified statistical analysis of two common sequence comparison algorithms: maximum-score (Smith-Waterman) alignments and their generalized probabilistic counterparts, including maximum-likelihood alignments and hidden Markov models. The most important statistical characteristic of these algorithms is the distribution function of the maximum score S(max), resp. the maximum free energy F(max), for mutually uncorrelated random sequences. This distribution is known empirically to be of the Gumbel form with an exponential tail P(S(max)>x) approximately exp(-lambdax) for maximum-score alignment and P(F(max)>x) approximately exp(-lambdax) for some classes of probabilistic alignment. We derive an exact expression for lambda for particular probabilistic alignments. This result is then used to obtain accurate lambda values for generic probabilistic and maximum-score alignments. Although the result demonstrated uses a simple match-mismatch scoring system, it is expected to be a good starting point for more general scoring functions.

Algorithms↗
PubMed2005

Spatial registration of multichannel multi-subject fNIRS data to MNI space without MRI.

The registration of functional brain data to the common brain space offers great advantages for inter-modal data integration and sharing. However, this is difficult to achieve in functional near-infrared spectroscopy (fNIRS) because fNIRS data are primary obtained from the head surface and lack structural information of the measured brain. Therefore, in our previous articles, we presented a method for probabilistic registration of fNIRS data to the standard Montreal Neurological Institute (MNI) template through international 10-20 system without using the subject's magnetic resonance image (MRI). In the current study, we demonstrate our method with a new statistical model to facilitate group studies and provide information on different components of variability. We adopt an analysis similar to the single-factor one-way classification analysis of variance based on random effects model to examine the variability involved in our improvised method of probabilistic registration of fNIRS data. We tested this method by registering head surface data of twelve subjects to seventeen reference MRI data sets and found that the standard deviation in probabilistic registration thus performed for given head surface points is approximately within the range of 4.7 to 7.0 mm. This means that, if the spatial registration error is within an acceptable tolerance limit, it is possible to perform multi-subject fNIRS analysis to make inference at the population level and to provide information on positional variability in the population, even when subjects' MRIs are not available. In essence, the current method enables the multi-subject fNIRS data to be presented in the MNI space with clear description of associated positional variability. Such data presentation on a common platform, will not only strengthen the validity of the population analysis of fNIRS studies, but will also facilitate both intra- and inter-modal data sharing among the neuroimaging community.

Adult↗
PubMed2001

Probabilistic exposure assessment of operator and residential non-dietary exposure.

This paper discusses the potential role of probabilistic non-dietary exposure and risk assessment in the regulatory processes for pesticide products. Regulatory decisions regarding operator or consumer exposure to pesticide products have traditionally been based on deterministic exposure assessments. With the development of computer modelling and exposure databases, the submission of non-dietary exposure probabilistic assessments are imminent. The current status of non-dietary probability analysis to support regulatory risk management decisions is provided with a case study presented to illustrate issues regarding the use of probability analysis for regulatory non-dietary risk assessment. The development of a guidance document for the use of non-dietary exposure probability analyses for regulatory purposes is proposed.

Environmental Exposure↗
PubMedDate not supplied

[Stochastic simulation of the instrumental reflex in probability learning].

A method of computer imitation modelling is worked out of the process of instrumental reflex elaboration allowing to prognosticate the rate of learning at various combinations of probabilistic medium parameters and individual properties of the learning subject. By means of imitating modelling it is easy to find out the localization of the optima and pessima zones in the space of parameters influencing the learning. For building the model the empiric data are not required--they are used only for checking the obtained results. Therefore the conformity obtained with the literature data and the results of own studies allows to suggest that firstly, the worked out model possesses a good forecasting power and secondly, it can be used for studying such conditions of learning for which the appropriate experimental material is not yet collected.

Animals↗
PubMedDate not supplied

Impact of model, methodological, and parameter uncertainty in the economic analysis of vaccination programs.

Guidelines for economic evaluations insist that the sensitivity of model results to alternative parameter values should be thoroughly explored. However, differences in model construction and analytical choices (such as the choice of a cost-effectiveness or cost-benefit framework) also introduce uncertainty in results, though these are rarely subjected to a thorough sensitivity analysis. In this article, the authors quantify the effect of model, methodological, and parameter uncertainty, taking varicella vaccination as an example. They used 3 different models (a static model, a dynamic model that only looks at the effect of vaccination on varicella, and a dynamic model that also assesses the implications of vaccination for zoster epidemiology) and 2 forms of analysis (cost-benefit and cost-utility). They also varied the discount rate and time frame of analysis. Probabilistic sensitivity analyses were performed to estimate the impact of parameter uncertainty. In their example, model and methodological choice had a profound effect on estimated cost-effectiveness, but parameter uncertainty played a relatively minor role. Under cost-utility analysis, the probabilistic sensitivity analysis suggested that there was a near certainty that vaccination dominates no vaccination, or the other way around, depending on model choice and perspective. Under cost-benefit analysis, vaccination always appeared to be attractive. Thus, the authors clearly show that model and methodological assumptions can have greater impact on results than parameter estimates, although sensitivity analyses are rarely performed on these sources of uncertainty.

Adolescent↗
PubMed2005

Genetic algorithm-based optimization of hydrophobicity tables.

SUMMARY: The genomic abundance and pharmacological importance of membrane proteins have fueled efforts to identify them based solely on sequence information. Previous methods based on the physicochemical principle of a sliding window of hydrophobicity (hydropathy analysis) have been replaced by approaches based on hidden Markov models or neural networks which prevail due to their probabilistic orientation. In the current study, an optimization of the hydrophobicity tables used in hydropathy analysis is performed using a genetic algorithm. As such, the approach can be viewed as a synthesis between the physicochemically and statistically based methods. The resulting hydrophobicity tables lead to significant improvement in the prediction accuracy of hydropathy analysis. Furthermore, since hydropathy analysis is less dependent on the basis set of membrane proteins is used to hone the statistically based methods, as well as being faster, it may be valuable in the analysis of new genomes. Finally, the values obtained for each of the amino acids in the new hydrophobicity tables are discussed.

Algorithms↗
PubMed1999

Probabilistic sensitivity analysis in cost-effectiveness. An application from a study of vaccination against pneumococcal bacteremia in the elderly.

OBJECTIVES: We explore the policy implications of probabilistic sensitivity analysis in cost-effectiveness analysis by applying simulation methods to a decision model. METHODS: We present the multiway sensitivity analysis results of a study of the cost-effectiveness of vaccination against pneumococcal bacteremia in the elderly. We then execute a probabilistic sensitivity analysis of the cost-effectiveness ratio by specifying posterior distributions for the uncertain parameters in our decision analysis model. In order to estimate probability intervals, we rank the numerical values of the simulated incremental cost-effectiveness ratios (ICERs) to take into account preferences along the cost-effectiveness plane. RESULTS: The 95% probability intervals for the ICER were generally much narrower than the difference between the best case and worst case results from a multiway sensitivity analysis. Although the multiway sensitivity analysis had indicated that, in the worst case, vaccination in the 85 and older age group was not acceptable from a policy standpoint, probabilistic methods indicated that the cost-effectiveness of vaccination was below $50,000 per quality-adjusted life-year in greater than 92% of the simulations and below $100,000 in greater than 95% of the simulations. CONCLUSIONS: Probabilistic methods can supplement multiway sensitivity analyses to provide a more comprehensive picture of the uncertainty associated with cost-effectiveness ratios and thereby inform policy decisions.

Aged↗
PubMed1984

Proposals for a prevention policy: an analysis of the French report.

We present here and criticize the "Proposals for a Prevention Policy" drawn up by a multidisciplinary commission in France during 1982. The Report provides original elements at three conceptual levels: (i) a socio-economic model of causality of disease with a temporal, a probabilistic and a polyfactorial dimension; (ii) a social point of view brought to the concepts of health, prevention and health promotion; (iii) general principles for preventive intervention at a population level, including a breaking with the "victim-blaming" ideology. In this Report, work is always at the very center of the analysis developed by the commission. Practically, proposals are concerning work and environment on the one hand and structures and functional of public health services on the other hand. They may appear somewhat unrealistic or not clearly detailed. But one should understand that the commission essentially proposes an attitude of active involvement on the part of social actors rather than a list of concrete proposals. Undoubtedly, this French Report poses and discusses questions which are of interest even outside of the French context.

France↗
PubMed2003

EXPOLIS simulation model: PM2.5 application and comparison with measurements in Helsinki.

PM(2.5) exposure distributions of adult Helsinki citizens were simulated using a probabilistic simulation framework. Simulation results were compared to corresponding personal exposure distributions measured in the EXPOLIS study in Helsinki. The simpler models 1 and 2 (with two and three microenvironments, respectively) predict the general outline of the exposure distributions reasonably well. Compared to the observed exposure distribution, the mean is underestimated by less than 3 microg m(-3) (20%) and the standard deviation by 23-35%. In the improved simulation models (3 and 4), the environmental tobacco smoke (ETS)-exposed subjects are excluded, the time-activity models of working and nonworking subpopulations are modeled separately, and the correlations of input concentration and time fraction variables have been accounted for. The output of these models was very close to the observed distributions; the differences in the means were less than 0.1 microg m(-3) and the differences in standard deviation less than 1%. We conclude that when the required input data are available or can be reliably estimated, the target population PM(2.5) exposure distributions can be predicted accurately enough for most practical purposes using this kind of a microenvironment model.

Adult↗
PubMed1989

Testing separate families of segregation hypotheses: bootstrap methods.

Aspects of the statistical modeling and assessment of hypotheses concerning quantitative traits in genetics research are discussed. It is suggested that a traditional approach to such modeling and hypothesis testing, whereby competing models are "nested" in an effort to simplify their probabilistic assessment, can be complimented by an alternative statistical paradigm - the separate-families-of-hypotheses approach to segregation analysis. Two bootstrap-based methods are described that allow testing of any two, possibly non-nested, parametric genetic hypotheses. These procedures utilize a strategy in which the unknown distribution of a likelihood ratio-based test statistic is simulated, thereby allowing the estimation of critical values for the test statistic. Though the focus of this paper concerns quantitative traits, the strategies described can be applied to qualitative traits as well. The conceptual advantages and computational ease of these strategies are discussed, and their significance levels and power are examined through Monte Carlo experimentation. It is concluded that the separate-families-of-hypotheses approach, when carried out with the methods described in this paper, not only possesses some favorable statistical properties but also is well suited for genetic segregation analysis.

Alleles↗
PubMed1996

Use of the DIAS model to predict unrecognised hypoglycaemia in patients with insulin-dependent diabetes.

The Diabetes Advisory System (DIAS) is a model of human glucose metabolism implemented in a causal probabilistic network. It handles data on insulin dose, carbohydrate intake and blood glucose concentration to predict hourly blood glucose concentrations and thus provide an indication of blood glucose values between home blood tests. DIAS was used to predict blood glucose profiles in eight patients with well-controlled insulin-dependent diabetes, who are at increased risk of hypoglycaemia (abnormally low blood glucose levels). DIAS predicted nocturnal hypoglycaemia in six patients and daytime hypoglycaemia in one patient. The occurrence of nocturnal hypoglycaemia was not recognised by the patient or suspected by their doctor but was subsequently confirmed by blood testing in five patients. It is known that unrecognised nocturnal hypoglycaemia is common in patients with well-controlled diabetes. The ability of DIAS to identify such periods of hypoglycaemia with reasonable accuracy illustrates how the advanced technology it employs may provide reliable decision support to clinicians.

Adolescent↗
PubMed2001

Changing the first line drug for malaria treatment--cost-effectiveness analysis with highly uncertain inter-temporal trade-offs.

Access to effective treatment would substantially reduce the burden of malaria in sub-Saharan Africa, but resistance to chloroquine, the most commonly used first line drug, is now widespread. There has been considerable debate over the level of chloroquine resistance at which a new first line drug should be adopted. Two issues make this an extremely complex decision: it involves trade-offs in costs and health outcomes over time; and many of the parameters are uncertain. A modelling approach was identified as appropriate for addressing these issues. The costs and effects of changing from chloroquine to sulphadoxine-pyrimethamine (SP) as the first line drug were modelled over 10 years, allowing for growth in drug resistance. Probabilistic sensitivity analysis was used to allow for the high levels of parameter uncertainty. The optimal year of switch was highly dependent on both empirical values, such as initial resistance and resistance growth rates, and on subjective values, such as the time preferences of policy-makers. It was not possible to provide policy-makers with a definitive threshold resistance level at which to switch, but the model can be used as an analytical tool to structure the problem, explore trade-offs, and identify areas for which data are lacking.

Africa South of the Sahara↗
PubMed2005

The effect of three-dimensional shape optimization on the probabilistic response of a cemented femoral hip prosthesis.

Probabilistic analyses allow the effect of uncertainty in system parameters on predicted model performance measures to be determined. Furthermore, using performance functions to describe a failure event, the probability of failure can be quantified. The effect of three-dimensional prosthesis shape optimization on the probabilistic response and failure probability of a cemented hip prosthesis system is investigated. Random variables include joint and muscle loading, cortical and cancellous bone and PMMA bone cement elastic properties, and strength parameters describing failure of the bone cement and the prosthesis-bone cement interface. Several performance functions describing the bone cement and prosthesis-cement interface are used to compute the probability of failure. When evaluated deterministically, most performance functions indicated a safe design, with the exception of interface tensile failure. However, when evaluated probabilistically, finite probabilities of failure were computed, some significant. The most likely mode of failure before shape optimization was prosthesis-bone cement interface tensile failure with a predicted probability of failure of 97.9%. Deterministic prosthesis shape optimization reduced the probability of failure for all performance functions and reduced prosthesis-bone cement interface tensile failure by 31.7%. Probability sensitivity factors indicate that the uncertainty in the joint loading, cement strength, and implant-cement interface strength have the greatest effect on the computed probability of failure. Implant shape optimization results in a more robust implant design that is less sensitive to uncertainties in joint loading, which cannot be easily controlled, and more sensitive to cement and interface properties, which are easier to modify.

Cementation↗
PubMed2001

Effects of uncertainties on exposure estimates to methylmercury: a Monte Carlo analysis of exposure biomarkers versus dietary recall estimation.

This article presents a general model for estimating population heterogeneity and "lack of knowledge" uncertainty in methylmercury (MeHg) exposure assessments using two-dimensional Monte Carlo analysis. Using data from fish-consuming populations in Bangladesh, Brazil, Sweden, and the United Kingdom, predictive model estimates of dietary MeHg exposures were compared against those derived from biomarkers (i.e., [Hg]hair and [Hg]blood). By disaggregating parameter uncertainty into components (i.e., population heterogeneity, measurement error, recall error, and sampling error) estimates were obtained of the contribution of each component to the overall uncertainty. Steady-state diet:hair and diet:blood MeHg exposure ratios were estimated for each population and were used to develop distributions useful for conducting biomarker-based probabilistic assessments of MeHg exposure. The 5th and 95th percentile modeled MeHg exposure estimates around mean population exposure from each of the four study populations are presented to demonstrate lack of knowledge uncertainty about a best estimate for a true mean. Results from a U.K. study population showed that a predictive dietary model resulted in a 74% lower lack of knowledge uncertainty around a central mean estimate relative to a hair biomarker model, and also in a 31% lower lack of knowledge uncertainty around central mean estimate relative to a blood biomarker model. Similar results were obtained for the Brazil and Bangladesh populations. Such analyses, used here to evaluate alternative models of dietary MeHg exposure, can be used to refine exposure instruments, improve information used in site management and remediation decision making, and identify sources of uncertainty in risk estimates.

Algorithms↗