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At least 1,189 records · Page 66Linked to original sources
PubMed1989

Consequences of stochastic release of neurotransmitters for network computation in the central nervous system.

Neuronal membrane potentials vary continuously due largely to background synaptic noise produced by ongoing discharges in their presynaptic afferents and shaped by probabilistic factors of transmitter release. We investigated how the random activity of an identified population of interneurons with known release properties influences the performance of central cells. In stochastic models such as thermodynamic ones, the probabilistic input-output function of a formal neuron is sigmoid, having its maximal slope inversely related to a variable called "temperature." Our results indicate that, for a biological neuron, the probability that given excitatory input signals reach threshold is also sigmoid, allowing definition of a temperature that is proportional to the mean number of quanta comprising noise and can be modified by activity in the presynaptic network, a notion which could be included in neural models. By introducing uncertainty to the input-output relation of central neurons, synaptic noise could be a critical determinant of neuronal computational systems, allowing assemblies of cells to undergo continuous transitions between states.

Animals↗
PubMed2004

An empirical analysis of training protocols for probabilistic gene finders.

BACKGROUND: Generalized hidden Markov models (GHMMs) appear to be approaching acceptance as a de facto standard for state-of-the-art ab initio gene finding, as evidenced by the recent proliferation of GHMM implementations. While prevailing methods for modeling and parsing genes using GHMMs have been described in the literature, little attention has been paid as of yet to their proper training. The few hints available in the literature together with anecdotal observations suggest that most practitioners perform maximum likelihood parameter estimation only at the local submodel level, and then attend to the optimization of global parameter structure using some form of ad hoc manual tuning of individual parameters. RESULTS: We decided to investigate the utility of applying a more systematic optimization approach to the tuning of global parameter structure by implementing a global discriminative training procedure for our GHMM-based gene finder. Our results show that significant improvement in prediction accuracy can be achieved by this method. CONCLUSIONS: We conclude that training of GHMM-based gene finders is best performed using some form of discriminative training rather than simple maximum likelihood estimation at the submodel level, and that generalized gradient ascent methods are suitable for this task. We also conclude that partitioning of training data for the twin purposes of maximum likelihood initialization and gradient ascent optimization appears to be unnecessary, but that strict segregation of test data must be enforced during final gene finder evaluation to avoid artificially inflated accuracy measurements.

Algorithms↗
PubMed2001

Renovation of the WWTP hove.

The provision of reliable treatment has always been a priority in sanitary engineering. However, there is an ever-increasing pressure to meet desirable standards at reduced costs. This paper sets out a methodology to enhance the likelihood of a satisfactory allocation of the resources while providing reliable treatment. The procedure was gradually developed to optimise the massive capital investment of the Flemish Government to comply with the effluent standards set in the EU Directive 271/91. The methodology makes an extended use of dynamic modelling. To facilitate the identification and estimation of the uncertainty, a probabilistic Monte Carlo engine is coupled to the dynamic models. In doing so this approach provides a way of explicitly incorporating uncertainty and variability in the process analysis. The procedure is examined in an upgrade of a conventional WWTP towards stricter effluent norms on nutrients. The results suggest that the procedure can support the decision-making process under uncertainty conditions and enhance the likelihood of meeting effluent standards not entailing above-normal capital investments. The analysis led to reducing the capital investment by 43%, producing savings of more than 1.2 million Euros.

Belgium↗
PubMed1990

Mathematical models of gene amplification with applications to cellular drug resistance and tumorigenicity.

An increased number of copies of specific genes may offer an advantage to cells when they grow in restrictive conditions such as in the presence of toxic drugs, or in a tumor. Three mathematical models of gene amplification and deamplification are proposed to describe the kinetics of unstable phenotypes of cells with amplified genes. The models differ in details but all assume probabilistic mechanisms of increase and decrease in gene copy number per cell (gene amplification/deamplification). Analysis of the models indicates that a stable distribution of numbers of copies of genes per cell, observed experimentally, exists only if the probability of deamplification exceeds the probability of amplification. The models are fitted to published data on the loss of methotrexate resistance in cultured cell lines, due to the loss of amplified dihydrofolate reductase gene. For two mouse cell lines unstably resistant to methotrexate the probabilities of amplification and deamplification of the dihydrofolate reductase gene on double minute chromosomes are estimated to be approximately 2% and 10%, respectively. These probabilities are much higher than widely presumed. The models explain the gradual disappearance of the resistant phenotype when selective pressure is withdrawn, by postulating that the rate of deamplification exceeds the rate of amplification. Thus it is not necessary to invoke a growth advantage of nonresistant cells which has been the standard explanation. For another analogous process, the loss of double minute chromosomes containing the myc oncogene from SEWA tumor cells, the growth advantage model does seem to be superior to the amplification and deamplification model. In a more theoretical section of the paper, it is demonstrated that gene amplification/deamplification can result in reduction to homozygosity, such as is observed in some tumors. Other applications are discussed.

Animals↗
PubMed2004

Statistical shape model of atria, ventricles and epicardium from short- and long-axis MR images.

We describe a new 3-D statistical shape model of the heart consisting of atria, ventricles and epicardium. The model was constructed by combining information on standard short- and long-axis cardiac MR images. In the model, the variability of the shape was modeled with PCA- and ICA-based shape models as well as with non-parametric landmark probability distributions and a probabilistic atlas. The statistical atlas was built from 25 healthy subjects. The shape model was evaluated by applying it to image segmentation. The probabilistic atlas was found to be superior to the other shape models (P < 0.001) in this study.

Adult↗
PubMed2002

Combining location and expression data for principled discovery of genetic regulatory network models.

We develop principled methods for the automatic induction (discovery) of genetic regulatory network models from multiple data sources and data modalities. Models of regulatory networks are represented as Bayesian networks, allowing the models to compactly and robustly capture probabilistic multivariate statistical dependencies between the various cellular factors in these networks. We build on previous Bayesian network validation results by extending the validation framework to the context of model induction, leveraging heuristic simulated annealing search algorithms and posterior model averaging. Using expression data in isolation yields results inconsistent with location data so we incorporate genomic location data to guide the model induction process. We combine these two data modalities by allowing location data to influence the model prior and expression data to influence the model likelihood. We demonstrate the utility of this approach by discovering genetic regulatory models of thirty-three variables involved in S. cerevisiae pheromone response. The models we automatically generate are consistent with the current understanding regarding this regulatory network, but also suggest new directions for future experimental investigation.

Bayes Theorem↗
PubMed2026

High-Purity Monovalent Functionalization of Carbon Nanotubes.

Single-walled carbon nanotubes (SWCNTs) show promise for probing molecular interactions at single-molecule resolution, yet generating SWCNT populations bearing a single defined functional tag remains challenging because surface functionalization is inherently stochastic. Here, we present a batch-scale strategy to produce predominantly singly tagged SWCNTs by leveraging the stochastic adsorption of single-stranded DNA (ssDNA). Specifically, SWCNTs are dispersed using a mixture of unmodified ssDNA (um-ssDNA) and a minor fraction of modified ssDNA (m-ssDNA) carrying an affinity handle. We developed a probabilistic ssDNA-SWCNT binding model that predicts the distribution of m-ssDNA per nanotube as a function of the input minor-strand fraction p = m-ssDNA/total ssDNA, enabling selection of conditions that maximize single-tag purity. Using magnetic-bead capture via a biotin affinity interaction and subsequent release, we isolate SWCNTs with 97.6% predicted single-tag purity at 2% recovery. Single-molecule fluorescence imaging further supports predominantly single-label occupancy under the model-selected conditions. Thus, this approach provides a general route to SWCNTs bearing a single molecular handle for downstream conjugation and assembly, supporting diverse future applications in SWCNT-based nanotechnologies.

Nanotubes, Carbon↗
PubMed2002

Statistical methods in finite element analysis.

Finite element analysis (FEA) is a commonly used tool within many areas of engineering and can provide useful information in structural analysis of mechanical systems. However, most analyses within the field of biomechanics usually take no account either of the wide variation in material properties and geometry that may occur in natural tissues or manufacturing imperfections in synthetic materials. This paper discusses two different methods of incorporating uncertainty in FE models. The first, Taguchi's robust parameter design, uses orthogonal matrices to determine how to vary the parameters in a series of FE models, and provides information on the sensitivity of a model to input parameters. The second, probabilistic analysis, enables the distribution of a response variable to be determined from the distributions of the input variables. The methods are demonstrated using a simple example of an FE model of a beam that is assigned material properties and geometry over a range similar to an orthopaedic fixation plate. In addition to showing how each method may be used on its own, we also show how computational effort may be minimised by first identifying the most important input variables before determining the effects of imprecision.

Cervical Vertebrae↗
PubMed2000

Evaluation of DIABNET, a decision support system for therapy planning in gestational diabetes.

DIABNET is a knowledge-based system designed to aid doctors with therapy planning in gestational diabetes. The system core is a qualitative model, implemented by a Causal Probabilistic Network, that is able to detect the insulin effectiveness on a daily basis. DIABNET analyses monitoring data and proposes quantitative changes in insulin therapy and qualitative diet modifications. This paper proposes an evaluation methodology to assess the system performance when working in a real scenario. The methodology manages the absence of a gold standard and includes: a subjective analysis based on questionnaires and an objective analysis based on a quantitative comparison of the system's and experts' proposals. The paper also shows the results of two experiments in which expert diabetologists evaluated the therapeutical advice provided by DIABNET during the follow up of 9 patients with gestational diabetes. DIABNET detected the need of a therapy modification in 92% of the cases showing its appropriateness for automatic alarm generation. Around 80% of the proposals were accepted by experts. The evaluation results are encouraging and allow characterisation of the system's performance when proposing therapy modifications. Evaluation in its turn helps to refine the knowledge managed by DIABNET and enables us to look towards the further clinical use of DIABNET as a decision tool in gestational diabetes integrated in a telemedicine service.

Artificial Intelligence↗
PubMed1998

Deterministic annealing EM algorithm.

This paper presents a deterministic annealing EM (DAEM) algorithm for maximum likelihood estimation problems to overcome a local maxima problem associated with the conventional EM algorithm. In our approach, a new posterior parameterized by `temperature' is derived by using the principle of maximum entropy and is used for controlling the annealing process. In the DAEM algorithm, the EM process is reformulated as the problem of minimizing the thermodynamic free energy by using a statistical mechanics analogy. Since this minimization is deterministically performed at each temperature, the total search is executed far more efficiently than in the simulated annealing. Moreover, the derived DAEM algorithm, unlike the conventional EM algorithm, can obtain better estimates free of the initial parameter values. We also apply the DAEM algorithm to the training of probabilistic neural networks using mixture models to estimate the probability density and demonstrate the performance of the DAEM algorithm.

Journal Article↗
PubMed2004

After Helsinki: a multidisciplinary review of the relationship between asbestos exposure and lung cancer, with emphasis on studies published during 1997-2004.

Despite an extensive literature, the relationship between asbestos exposure and lung cancer remains the subject of controversy, related to the fact that most asbestos-associated lung cancers occur in those who are also cigarette smokers: because smoking represents the strongest identifiable lung cancer risk factor among many others, and lung cancer is not uncommon across industrialised societies, analysis of the combined (synergistic) effects of smoking and asbestos on lung cancer risk is a more complex exercise than the relationship between asbestos inhalation and mesothelioma. As a follow-on from previous reviews of prevailing evidence, this review critically evaluates more recent studies on this relationship--concentrating on those published between 1997 and 2004--including lung cancer to mesothelioma ratios, the interactive effects of cigarette smoke and asbestos in combination, and the cumulative exposure model for lung cancer induction as set forth in The Helsinki Criteria and The AWARD Criteria (as opposed to the asbestosis-->cancer model), together with discussion of differential genetic susceptibility/resistance factors for lung carcinogenesis by both cigarette smoke and asbestos. The authors conclude that: (i) the prevailing evidence strongly supports the cumulative exposure model; (ii) the criteria for probabilistic attribution of lung cancer to mixed asbestos exposures as a consequence of the production and end-use of asbestos-containing products such as insulation and asbestos-cement building materials--as embodied in The Helsinki and AWARD Criteria--conform to, and are further consolidated by, the new evidence discussed in this review; (iii) different attribution criteria (e.g., greater cumulative exposures) are appropriate for chrysotile mining/milling and perhaps for other chrysotile-only exposures, such as friction products manufacture, than for amphibole-only exposures or mixed asbestos exposures; and (iv) emerging evidence on genetic susceptibility/resistance factors for lung cancer risk as a consequence of cigarette smoking, and potentially also asbestos exposure, suggests that genotypic variation may represent an additional confounding factor potentially affecting the strength of association and hence the probability of causal contribution in the individual subject, but at present there is insufficient evidence to draw any meaningful conclusions concerning variation in asbestos-mediated lung cancer risk relative to such resistance/susceptibility factors.

Adenocarcinoma↗
PubMed2007

Detailed analysis of the response of different cell lines to carbon irradiation.

Published survival data for Chinese hamster ovarian cells CHO-K1 and their radiosensitive mutant xrs5 after irradiation by carbon ions of energies from 2.4 to 266.4 MeV u(-1) have been analysed using the probabilistic two-stage radiobiological model, which enables to represent the interplay of damage induction and repair processes. The results give support for the hypothesis that the differences in radiation sensitivity of diverse cell lines are given primarily by their different repair capabilities, and indicate the need for explicitly representing the outcome of repair processes in radiobiological models and treatment-planning approaches in radiotherapy.

Animals↗
PubMed2002

Diffusion of epicenters of earthquake aftershocks, Omori's law, and generalized continuous-time random walk models.

The epidemic-type aftershock sequence (ETAS) model is a simple stochastic process modeling seismicity, based on the two best-established empirical laws, the Omori law (power-law decay approximately 1/t(1+theta) of seismicity after an earthquake) and Gutenberg-Richter law (power-law distribution of earthquake energies). In order to describe also the space distribution of seismicity, we use in addition a power-law distribution approximately 1/r(1+mu) of distances between triggered and triggering earthquakes. The ETAS model has been studied for the last two decades to model real seismicity catalogs and to obtain short-term probabilistic forecasts. Here, we present a mapping between the ETAS model and a class of CTRW (continuous time random walk) models, based on the identification of their corresponding master equations. This mapping allows us to use the wealth of results previously obtained on anomalous diffusion of CTRW. After translating into the relevant variable for the ETAS model, we provide a classification of the different regimes of diffusion of seismic activity triggered by a mainshock. Specifically, we derive the relation between the average distance between aftershocks and the mainshock as a function of the time from the mainshock and of the joint probability distribution of the times and locations of the aftershocks. The different regimes are fully characterized by the two exponents theta and mu. Our predictions are checked by careful numerical simulations. We stress the distinction between the "bare" Omori law describing the seismic rate activated directly by a mainshock and the "renormalized" Omori law taking into account all possible cascades from mainshocks to aftershocks of aftershock of aftershock, and so on. In particular, we predict that seismic diffusion or subdiffusion occurs and should be observable only when the observed Omori exponent is less than 1, because this signals the operation of the renormalization of the bare Omori law, also at the origin of seismic diffusion in the ETAS model. We present predictions and insights provided by the ETAS to CTRW mapping which suggest different ways for studying seismic catalogs. Finally, we discuss the present evidence for our predicted subdiffusion of seismicity triggered by a main shock, stressing the caveats and limitations of previous empirical works.

Journal Article↗
PubMed2000

Fracture and second-order phase transitions.

Using the global fiber bundle model as a tractable scheme of progressive fracture in heterogeneous materials, we define the branching ratio in avalanches as a suitable order parameter to clarify the order of the phase transition occurring at the collapse of the system. The model is analyzed using a probabilistic approach suited to smooth fluctuations. The branching ratio shows a behavior analogous to the magnetization in known magnetic systems with second-order phase transitions. We obtain a universal critical exponent beta approximately = 0.5 independent of the probability distribution used to assign the strengths of individual fibers.

Journal Article↗
PubMed2001

The method of joint probability distribution functions applied to MAD techniques. The two-wavelength case for acentric crystals.

MAD (multiple-wavelength anomalous dispersion) techniques are often considered as a special MIR (multiple isomorphous replacement) case. The rigorous method of the joint probability distribution functions is applied to solve the phase problem for acentric crystals, on the assumption that the anomalous scatterer's substructure is a priori known. The two-wavelength case is considered: errors in measurements and in the model substructure are handled. The probabilistic approach provides a very simple and efficient formula for estimating structure-factor phases.

Journal Article↗
PubMed2005

Spatial reasoning with incomplete information on relative positioning.

This paper describes a probabilistic method of inferring the position of a point with respect to a reference point knowing their relative spatial position to a third point. We address this problem in the case of incomplete information where only the angular spatial relationships are known. The use of probabilistic representations allows us to model prior knowledge. We derive exact formulae expressing the conditional probability of the position given the two known angles, in typical cases: uniform or Gaussian random prior distributions within rectangular or circular regions. This result is illustrated with respect to two different simulations: The first is devoted to the localization of a mobile phone using only angular relationships, the second, to geopositioning within a city. This last example uses angular relationships and some additional knowledge about the position.

Algorithms↗
PubMed1997

Language acquisition and use: learning and applying probabilistic constraints.

What kinds of knowledge underlie the use of language and how is this knowledge acquired? Linguists equate knowing a language with knowing a grammar. Classic "poverty of the stimulus" arguments suggest that grammar identification is an intractable inductive problem and that acquisition is possible only because children possess innate knowledge of grammatical structure. An alternative view is emerging from studies of statistical and probabilistic aspects of language, connectionist models, and the learning capacities of infants. This approach emphasizes continuity between how language is acquired and how it is used. It retains the idea that innate capacities constrain language learning, but calls into question whether they include knowledge of grammatical structure.

Adult↗
PubMed2004

[Prognostication of coronary atherosclerosis for selection of tactics of management of patients with ischemic heart disease].

AIM: To create an artificial neural network for prognostication of coronary atherosclerosis and thus selection of patients requiring invasive interventions. METHODS AND RESULTS: Efficacy of prognostication made by a physician and by a model was compared basing on probabilistic estimates with consideration of individual level of risk. Nineteen clinical and instrumental parameters were entered into multifactorial analysis. These parameters included data from anamnesis, results of instrumental examination, a priori likelihood of ischemic heart disease, and magnitude of individual risk of cardiovascular events. The following factors were selected as significant at neural network prognostication of coronary atherosclerosis: result of exercise test, typical angina, history of myocardial infarction, ECG-signs of left ventricular hypertrophy and a priori likelihood of ischemic heart disease. Basing on these data two stage scheme of prognostication of coronary atherosclerosis was suggested. CONCLUSION: Thus artificial neural network suitable for screening and selection of patients for invasive interventions in ambulatory practice was created. Prognosis made with the use of artificial neural network was 1.5-3 times more accurate than that made by a physician.

Chi-Square Distribution↗