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At least 1,693 records · Page 94Linked to original sources
PubMed2006

Predicting abundance of desert riparian birds: validation and calibration of the Effective Area Model.

Reliable prediction of the effects of landscape change on species abundance is critical to land managers who must make frequent, rapid decisions with long-term consequences. However, due to inherent temporal and spatial variability in ecological systems, previous attempts to predict species abundance in novel locations and/or time frames have been largely unsuccessful. The Effective Area Model (EAM) uses change in habitat composition and geometry coupled with response of animals to habitat edges to predict change in species abundance at a landscape scale. Our research goals were to validate EAM abundance predictions in new locations and to develop a calibration framework that enables absolute abundance predictions in novel regions or time frames. For model validation, we compared the EAM to a null model excluding edge effects in terms of accurate prediction of species abundance. The EAM outperformed the null model for 83.3% of species (N=12) for which it was possible to discern a difference when considering 50 validation sites. Likewise, the EAM outperformed the null model when considering subsets of validation sites categorized on the basis of four variables (isolation, presence of water, region, and focal habitat). Additionally, we explored a framework for producing calibrated models to decrease prediction error given inherent temporal and spatial variability in abundance. We calibrated the EAM to new locations using linear regression between observed and predicted abundance with and without additional habitat covariates. We found that model adjustments for unexplained variability in time and space, as well as variability that can be explained by incorporating additional covariates, improved EAM predictions. Calibrated EAM abundance estimates with additional site-level variables explained a significant amount of variability (P < 0.05) in observed abundance for 17 of 20 species, with R2 values >25% for 12 species, >48% for six species, and >60% for four species when considering all predictive models. The calibration framework described in this paper can be used to predict absolute abundance in sites different from those in which data were collected if the target population of sites to which one would like to statistically infer is sampled in a probabilistic way.

Animals↗
PubMed2001

A spike-train probability model.

Poisson processes usually provide adequate descriptions of the irregularity in neuron spike times after pooling the data across large numbers of trials, as is done in constructing the peristimulus time histogram. When probabilities are needed to describe the behavior of neurons within individual trials, however, Poisson process models are often inadequate. In principle, an explicit formula gives the probability density of a single spike train in great generality, but without additional assumptions, the firing-rate intensity function appearing in that formula cannot be estimated. We propose a simple solution to this problem, which is to assume that the time at which a neuron fires is determined probabilistically by, and only by, two quantities: the experimental clock time and the elapsed time since the previous spike. We show that this model can be fitted with standard methods and software and that it may used successfully to fit neuronal data.

Animals↗
PubMed2005

Airborne particle deposition onto the ocular surface.

UNLABELLED: Eye irritation is one of the most frequently reported symptoms in relation to working in office-like environments. So far, no consistent association has been found between concentration of airborne particles and prevalence of eye irritation. External physical factors not hitherto considered could have a large influence on particle deposition velocity onto the ocular surface. This may have obscured the role airborne particles play. Based on previously published semi-empirical models, the paper describes the influence of turbulence, gravitational settling, electrical fields, and thermophoresis on deposition velocity. A probabilistic approach was used to determine percentile ranges in deposition velocity when the magnitude of these parameters varied within typical ranges. The calculations suggest that differences in external factors other than particle size may cause differences in the deposition velocity of one order of magnitude or more. Studies trying to find associations between airborne particle concentration levels and eye irritation should take into consideration the influence of external physical factors on deposition velocity. PRACTICAL IMPLICATIONS: External physical factors other than concentration are likely to have a large influence on the deposition rate of airborne particles onto the ocular surface. Thus, future studies on associations between airborne particle levels and eye symptoms should only be conducted if particle concentration measurements are suitably size resolved. Other relevant external physical factors should also be assessed and included in a multivariate analysis of exposure-response associations.

Air Movements↗
PubMed2002

Effects of lesions of the orbitofrontal cortex on sensitivity to delayed and probabilistic reinforcement.

RATIONALE: Lesions of the orbital prefrontal cortex (OPFC) can cause pathologically impulsive behaviour in humans. Inter-temporal choice behaviour (choice between reinforcers differing in size, delay and/or probability) has been proposed as a model of "impulsive choice" in animals. OBJECTIVE: The effect of lesions of the OPFC on rats' inter-temporal choice behaviour was examined in two experiments: (1) rats chose between a smaller immediate reinforcer and a larger delayed reinforcer; (2) rats chose between a smaller certain reinforcer and a larger probabilistic reinforcer. METHODS: Under halothane anaesthesia, rats received injections of the excitotoxin quinolinate into the OPFC (0.1 M, 0.5 microl, two injections in each hemisphere), or sham lesions (injections of vehicle). They were trained to press two levers (A and B) for food-pellet reinforcers in discrete-trials schedules. In free-choice trials, a press on A resulted in immediate delivery of one food pellet; a press on B resulted in delivery of two pellets, either following a delay ( d) (experiment 1), or with a probability ( p) <1 (experiment 2). The values of d and p were manipulated across phases of the experiments. The locations of the lesions were verified histologically at the end of the experiment. RESULTS: In experiment 1, both groups showed declining choice of lever B as a function of d. The lesioned rats showed significantly shorter indifference delays ( D50: the value of d corresponding to 50% choice of lever B) than the sham-lesioned rats. In experiment 2, both groups showed declining choice of lever B as a function of the odds against delivery of the two-pellet reinforcer, theta ( theta =[1/ p]-1). The lesioned rats showed lower indifference odds ( theta50: the value of theta corresponding to 50% choice of lever B) than the sham-lesioned rats. In both experiments, the lesioned rats showed extensive atrophy of the OPFC, with sparing of the dorsolateral prefrontal cortex. CONCLUSIONS: The results show that lesions of the OPFC can promote preference for the smaller and more immediate, and the smaller and more certain of two reinforcers. The results are consistent with two interpretations: the lesion may have altered (i) the rates of delay and odds discounting, and/or (ii) sensitivity to the ratio of the sizes of the two reinforcers.

Animals↗
PubMed2002

Measuring probabilistic reaction norms for age and size at maturation.

We present a new probabilistic concept of reaction norms for age and size at maturation that is applicable when observations are carried out at discrete time intervals. This approach can also be used to estimate reaction norms for age and size at metamorphosis or at other ontogenetic transitions. Such estimations are critical for understanding phenotypic plasticity and life-history changes in variable environments, assessing genetic changes in the presence of phenotypic plasticity, and calibrating size- and age-structured population models. We show that previous approaches to this problem, based on regressing size against age at maturation, give results that are systematically biased when compared to the probabilistic reaction norms. The bias can be substantial and is likely to lead to qualitatively incorrect conclusions; it is caused by failing to account for the probabilistic nature of the maturation process. We explain why, instead, robust estimations of maturation reaction norms should be based on logistic regression or on other statistical models that treat the probability of maturing as a dependent variable. We demonstrate the utility of our approach with two examples. First, the analysis of data generated for a known reaction norm highlights some crucial limitations of previous approaches. Second, application to the northeast arctic cod (Gadus morhua) illustrates how our approach can be used to shed new light on existing real-world data.

Age Factors↗
PubMedDate not supplied

Probabilistic small area risk assessment using GIS-based data: a case study on Finnish childhood diabetes. Geographic information systems.

A Bayesian hierarchical spatial model is constructed to describe the regional incidence of insulin dependent diabetes mellitus (IDDM) among the under 15-year-olds in Finland. The model exploits aggregated pixel-wise locations for both the cases and the population at risk. Typically such data arise from combining geographic information systems (GIS) with large databases. The dates of diagnosis and locations of the cases are observed from 1987 to 1996. The population at risk counts are available for every second year during the same period. A hierarchical model is suggested for the pixel wise case counts, including a population model to account for the uncertainty of the population at risk over the years. The model is applied in the construction of disease maps (aggregated 100 km(2) pixels), and spatial posterior predictive distributions are computed to study whether there can be found a statistically exceptional number of cases in a small area of interest.

Bayes Theorem↗
PubMed1999

Biological probability: cognitive processes of generating probabilities of events in biological systems.

This paper analyses relationships between probabilities of events happening in biological systems (or probabilistic disposition of systems) and cognitive properties of biological entities comprising such systems. Two kinds of cognitive properties are identified as relevant to the current problem: the ability to respond differently against different configurations of the environment (discriminability of cognition), and the ability to make an appropriate response to maintain a particular relation with the environment (selectivity of cognition). A basic framework bridging the two features of living systems, probabilistic disposition and the cognitive properties, is presented towards a general theory explaining the process generating probabilities of biological events. In this framework, a deterministic model of a system of entities is developed, in which objects are described as subjects that cognize events (i.e. entities as cognizers). Cognition is used in a wider sense, including not only biotic but also abiotic, and cognizers are conceptually distinguished from the meta-observer who describes the system externally. Based on this perspective, this paper seeks to explicate how events can occur in an uncertain, probabilistic manner, if observed from a cognizer viewpoint, even under a deterministic system. Each cognizer is identified with both the set of states that are actually taken, and its motion function which maps its state uniquely to a successor state depending on the current states of itself and of the rest of cognizers constituting the system. The model analysis reveals that the cognitive properties, discriminability and selectivity, of a cognizer can contribute to determining the probability of an event encountered by the cognizer itself-in particular, discrimination reducing the uncertainty in events occurrence for the cognizer. Biological implication of this result is discussed focusing on the concept of the probability of survival and reproduction.

Animals↗
PubMed2002

Bootstrapping the lexicon: a computational model of infant speech segmentation.

Prelinguistic infants must find a way to isolate meaningful chunks from the continuous streams of speech that they hear. BootLex, a new model which uses distributional cues to build a lexicon, demonstrates how much can be accomplished using this single source of information. This conceptually simple probabilistic algorithm achieves significant segmentation results on various kinds of language corpora - English, Japanese, and Spanish; child- and adult-directed speech, and written texts; and several variations in coding structure - and reveals which statistical characteristics of the input have an influence on segmentation performance. BootLex is then compared, quantitatively and qualitatively, with three other groups of computational models of the same infant segmentation process, paying particular attention to functional characteristics of the models and their similarity to human cognition. Commonalities and contrasts among the models are discussed, as well as their implications both for theories of the cognitive problem of segmentation itself, and for the general enterprise of computational cognitive modeling.

Algorithms↗
PubMed1991

Absorption mechanisms of secondary aliphatic amines in rat colon and small intestine.

This study is intended to be a further and conclusive validation of the bihyperbolic model equation proposed by Plá-Delfina and Moreno to describe passive intestinal absorption mechanisms through the analysis of absorption-lipophilicity correlations for homologous series of xenobiotics and drugs. Secondary aliphatic amines, largely differing from previously tested substances, were selected as model compounds. Evidence is given which demonstrates that a minimum lipophilicity value exists for absorption in small intestine, instead of a maximum, as probabilistic theories predict. Moreover, bihyperbolic equation provides an excellent fit, with AIC figures up to -29. Aqueous pore absorption was small, presumably due to ionic interactions with pore charges (kp approximately 1.0 h-1), whereas membrane penetration was highly lucrative (km approximately 7.4 h-1), thus indicating that some part of the lipophilic ionic species is capable of penetrating "per se" the lipoidal membrane. As model predicts, bihyperbolic equation collapses to monohyperbolic for colonic absorption, where AIC figures up to -39 were found, with a km value of about 4.0 h-1. Membrane absorption efficiency was, surprisingly, similar in colon and small intestine for the tested solutes; it was attributed to the basic character of the compounds associated with working pH and ion absorption. This latter effect would deserve further investigation in oral sustained-release medication with basic drugs having similar pKa values.

Amines↗
PubMed2006

Cost-effectiveness of chronic hepatitis C treatment with thymosin alpha-1.

BACKGROUND: More than one million individuals in Mexico are infected with hepatitis C virus (HCV), and 80% are at risk for developing a chronic infection that could lead to hepatic cirrhosis and other complications that impact quality of life and institutional costs. The objective of the study was to determine the most cost-effective treatment against HCV among the following: peginterferon, peginterferon plus ribavirin, peginterferon plus ribavirin plus thymosin, and no treatment. METHODS: We carried out cost-effectiveness analysis using the institutional perspective, including a 45-year time frame and a 3% discount rate for costs and effectiveness. We employed a Bayesian-focused decision tree and a Markov model. One- and two-way sensitivity analyses were performed, as well as threshold-oriented and probabilistic analyses, and we obtained acceptability curves and net health benefits. RESULTS: Triple therapy (peginterferon plus ribavirin plus thymosin alpha-1) was dominant with lower cost and higher utility in relationship with peginterferon + ribavirin option, peginterferon alone and no-treatment option. In triple therapy the cost per unit of success was of 1,908 [USD/quality-adjusted life years (QALY)] compared with peginterferon plus ribavirin 2,277/QALY, peginterferon alone 2,929/QALY, and no treatment 4,204/QALY. Sensitivity analyses confirmed the robustness of the base case. CONCLUSIONS: Peginterferon plus ribavirin plus thymosin alpha-1 option was dominant (lowest cost and highest effectiveness). Using no drug was the most expensive and least effective option.

Adjuvants, Immunologic↗
PubMed2005

Children can solve Bayesian problems: the role of representation in mental computation.

Can children reason the Bayesian way? We argue that the answer to this question depends on how numbers are represented, because a representation can do part of the computation. We test, for the first time, whether Bayesian reasoning can be elicited in children by means of natural frequencies. We show that when information was presented to fourth, fifth, and sixth graders in terms of probabilities, their ability to estimate the Bayesian posterior probability was zero. Yet when the same information was presented in natural frequencies, Bayesian reasoning showed a steady increase from fourth to sixth grade, reaching an average level of 19, 39, and 53%, respectively, in two studies. Sixth graders' performance with natural frequencies matched the performance of adults with probabilities. But this general increase was accompanied by striking individual differences. More than half of the sixth graders solved most or all problems, whereas one third could not solve a single one. An analysis of the children's responses provides evidence for the use of three non-Bayesian strategies. These follow an overlapping wave model of development and continue to be observed in the minds of adults. More so than adults' probabilistic reasoning, children's reasoning depends on a proper representation of information.

Adult↗
PubMed2003

The study of structured populations--new hope for a difficult and divided science.

Natural populations, including those of humans, have complex geographies and histories. Studying how they evolve is difficult, but it is possible with population-based DNA sequence data. However, the study of structured populations is divided by two distinct schools of thought and analysis. The phylogeographic approach is fundamentally graphical and begins with a gene-tree estimate. By contrast, the more traditional approach of using summary statistics is fundamentally mathematical. Both approaches have limitations, but there is promise in newer probabilistic methods that offer the flexibility and data exploitation of the phylogeographic approach in an explicitly model-based mathematical framework.

Animals↗
PubMed2002

Is there a toxicological advantage for non-hyperbolic kinetics in cytochrome P450 catalysis? Functional allostery from "distributive catalysis".

The cytochrome P450s (CYPs) are the major enzymatic detoxification and drug metabolism system. Recently, it has become clear that several CYP isoforms exhibit positive and negative homotropic cooperativity. However, the toxicological implications of allosteric kinetics have not been considered, nor understood. The allosteric kinetics are particularly enigmatic in several respects. In many cases, CYPs bioactivate substrates to more toxic products, thus making it difficult to rationalize a functional advantage for positive cooperativity. Also, CYPs exhibit cooperativity with many structurally diverse ligands, in marked contrast to the specificity observed with other allosteric systems. Here, kinetic simulations are used to compare the probabilistic time- and concentration-dependent integrated toxicity function during conversion of substrate to product for CYP models exhibiting Michaelis-Menten (non-cooperative) kinetics, positive cooperativity, or negative cooperativity. The results demonstrate that, at low substrate concentrations, the slower substrate turnover afforded by cooperative CYPs compared with Michaelis-Menten enzymes can be a significant toxicological advantage, when toxic thresholds exist. When present, the advantage results from enhanced "distribution" of toxin in two pools, substrate and product, for an extended period, thus minimizing the chance that either exceeds its toxic threshold. At intermediate concentrations, the allosteric kinetics can be a modest advantage or modest disadvantage, depending on the kinetic parameters. However, at high substrate concentrations associated with a high probability of toxicity, fast turnover is desirable, and this advantage is provided also by the cooperative enzymes. For the positive homotropic cooperativity, the allosteric kinetics minimize the probability of toxicity over the widest range of system parameters. Furthermore, this apparent functional cooperativity is achieved without specific molecular recognition that is the hallmark of "traditional" allostery.

Allosteric Site↗
PubMed2006

Relevance feedback for CBIR: a new approach based on probabilistic feature weighting with positive and negative examples.

In content-based image retrieval, understanding the user's needs is a challenging task that requires integrating him in the process of retrieval. Relevance feedback (RF) has proven to be an effective tool for taking the user's judgement into account. In this paper, we present a new RF framework based on a feature selection algorithm that nicely combines the advantages of a probabilistic formulation with those of using both the positive example (PE) and the negative example (NE). Through interaction with the user, our algorithm learns the importance he assigns to image features, and then applies the results obtained to define similarity measures that correspond better to his judgement. The use of the NE allows images undesired by the user to be discarded, thereby improving retrieval accuracy. As for the probabilistic formulation of the problem, it presents a multitude of advantages and opens the door to more modeling possibilities that achieve a good feature selection. It makes it possible to cluster the query data into classes, choose the probability law that best models each class, model missing data, and support queries with multiple PE and/or NE classes. The basic principle of our algorithm is to assign more importance to features with a high likelihood and those which distinguish well between PE classes and NE classes. The proposed algorithm was validated separately and in image retrieval context, and the experiments show that it performs a good feature selection and contributes to improving retrieval effectiveness.

Algorithms↗
PubMed2005

Probabilistic finite-state machines--part I.

Probabilistic finite-state machines are used today in a variety of areas in pattern recognition, or in fields to which pattern recognition is linked: computational linguistics, machine learning, time series analysis, circuit testing, computational biology, speech recognition, and machine translation are some of them. In Part I of this paper, we survey these generative objects and study their definitions and properties. In Part II, we will study the relation of probabilistic finite-state automata with other well-known devices that generate strings as hidden Markov models and n-grams and provide theorems, algorithms, and properties that represent a current state of the art of these objects.

Algorithms↗
PubMed2005

Probabilistic finite-state machines--part II.

Probabilistic finite-state machines are used today in a variety of areas in pattern recognition or in fields to which pattern recognition is linked. In Part I of this paper, we surveyed these objects and studied their properties. In this Part II, we study the relations between probabilistic finite-state automata and other well-known devices that generate strings like hidden Markov models and n-grams and provide theorems, algorithms, and properties that represent a current state of the art of these objects.

Algorithms↗
PubMed2003

A hierarchical method for finding optimal architecture and weights using evolutionary least square based learning.

In this paper, we present a novel approach of implementing a combination methodology to find appropriate neural network architecture and weights using an evolutionary least square based algorithm (GALS).1 This paper focuses on aspects such as the heuristics of updating weights using an evolutionary least square based algorithm, finding the number of hidden neurons for a two layer feed forward neural network, the stopping criterion for the algorithm and finally some comparisons of the results with other existing methods for searching optimal or near optimal solution in the multidimensional complex search space comprising the architecture and the weight variables. We explain how the weight updating algorithm using evolutionary least square based approach can be combined with the growing architecture model to find the optimum number of hidden neurons. We also discuss the issues of finding a probabilistic solution space as a starting point for the least square method and address the problems involving fitness breaking. We apply the proposed approach to XOR problem, 10 bit odd parity problem and many real-world benchmark data sets such as handwriting data set from CEDAR, breast cancer and heart disease data sets from UCI ML repository. The comparative results based on classification accuracy and the time complexity are discussed.

Algorithms↗
PubMed1998

Guideline generation from data by induction of decision tables using a Bayesian network framework.

Decision tables can be used to represent practice guidelines effectively. In this study we adopt the powerful probabilistic framework of Bayesian Networks (BN) for the induction of decision tables. We discuss the simplest BN model, the Naive Bayes and extend it to the Two-Stage Naive Bayes. We show that reversal of edges in Naive Bayes and Two-stage Naive Bayes results in simple decision table and hierarchical decision table respectively. We induce these graphical models for dementia severity staging using the Clinical Dementia Rating Scale (CDRS) database from the University of California, Irvine, Alzheimer's Disease Research Center. These induced models capture the two-stage methodology clinicians use in computing the global CDR score by first computing the six category scores of memory, orientation, judgment and problem solving, community affairs, home and hobbies and personal care, and then the global CDRS. The induced Two-Stage models also attain a clinically acceptable performance when compared to domain experts and could serve as useful guidelines for dementia severity staging.

Algorithms↗