Luis Aparecido Milan

Chironomid species richness in low-order streams in the Brazilian Atlantic Forest: a first approximation through a Bayesian approach

Abstract The Atlantic Forest of Brazil has been identified as a biodiversity hotspot of global significance. We assessed chironomid (Diptera:Chironomidae) taxa richness in 2 vegetation types in this region: the Atlantic Rain Forest and the Atlantic Semi-deciduous Forest. Taxa were collected from 15 low-order streams across multiple habitats. A total of 191 morphospecies were recognized (125 Chironominae, 28 Tanypodinae, and 38 Orthocladiinae). We estimated chironomid richness using a Bayesian statistical approach. Species-richness estimates ranged from 200 (credibility interval, 195–207) to 267 (248–288). These results place low-order streams from Atlantic Forest among the most chironomid speciose areas in the world.

A Bayesian analysis for estimating the number of species in a population using nonhomogeneous Poisson process

We propose a Bayesian approach using nonhomogeneous Poisson process to estimate the number of species of a population. The proposed methodology uses a [pi]-mixture to eliminate the unknown total mean of each species. One contribution of the article is to apply the Metropolis-within-Gibbs algorithm to obtain the marginal posterior distribution of the number of species and the capture mean time.

Hierarchical Bayesian estimation for the number of species

This paper is concerned with the estimation of the number of species in a population through a fully hierarchical Bayesian model using the Metropolis algorithm. The proposed Bayesian estimator is based on Poisson random variables with means that are distributed according to some prior distributions with unknown hyperparameters. An empirical Bayes approach is considered and compared with the fully Bayesian approach based on biological data.

Bayesian Estimation of the Size of a Closed Population Using Photo-ID Data with Part of the Population Uncatchable

Abstract We develop a Bayesian statistical model for estimating bowhead whale population size from photo-identification data when most of the population is uncatchable. The proposed conditional likelihood function is a product of Darrochs model, formulated as a function of the number of good photos, and a binomial distribution of captured whales given the total number of good photos at each occasion. The full Bayesian model is implemented via adaptive rejection sampling for log concave densities. We apply the model to data from 1985 and 1986 bowhead whale photographic studies and the results compare favorably with the ones obtained in the literature. Also, a comparison with the maximum likelihood procedure with bootstrap simulation is considered using different vague priors for the capture probabilities.

Improved assessment of accuracy and performance indicators in paper-based ELISA

Paper-based devices are an excellent match for low-cost point-of-care testing (POCT) tools. Their user-friendliness, portability, and short time of analysis, coupled with ease of local manufacture make these devices the best option for inexpensive diagnostic testing tools. However, despite all their positive features, these low-cost diagnostic devices must present good performance indicators, such as sensitivity, specificity, and accuracy. We developed and validated a paper-based ELISA for toxoplasmosis diagnosis through the detection of Toxoplasma gondii immunoglobulin G (IgG) antibodies in 100 human serum samples. From among the different ways to define the cut-off value, we chose Youdens J index (cut-off = 21.73 A.U.), which presented a higher sensitivity value. Our paper-based assay presented a sensitivity of 0.96, a specificity of 0.87, and a gray zone comprising 16 samples (±15% of the cut-off value, with 3 false positive outputs). The accuracy of the test was estimated by using ROC curves (AUC = 0.97). We also created a macro in Microsoft Excel® to estimate the accuracy of the test (m-Accuracy) based on a non-parametric method, which evidenced a value = 0.88, which classifies our test as moderately to highly accurate. We also provide the m-Accuracy macro for download and the paper-based microplate designs for printing, in order to collaborate with the scientific community and facilitate further studies using this platform. The improvement of these diagnostic tools can bring this technology for those who need it, contributing to population health and well-being.

Determinação do limiar de anaerobiose de idosos saudáveis: comparação entre diferentes métodos

OBJECTIVE: To determine the anaerobic threshold by the graphic visual ventilatory method and the Hinkley and heteroscedastic mathematical models, applied to heart rate, myoelectric root mean square (RMS) signal and VCO2 datasets, and to compare the anaerobic threshold obtained by the three methods. METHOD: Nine active elderly subjects were studied (aged 61.4 ± 1.8 years) during a ramp-load continuous dynamic physical exercise test on a cycle ergometer, with power ranging from 10 to 15 Watts/min. Beat-to-beat heart rate data, electromyographic data from the surface of the vastus lateralis muscle, and breath-to-breath ventilatory data were collected. After applying mathematical models and identifying the behavioral shift points, these power levels, heart rates and VO2 values were noted and these were compared and correlated with those obtained by the graphic visual model (gold standard). The Friedman test for multiple comparisons and the Spearman correlation test were utilized (significance level: 5%). RESULTS: No significant differences were found in relation to the gold standard, between the power levels, VO2 values and heart rates at the anaerobic threshold identified by the different models. Significant correlations were found between the heart rates identified by the mathematical models, between the VO2 values identified by the heart rates, and between power rates only when identified by the Hinkley model applied to myoelectric RMS signal data. CONCLUSION: In this study group, the mathematical models were shown to be adequate for non-invasively determining the anaerobic threshold. Both models worked best on the heart rate data, followed by VCO2 and RMS.

Data-Driven Reversible Jump for QTL Mapping.

We propose a birth–death–merge data-driven reversible jump (DDRJ) for multiple-QTL mapping where the phenotypic trait is modeled as a linear function of the additive and dominance effects of the unknown QTL genotypes. We compare the performance of the proposed methodology, usual reversible jump (RJ) and multiple-interval mapping (MIM), using simulated and real data sets. Compared with RJ, DDRJ shows a better performance to estimate the number of QTLs and their locations on the genome mainly when the QTLs effect is moderate, basically as a result of better mixing for transdimensional moves. The inclusion of a merge step of consecutive QTLs in DDRJ is efficient, under tested conditions, to avoid the split of true QTL’s effects between false QTLs and, consequently, selection of the wrong model. DDRJ is also more precise to estimate the QTLs location than MIM in which the number of QTLs need to be specified in advance. As DDRJ is more efficient to identify and characterize QTLs with smaller effect, this method also appears to be useful and brings contributions to identifying single-nucleotide polymorphisms (SNPs) that usually have a small effect on phenotype.

Mixture models with an unknown number of components via a new posterior split–merge MCMC algorithm

Abstract In this paper we introduce a Bayesian analysis for mixture models with an unknown number of components via a new posterior split–merge MCMC algorithm. Our strategy for splitting is based on data in which allocation probabilities are calculated based on posterior distribution from the previously allocated observations. This procedure is easy to be implemented and determines a quick split proposal. The acceptance probability for split–merge movements are calculated according to metropolised Carlin and Chib’s procedure. The performance of the proposed algorithm is verified using artificial datasets as well as two real datasets. The first real data set is the benchmark galaxy data, while the second is the publicly available data set on Escherichia coli bacterium.

Bayesian computational methods for sampling from the posterior distribution of a bivariate survival model, based on AMH copula in the presence of right-censored data

In this paper, we study the performance of Bayesian computational methods to estimate the parameters of a bivariate survival model based on the Ali–Mikhail–Haq copula with marginal distributions given by Weibull distributions. The estimation procedure was based on Monte Carlo Markov Chain (MCMC) algorithms. We present three version of the Metropolis–Hastings algorithm: Independent Metropolis–Hastings (IMH), Random Walk Metropolis (RWM) and Metropolis–Hastings with a natural-candidate generating density (MH). Since the creation of a good candidate generating density in IMH and RWM may be difficult, we also describe how to update a parameter of interest using the slice sampling (SS) method. A simulation study was carried out to compare the performances of the IMH, RWM and SS. A comparison was made using the sample root mean square error as an indicator of performance. Results obtained from the simulations show that the SS algorithm is an effective alternative to the IMH and RWM methods when simulating values from the posterior distribution, especially for small sample sizes. We also applied these methods to a real data set.

Bayesian estimation for a mixture of simplex distributions with an unknown number of components: HDI analysis in Brazil

ABSTRACTVariables taking value in (0,1), such as rates or proportions, are frequently analyzed by researchers, for instance, political and social data, as well as the Human Development Index (HDI). However, sometimes this type of data cannot be modeled adequately using a unique distribution. In this case, we can use a mixture of distributions, which is a powerful and flexible probabilistic tool. This manuscript deals with a mixture of simplex distributions to model proportional data. A fully Bayesian approach is proposed for inference which includes a reversible-jump Markov Chain Monte Carlo procedure. The usefulness of the proposed approach is confirmed by using of the simulated mixture data from several different scenarios and by using the methodology to analyze municipal HDI data of cities (or towns) in the Northeast region and Sao Paulo state in Brazil. The analysis shows that among the cities in the Northeast, some appear to have a similar HDI to other cities in Sao Paulo state.ABSTRACT Variables taking value in , such as rates or proportions, are frequently analyzed by researchers, for instance, political and social data, as well as the Human Development Index (HDI). However, sometimes this type of data cannot be modeled adequately using a unique distribution. In this case, we can use a mixture of distributions, which is a powerful and flexible probabilistic tool. This manuscript deals with a mixture of simplex distributions to model proportional data. A fully Bayesian approach is proposed for inference which includes a reversible-jump Markov Chain Monte Carlo procedure. The usefulness of the proposed approach is confirmed by using of the simulated mixture data from several different scenarios and by using the methodology to analyze municipal HDI data of cities (or towns) in the Northeast region and São Paulo state in Brazil. The analysis shows that among the cities in the Northeast, some appear to have a similar HDI to other cities in São Paulo state.