A Bayesian Approach for Modeling and Forecasting Solar Photovoltaic Power Generation.

In this paper, we propose a Bayesian approach to estimate the curve of a function f(·) that models the solar power generated at moments per day for days and to forecast the curve for the (n+1)th day by using the history of recorded values. We assume that f(·) is an unknown function and adopt a Bayesian model with a Gaussian-process prior on the vector of values f(t)=f(1),…, f(k). An advantage of this approach is that we may estimate the curves of f(·) and fn+1(·) as "smooth functions" obtained by interpolating between the points generated from a -variate normal distribution with appropriate mean vector and covariance matrix.

Brain activation during fear extinction recall in unmedicated patients with obsessive-compulsive disorder.

Specific brain activation patterns during fear conditioning and the recall of previously extinguished fear responses have been associated with obsessive-compulsive disorder (OCD). However, further replication studies are necessary. We measured skin-conductance response and blood oxygenation level-dependent responses in unmedicated adult patients with OCD (n = 27) and healthy participants (n = 22) submitted to a two-day fear-conditioning experiment comprising fear conditioning, extinction (day 1) and extinction recall (day 2).

Modeling Overdispersed Dengue Data via Poisson Inverse Gaussian Regression Model: A Case Study in the City of Campo Grande, MS, Brazil.

Dengue fever is a tropical disease transmitted mainly by the female mosquito that affects millions of people every year. As there is still no safe and effective vaccine, currently the best way to prevent the disease is to control the proliferation of the transmitting mosquito. Since the proliferation and life cycle of the mosquito depend on environmental variables such as temperature and water availability, among others, statistical models are needed to understand the existing relationships between environmental variables and the recorded number of dengue cases and predict the number of cases for some future time interval.

Accuracy and Sensitivity of Clinical Parameters in Predicting Successful Extubation in Patients with Acute Brain Injury.

Background: Brain injuries are frequent causes of intubation and mechanical ventilation. The aim of this study was to investigate the accuracy and sensitivity of clinical parameters in predicting successful extubation in patients with acute brain injury.

Methods: Six hundred and forty-four patients assisted at a high-complexity hospital were recruited.

Piecewise Modeling the Accumulated Daily Growth of COVID-19 Deaths: The Case of the State of São Paulo, Brazil.

The pandemic scenery caused by the new coronavirus, called SARS-CoV-2, increased interest in statistical models capable of projecting the evolution of the number of cases (and associated deaths) due to COVID-19 in countries, states and/or cities. This interest is mainly due to the fact that the projections may help the government agencies in making decisions in relation to procedures of prevention of the disease. Since the growth of the number of cases (and deaths) of COVID-19, in general, has presented a heterogeneous evolution over time, it is important that the modeling procedure is capable of identifying periods with different growth rates and proposing an adequate model for each period.

Ventilatory inefficiency during graded exercise in COPD: A pragmatic approach.

Background/objective: The current approach to measuring ventilatory (in)efficiency (V' -V'CO slope, nadir and intercept) presents critical drawbacks in the evaluation of COPD subjects, owing mainly to mechanical ventilatory constraints. Thus, we aimed to compare the current approach with a new method we have developed for ventilatory efficiency calculation.

Methods: The new procedure was based on measuring the amount of CO cleared by the lungs (V'CO , L/min) plotted against a predefined range of increase in minute ventilation (V' ) (ten-fold increase based on semilog scale) during incremental exercise to symptom-limited maximum tolerance.

Left ventricular diastolic dysfunction and exertional ventilatory inefficiency in COPD.

Background: Left ventricular diastolic dysfunction (LVDD) is highly prevalent in COPD and conflicting results have emerged regarding the consequences on exercise capacity in the 6MWT. We sought to examine the ventilatory efficiency and variability metrics as the primary endpoint and aerobic capacity (V'O) as the secondary endpoint.

Methods: Forty subjects were included and submitted to comprehensive lung function tests, detailed pulsed-Doppler echocardiography, and cardiopulmonary exercise testing.

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).

A Bayesian approach for decision making on the identification of genes with different expression levels: an application to Escherichia coli bacterium data.

A common interest in gene expression data analysis is to identify from a large pool of candidate genes the genes that present significant changes in expression levels between a treatment and a control biological condition. Usually, it is done using a statistic value and a cutoff value that are used to separate the genes differentially and nondifferentially expressed. In this paper, we propose a Bayesian approach to identify genes differentially expressed calculating sequentially credibility intervals from predictive densities which are constructed using the sampled mean treatment effect from all genes in study excluding the treatment effect of genes previously identified with statistical evidence for difference.