Modelagem da irradiação direta na incidência normal em Botucatu: aprendizado de máquina, estatístico e linke

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Mayaro Virus Infection in Amazonia: A Multimodel Inference Approach to Risk Factor Assessment

Background: Arboviral diseases are major global public health threats. Yet, our understanding of infection risk factors is, with a few exceptions, considerably limited. A crucial shortcoming is the widespread use of analytical methods generally not suited for observational data - particularly null hypothesis-testing (NHT) and step-wise regression (SWR). Using Mayaro virus (MAYV) as a case study, here we compare information theory-based multimodel inference (MMI) with conventional analyses for arboviral infection risk factor assessment. Methodology/Principal Findings: A cross-sectional survey of anti-MAYV antibodies revealed 44% prevalence (n = 270 subjects) in a central Amazon rural settlement. NHT suggested that residents of village-like household clusters and those using closed toilet/latrines were at higher risk, while living in non-village-like areas, using bednets, and owning fowl, pigs or dogs were protective. The "minimum adequate" SWR model retained only residence area and bednet use. Using MMI, we identified relevant covariates, quantified their relative importance, and estimated effect-sizes (beta +/- SE) on which to base inference. Residence area (beta(Village) = 2.93 +/- 0.41; beta(Upland) = -0.56 +/- 0.33, beta(Riverbanks) = -2.37 +/- 0.55) and bednet use (beta = -0.95 +/- 0.28) were the most important factors, followed by crop-plot ownership (beta = 0.39 +/- 0.22) and regular use of a closed toilet/latrine (beta = 0.19 +/- 0.13); domestic animals had insignificant protective effects and were relatively unimportant. The SWR model ranked fifth among the 128 models in the final MMI set. Conclusions/Significance: Our analyses illustrate how MMI can enhance inference on infection risk factors when compared with NHT or SWR. MMI indicates that forest crop-plot workers are likely exposed to typical MAYV cycles maintained by diurnal, forest dwelling vectors; however, MAYV might also be circulating in nocturnal, domestic-peridomestic cycles in village-like areas. This suggests either a vector shift (synanthropic mosquitoes vectoring MAYV) or a habitat/habits shift (classical MAYV vectors adapting to densely populated landscapes and nocturnal biting); any such ecological/adaptive novelty could increase the likelihood of MAYV emergence in Amazonia.

Predicting Delta Ferrite Content in Stainless Steel Castings

The distribution of delta ferrite fraction was measured with the magnetic method in specimens of different stainless steel compositions cast by the investment casting (lost wax) process. Ferrite fraction measurements published in the literature for stainless steel cast samples were added to the present work data, enabling an extensive analysis about practical methods to calculate delta ferrite fractions in stainless steel castings. Nineteen different versions of practical methods were formed using Schaeffler, DeLong, and Siewert diagrams and the nickel and chromium equivalent indexes suggested by several authors. These methods were evaluated by a detailed statistical analysis, showing that the Siewert diagram, including its equivalent indexes and iso-ferrite lines, gives the lowest relative errors between calculated and measured delta ferrite fractions. Although originally created for stainless steel welds, this diagram gives relative errors lower than those for the current ASTM standard method (800/A 800M-01), developed to predict ferrite fractions in stainless steel castings. Practical methods originated from a combination of different chromium/nickel equivalent indexes and the iso-ferrite lines from Schaeffler diagram give the lowest relative errors when compared with combinations using other iso-ferrite line diagrams. For the samples cast in the present work, an increase in cooling rate from 0.78 to 2.7 K/s caused a decrease in the delta ferrite fraction, but a statistical hypothesis test revealed that this effect is significant in only 50% of the samples that have ferrite in their microstructures.

Assessment of variance components in nonlinear mixed-effects elliptical models

The issue of assessing variance components is essential in deciding on the inclusion of random effects in the context of mixed models. In this work we discuss this problem by supposing nonlinear elliptical models for correlated data by using the score-type test proposed in Silvapulle and Silvapulle (1995). Being asymptotically equivalent to the likelihood ratio test and only requiring the estimation under the null hypothesis, this test provides a fairly easy computable alternative for assessing one-sided hypotheses in the context of the marginal model. Taking into account the possible non-normal distribution, we assume that the joint distribution of the response variable and the random effects lies in the elliptical class, which includes light-tailed and heavy-tailed distributions such as Student-t, power exponential, logistic, generalized Student-t, generalized logistic, contaminated normal, and the normal itself, among others. We compare the sensitivity of the score-type test under normal, Student-t and power exponential models for the kinetics data set discussed in Vonesh and Carter (1992) and fitted using the model presented in Russo et al. (2009). Also, a simulation study is performed to analyze the consequences of the kurtosis misspecification.

Cointegration: Bayesian Significance Test

To estimate causal relationships, time series econometricians must be aware of spurious correlation, a problem first mentioned by Yule (1926). To deal with this problem, one can work either with differenced series or multivariate models: VAR (VEC or VECM) models. These models usually include at least one cointegration relation. Although the Bayesian literature on VAR/VEC is quite advanced, Bauwens et al. (1999) highlighted that "the topic of selecting the cointegrating rank has not yet given very useful and convincing results". The present article applies the Full Bayesian Significance Test (FBST), especially designed to deal with sharp hypotheses, to cointegration rank selection tests in VECM time series models. It shows the FBST implementation using both simulated and available (in the literature) data sets. As illustration, standard non informative priors are used.

Comparing non-stationary and irregularly spaced time series

In this paper, we present approximate distributions for the ratio of the cumulative wavelet periodograms considering stationary and non-stationary time series generated from independent Gaussian processes. We also adapt an existing procedure to use this statistic and its approximate distribution in order to test if two regularly or irregularly spaced time series are realizations of the same generating process. Simulation studies show good size and power properties for the test statistic. An application with financial microdata illustrates the test usefulness. We conclude advocating the use of these approximate distributions instead of the ones obtained through randomizations, mainly in the case of irregular time series. (C) 2012 Elsevier B.V. All rights reserved.

Coping Style and Depressive Symptomatology during Pregnancy in a Private Setting Sample

Aim: to evaluate the association of antenatal depressive symptomatology (AD) with life events and coping styles, the hypothesis was that certain coping strategies are associated to depressive symptomatology. Methods: we performed a cross sectional study of 312 women attending a private clinic in the city of Osasco, Sao Paulo from 27/05/1998 to 13/05/2002. The following instruments were used: Beck Depression Inventory (BDI), Holmes and Rahe Schedule of Recent Events (SSRS), Folkman and Lazarus Ways of Coping Questionnaire and questionnaire with social-demographic and obstetric data. Inclusion criteria: women with 110 past history of depression, psychiatric treatment, alcohol or drug abuse and no clinical-obstetrical complications. Odds ratios and 95% CI were used to examine the association between AD (according to BDI) and exposures variables. Hypothesis testing was done with chi(2) tests and a p value < .05. Results: AD occurred in 21.1% of pregnant women. By the univariate analyses, education, number of pregnancies, previous abortion, husband income, situation of marriage and score of SSRS were associated with AD. All coping styles were associated with AD, except seeking support and positive reappraisal. By the multivariate analyses, four coping styles were kept in the final model: confront (p = .039), accepting responsibility (p < .001), escape-avoidance (p = .002), problem-solving (p = .005). Conclusions: AD was highly prevalent and was associated with maladaptive coping styles.

Aplicaciones del caos determinista a la detección de outliers

[EN]In this paper the authors show that techniques employed in the prediction of chaotic time series" can also be applied to detection of outliers. A definition of outlier" lS provided and a theorem on hypothesis testing is also proved.

Kinematics of the Sicily and Calabria Subduction System from Elastic Block Modeling of GPS Data

We use data from about 700 GPS stations in the EuroMediterranen region to investigate the present-day behavior of the the Calabrian subduction zone within the Mediterranean-scale plates kinematics and to perform local scale studies about the strain accumulation on active structures. We focus attenction on the Messina Straits and Crati Valley faults where GPS data show extentional velocity gradients of ∼3 mm/yr and ∼2 mm/yr, respectively. We use dislocation model and a non-linear constrained optimization algorithm to invert for fault geometric parameters and slip-rates and evaluate the associated uncertainties adopting a bootstrap approach. Our analysis suggest the presence of two partially locked normal faults. To investigate the impact of elastic strain contributes from other nearby active faults onto the observed velocity gradient we use a block modeling approach. Our models show that the inferred slip-rates on the two analyzed structures are strongly impacted by the assumed locking width of the Calabrian subduction thrust. In order to frame the observed local deformation features within the present- day central Mediterranean kinematics we realyze a statistical analysis testing the indipendent motion (w.r.t. the African and Eurasias plates) of the Adriatic, Cal- abrian and Sicilian blocks. Our preferred model confirms a microplate like behaviour for all the investigated blocks. Within these kinematic boundary conditions we fur- ther investigate the Calabrian Slab interface geometry using a combined approach of block modeling and χ2ν statistic. Almost no information is obtained using only the horizontal GPS velocities that prove to be a not sufficient dataset for a multi-parametric inversion approach. Trying to stronger constrain the slab geometry we estimate the predicted vertical velocities performing suites of forward models of elastic dislocations varying the fault locking depth. Comparison with the observed field suggest a maximum resolved locking depth of 25 km.

Advantages and Applications of Transforming Empirical Model Input Space with Dimensional Models

Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.

Modeling Breastmilk Infectivity in HIV-1 Infected Mothers

Estimation of breastmilk infectivity in HIV-1 infected mothers is difficult because transmission can occur while the fetus is in-utero, during delivery, or through breastfeeding. Since transmission can only be detected through periodic testing, however, it may be impossible to determine the actual mode of transmission in any individual child. In this paper we develop a model to estimate breastmilk infectivity as well as the probabilities of in-utero and intrapartum transmission. In addition, the model allows separate estimation of early and late breastmilk infectivity and individual variation in maternal infectivity. Methods for hypothesis testing of binary risk factors and a method for assessing goodness of fit are also described. Data from a randomized trial of breastfeeding versus formula feeding among HIV-1 infected mothers in Nairobi, Kenya are used to illustrate the methods.

Plurality of tree species responses to drought perturbation in Bornean tropical rain forest

Drought perturbation driven by the El Niño Southern Oscillation (ENSO) is a principal stochastic variable determining the dynamics of lowland rain forest in S.E. Asia. Mortality, recruitment and stem growth rates at Danum in Sabah (Malaysian Borneo) were recorded in two 4-ha plots (trees ≥ 10 cm gbh) for two periods, 1986–1996 and 1996–2001. Mortality and growth were also recorded in a sample of subplots for small trees (10 to <50 cm gbh) in two sub-periods, 1996–1999 and 1999–2001. Dynamics variables were employed to build indices of drought response for each of the 34 most abundant plot-level species (22 at the subplot level), these being interval-weighted percentage changes between periods and sub-periods. A significant yet complex effect of the strong 1997/1998 drought at the forest community level was shown by randomization procedures followed by multiple hypothesis testing. Despite a general resistance of the forest to drought, large and significant differences in short-term responses were apparent for several species. Using a diagrammatic form of stability analysis, different species showed immediate or lagged effects, high or low degrees of resilience or even oscillatory dynamics. In the context of the local topographic gradient, species’ responses define the newly termed perturbation response niche. The largest responses, particularly for recruitment and growth, were among the small trees, many of which are members of understorey taxa. The results bring with them a novel approach to understanding community dynamics: the kaleidoscopic complexity of idiosyncratic responses to stochastic perturbations suggests that plurality, rather than neutrality, of responses may be essential to understanding these tropical forests. The basis to the various responses lies with the mechanisms of tree-soil water relations which are physiologically predictable: the timing and intensity of the next drought, however, is not. To date, environmental stochasticity has been insufficiently incorporated into models of tropical forest dynamics, a step that might considerably improve the reality of theories about these globally important ecosystems.

Chronic schizophrenics with positive symptomatology have shortened EEG microstate durations

OBJECTIVE: In young, first-episode, never-treated schizophrenics compared with controls, (a) generally shorter durations of EEG microstates were reported (Koukkou et al., Brain Topogr 6 (1994) 251; Kinoshita et al., Psychiatry Res Neuroimaging 83 (1998) 58), and (b) specifically, shorter duration of a particular class of microstates (Koenig et al., Eur Arch Psychiatry Clin Neurosci 249 (1999) 205). We now examined whether older, chronic schizophrenic patients with positive symptomatology also show these characteristics. METHODS: Multichannel resting EEG (62.2 s/subject) from two subject groups, 14 patients (36.1+/-10.2 years old) and 13 controls (35.1+/-8.2 years old), all males, was analyzed into microstates using a global approach for microstate analysis that clustered the microstates into 4 classes (Koenig et al., 1999). RESULTS: (a) Hypothesis testing of general microstate shortening supported a trend (P=0.064). (b) Two-way repeated measure ANOVA (two subject groupsx4 microstate classes) showed a significant group effect for microstate duration. Posthoc tests revealed that a microstate class with brain electric field orientation from left central to right central-posterior had significantly shorter microstates in patients than controls (68.5 vs. 76.1 ms, P=0.034). CONCLUSIONS: The results were in line with the results from young, never-treated, productive patients, thus suggesting that in schizophrenic information processing, one class of mental operations might intermittently cause deviant mental constructs because of premature termination of processing.

Computer simulation of glioma growth and morphology.

Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. Here, we address this multi-scalar problem by employing a novel predictive three-dimensional mathematical and computational model based on first-principle equations (conservation laws of physics) that describe mathematically the diffusion of cell substrates and other processes determining tumor mass growth and invasion. The model uses conserved variables to represent known determinants of glioma behavior, e.g., cell density and oxygen concentration, as well as biological functional relationships and parameters linking phenomena at different scales whose specific forms and values are hypothesized and calculated based on in vitro and in vivo experiments and from histopathology of tissue specimens from human gliomas. This model enables correlation of glioma morphology to tumor growth by quantifying interdependence of tumor mass on the microenvironment (e.g., hypoxia, tissue disruption) and on the cellular phenotypes (e.g., mitosis and apoptosis rates, cell adhesion strength). Once functional relationships between variables and associated parameter values have been informed, e.g., from histopathology or intra-operative analysis, this model can be used for disease diagnosis/prognosis, hypothesis testing, and to guide surgery and therapy. In particular, this tool identifies and quantifies the effects of vascularization and other cell-scale glioma morphological characteristics as predictors of tumor-scale growth and invasion.

Computer simulation of glioma growth and morphology.

Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. Here, we address this multi-scalar problem by employing a novel predictive three-dimensional mathematical and computational model based on first-principle equations (conservation laws of physics) that describe mathematically the diffusion of cell substrates and other processes determining tumor mass growth and invasion. The model uses conserved variables to represent known determinants of glioma behavior, e.g., cell density and oxygen concentration, as well as biological functional relationships and parameters linking phenomena at different scales whose specific forms and values are hypothesized and calculated based on in vitro and in vivo experiments and from histopathology of tissue specimens from human gliomas. This model enables correlation of glioma morphology to tumor growth by quantifying interdependence of tumor mass on the microenvironment (e.g., hypoxia, tissue disruption) and on the cellular phenotypes (e.g., mitosis and apoptosis rates, cell adhesion strength). Once functional relationships between variables and associated parameter values have been informed, e.g., from histopathology or intra-operative analysis, this model can be used for disease diagnosis/prognosis, hypothesis testing, and to guide surgery and therapy. In particular, this tool identifies and quantifies the effects of vascularization and other cell-scale glioma morphological characteristics as predictors of tumor-scale growth and invasion.