The B0.5IVe CoRoT target HD 49330 I. Photometric analysis from CoRoT data

Context. Be stars undergo outbursts producing a circumstellar disk from the ejected material. The beating of non-radial pulsations has been put forward as a possible mechanism of ejection. Aims. We analyze the pulsational behavior of the early B0.5IVe star HD 49330 observed during the first CoRoT long run towards the Galactical anticenter (LRA1). This Be star is located close to the lower edge of the beta Cephei instability strip in the HR diagram and showed a 0.03 mag outburst during the CoRoT observations. It is thus an ideal case for testing the aforementioned hypothesis. Methods. We analyze the CoRoT light curve of HD 49330 using Fourier methods and non-linear least square fitting. Results. In this star, we find pulsation modes typical of beta Cep stars (p modes) and SPB stars (g modes) with amplitude variations along the run directly correlated with the outburst. These results provide new clues about the origin of the Be phenomenon as well as strong constraints on the seismic modelling of Be stars.

Dispersive analysis of KL mu 3 and KLe3 scalar and vector form factors using KTeV data

Using the published KTeV samples of K(L) -> pi(+/-)e(-/+)nu and K(L) -> pi(+/-)mu(-/+)nu decays, we perform a reanalysis of the scalar and vector form factors based on the dispersive parametrization. We obtain phase-space integrals I(K)(e) = 0.15446 +/- 0.00025 and I(K)(mu) = 0.10219 +/- 0.00025. For the scalar form factor parametrization, the only free parameter is the normalized form factor value at the Callan-Treiman point (C); our best-fit results in InC = 0.1915 +/- 0.0122. We also study the sensitivity of C to different parametrizations of the vector form factor. The results for the phase-space integrals and C are then used to make tests of the standard model. Finally, we compare our results with lattice QCD calculations of F(K)/F(pi) and f(+)(0).

Multidimensional cluster stability analysis from a Brazilian Bradyrhizobium sp RFLP/PCR data set

The taxonomy of the N(2)-fixing bacteria belonging to the genus Bradyrhizobium is still poorly refined, mainly due to conflicting results obtained by the analysis of the phenotypic and genotypic properties. This paper presents an application of a method aiming at the identification of possible new clusters within a Brazilian collection of 119 Bradryrhizobium strains showing phenotypic characteristics of B. japonicum and B. elkanii. The stability was studied as a function of the number of restriction enzymes used in the RFLP-PCR analysis of three ribosomal regions with three restriction enzymes per region. The method proposed here uses Clustering algorithms with distances calculated by average-linkage clustering. Introducing perturbations using sub-sampling techniques makes the stability analysis. The method showed efficacy in the grouping of the species B. japonicum and B. elkanii. Furthermore, two new clusters were clearly defined, indicating possible new species, and sub-clusters within each detected cluster. (C) 2008 Elsevier B.V. All rights reserved.

Artificial neural networks analysis of Sao Paulo subway tunnel settlement data

Artificial neural networks have been used to analyze a number of engineering problems, including settlement caused by different tunneling methods in various types of ground mass. This paper focuses on settlement over shotcrete- supported tunnels on Sao Paulo subway line 2 (West Extension) that were excavated in Tertiary sediments using the sequential excavation method. The adjusted network is a good tool for predicting settlement above new tunnels to be excavated in similar conditions. The influence of network training parameters on the quality of results is also discussed. (C) 2007 Elsevier Ltd. All rights reserved.

Log-modified Weibull regression models with censored data: Sensitivity and residual analysis

This paper proposes a regression model considering the modified Weibull distribution. This distribution can be used to model bathtub-shaped failure rate functions. Assuming censored data, we consider maximum likelihood and Jackknife estimators for the parameters of the model. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and we also present some ways to perform global influence. Besides, for different parameter settings, sample sizes and censoring percentages, various simulations are performed and the empirical distribution of the modified deviance residual is displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended for a martingale-type residual in log-modified Weibull regression models with censored data. Finally, we analyze a real data set under log-modified Weibull regression models. A diagnostic analysis and a model checking based on the modified deviance residual are performed to select appropriate models. (c) 2008 Elsevier B.V. All rights reserved.

Regression models for grouped survival data: Estimation and sensitivity analysis

In this study, regression models are evaluated for grouped survival data when the effect of censoring time is considered in the model and the regression structure is modeled through four link functions. The methodology for grouped survival data is based on life tables, and the times are grouped in k intervals so that ties are eliminated. Thus, the data modeling is performed by considering the discrete models of lifetime regression. The model parameters are estimated by using the maximum likelihood and jackknife methods. To detect influential observations in the proposed models, diagnostic measures based on case deletion, which are denominated global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to those measures, the local influence and the total influential estimate are also employed. Various simulation studies are performed and compared to the performance of the four link functions of the regression models for grouped survival data for different parameter settings, sample sizes and numbers of intervals. Finally, a data set is analyzed by using the proposed regression models. (C) 2010 Elsevier B.V. All rights reserved.

Equational Reasoning as a Tool for Data Analysis

A combination of deductive reasoning, clustering, and inductive learning is given as an example of a hybrid system for exploratory data analysis. Visualization is replaced by a dialogue with the data.

A framework for electricity price spike analysis with advanced data mining methods

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.

Data analysis in multiple-frequency bioelectrical impedance analysis

The performance of three analytical methods for multiple-frequency bioelectrical impedance analysis (MFBIA) data was assessed. The methods were the established method of Cole and Cole, the newly proposed method of Siconolfi and co-workers and a modification of this procedure. Method performance was assessed from the adequacy of the curve fitting techniques, as judged by the correlation coefficient and standard error of the estimate, and the accuracy of the different methods in determining the theoretical values of impedance parameters describing a set of model electrical circuits. The experimental data were well fitted by all curve-fitting procedures (r = 0.9 with SEE 0.3 to 3.5% or better for most circuit-procedure combinations). Cole-Cole modelling provided the most accurate estimates of circuit impedance values, generally within 1-2% of the theoretical values, followed by the Siconolfi procedure using a sixth-order polynomial regression (1-6% variation). None of the methods, however, accurately estimated circuit parameters when the measured impedances were low (<20 Omega) reflecting the electronic limits of the impedance meter used. These data suggest that Cole-Cole modelling remains the preferred method for the analysis of MFBIA data.

Analysis of long-term light-trap data for Helicoverpa spp. (Lepidoptera : Noctuidae) in Australia: the effect of climate and crop host plants

Regression analyses of a long series of light-trap catches at Narrabri, Australia, were used to describe the seasonal dynamics of Helicoverpa armigera (Hubner). The size of the second generation was significantly related to the size of the first generation, to winter rainfall, which had a positive effect, and to spring rainfall which had a negative effect. These variables accounted for up to 96% of the variation in size of the second generation from year to year. Rainfall and crop hosts were also important for the size of the third generation. The area and tonnage of many potential host crops were significantly correlated with winter rain. When winter rain was omitted from the analysis, the sizes of both the second and third generations could be expressed as a function of the size of the previous generation and of the areas planted to lucerne, sorghum and maize. Lucerne and maize always had positive coefficients and sorghum a negative one. We extended our analysis to catches of H. punctigera (Wallengren), which declines in abundance after the second generation. Winter rain had a positive effect on the sizes of the second and third generations, and rain in spring or early summer had a negative effect. Only the area grown to lucerne had a positive effect on abundance. Forecasts of pest levels from a few months to a few weeks in advance are discussed, along with the improved understanding of the seasonal dynamics of both species and the significance of crops in the management of insecticide resistance for H. armigera.

Analysis of proximal femur DXA scans in growing children: Comparisons of different protocols for cross-sectional 8-month and 7-year longitudinal data

Dual-energy X-ray absorptiometry (DXA) is a widely used method for measuring bone mineral in the growing skeleton. Because scan analysis in children offers a number of challenges, we compared DXA results using six analysis methods at the total proximal femur (PF) and five methods at the femoral neck (FN), In total we assessed 50 scans (25 boys, 25 girls) from two separate studies for cross-sectional differences in bone area, bone mineral content (BMC), and areal bone mineral density (aBMD) and for percentage change over the short term (8 months) and long term (7 years). At the proximal femur for the short-term longitudinal analysis, there was an approximate 3.5% greater change in bone area and BMC when the global region of interest (ROI) was allowed to increase in size between years as compared with when the global ROI was held constant. Trend analysis showed a significant (p < 0.05) difference between scan analysis methods for bone area and BMC across 7 years. At the femoral neck, cross-sectional analysis using a narrower (from default) ROI, without change in location, resulted in a 12.9 and 12.6% smaller bone area and BMC, respectively (both p < 0.001), Changes in FN area and BMC over 8 months were significantly greater (2.3 %, p < 0.05) using a narrower FN rather than the default ROI, Similarly, the 7-year longitudinal data revealed that differences between scan analysis methods were greatest when the narrower FN ROI was maintained across all years (p < 0.001), For aBMD there were no significant differences in group means between analysis methods at either the PF or FN, Our findings show the need to standardize the analysis of proximal femur DXA scans in growing children.

Long-term safety and efficacy of percutaneous coronary intervention with stenting and coronary artery bypass surgery for multivessel coronary artery disease - A meta-analysis with 5-year patient-level data from the ARTS, ERACI-II, MASS-II, and SoS trials

Background-Randomized trials that studied clinical outcomes after percutaneous coronary intervention (PCI) with bare metal stenting versus coronary artery bypass grafting (CABG) are underpowered to properly assess safety end points like death, stroke, and myocardial infarction. Pooling data from randomized controlled trials increases the statistical power and allows better assessment of the treatment effect in high-risk subgroups. Methods and Results-We performed a pooled analysis of 3051 patients in 4 randomized trials evaluating the relative safety and efficacy of PCI with stenting and CABG at 5 years for the treatment of multivessel coronary artery disease. The primary end point was the composite end point of death, stroke, or myocardial infarction. The secondary end point was the occurrence of major adverse cardiac and cerebrovascular accidents, death, stroke, myocardial infarction, and repeat revascularization. We tested for heterogeneities in treatment effect in patient subgroups. At 5 years, the cumulative incidence of death, myocardial infarction, and stroke was similar in patients randomized to PCI with stenting versus CABG (16.7% versus 16.9%, respectively; hazard ratio, 1.04, 95% confidence interval, 0.86 to 1.27; P = 0.69). Repeat revascularization, however, occurred significantly more frequently after PCI than CABG (29.0% versus 7.9%, respectively; hazard ratio, 0.23; 95% confidence interval, 0.18 to 0.29; P<0.001). Major adverse cardiac and cerebrovascular events were significantly higher in the PCI than the CABG group (39.2% versus 23.0%, respectively; hazard ratio, 0.53; 95% confidence interval, 0.45 to 0.61; P<0.001). No heterogeneity of treatment effect was found in the subgroups, including diabetic patients and those presenting with 3-vessel disease. Conclusions-In this pooled analysis of 4 randomized trials, PCI with stenting was associated with a long-term safety profile similar to that of CABG. However, as a result of persistently lower repeat revascularization rates in the CABG patients, overall major adverse cardiac and cerebrovascular event rates were significantly lower in the CABG group at 5 years.

Analyzing the connectivity between regions of interest: An approach based on cluster Granger causality for NRI data analysis

The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. In this context, in functional neuroimaging, connectivity analysis is a major tool for the exploration and characterization of the information flow between specialized brain regions. In most functional magnetic resonance imaging (fMRI) studies, connectivity analysis is carried out by first selecting regions of interest (ROI) and then calculating an average BOLD time series (across the voxels in each cluster). Some studies have shown that the average may not be a good choice and have suggested, as an alternative, the use of principal component analysis (PCA) to extract the principal eigen-time series from the ROI(s). In this paper, we introduce a novel approach called cluster Granger analysis (CGA) to study connectivity between ROIs. The main aim of this method was to employ multiple eigen-time series in each ROI to avoid temporal information loss during identification of Granger causality. Such information loss is inherent in averaging (e.g., to yield a single ""representative"" time series per ROI). This, in turn, may lead to a lack of power in detecting connections. The proposed approach is based on multivariate statistical analysis and integrates PCA and partial canonical correlation in a framework of Granger causality for clusters (sets) of time series. We also describe an algorithm for statistical significance testing based on bootstrapping. By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI. (c) 2010 Elsevier Inc. All rights reserved.