Bootstrap confidence intervals for industrial recurrent event data

Industrial recurrent event data where an event of interest can be observed more than once in a single sample unit are presented in several areas, such as engineering, manufacturing and industrial reliability. Such type of data provide information about the number of events, time to their occurrence and also their costs. Nelson (1995) presents a methodology to obtain asymptotic confidence intervals for the cost and the number of cumulative recurrent events. Although this is a standard procedure, it can not perform well in some situations, in particular when the sample size available is small. In this context, computer-intensive methods such as bootstrap can be used to construct confidence intervals. In this paper, we propose a technique based on the bootstrap method to have interval estimates for the cost and the number of cumulative events. One of the advantages of the proposed methodology is the possibility for its application in several areas and its easy computational implementation. In addition, it can be a better alternative than asymptotic-based methods to calculate confidence intervals, according to some Monte Carlo simulations. An example from the engineering area illustrates the methodology.

A multiple time scale survival model with a cure fraction

Many recent survival studies propose modeling data with a cure fraction, i.e., data in which part of the population is not susceptible to the event of interest. This event may occur more than once for the same individual (recurrent event). We then have a scenario of recurrent event data in the presence of a cure fraction, which may appear in various areas such as oncology, finance, industries, among others. This paper proposes a multiple time scale survival model to analyze recurrent events using a cure fraction. The objective is analyzing the efficiency of certain interventions so that the studied event will not happen again in terms of covariates and censoring. All estimates were obtained using a sampling-based approach, which allows information to be input beforehand with lower computational effort. Simulations were done based on a clinical scenario in order to observe some frequentist properties of the estimation procedure in the presence of small and moderate sample sizes. An application of a well-known set of real mammary tumor data is provided.

The polysurvival model with long-term survivors

Long-term survival models have historically been considered for analyzing time-to-event data with long-term survivors fraction. However, situations in which a fraction (1 - p) of systems is subject to failure from independent competing causes of failure, while the remaining proportion p is cured or has not presented the event of interest during the time period of the study, have not been fully considered in the literature. In order to accommodate such situations, we present in this paper a new long-term survival model. Maximum likelihood estimation procedure is discussed as well as interval estimation and hypothesis tests. A real dataset illustrates the methodology.

Vertical distribution of benthic invertebrate larvae during an upwelling event along a transect off the tropical Brazilian continental margin

Abundance and composition of marine benthic communities have been relatively well studied in the SE Brazilian coast, but little is known on patterns controlling the distribution of their planktonic larval stages. A survey of larval abundance in the continental margin, using a Multi-Plankton Sampler, was conducted in a cross-shelf transect off Cabo Frio (23 degrees S and 42 degrees W) during a costal upwelling event. Hydrographic conditions were monitored through discrete CDT casts. Chlorophyll-a in the top 100 m of the water column was determined and changes in surface chlorophyll-a was estimated using SeaWiFS images. Based on the larval abundances and the meso-scale hydrodynamics scenario, our results suggest two different processes affecting larval distributions. High larval densities were found nearshore due to the upwelling event associated with high chlorophyll a and strong along shore current. On the continental slope, high larval abundance was associated with a clockwise rotating meander, which may have entrapped larvae from a region located further north (Cabo de Sao Tome, 22 degrees S and 41 degrees W). In mid-shelf areas, our data suggests that vertical migration may likely occur as a response to avoid offshore transport by upwelling plumes and/or cyclonic meanders. The hydrodynamic scenario observed in the study area has two distinct yet extremely important consequences: larval retention on food-rich upwelling areas and the broadening of the tropical domain to southernmost subtropical areas. (C) 2009 Elsevier B.V. All rights reserved.

Detecting an abnormality in a recovery boiler using dynamic multivariate data analysis with parallel coordinate plots

Data visualization techniques are powerful in the handling and analysis of multivariate systems. One such technique known as parallel coordinates was used to support the diagnosis of an event, detected by a neural network-based monitoring system, in a boiler at a Brazilian Kraft pulp mill. Its attractiveness is the possibility of the visualization of several variables simultaneously. The diagnostic procedure was carried out step-by-step going through exploratory, explanatory, confirmatory, and communicative goals. This tool allowed the visualization of the boiler dynamics in an easier way, compared to commonly used univariate trend plots. In addition it facilitated analysis of other aspects, namely relationships among process variables, distinct modes of operation and discrepant data. The whole analysis revealed firstly that the period involving the detected event was associated with a transition between two distinct normal modes of operation, and secondly the presence of unusual changes in process variables at this time.

Underlying Event measurements in pp collisions at root s=0.9 and 7 TeV with the ALICE experiment at the LHC

We present measurements of Underlying Event observables in pp collisions at root s = 0 : 9 and 7 TeV. The analysis is performed as a function of the highest charged-particle transverse momentum p(T),L-T in the event. Different regions are defined with respect to the azimuthal direction of the leading (highest transverse momentum) track: Toward, Transverse and Away. The Toward and Away regions collect the fragmentation products of the hardest partonic interaction. The Transverse region is expected to be most sensitive to the Underlying Event activity. The study is performed with charged particles above three different p(T) thresholds: 0.15, 0.5 and 1.0 GeV/c. In the Transverse region we observe an increase in the multiplicity of a factor 2-3 between the lower and higher collision energies, depending on the track p(T) threshold considered. Data are compared to PYTHIA 6.4, PYTHIA 8.1 and PHOJET. On average, all models considered underestimate the multiplicity and summed p(T) in the Transverse region by about 10-30%.

Repression of osteocyte Wnt/beta-catenin signaling is an early event in the progression of renal osteodystrophy

Chronic kidney diseasemineral bone disorder (CKD-MBD) is defined by abnormalities in mineral and hormone metabolism, bone histomorphometric changes, and/or the presence of soft-tissue calcification. Emerging evidence suggests that features of CKD-MBD may occur early in disease progression and are associated with changes in osteocyte function. To identify early changes in bone, we utilized the jck mouse, a genetic model of polycystic kidney disease that exhibits progressive renal disease. At 6 weeks of age, jck mice have normal renal function and no evidence of bone disease but exhibit continual decline in renal function and death by 20 weeks of age, when approximately 40% to 60% of them have vascular calcification. Temporal changes in serum parameters were identified in jck relative to wild-type mice from 6 through 18 weeks of age and were subsequently shown to largely mirror serum changes commonly associated with clinical CKD-MBD. Bone histomorphometry revealed progressive changes associated with increased osteoclast activity and elevated bone formation relative to wild-type mice. To capture the early molecular and cellular events in the progression of CKD-MBD we examined cell-specific pathways associated with bone remodeling at the protein and/or gene expression level. Importantly, a steady increase in the number of cells expressing phosphor-Ser33/37-beta-catenin was observed both in mouse and human bones. Overall repression of Wnt/beta-catenin signaling within osteocytes occurred in conjunction with increased expression of Wnt antagonists (SOST and sFRP4) and genes associated with osteoclast activity, including receptor activator of NF-?B ligand (RANKL). The resulting increase in the RANKL/osteoprotegerin (OPG) ratio correlated with increased osteoclast activity. In late-stage disease, an apparent repression of genes associated with osteoblast function was observed. These data confirm that jck mice develop progressive biochemical changes in CKD-MBD and suggest that repression of the Wnt/beta-catenin pathway is involved in the pathogenesis of renal osteodystrophy. (C) 2012 American Society for Bone and Mineral Research.

Measurement of event background fluctuations for charged particle jet reconstruction in Pb-Pb collisions at root s(NN)=2.76 TeV

The effect of event background fluctuations on charged particle jet reconstruction in Pb-Pb collisions at root s(NN) = 2.76 TeV has been measured with the ALICE experiment. The main sources of non-statistical fluctuations are characterized based purely on experimental data with an unbiased method, as well as by using single high p(t) particles and simulated jets embedded into real Pb-Pb events and reconstructed with the anti-k(t) jet finder. The influence of a low transverse momentum cut-off on particles used in the jet reconstruction is quantified by varying the minimum track p(t) between 0.15 GeV/c and 2 GeV/c. For embedded jets reconstructed from charged particles with p(t) > 0.15 GeV/c, the uncertainty in the reconstructed jet transverse momentum due to the heavy-ion background is measured to be 11.3 GeV/c (standard deviation) for the 10% most central Pb-Pb collisions, slightly larger than the value of 11.0 GeV/c measured using the unbiased method. For a higher particle transverse momentum threshold of 2 GeV/c, which will generate a stronger bias towards hard fragmentation in the jet finding process, the standard deviation of the fluctuations in the reconstructed jet transverse momentum is reduced to 4.8-5.0 GeV/c for the 10% most central events. A non-Gaussian tail of the momentum uncertainty is observed and its impact on the reconstructed jet spectrum is evaluated for varying particle momentum thresholds, by folding the measured fluctuations with steeply falling spectra.

Spatial-temporal variations in phytoplankton size and colored detrital matter absorption at global and regional scales, as derived from twelve years of SeaWiFS data (1998-2009)

A procedure has been proposed by Ciotti and Bricaud (2006) to retrieve spectral absorption coefficients of phytoplankton and colored detrital matter (CDM) from satellite radiance measurements. This was also the first procedure to estimate a size factor for phytoplankton, based on the shape of the retrieved algal absorption spectrum, and the spectral slope of CDM absorption. Applying this method to the global ocean color data set acquired by SeaWiFS over twelve years (1998-2009), allowed for a comparison of the spatial variations of chlorophyll concentration ([Chl]), algal size factor (S-f), CDM absorption coefficient (a(cdm)) at 443 nm, and spectral slope of CDM absorption (S-cdm). As expected, correlations between the derived parameters were characterized by a large scatter at the global scale. We compared temporal variability of the spatially averaged parameters over the twelve-year period for three oceanic areas of biogeochemical importance: the Eastern Equatorial Pacific, the North Atlantic and the Mediterranean Sea. In all areas, both S-f and a(cdm)(443) showed large seasonal and interannual variations, generally correlated to those of algal biomass. The CDM maxima appeared in some occasions to last longer than those of [Chl]. The spectral slope of CDM absorption showed very large seasonal cycles consistent with photobleaching, challenging the assumption of a constant slope commonly used in bio-optical models. In the Equatorial Pacific, the seasonal cycles of [Chl], S-f, a(cdm)(443) and S-cdm, as well as the relationships between these parameters, were strongly affected by the 1997-98 El Ni o/La Ni a event.

A Bayesian destructive weighted Poisson cure rate model and an application to a cutaneous melanoma data

In this article, we propose a new Bayesian flexible cure rate survival model, which generalises the stochastic model of Klebanov et al. [Klebanov LB, Rachev ST and Yakovlev AY. A stochastic-model of radiation carcinogenesis - latent time distributions and their properties. Math Biosci 1993; 113: 51-75], and has much in common with the destructive model formulated by Rodrigues et al. [Rodrigues J, de Castro M, Balakrishnan N and Cancho VG. Destructive weighted Poisson cure rate models. Technical Report, Universidade Federal de Sao Carlos, Sao Carlos-SP. Brazil, 2009 (accepted in Lifetime Data Analysis)]. In our approach, the accumulated number of lesions or altered cells follows a compound weighted Poisson distribution. This model is more flexible than the promotion time cure model in terms of dispersion. Moreover, it possesses an interesting and realistic interpretation of the biological mechanism of the occurrence of the event of interest as it includes a destructive process of tumour cells after an initial treatment or the capacity of an individual exposed to irradiation to repair altered cells that results in cancer induction. In other words, what is recorded is only the damaged portion of the original number of altered cells not eliminated by the treatment or repaired by the repair system of an individual. Markov Chain Monte Carlo (MCMC) methods are then used to develop Bayesian inference for the proposed model. Also, some discussions on the model selection and an illustration with a cutaneous melanoma data set analysed by Rodrigues et al. [Rodrigues J, de Castro M, Balakrishnan N and Cancho VG. Destructive weighted Poisson cure rate models. Technical Report, Universidade Federal de Sao Carlos, Sao Carlos-SP. Brazil, 2009 (accepted in Lifetime Data Analysis)] are presented.

Functional clustering of time series gene expression data by Granger causality

Background: A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes. Results: In this study we perform gene clustering through the identification of Granger causality between and within sets of time series gene expression data. Granger causality is based on the idea that the cause of an event cannot come after its consequence. Conclusions: This kind of analysis can be used as a complementary approach for functional clustering, wherein genes would be clustered not solely based on their expression similarity but on their topological proximity built according to the intensity of Granger causality among them.