Bayesian outlier analysis in binary regression

We propose alternative approaches to analyze residuals in binary regression models based on random effect components. Our preferred model does not depend upon any tuning parameter, being completely automatic. Although the focus is mainly on accommodation of outliers, the proposed methodology is also able to detect them. Our approach consists of evaluating the posterior distribution of random effects included in the linear predictor. The evaluation of the posterior distributions of interest involves cumbersome integration, which is easily dealt with through stochastic simulation methods. We also discuss different specifications of prior distributions for the random effects. The potential of these strategies is compared in a real data set. The main finding is that the inclusion of extra variability accommodates the outliers, improving the adjustment of the model substantially, besides correctly indicating the possible outliers.

Expression of fibroblast growth factor receptors during development and regression of the bovine corpus luteum

There is evidence that fibroblast growth factors (FGFs) are involved in the regulation of growth and regression of the corpus luteum (CL). However, the expression pattern of most FGF receptors (FGFRs) during CL lifespan is still unknown. The objective of the present study was to determine the pattern of expression of 'B' and 'C' splice variants of FGFRs in the bovine CL. Bovine CL were collected from an abattoir and classed as corpora hemorrhagica (Stage I), developing (Stage II), developed (Stage III) or regressed (Stage IV) CL. Expression of FGFR mRNA was measured by semiquantitative reverse transcription-polymerase chain reaction and FGFR protein was localised by immunohistochemistry. Expression of mRNA encoding the 'B' and 'C' spliced forms of FGFR1 and FGFR2 was readily detectable in the bovine CL and was accompanied by protein localisation. FGFR1C and FGFR2C mRNA expression did not vary throughout CL lifespan, whereas FGFR1B was upregulated in the developed (Stage III) CL. FGFR3B, FGFR3C and FGFR4 expression was inconsistent in the bovine CL. The present data indicate that FGFR1 and FGFR2 splice variants are the main receptors for FGF action in the bovine CL.

Well-conditioned robust observer for detection of faulty systems components

A new concept of fault detection and isolation using robust observation for systems with random noises is presented. The method selects the parameters from components that may fault during the process and constructs well conditioned robust observers, considering sensors faults. To isolate component failures via robust observation, a bank of detection observers is constructed, where each observer is only sensitive to one specified component failure while robust to all other component failures.

Regression of the lateral oviducts during the larval-adult transformation of the reproductive system of Melipona quadrifasciata and Frieseomelitta varia

Toward the end of the larval phase (pre-pupa), the reproductive systems of Melipona quadrifasciata and Frieseomelitta varia workers are anatomically similar. Scanning electron microscopy showed that during this developmental phase the right and left ovaries are fused and form a heart-shaped structure located above the midgut. Each ovary is connected to the genital chamber by a long and slender lateral oviduct. During pupal development, the lateral oviducts of workers from both species become extremely reduced due to a drastic process of cell death, as shown by transmission electron microscopy. During the lateral oviduct shortening, their simple columnar epithelial cells show some signs of apoptosis in addition to necrosis. Cell death was characterized by cytoplasmic vesiculation, peculiar accumulation of glycogen, and dilation of cytoplasmic organelles such as mitochondria and rough endoplasmic reticulum. The nuclei, at first irregularly contoured, became swollen, with chromatin flocculation and various areas of condensed chromatin next to the nuclear envelope. At the end of the pupal phase, deep recesses marked the nuclei. At emergence, worker and queen reproductive systems showed marked differences, although reduction in the lateral oviducts was an event occurring in both castes. However, in queens the ovarioles increased in length and the spermatheca was larger than that of workers. At the external anatomical level, the reproductive system of workers and queens could be distinguished in the white- and pink-eyed pupal phase. The metamorphic function of the death of lateral oviduct cells, with consequent oviduct shortening, is discussed in terms of the anatomical reorganization of the reproductive system and of the ventrolateral positioning of adult worker bee ovaries. (C) 2000 Wiley-Liss, Inc.

Morphometric evaluation of gingival overgrowth and regression caused by cyclosporin in rats

Cyclosporin A is a selective immunosuppressant, used in organ transplants to prevent graft rejection. Cyclosporin A can cause various side effects including gingival overgrowth. The aim of this work was to evaluate gingival overgrowth of rats treated daily with 10 mg/kg body weight of Cyclosporin A for 60 days, as well as the regression after the interruption of treatment. All rats treated with Cyclosporin A developed gingival overgrowth, with increased thickness of the epithelium, height and width of the connective tissue. The density of fibroblasts and collagen fibers also increased. Five to 90 days after the interruption of treatment with Cyclosporin A, there was a progressive reduction of the gingival volume and of collagen fibers and fibroblast densities. The reduction was more pronounced in the initial periods and after 90 days did not return to the normal values.

Robust linear mixed models with normal/independent distributions and Bayesian MCMC implementation

Linear mixed effects models have been widely used in analysis of data where responses are clustered around some random effects, so it is not reasonable to assume independence between observations in the same cluster. In most biological applications, it is assumed that the distributions of the random effects and of the residuals are Gaussian. This makes inferences vulnerable to the presence of outliers. Here, linear mixed effects models with normal/independent residual distributions for robust inferences are described. Specific distributions examined include univariate and multivariate versions of the Student-t, the slash and the contaminated normal. A Bayesian framework is adopted and Markov chain Monte Carlo is used to carry out the posterior analysis. The procedures are illustrated using birth weight data on rats in a texicological experiment. Results from the Gaussian and robust models are contrasted, and it is shown how the implementation can be used for outlier detection. The thick-tailed distributions provide an appealing robust alternative to the Gaussian process in linear mixed models, and they are easily implemented using data augmentation and MCMC techniques.

Robust control to parametric uncertainties in smart structures using linear matrix inequalities

The study of algorithms for active vibrations control in flexible structures became an area of enormous interest, mainly due to the countless demands of an optimal performance of mechanical systems as aircraft and aerospace structures. Smart structures, formed by a structure base, coupled with piezoelectric actuators and sensor are capable to guarantee the conditions demanded through the application of several types of controllers. This article shows some steps that should be followed in the design of a smart structure. It is discussed: the optimal placement of actuators, the model reduction and the controller design through techniques involving linear matrix inequalities (LMI). It is considered as constraints in LMI: the decay rate, voltage input limitation in the actuators and bounded output peak (output energy). Two controllers robust to parametric variation are designed: the first one considers the actuator in non-optimal location and the second one the actuator is put in an optimal placement. The performance are compared and discussed. The simulations to illustrate the methodology are made with a cantilever beam with bonded piezoelectric actuators.

Robust control applications for smart truss structure

A finite element modeling of an intelligent truss structure with piezoelectric stack actuators for the purpose of active damping and structural vibration attenuation is presented. This paper concerns with the following issues aspects: the design of intelligent truss structure considering electro-mechanical coupling between the host structure and piezoelectric stack actuators; the H 2 norm approach to search for optimal placement of actuators and sensors; and finally some aspects in robust control techniques. The electro-mechanical behavior of piezoelectric elements is directly related to the successful application of the actuators in truss structures. In order to achieve the desired damping in the interested bandwidth frequency it is used the H ∞ output feedback solved by convex optimization. The constraints to be reached are written by linear matrix inequalities (LMI). The paper concludes with a numerical example, using Matlab and Simulink, in a cantilevered, 2-bay space truss structure. The results demonstrated the approach applicability.

Double Higgs production at the LHC as a robust test of little Higgs models

We analyze double Higgs boson production at the LHC in the context of Little Higgs models. In double Higgs production, the diagrams involved are directly related to those that cause the cancellation of the quadratic divergence of the Higgs self-energy, so this mode provides a robust prediction for this class of models. We find that in extensions of this model with the inclusion of a so-called T-parity, there is a significant enhancement in the cross sections as compared to the Standard Model. © 2006 American Institute of Physics.

Avaliação dos resultados neonatais do método canguru no Brasil

Objective: To evaluate the results of the kangaroo mother method in Brazil. Methods: A prospective cohort study comparing 16 units that have or do not have the second phase of the kangaroo mother method: eight were national centers of excellence for the kangaroo mother method (study group) and eight were part of the Brazilian Neonatal Research Network (control group). A total of 985 newborn infants with birth weights of 500 to 1,749 g were enrolled. Multivariate analyses employedmultiple linear regression and Poisson regression with robust adjustment. Results: The adjusted analysis (controlled for birth weight, gestational age, Score for Neonatal Acute Physiology Perinatal Extension II, Neonatal Therapeutic Intervention Scoring System, and maternal age and educational level) demonstrated that mean length of hospital stay (p = 0.14) and intercurrent clinical conditions in the intermediate or kangaroo unit were equal for both groups. Weight (p = 0.012), length (p = 0.039) and head circumference (p = 0.006) at 36 weeks' corrected gestational age were all lower at the kangaroo units. The kangaroo units exhibited superior performance in relation to exclusive breastfeeding at discharge (69.2 vs. 23.8%, p=0.022). Conclusions: The evidence suggests that the humanization strategy adopted by the Brazilian Ministry of Health is a safe alternative to conventional treatment and a good strategy for promoting breastfeeding. Copyright © 2008 by Sociedade Brasileira de Pediatria.

Fusion of fingerprint recognition methods for robust human identification

Biometrics is one of the biggest tendencies in human identification. The fingerprint is the most widely used biometric. However considering the automatic fingerprint recognition a completely solved problem is a common mistake. The most popular and extensively used methods, the minutiae-based, do not perform well on poor-quality images and when just a small area of overlap between the template and the query images exists. The use of multibiometrics is considered one of the keys to overcome the weakness and improve the accuracy of biometrics systems. This paper presents the fusion of a minutiae-based and a ridge-based fingerprint recognition method at rank, decision and score level. The fusion techniques implemented leaded to a reduction of the Equal Error Rate by 31.78% (from 4.09% to 2.79%) and a decreasing of 6 positions in the rank to reach a Correct Retrieval (from rank 8 to 2) when assessed in the FVC2002-DB1A database. © 2008 IEEE.

Multi-trait and random regression mature weight heritability and breeding value estimates in Nelore cattle

Mature weight breeding values were estimated using a multi-trait animal model (MM) and a random regression animal model (RRM). Data consisted of 82 064 weight records from 8 145 animals, recorded from birth to eight years of age. Weights at standard ages were considered in the MM. All models included contemporary groups as fixed effects, and age of dam (linear and quadratic effects) and animal age as covariates. In the RRM, mean trends were modelled through a cubic regression on orthogonal polynomials of animal age and genetic maternal and direct and maternal permanent environmental effects were also included as random. Legendre polynomials of orders 4, 3, 6 and 3 were used for animal and maternal genetic and permanent environmental effects, respectively, considering five classes of residual variances. Mature weight (five years) direct heritability estimates were 0.35 (MM) and 0.38 (RRM). Rank correlation between sires' breeding values estimated by MM and RRM was 0.82. However, selecting the top 2% (12) or 10% (62) of the young sires based on the MM predicted breeding values, respectively 71% and 80% of the same sires would be selected if RRM estimates were used instead. The RRM modelled the changes in the (co)variances with age adequately and larger breeding value accuracies can be expected using this model. © South African Society for Animal Science.

Robust and fast vowel recognition using optimum-path forest

The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamental importance in most of the speech processing systems, vary from automatic interpretation of spoken language to biometrics. State-of-the-art systems for AVR are based on traditional machine learning models such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), however, such classifiers can not deal with efficiency and effectiveness at the same time, existing a gap to be explored when real-time processing is required. In this work, we present an algorithm for AVR based on the Optimum-Path Forest (OPF), which is an emergent pattern recognition technique recently introduced in literature. Adopting a supervised training procedure and using speech tags from two public datasets, we observed that OPF has outperformed ANNs, SVMs, plus other classifiers, in terms of training time and accuracy. ©2010 IEEE.

The challenge of finding robust poverty indicators for rapid monitoring of changes

Includes bibliography