Bayesian Analysis for Errors in Variables with Changepoint Models

Changepoint regression models have originally been developed in connection with applications in quality control, where a change from the in-control to the out-of-control state has to be detected based on the avaliable random observations. Up to now various changepoint models have been suggested for differents applications like reliability, econometrics or medicine. In many practical situations the covariate cannot be measured precisely and an alternative model are the errors in variable regression models. In this paper we study the regression model with errors in variables with changepoint from a Bayesian approach. From the simulation study we found that the proposed procedure produces estimates suitable for the changepoint and all other model parameters.

Qualitative analysis by online nuclear magnetic resonance using Carr-Purcell-Meiboom-Gill sequence with low refocusing flip angles

The Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence has been used in many applications of magnetic resonance imaging (MRI) and low-resolution NMR (LRNMR) spectroscopy. Recently. CPMG was used in online LRNMR measurements that use long RF pulse trains, causing an increase in probe temperature and, therefore, tuning and matching maladjustments. To minimize this problem, the use of a low-power CPMG sequence based on low refocusing pulse flip angles (LRFA) was studied experimentally and theoretically. This approach has been used in several MRI protocols to reduce incident RF power and meet the specific absorption rate. The results for CPMG with LRFA of 3 pi/4 (CPMG(135)), pi/2 (CPMG(90)) and pi/4 (CPMG(45)) were compared with conventional CPMG with refocusing pi pulses. For a homogeneous field, with linewidth equal to Delta nu = 15 Hz, the refocusing flip angles can be as low as pi/4 to obtain the transverse relaxation time (T(2)) value with errors below 5%. For a less homogeneous magnetic field. Delta nu = 100 Hz, the choice of the LRFA has to take into account the reduction in the intensity of the CPMG signal and the increase in the time constant of the CPMG decay that also becomes dependent on longitudinal relaxation time (T(1)). We have compared the T(2) values measured by conventional CPMG and CPMG(90) for 30 oilseed species, and a good correlation coefficient, r = 0.98, was obtained. Therefore, for oilseeds, the T(2) measurements performed with pi/2 refocusing pulses (CPMG(90)), with the same pulse width of conventional CPMG, use only 25% of the RF power. This reduces the heating problem in the probe and reduces the power deposition in the samples. (C) 2011 Elsevier B.V. All rights reserved.

OBSERVING BEHAVIOR AND ATYPICALLY RESTRICTED STIMULUS CONTROL

Restricted stimulus control refers to discrimination learning with atypical limitations in the range of controlling stimuli or stimulus features In the study reported here 4 normally capable individuals and 10 individuals with Intellectual disabilities (ID) performed two-sample delayed matching to sample Sample stimulus observing was recorded with an eye tracking apparatus High accuracy scores indicated stimulus control by both sample stimuli for the 4 nondisabled participants and 4 participants with ID and eye tracking data showed reliable observing of all stimuli Intermediate accuracy scores indicated restricted stimulus control for the remaining 6 participants Their eye tracking data showed that errors were related to failures to observe sample stimuli and relatively brief observing durations Five of these participants were then given interventions designed to improve observing behavior For 4 participants the interventions resulted initially in elimination of observing failures increased observing durations and Increased accuracy For 2 of these participants contingencies sufficient to maintain adequate observing were not always sufficient to maintain high accuracy subsequent procedure modifications restored It however For the 5th participant initial improvements in observing were not accompanied by improved accuracy in apparent Instance of observing without attending accuracy improved only after an additional intervention that imposed contingencies on observing behavior Thus interventions that control observing behavior seem necessary but may not always be sufficient for the remediation of restricted stimulus control

Playing to Create New Ways of Playing: A Child with Prader-Willi Syndrome

This investigation evaluates the possibility of constructing new ways of playing for a child with Prader-Willi syndrome, by means of occupational therapy. It is a qualitative study which makes use of the case study methodology, whose starting point is the clinical intervention as data collect field. It also presents a short revision of the literature to subside discussions and reflections. It was observed that through the playing experience the occupational therapist led the child to know his own limitations and possibilities, by making him discover new ways of doing activities. Observing the therapist and learning with her, the patient experienced different situations throughout the therapeutic relationship, what enabled him to experiment them in his everyday life. Finally, this study aims at showing the clinical reasoning of an occupational therapist with a view to demonstrate Brazilian therapeutical conduct.

Combining meta-learning and search techniques to select parameters for support vector machines

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.

Caffeine Determination at a Carbon Fiber Ultramicroelectrodes by Fast-Scan Cyclic Voltammetry

Caffeine determination using a fast-scan voltammetric procedure at a carbon fiber ultramicroelectrode (CF-UME) is described. The CF-UME was submitted to electrochemical pretreatment. Parameters such as number of acquisition cycles, scan rate, potential window, and the electrochemical surface pretreatment were optimized. Using the optimized conditions, it was possible to achieve a LDR from 10.0 up to 200 mu mol L-1, with a LOD of 3.33 mu mol L-1. The method has been applied in the determination of caffeine in commercial samples, with errors of 1.0-3.5% in relation to the label values and recoveries of 97-114% within the linear range.

Caffeine determination at a carbon fiber ultramicroelectrodes by fast-scan cyclic voltammetry

Caffeine determination using a fast-scan voltammetric procedure at a carbon fiber ultramicroelectrode (CF-UME) is described. The CF-UME was submitted to electrochemical pretreatment. Parameters such as number of acquisition cycles, scan rate, potential window, and the electrochemical surface pretreatment were optimized. Using the optimized conditions, it was possible to achieve a LDR from 10.0 up to 200 μmol L-1, with a LOD of 3.33 μmol L-1. The method has been applied in the determination of caffeine in commercial samples, with errors of 1.0-3.5% in relation to the label values and recoveries of 97-114% within the linear range.

Prediction with measurement errors in finite populations

We address the problem of selecting the best linear unbiased predictor (BLUP) of the latent value (e.g., serum glucose fasting level) of sample subjects with heteroskedastic measurement errors. Using a simple example, we compare the usual mixed model BLUP to a similar predictor based on a mixed model framed in a finite population (FPMM) setup with two sources of variability, the first of which corresponds to simple random sampling and the second, to heteroskedastic measurement errors. Under this last approach, we show that when measurement errors are subject-specific, the BLUP shrinkage constants are based on a pooled measurement error variance as opposed to the individual ones generally considered for the usual mixed model BLUP. In contrast, when the heteroskedastic measurement errors are measurement condition-specific, the FPMM BLUP involves different shrinkage constants. We also show that in this setup, when measurement errors are subject-specific, the usual mixed model predictor is biased but has a smaller mean squared error than the FPMM BLUP which points to some difficulties in the interpretation of such predictors. (C) 2011 Elsevier By. All rights reserved.

A log-Birnbaum-Saunders regression model with asymmetric errors

This paper introduces a skewed log-Birnbaum-Saunders regression model based on the skewed sinh-normal distribution proposed by Leiva et al. [A skewed sinh-normal distribution and its properties and application to air pollution, Comm. Statist. Theory Methods 39 (2010), pp. 426-443]. Some influence methods, such as the local influence and generalized leverage, are presented. Additionally, we derived the normal curvatures of local influence under some perturbation schemes. An empirical application to a real data set is presented in order to illustrate the usefulness of the proposed model.

Saethre-Chotzen phenotype with learning disability and hyper IgE phenotype in a patient due to complex chromosomal rearrangement involving chromosomes 3 and 7

The authors describe on a Brazilian girl with coronal synostosis, facial asymmetry, ptosis, brachydactyly, significant learning difficulties, recurrent scalp infections with marked hair loss, and elevated serum immunoglobulin E. Standard lymphocyte karyotype showed a small additional segment in 7p21[46,XX,add(7)(p21)]. Deletion of the TWIST1 gene, detected by Multiplex Ligation Probe-dependent Amplification (MPLA) and array-CGH, was consistent with phenotype of SaethreChotzen syndrome. Array CGH also showed deletion of four other genes at 7p21.1 (SNX13, PRPS1L1, HD9C9, and FERD3L) and the deletion of six genes (CACNA2D2, C3orf18, HEMK1, CISH, MAPKAPK3, and DOCK3) at 3p21.31. Our case reinforces FERD3L as candidate gene for intellectual disability and suggested that genes located in 3p21.3 can be related to hyper IgE phenotype. (C) 2012 Wiley Periodicals, Inc.

Flow Visualization with Quantified Spatial and Temporal Errors Using Edge Maps

Robust analysis of vector fields has been established as an important tool for deriving insights from the complex systems these fields model. Traditional analysis and visualization techniques rely primarily on computing streamlines through numerical integration. The inherent numerical errors of such approaches are usually ignored, leading to inconsistencies that cause unreliable visualizations and can ultimately prevent in-depth analysis. We propose a new representation for vector fields on surfaces that replaces numerical integration through triangles with maps from the triangle boundaries to themselves. This representation, called edge maps, permits a concise description of flow behaviors and is equivalent to computing all possible streamlines at a user defined error threshold. Independent of this error streamlines computed using edge maps are guaranteed to be consistent up to floating point precision, enabling the stable extraction of features such as the topological skeleton. Furthermore, our representation explicitly stores spatial and temporal errors which we use to produce more informative visualizations. This work describes the construction of edge maps, the error quantification, and a refinement procedure to adhere to a user defined error bound. Finally, we introduce new visualizations using the additional information provided by edge maps to indicate the uncertainty involved in computing streamlines and topological structures.