Combined bias and outlier identification in dynamic data reconciliation

Measured process data normally contain inaccuracies because the measurements are obtained using imperfect instruments. As well as random errors one can expect systematic bias caused by miscalibrated instruments or outliers caused by process peaks such as sudden power fluctuations. Data reconciliation is the adjustment of a set of process data based on a model of the process so that the derived estimates conform to natural laws. In this paper, techniques for the detection and identification of both systematic bias and outliers in dynamic process data are presented. A novel technique for the detection and identification of systematic bias is formulated and presented. The problem of detection, identification and elimination of outliers is also treated using a modified version of a previously available clustering technique. These techniques are also combined to provide a global dynamic data reconciliation (DDR) strategy. The algorithms presented are tested in isolation and in combination using dynamic simulations of two continuous stirred tank reactors (CSTR).

Detection and attribution of climate change: a regional perspective

The Intergovernmental Panel on Climate Change fourth assessment report, published in 2007 came to a more confident assessment of the causes of global temperature change than previous reports and concluded that ‘it is likely that there has been significant anthropogenic warming over the past 50 years averaged over each continent except Antarctica.’ Since then, warming over Antarctica has also been attributed to human influence, and further evidence has accumulated attributing a much wider range of climate changes to human activities. Such changes are broadly consistent with theoretical understanding, and climate model simulations, of how the planet is expected to respond. This paper reviews this evidence from a regional perspective to reflect a growing interest in understanding the regional effects of climate change, which can differ markedly across the globe. We set out the methodological basis for detection and attribution and discuss the spatial scales on which it is possible to make robust attribution statements. We review the evidence showing significant human-induced changes in regional temperatures, and for the effects of external forcings on changes in the hydrological cycle, the cryosphere, circulation changes, oceanic changes, and changes in extremes. We then discuss future challenges for the science of attribution. To better assess the pace of change, and to understand more about the regional changes to which societies need to adapt, we will need to refine our understanding of the effects of external forcing and internal variability

Change detection evokes a Simon-like effect

A change detection paradigm was used to estimate the role of explicit change detection in the generation of the irrelevant spatial stimulus coding underlying the Simon effect. In one condition, no blank was interposed between two successive displays, which produced efficient change detection. In another condition, the presence of a blank frame produced a robust change blindness effect, which is crucially assumed to occur as the consequence of impaired attentional orienting to the change location. The results showed a strong Simon-like effect under conditions of efficient change detection. By contrast, no Simon-like effect was observed under conditions of change blindness, namely when attention shifting towards the change location was hampered. Experiment 2 supported this pattern by showing that a Simon-like effect could be observed when the blank was present, but only when participants detected the change by means of a cue that was informative as to change location. Overall, our findings show that a Simon-like effect can only be observed under conditions of explicit change detection, likely because a shift of attention towards the change location has occurred.

A multi-level analytical approach for detection and visualization of intracellular NO production and nitrosation events using diaminofluoresceins

Diaminofluoresceins are widely used probes for detection and intracellular localization of NO formation in cultured/isolated cells and intact tissues. The fluorinated derivative, 4-amino-5-methylamino-2′,7′-difluorofluorescein (DAF-FM), has gained increasing popularity in recent years due to its improved NO-sensitivity, pH-stability, and resistance to photo-bleaching compared to the first-generation compound, DAF-2. Detection of NO production by either reagent relies on conversion of the parent compound into a fluorescent triazole, DAF-FM-T and DAF-2-T, respectively. While this reaction is specific for NO and/or reactive nitrosating species, it is also affected by the presence of oxidants/antioxidants. Moreover, the reaction with other molecules can lead to the formation of fluorescent products other than the expected triazole. Thus additional controls and structural confirmation of the reaction products are essential. Using human red blood cells as an exemplary cellular system we here describe robust protocols for the analysis of intracellular DAF-FM-T formation using an array of fluorescence-based methods (laser-scanning fluorescence microscopy, flow cytometry and fluorimetry) and analytical separation techniques (reversed-phase HPLC and LC-MS/MS). When used in combination, these assays afford unequivocal identification of the fluorescent signal as being derived from NO and are applicable to most other cellular systems without or with only minor modifications.

IMILAST: a community effort to intercompare extratropical cyclone detection and tracking algorithms

The variability of results from different automated methods of detection and tracking of extratropical cyclones is assessed in order to identify uncertainties related to the choice of method. Fifteen international teams applied their own algorithms to the same dataset—the period 1989–2009 of interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERAInterim) data. This experiment is part of the community project Intercomparison of Mid Latitude Storm Diagnostics (IMILAST; see www.proclim.ch/imilast/index.html). The spread of results for cyclone frequency, intensity, life cycle, and track location is presented to illustrate the impact of using different methods. Globally, methods agree well for geographical distribution in large oceanic regions, interannual variability of cyclone numbers, geographical patterns of strong trends, and distribution shape for many life cycle characteristics. In contrast, the largest disparities exist for the total numbers of cyclones, the detection of weak cyclones, and distribution in some densely populated regions. Consistency between methods is better for strong cyclones than for shallow ones. Two case studies of relatively large, intense cyclones reveal that the identification of the most intense part of the life cycle of these events is robust between methods, but considerable differences exist during the development and the dissolution phases.

Fold-change detection in a whole-pathway model of Escherichia coli chemotaxis

There has been recent interest in sensory systems that are able to display a response which is proportional to a fold change in stimulus concentration, a feature referred to as fold-change detection (FCD). Here, we demonstrate FCD in a recent whole-pathway mathematical model of Escherichia coli chemotaxis. FCD is shown to hold for each protein in the signalling cascade and to be robust to kinetic rate and protein concentration variation. Using a sensitivity analysis, we find that only variations in the number of receptors within a signalling team lead to the model not exhibiting FCD. We also discuss the ability of a cell with multiple receptor types to display FCD and explain how a particular receptor configuration may be used to elucidate the two experimentally determined regimes of FCD behaviour. All findings are discussed in respect of the experimental literature.

Biologically-inspired robust motion segmentation using mutual information

This paper presents a neuroscience inspired information theoretic approach to motion segmentation. Robust motion segmentation represents a fundamental first stage in many surveillance tasks. As an alternative to widely adopted individual segmentation approaches, which are challenged in different ways by imagery exhibiting a wide range of environmental variation and irrelevant motion, this paper presents a new biologically-inspired approach which computes the multivariate mutual information between multiple complementary motion segmentation outputs. Performance evaluation across a range of datasets and against competing segmentation methods demonstrates robust performance.

Development and validation of a parent report measure for detection of cognitive delay in infancy

Aim To develop a brief, parent-completed instrument (‘ERIC’) for detection of cognitive delay in 10-24 month-olds born preterm, or with low birth weight, or with perinatal complications, and to establish its diagnostic properties. Method Scores were collected from parents of 317 children meeting ≥1 inclusion criteria (birth weight <1500g; gestational age <34 completed weeks; 5-minute Apgar <7; presence of hypoxic-ischemic encephalopathy) and meeting no exclusion criteria. Children were assessed for cognitive delay using a criterion score on the Bayley Scales of Infant and Toddler Development Cognitive Scale III1 <80. Items were retained according to their individual associations with delay. Sensitivity, specificity, Positive and Negative Predictive Values were estimated and a truncated ERIC was developed for use <14 months. Results ERIC detected 17 out of 18 delayed children in the sample, with 94.4% sensitivity (95% CI [confidence interval] 83.9-100%), 76.9% specificity (72.1-81.7%), 19.8% positive predictive value (11.4-28.2%); 99.6% negative predictive value (98.7-100%); 4.09 likelihood ratio positive; and 0.07 likelihood ratio negative; the associated Area under the Curve was .909 (.829-.960). Interpretation ERIC has potential value as a quickly-administered diagnostic instrument for the absence of early cognitive delay in preterm or premature infants of 10-24 months, and as a screen for cognitive delay. Further research may be needed before ERIC can be recommended for wide-scale use.

Robust multimodal face and fingerprint fusion in the presence of spoofing attacks

Anti-spoofing is attracting growing interest in biometrics, considering the variety of fake materials and new means to attack biometric recognition systems. New unseen materials continuously challenge state-of-the-art spoofing detectors, suggesting for additional systematic approaches to target anti-spoofing. By incorporating liveness scores into the biometric fusion process, recognition accuracy can be enhanced, but traditional sum-rule based fusion algorithms are known to be highly sensitive to single spoofed instances. This paper investigates 1-median filtering as a spoofing-resistant generalised alternative to the sum-rule targeting the problem of partial multibiometric spoofing where m out of n biometric sources to be combined are attacked. Augmenting previous work, this paper investigates the dynamic detection and rejection of livenessrecognition pair outliers for spoofed samples in true multi-modal configuration with its inherent challenge of normalisation. As a further contribution, bootstrap aggregating (bagging) classifiers for fingerprint spoof-detection algorithm is presented. Experiments on the latest face video databases (Idiap Replay- Attack Database and CASIA Face Anti-Spoofing Database), and fingerprint spoofing database (Fingerprint Liveness Detection Competition 2013) illustrate the efficiency of proposed techniques.

A powder X-ray diffraction method for detection of polyprenylated benzophenones in plant extracts associated with HPLC for quantitative analysis

A robust, direct, rapid and non-destructive X-ray diffraction crystallography method to detect the polyprenylated benzophenones 7-epi-clusianone (1) and guttiferone A (2) in extracts from Garcinia brasiliensis is presented. Powder samples of benzophenones 1 and 2, dried hexane extracts from G. brasiliensis seeds and fruit`s pericarp, and the dried ethanolic extract from G. brasiliensis seeds were unambiguously characterized by powder X-ray diffractometry. The calculated X-ray diffraction peaks from crystal structures of analytes 1 and 2, previously determined by single-crystal X-ray diffraction technique, were overlaid to those of the experimental powder diffractograms, providing a practical identification of these compounds in the analyzed material and confirming the pure contents of the powder samples. Using the X-ray diffraction crystallography method, the studied polyprenylated benzophenones were selectively and simultaneously detected in the extracts which were mounted directly on sample holder. In addition, reference materials of the analytes were not required for analyses since the crystal structures of the compounds are known. High performance liquid chromatography analyses also were comparatively carried out to quantify the analytes in the same plant extracts showing to be in agreement with X-ray diffraction crystallography method. (C) 2010 Elsevier B.V. All rights reserved.

Multivariate measurement error models based on scale mixtures of the skew-normal distribution

Scale mixtures of the skew-normal (SMSN) distribution is a class of asymmetric thick-tailed distributions that includes the skew-normal (SN) distribution as a special case. The main advantage of these classes of distributions is that they are easy to simulate and have a nice hierarchical representation facilitating easy implementation of the expectation-maximization algorithm for the maximum-likelihood estimation. In this paper, we assume an SMSN distribution for the unobserved value of the covariates and a symmetric scale mixtures of the normal distribution for the error term of the model. This provides a robust alternative to parameter estimation in multivariate measurement error models. Specific distributions examined include univariate and multivariate versions of the SN, skew-t, skew-slash and skew-contaminated normal distributions. The results and methods are applied to a real data set.

Factors associated with increased prevalence of human papillomavirus infection in a cohort of HIV-Infected Brazilian women

Objectives: Human papillomavirus (HPV) infection is a major risk factor for cervical disease. Using baseline data from the HIV-infected cohort of Evandro Chagas Clinical Research Institute at Fiocruz, Rio de Janeiro, Brazil, factors associated with an increased prevalence of HPV were assessed. Methods: Samples from 634 HIV-infected women were tested for the presence of HPV infection using hybrid capture 11 and polymerase chain reaction. Prevalence ratios (PR) were estimated using Poisson regression analysis with robust variance. Results: The overall prevalence of HPV infection was 48%, of which 94% were infected with a high-risk HPV. In multivariate analysis, factors independently associated with infection with high-risk HPV type were: younger age (<30 years of age; PR 1.5, 95% confidence interval (CI) 1.1-2.1), current or prior drug use (PR 1.3, 95% CI 1.0-1.6), self-reported history of HPV infection (PR 1.2, 95% CI 0.96-1.6), condom use in the last sexual intercourse (PR 1.3, 95% CI 1.1-1.7), and nadir CD4+ T-cell count <100 cells/mm(3) (PR 1.6, 95% CI 1.2-2.1). Conclusions: The estimated prevalence of high-risk HPV-infection among HIV-infected women from Rio de Janeiro, Brazil, was high. Close monitoring of HPV-related effects is warranted in all HIV-infected women, in particular those of younger age and advanced immunosuppression. (C) 2008 International Society for Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

A robust Bayesian approach to null intercept measurement error model with application to dental data

Measurement error models often arise in epidemiological and clinical research. Usually, in this set up it is assumed that the latent variable has a normal distribution. However, the normality assumption may not be always correct. Skew-normal/independent distribution is a class of asymmetric thick-tailed distributions which includes the Skew-normal distribution as a special case. In this paper, we explore the use of skew-normal/independent distribution as a robust alternative to null intercept measurement error model under a Bayesian paradigm. We assume that the random errors and the unobserved value of the covariate (latent variable) follows jointly a skew-normal/independent distribution, providing an appealing robust alternative to the routine use of symmetric normal distribution in this type of model. Specific distributions examined include univariate and multivariate versions of the skew-normal distribution, the skew-t distributions, the skew-slash distributions and the skew contaminated normal distributions. The methods developed is illustrated using a real data set from a dental clinical trial. (C) 2008 Elsevier B.V. All rights reserved.

Structure effects of self-assembled Prussian blue confined in highly organized mesoporous TiO(2) on the electrocatalytic properties towards H(2)O(2) detection

Here we report the derivatization of mesoporous TiO(2) thin films for the preparation of H(2)O(2) amperometric sensors. The coordination of the bifunctional ligand 1,10 phenantroline, 5,6 dione on the surface Ti(IV) ions provides open coordination sites for Fe(II) cations which are the starting point for the growth of a layer of Prussian blue polymer. The porous structure of the mesoporous TiO(2) allows the growth, ion by ion of the coordination polymer. Up to four layer of Prussian blue can be deposit without losing the porous structure of the film, which results in an enhanced response of these materials as H(2)O(2) sensors. These porous confined PB modified electrodes are robust sensors that exhibit good reproducibility, environmental stability and high sensitivity towards H(2)O(2) detection. (C) 2010 Elsevier B.V. All rights reserved.

Flow injection analysis of ethyl xanthate by gas diffusion and UV detection as CS(2) for process monitoring of sulfide ore flotation

A sensitive and robust analytical method for spectrophotometric determination of ethyl xanthate, CH(3)CH(2)OCS(2)(-) at trace concentrations in pulp solutions from froth flotation process is proposed. The analytical method is based on the decomposition of ethyl xanthate. EtX(-), with 2.0 mol L(-1) HCl generating ethanol and carbon disulfide. CS(2). A gas diffusion cell assures that only the volatile compounds diffuse through a PTFE membrane towards an acceptor stream of deionized water, thus avoiding the interferences of non-volatile compounds and suspended particles. The CS(2) is selectively detected by UV absorbance at 206 nm (epsilon = 65,000 L mol(-1) cm(-1)). The measured absorbance is directly proportional to EtX(-) concentration present in the sample solutions. The Beer`s law is obeyed in a 1 x 10(-6) to 2 x 10(-4) mol L(-1) concentration range of ethyl xanthate in the pulp with an excellent correlation coefficient (r = 0.999) and a detection limit of 3.1 x 10(-7) mol L(-1), corresponding to 38 mu g L. At flow rates of 200 mu L min(-1) of the donor stream and 100 mu L min(-1) of the acceptor channel a sampling rate of 15 injections per hour could be achieved with RSD < 2.3% (n = 10, 300 mu L injections of 1 x 10(-5) mol L(-1) EtX(-)). Two practical applications demonstrate the versatility of the FIA method: (i) evaluation the free EtX(-) concentration during a laboratory study of the EtX(-) adsorption capacity on pulverized sulfide ore (pyrite) and (ii) monitoring of EtX(-) at different stages (from starting load to washing effluents) of a flotation pilot plant processing a Cu-Zn sulfide ore. (C) 2010 Elsevier By. All rights reserved.