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PubMed2005

Molecular mechanisms involved in robustness of yeast central metabolism against null mutations.

Adaptive strategies employed by the yeast Saccharomyces cerevisiae provide robustness and adaptability of its central metabolism. Since central metabolism in yeast has been well studied at the enzymatic and genetic levels, it represents an excellent system for evaluating the relative roles of duplicate genes and alternative metabolic pathways as possible mechanisms for the stability of central metabolism against null mutations. Yeast appears to employ a variety of mechanisms to ensure functional robustness of its central metabolism. Uninterrupted flow of energy and precursor metabolites through the pathways of central metabolism via glycolysis (EMP), pentose phosphate shunt (PPS), and the tricarboxylic acid (TCA) cycle are ensured by a variety of adaptive mechanisms. One of the most significant mechanisms appears to be gene duplication events that have produced a number of isozymes functioning under variable environmental and physiological conditions. Alternative pathways represent another important mechanism for increasing the robustness of the system. The robustness of the pathways of central metabolism is apparently higher than that of the other parts of metabolism, because of its exceptional importance to the organism's vitality. The proportion of duplicated viable genes also is substantially larger in central metabolism than that in a pool of other metabolic genes.

Citric Acid Cycle↗
PubMed2006

In silico predicted robustness of viroid RNA secondary structures. II. Interaction between mutation pairs.

Viroids are plant subviral pathogens whose genomes are constituted by a single-stranded and covalently closed small RNA molecule that does not encode for any protein. Most of the 29 described viroid species fold into a rodlike or quasi-rodlike structure, whereas a few of them fold as highly branched structures. In a previous study, we used RNA thermodynamic secondary structure prediction algorithms to compare the mutational robustness of all viroid species. Here we used the same approach to explore the sign and strength of epistasis among pairs of random mutations. We found that antagonistic interactions were more abundant than synergistic ones. However, despite their lower frequency, synergistic interactions tended to be more intense. Mutational robustness and the intensity of epistasis were correlated such that viroid species with large average mutational effects showed stronger antagonistic epistasis, whereas viroids with mild average mutational effects showed weaker antagonistic interactions. The strength of antagonistic epistasis decreased with genome complexity as a consequence of the gained robustness of duplicated genomes. In good agreement with our previous finding of an evolutionary trend toward increased robustness, we now found a trend toward reduced antagonistic epistasis.

Base Pairing↗
PubMed2001

What do we need for robust, quantitative health impact assessment?

Health impact assessment (HIA) aims to make the health consequences of decisions explicit. Decision-makers need to know that the conclusions of HIA are robust. Quantified estimates of potential health impacts may be more influential but there are a number of concerns. First, not everything that can be quantified is important. Second, not everything that is being quantified at present should be, if this cannot be done robustly. Finally, not everything that is important can be quantified: rigorous qualitative HIA will still be needed for a thorough assessment. This paper presents the first published attempt to provide practical guidance on what is required to perform robust, quantitative HIA. Initial steps include profiling the affected populations, obtaining evidence for postulated impacts, and determining how differences in subgroups' exposures and susceptibilities affect impacts. Using epidemiological evidence for HIA is different from carrying out a new study. Key steps in quantifying impacts are mapping the causal pathway, selecting appropriate outcome measures and selecting or developing a statistical model. Evidence from different sources is needed. For many health impacts, evidence of an effect may be scarce and estimates of the size and nature of the relationship may be inadequate. Assumptions and uncertainties must therefore be explicit. Modelled data can sometimes be tested against empirical data but sensitivity analyses are crucial. When scientific problems occur, discontinuing the study is not an option, as HIA is usually intended to inform real decisions. Both qualitative and quantitative elements of HIA must be performed robustly to be of value.

Guidelines as Topic↗
PubMed2004

On the robustness of stride frequency estimation.

The robustness of stride frequency estimation (location and spread) from stride period data is investigated using influence functions. Theoretical analysis reveals that stride frequency estimates by Stokes et al. and by direct calculation have unbounded influence functions and zero breakdown points, implying a lack of both local and global robustness. Comparison of estimates obtained from an ensemble of pathological gait stride time series shows that on average, differences among estimators are not statistically significant (p > or = 0.59) for long time series (hundreds of strides). Specific circumstances under which nonrobust estimates depart from robust estimates are investigated in terms of outlier influence. We recommend some heuristic rules-of-thumb for prudent selection of nonrobust stride frequency estimators for a given stride time series. The theoretical and empirical estimator comparisons suggest that in general, more research on estimator robustness in quantitative gait analysis is warranted.

Adult↗
PubMed2006

Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy.

A fully automated approach is presented for robust detection and classification of changes in longitudinal time-series of color retinal fundus images of diabetic retinopathy. The method is robust to: 1) spatial variations in illumination resulting from instrument limitations and changes both within, and between patient visits; 2) imaging artifacts such as dust particles; 3) outliers in the training data; 4) segmentation and alignment errors. Robustness to illumination variation is achieved by a novel iterative algorithm to estimate the reflectance of the retina exploiting automatically extracted segmentations of the retinal vasculature, optic disk, fovea, and pathologies. Robustness to dust artifacts is achieved by exploiting their spectral characteristics, enabling application to film-based, as well as digital imaging systems. False changes from alignment errors are minimized by subpixel accuracy registration using a 12-parameter transformation that accounts for unknown retinal curvature and camera parameters. Bayesian detection and classification algorithms are used to generate a color-coded output that is readily inspected. A multiobserver validation on 43 image pairs from 22 eyes involving nonproliferative and proliferative diabetic retinopathies, showed a 97% change detection rate, a 3% miss rate, and a 10% false alarm rate. The performance in correctly classifying the changes was 99.3%. A self-consistency metric, and an error factor were developed to measure performance over more than two periods. The average self consistency was 94% and the error factor was 0.06%. Although this study focuses on diabetic changes, the proposed techniques have broader applicability in ophthalmology.

Algorithms↗
PubMed2005

Unsupervised segmentation based on robust estimation and color active contour models.

One of the most commonly used clinical tests performed today is the routine evaluation of peripheral blood smears. In this paper, we investigate the design, development, and implementation of a robust color gradient vector flow (GVF) active contour model for performing segmentation, using a database of 1791 imaged cells. The algorithms developed for this research operate in Luv color space, and introduce a color gradient and L2E robust estimation into the traditional GVF snake. The accuracy of the new model was compared with the segmentation results using a mean-shift approach, the traditional color GVF snake, and several other commonly used segmentation strategies. The unsupervised robust color snake with L2E robust estimation was shown to provide results which were superior to the other unsupervised approaches, and was comparable with supervised segmentation, as judged by a panel of human experts.

Algorithms↗
PubMed2005

Robust active appearance models and their application to medical image analysis.

Active appearance models (AAMs) have been successfully used for a variety of segmentation tasks in medical image analysis. However, gross disturbances of objects can occur in routine clinical setting caused by pathological changes or medical interventions. This poses a problem for AAM-based segmentation, since the method is inherently not robust. In this paper, a novel robust AAM (RAAM) matching algorithm is presented. Compared to previous approaches, no assumptions are made regarding the kind of gray-value disturbance and/or the expected magnitude of residuals during matching. The method consists of two main stages. First, initial residuals are analyzed by means of a mean-shift-based mode detection step. Second, an objective function is utilized for the selection of a mode combination not representing the gross outliers. We demonstrate the robustness of the method in a variety of examples with different noise conditions. The RAAM performance is quantitatively demonstrated in two substantially different applications, diaphragm segmentation and rheumatoid arthritis assessment. In all cases, the robust method shows an excellent behavior, with the new method tolerating up to 50% object area covered by gross gray-level disturbances.

Algorithms↗
PubMed2005

Precision range image registration using a robust surface interpenetration measure and enhanced genetic algorithms.

This paper addresses the range image registration problem for views having low overlap and which may include substantial noise. The current state of the art in range image registration is best represented by the well-known iterative closest point (ICP) algorithm and numerous variations on it. Although this method is effective in many domains, it nevertheless suffers from two key limitations: It requires prealignment of the range surfaces to a reasonable starting point and it is not robust to outliers arising either from noise or low surface overlap. This paper proposes a new approach that avoids these problems. To that end, there are two key, novel contributions in this work: a new, hybrid genetic algorithm (GA) technique, including hillclimbing and parallel-migration, combined with a new, robust evaluation metric based on surface interpenetration. Up to now, interpenetration has been evaluated only qualitatively; we define the first quantitative measure for it. Because they search in a space of transformations, GAs are capable of registering surfaces even when there is low overlap between them and without need for prealignment. The novel GA search algorithm we present offers much faster convergence than prior GA methods, while the new robust evaluation metric ensures more precise alignments, even in the presence of significant noise, than mean squared error or other well-known robust cost functions. The paper presents thorough experimental results to show the improvements realized by these two contributions.

Algorithms↗
PubMed2006

Channel smoothing: efficient robust smoothing of low-level signal features.

In this paper, we present a new and efficient method to implement robust smoothing of low-level signal features: B-spline channel smoothing. This method consists of three steps: encoding of the signal features into channels, averaging of the channels, and decoding of the channels. We show that linear smoothing of channels is equivalent to robust smoothing of the signal features if we make use of quadratic B-splines to generate the channels. The linear decoding from B-spline channels allows the derivation of a robust error norm, which is very similar to Tukey's biweight error norm. We compare channel smoothing with three other robust smoothing techniques: nonlinear diffusion, bilateral filtering, and mean-shift filtering, both theoretically and on a 2D orientation-data smoothing task. Channel smoothing is found to be superior in four respects: It has a lower computational complexity, it is easy to implement, it chooses the global minimum error instead of the nearest local minimum, and it can also be used on nonlinear spaces, such as orientation space.

Algorithms↗
PubMed2004

Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure.

Fuzzy c-means clustering (FCM) with spatial constraints (FCM_S) is an effective algorithm suitable for image segmentation. Its effectiveness contributes not only to the introduction of fuzziness for belongingness of each pixel but also to exploitation of spatial contextual information. Although the contextual information can raise its insensitivity to noise to some extent, FCM_S still lacks enough robustness to noise and outliers and is not suitable for revealing non-Euclidean structure of the input data due to the use of Euclidean distance (L2 norm). In this paper, to overcome the above problems, we first propose two variants, FCM_S1 and FCM_S2, of FCM_S to aim at simplifying its computation and then extend them, including FCM_S, to corresponding robust kernelized versions KFCM_S, KFCM_S1 and KFCM_S2 by the kernel methods. Our main motives of using the kernel methods consist in: inducing a class of robust non-Euclidean distance measures for the original data space to derive new objective functions and thus clustering the non-Euclidean structures in data; enhancing robustness of the original clustering algorithms to noise and outliers, and still retaining computational simplicity. The experiments on the artificial and real-world datasets show that our proposed algorithms, especially with spatial constraints, are more effective.

Algorithms↗
PubMed2006

Robust and fast learning for fuzzy cerebellar model articulation controllers.

In this paper, the online learning capability and the robust property for the learning algorithms of cerebellar model articulation controllers (CMAC) are discussed. Both the traditional CMAC and fuzzy CMAC are considered. In the study, we find a way of embeding the idea of M-estimators into the CMAC learning algorithms to provide the robust property against outliers existing in training data. An annealing schedule is also adopted for the learning constant to fulfill robust learning. In the study, we also extend our previous work of adopting the credit assignment idea into CMAC learning to provide fast learning for fuzzy CMAC. From demonstrated examples, it is clearly evident that the proposed algorithm indeed has faster and more robust learning. In our study, we then employ the proposed CMAC for an online learning control scheme used in the literature. In the implementation, we also propose to use a tuning parameter instead of a fixed constant to achieve both online learning and fine-tuning effects. The simulation results indeed show the effectiveness of the proposed approaches.

Algorithms↗
PubMed2006

Robust H infinity static output feedback control of fuzzy systems: an ILMI approach.

This paper examines the problem of robust H infinity static output feedback control of a Takagi-Sugeno fuzzy system. The proposed robust H infinity static output feedback controller guarantees the pounds 2 gain of the mapping from the exogenous disturbances to the regulated output to be less than or equal to a prescribed level. The existence of a robust H infinity static output feedback control is given in terms of the solvability of bilinear matrix inequalities. An iterative algorithm based on the linear matrix inequality is developed to compute robust H infinity static output feedback gains. To reduce the conservatism of the design, the structural information of membership function characteristics is incorporated. A numerical example is used to illustrate the validity of the design methodologies.

Algorithms↗
PubMed2006

CATSMLP: toward a robust and interpretable multilayer perceptron with sigmoid activation functions.

Enhancing the robustness and interpretability of a multilayer perceptron (MLP) with a sigmoid activation function is a challenging topic. As a particular MLP, additive TS-type MLP (ATSMLP) can be interpreted based on single-stage fuzzy IF-THEN rules, but its robustness will be degraded with the increase in the number of intermediate layers. This paper presents a new MLP model called cascaded ATSMLP (CATSMLP), where the ATSMLPs are organized in a cascaded way. The proposed CATSMLP is a universal approximator and is also proven to be functionally equivalent to a fuzzy inference system based on syllogistic fuzzy reasoning. Therefore, the CATSMLP may be interpreted based on syllogistic fuzzy reasoning in a theoretical sense. Meanwhile, due to the fact that syllogistic fuzzy reasoning has distinctive advantage over single-stage IF-THEN fuzzy reasoning in robustness, this paper proves in an indirect way that the CATSMLP is more robust than the ATSMLP in an upper-bound sense. Several experiments were conducted to confirm such a claim.

Journal Article↗
PubMed2001

Robust two-stage estimation in hierarchical nonlinear models.

Hierarchical models encompass two sources of variation, namely within and among individuals in the population; thus, it is important to identify outliers that may arise at each sampling level. A two-stage approach to analyzing nonlinear repeated measurements naturally allows parametric modeling of the respective variance structure for the intraindividual random errors and interindividual random effects. We propose a robust two-stage procedure based on Huber's (1981, Robust Statistics) theory of M-estimation to accommodate separately aberrant responses within an experimental unit and subjects deviating from the study population when the usual assumptions of normality are violated. A toxicology study of chronic ozone exposure in rats illustrates the impact of outliers on the population inference and hence the advantage of adopting the robust methodology. The robust weights generated by the two-stage M-estimation process also serve as diagnostics for gauging the relative influence of outliers at each level of the hierarchical model. A practical appeal of our proposal is the computational simplicity since the estimation algorithm may be implemented using standard statistical software with a nonlinear least squares routine and iterative capability.

Air Pollutants↗
PubMed2004

Loglinear models for the robust design in mark-recapture experiments.

The robust design is a method for implementing a mark-recapture experiment featuring a nested sampling structure. The first level consists of primary sampling sessions; the population experiences mortality and immigration between primary sessions so that open population models apply at this level. The second level of sampling has a short mark-recapture study within each primary session. Closed population models are used at this stage to estimate the animal abundance at each primary session. This article suggests a loglinear technique to fit the robust design. Loglinear models for the analysis of mark-recapture data from closed and open populations are first reviewed. These two types of models are then combined to analyze the data from a robust design. The proposed loglinear approach to the robust design allows incorporating parameters for a heterogeneity in the capture probabilities of the units within each primary session. Temporary emigration out of the study area can also be accounted for in the loglinear framework. The analysis is relatively simple; it relies on a large Poisson regression with the vector of frequencies of the capture histories as dependent variable. An example concerned with the estimation of abundance and survival of the red-back vole in an area of southeastern Québec is presented.

Animals↗
PubMed2006

Genetic robustness and selection at the protein level for synonymous codons.

Synonymous codons are neutral at the protein level, therefore natural selection at the protein level should have no effect on their frequencies. Synonymous codons, however, differ in their capacity to reduce the effects of errors: after mutation, certain codons keep on coding for the same amino acid or for amino acids with similar properties, while other synonymous codons produce very different amino acids. Therefore, the impact of errors on a coding sequence (genetic robustness) can be measured by analysing its codon usage. I analyse the codon usage of sequenced nuclear and cytoplasmic genomes and I show that there is an extensive variation in genetic robustness at the DNA sequence level, both among genomes and among genes of the same genome. I also show theoretically that robustness can be adaptive, that is natural selection may lead to a preference for codons that reduce the impact of errors. If selection occurs only among the mutants of a codon (e.g. among the progeny before the adult phase), however, the codons that are more sensitive to the effects of mutations may increase in frequency because they manage to get rid more easily of deleterious mutations. I also suggest other possible explanations for the evolution of genetic robustness at the codon level.

Amino Acid Sequence↗
PubMed2005

The robustness of perception.

The natural environment around us, which is often crowded, cluttered or even foggy, is subject to a dynamically changing composition of objects and events. The human brain is continuously perceiving, recognizing and evaluating this dynamic scene composition. If the perception of degraded visual objects is important, e.g. in the case of potential threat stimuli, the brain needs to be more sensitive in detecting these objects from the natural environment. It is therefore hypothesized that reacting to the dynamically changing environment involves a robust and quick processing of salient information, which can be either with or without conscious awareness. We investigated the dynamics and robustness of perception using pictures of three salience levels, i.e. fearful faces (most salient), neutral faces (salient) and houses (nonsalient), which appear from dynamically decreasing random visual noise. Stimuli were matched for luminance, contrast, brightness and spatial frequency information. Reaction times show a significantly earlier response for faces than for houses. Fearful faces were significantly more quickly detected than neutral faces. The neural correlates sustaining robust perception were investigated with event-related functional magnetic resonance imaging (fMRI). The amygdala showed a significant perception-related response for faces, as compared to houses, that was further enhanced for fearful faces as compared to neutral faces. Our data indicate that emotionally salient information processing is (i) mediated by the amygdala and (ii) more robust than for nonsalient stimuli as it shows a significantly lower perceptual threshold.

Adolescent↗
PubMed1991

Scaled test statistics and robust standard errors for non-normal data in covariance structure analysis: a Monte Carlo study.

Research studying robustness of maximum likelihood (ML) statistics in covariance structure analysis has concluded that test statistics and standard errors are biased under severe non-normality. An estimation procedure known as asymptotic distribution free (ADF), making no distributional assumption, has been suggested to avoid these biases. Corrections to the normal theory statistics to yield more adequate performance have also been proposed. This study compares the performance of a scaled test statistic and robust standard errors for two models under several non-normal conditions and also compares these with the results from ML and ADF methods. Both ML and ADF test statistics performed rather well in one model and considerably worse in the other. In general, the scaled test statistic seemed to behave better than the ML test statistic and the ADF statistic performed the worst. The robust and ADF standard errors yielded more appropriate estimates of sampling variability than the ML standard errors, which were usually downward biased, in both models under most of the non-normal conditions. ML test statistics and standard errors were found to be quite robust to the violation of the normality assumption when data had either symmetric and platykurtic distributions, or non-symmetric and zero kurtotic distributions.

Analysis of Variance↗