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At least 199 records · Page 11Linked to original sources
PubMed2006

The assessment of hemispheric lateralization in functional MRI--robustness and reproducibility.

Various methods have been proposed to calculate a lateralization index (LI) on the basis of functional magnetic resonance imaging (fMRI) data. Most of them are either based on the extent of the activated brain region (i.e., the number of "active" voxels) or the magnitude of the fMRI signal change. The purpose of the present study was to investigate the characteristics of various variants of these approaches and to identify the one that yields the most robust and reproducible results. Robustness was assessed by evaluating the dependence on arbitrary external parameters, reproducibility was assessed by Pearson's correlation coefficient. LIs based on active voxels counts at one single fixed statistical threshold as well as LIs based on unthresholded signal intensity changes (i.e., based on all voxels in a region of interest) yielded neither robust nor reproducible laterality results. Instead, the lateralization of a cognitive function was best described by "thresholded" signal intensity changes where the activity measure was based on signal intensity changes in those voxels in a region of interest that exceeded a predefined activation level. However, not all other approaches should be discarded completely since they have their own specific application fields. First, LIs based on active voxel counts in the form of p-value-dependent lateralization plots (LI=LI(p)) can be used as a straightforward measure to describe hemispheric dominance. Second, LIs based on active voxel counts at variable thresholds (standardized by the total number of active voxels) are a good alternative for big regions of interest since LIs based on signal intensity changes are restricted to small ROIs.

Adult↗
PubMed2006

The anti-invasive compound motuporamine C is a robust stimulator of neuronal growth cone collapse.

Neuronal outgrowth is a fundamental process for normal development of the nervous system. Despite recent advances, the molecular mechanisms governing neuronal motility are still poorly understood. To provide insight into the intracellular signaling mechanisms required for neuronal outgrowth, we have characterized the effects of a compound previously identified for its anti-motility effects on transformed cells. We show that this compound, motuporamine C, acts as a robust inhibitor of chick neurite outgrowth in a dose-dependent fashion. Furthermore, in the presence of motuporamine C, growth cone collapse is observed, followed by neurite retraction. After removal, growth cones re-extend lamellipodial and filopodial processes and re-establish motility. Neurons exposed to motuporamine C exhibit a significant upregulation of active Rho-GTP. Additionally, effector-blocking experiments using Rho and Rho-associated kinase inhibitors indicate that the Rho pathway plays a critical role in motuporamine C-mediated growth cone collapse. Thus, we have characterized a novel anti-motility compound that has a robust inhibitory effect on neuronal outgrowth and involves signaling through the Rho-Rho kinase collapse pathway. Due to these robust effects, motuporamine C may serve as a valuable tool in further examining the intracellular mechanisms associated with growth cone motility.

Alkaloids↗
PubMed2005

Robustness of reconstructing the Young's modulus distribution of vulnerable atherosclerotic plaques using a parametric plaque model.

Assessment of atherosclerotic plaque composition is crucial for quantitative monitoring of atherosclerosis and for quantifying the effect of pharmaceutical plaque-stabilizing treatments during clinical trials. We assessed this composition by applying a geometrically constrained, iterative inverse solution method to reconstruct a modulus elastogram (i.e., Young's modulus image) from a plaque strain elastogram (i.e., radial strain image) that is measured using intravascular ultrasound strain elastography. This reconstruction method is especially suited for thin-cap fibroatheromas (TCFAs) (i.e., plaques with a thin fibrous cap overlaying a lipid pool). Because a strain elastogram of a plaque depends upon the plaque material composition, catheter position within the vessel and measurement noise, this paper investigates how robust the reconstruction is when these parameters are varied. To this end, a standard plaque was defined as the modulus elastogram that was reconstructed from an in vivo measured strain elastogram of a human coronary plaque. This standard plaque was used to computer-simulate different strain elastograms, by varying the 1. geometry and material properties of its plaque components, 2. catheter position and 3. level of added strain noise. Robustness was evaluated by quantifying the correctly reconstructed size, shape and Young's modulus of each plaque component region and minimal cap thickness. The simulations showed that TCFAs can be adequately reconstructed; the thinner and stiffer the cap or the softer and larger the lipid pool, the better is the reconstruction of these components and minimal cap thickness. Furthermore, reconstructions were 1. independent of catheter position and 2. independent of strain noise. As such, this method has potential to monitor robustly and quantitatively atherosclerosis in vivo.

Algorithms↗
PubMed2006

Single dose glucagon (0.5 mg IV bolus) administration in healthy human volunteers is a robust model for assessment of glycogenolysis: characterisation of the glucose excursion after glucagon challenge.

INTRODUCTION: In the assessment of potential new treatments for Type 2 diabetes, robust pharmacological methods are helpful in assessing efficacy, defining dose response, duration of effect and ultimately in deciding whether to progress compounds to the next phase of drug development. Hepatic glucose handling is abnormal in Type 2 diabetes. We evaluated glucagon challenge as a way of assessing effects on the glycogenolytic pathway. METHODS: In each of 2 studies healthy subjects received glucagon as an IV bolus of 0.5 mg studied after an overnight fast and plasma glucose was monitored before and for 180 min after glucagon challenge. Study 1 was a double-blind placebo controlled study comparing glucagon administered twice with saline placebo. In study 2, subjects were studied on a single occasion and the glucagon challenge was carried out in the morning and then repeated 7 h later. In study 2, insulin concentrations were also monitored before and after the glucagon challenge. RESULTS: In study 1, glucose rose in a reproducible manner with a peak glucose 20 min after challenge falling to baseline values by 120 min and then fell below values for saline challenge between 120 and 180 min. Analysis of the data showed that the corrected AUC(0-20) min was the most robust variable and could be expected to detect clinically relevant changes in small numbers (<10) of subjects. In study 2, we demonstrated that when glucagon challenge was repeated 7 h after the first challenge, the glucose excursion was highly variable. The plasma insulin response was robust following the initial challenge but variable following the second challenge. DISCUSSION: We have demonstrated that an IV bolus glucagon challenge (0.5 mg) results in a reproducible rise in glucose in healthy volunteers and can be repeated within a week but when repeated on the same day gave a poorly reproducible rise in glucose. Glucagon challenge may be useful in studying novel drugs that affect glycogen handling in the liver.

Adult↗
PubMed2005

A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset.

Development of a robust and efficient approach for extracting useful information from microarray data continues to be a significant and challenging task. Microarray data are characterized by a high dimension, high signal-to-noise ratio, and high correlations between genes, but with a relatively small sample size. Current methods for dimensional reduction can further be improved for the scenario of the presence of a single (or a few) high influential gene(s) in which its effect in the feature subset would prohibit inclusion of other important genes. We have formalized a robust gene selection approach based on a hybrid between genetic algorithm and support vector machine. The major goal of this hybridization was to exploit fully their respective merits (e.g., robustness to the size of solution space and capability of handling a very large dimension of feature genes) for identification of key feature genes (or molecular signatures) for a complex biological phenotype. We have applied the approach to the microarray data of diffuse large B cell lymphoma to demonstrate its behaviors and properties for mining the high-dimension data of genome-wide gene expression profiles. The resulting classifier(s) (the optimal gene subset(s)) has achieved the highest accuracy (99%) for prediction of independent microarray samples in comparisons with marginal filters and a hybrid between genetic algorithm and K nearest neighbors.

Algorithms↗
PubMed2006

Robust systemic transduction with AAV9 vectors in mice: efficient global cardiac gene transfer superior to that of AAV8.

It has been recently shown that recombinant adeno-associated virus serotype 8 (rAAV8) is a robust alternative serotype vector that overcomes many of the limitations of rAAV2 and transduces various tissues efficiently and globally through systemic vector administration. AAV9 is a serotype newly isolated from human tissues, but our knowledge of the biology of rAAV9 in vivo is currently limited. Here, we demonstrate by a series of comprehensive side-by-side experiments with rAAV8 and 9 vectors delivered via different routes or at various doses in mice that rAAV9 vectors share the robustness of rAAV8, i.e., (1) very high liver transduction efficiency irrespective of whether vectors are administered intravascularly or extravascularly and (2) substantial transduction in the heart, skeletal muscle, and pancreas by peripheral vein injection. Importantly, rAAV9 transduced myocardium 5- to 10-fold higher than rAAV8, resulting in over 80% cardiomyocyte transduction following tail vein injection of as low as 1.0 x 10(11) particles per mouse. Thus rAAV9, as well as rAAV8, is a robust vector for gene therapy applications and rAAV9 is superior to rAAV8 specifically for cardiac gene delivery by systemic vector administration.

Animals↗
PubMed2002

Conservation of the segmented germband stage: robustness or pleiotropy?

Gene expression patterns of the segment polarity genes in the extended and segmented germband stage are remarkably conserved among insects. To explain the conservation of these stages, two hypotheses have been proposed. One hypothesis states that the conservation reflects a high interactivity between modules, so that mutations would have several pleiotropic effects in other parts of the body, resulting in stabilizing selection against mutational variation. The other hypothesis states that the conservation is caused by robustness of the segment polarity network against mutational changes. When evaluating the empirical evidence for these hypotheses, we found strong support for pleiotropy and little evidence supporting robustness of the segment polarity network. This points to a key role for stabilizing selection in the conservation of these stages. Finally, we discuss the implications for robustness of organizers and long-term conservation in general.

Animals↗
PubMed2003

On the physics of the symbol--matter problem in biological systems and the origin of life: affine Hilbert spaces model of the robustness of the internal quantum dynamics of biological systems.

In the present paper, some physical considerations of the biological symbol-matter problem is exposed. First of all, the physical concept of quantum dynamical internal measuremental robustness is discussed. In this context, the significance of introducing affine molecular Hilbert spaces, the original (primordeal) internal quantum measurement, and the global constraining nature of time-inversion symmetry restoring, as a special restoration force, is discussed at some length. It is pointed out, as a summary, that global robustness of the internal dynamics of quantum measurements is due to two basic factors: on one hand, the global constraining nature of the chosen specific (symmetry-) restoring force, and on the other, the individual robustness of the discrete local internal measuremental interactions. The second condition is supposed to follow from a system-internalised ("objective") Bohr-type Copenhagen interpretation of quantum mechanics, corresponding, in an external context, to the Generalized Complementarity Principle of Bohr and Elsasser. It is not claimed, however, that this latter problem has been, as yet, satisfactorily settled physically. In fact, if it were, it would amount to a specifically biological quantum theory of internal measurement, which had to be rooted in the original primordeal global internal measurement, amounting to the origin of the genetic code.

Adaptation, Physiological↗
PubMed1997

Experimental design strategies in the optimization and robustness testing of adsorptive stripping voltammetric conditions for kynurenic acid determination.

Experimental design was used for the optimization and robustness testing of an adsorptive stripping voltammetric procedure for kynurenic acid determination. The optimization of the peak height response proceeded through a screening phase (D-optimal design strategy) followed by a response surface study (Doehlert design) applied to the variables pH, pulse amplitude and stirring rate. An interaction between pH and stirring rate was pointed out. The optimized method was validated and the variation of factors that was expected to occur in practice was simulated in a robustness test. A composite fractional matrix for the evaluation of method robustness was used and pH emerged as the only critical factor. The linear range found applying the optimized conditions was 2.5 x 10(-9) to 2.5 x 10(-7) M and the calculated limit of detection was 1.72 x 10(-9) M.

Adsorption↗
PubMedDate not supplied

Subject independent facial expression recognition with robust face detection using a convolutional neural network.

Reliable detection of ordinary facial expressions (e.g. smile) despite the variability among individuals as well as face appearance is an important step toward the realization of perceptual user interface with autonomous perception of persons. We describe a rule-based algorithm for robust facial expression recognition combined with robust face detection using a convolutional neural network. In this study, we address the problem of subject independence as well as translation, rotation, and scale invariance in the recognition of facial expression. The result shows reliable detection of smiles with recognition rate of 97.6% for 5600 still images of more than 10 subjects. The proposed algorithm demonstrated the ability to discriminate smiling from talking based on the saliency score obtained from voting visual cues. To the best of our knowledge, it is the first facial expression recognition model with the property of subject independence combined with robustness to variability in facial appearance.

Discrimination, Psychological↗
PubMed1998

Robust maximum likelihood training of heteroscedastic probabilistic neural networks.

We consider the probabilistic neural network (PNN) that is a mixture of Gaussian basis functions having different variances. Such a Gaussian heteroscedastic PNN is more economic, in terms of the number of kernel functions required, than the Gaussian mixture PNN of a common variance. The expectation-maximisation (EM) algorithm, although a powerful technique for constructing maximum likelihood (ML) homoscedastic PNNs, often encounters numerical difficulties when training heteroscedastic PNNs. We combine a robust statistical technique known as the Jack-knife with the EM algorithm to provide a robust ML training algorithm. An artificial-data case, the two-dimensional XOR problem, and a real-data case, success or failure prediction of UK private construction companies, are used to evaluate the performance of this robust learning algorithm.

Journal Article↗
PubMed2003

A fully automatic and robust brain MRI tissue classification method.

A novel, fully automatic, adaptive, robust procedure for brain tissue classification from 3D magnetic resonance head images (MRI) is described in this paper. The procedure is adaptive in that it customizes a training set, by using a 'pruning' strategy, such that the classification is robust against anatomical variability and pathology. Starting from a set of samples generated from prior tissue probability maps (a 'model') in a standard, brain-based coordinate system ('stereotaxic space'), the method first reduces the fraction of incorrectly labeled samples in this set by using a minimum spanning tree graph-theoretic approach. Then, the corrected set of samples is used by a supervised kNN classifier for classifying the entire 3D image. The classification procedure is robust against variability in the image quality through a non-parametric implementation: no assumptions are made about the tissue intensity distributions. The performance of this brain tissue classification procedure is demonstrated through quantitative and qualitative validation experiments on both simulated MRI data (10 subjects) and real MRI data (43 subjects). A significant improvement in output quality was observed on subjects who exhibit morphological deviations from the model due to aging and pathology.

Adult↗
PubMedDate not supplied

Robustness analysis of the Escherichia coli metabolic network.

Genomic, biochemical, and strain-specific data can be assembled to define an in silico representation of the metabolic network for a select group of single cellular organisms. Flux-balance analysis and phenotypic phase planes derived therefrom have been developed and applied to analyze the metabolic capabilities and characteristics of Escherichia coli K-12. These analyses have shown the existence of seven essential reactions in the central metabolic pathways (glycolysis, pentose phosphate pathway, tricarboxylic acid cycle) for the growth in glucose minimal media. The corresponding seven gene products can be grouped into three categories: (1) pentose phosphate pathway genes, (2) three-carbon glycolytic genes, and (3) tricarboxylic acid cycle genes. Here we develop a procedure that calculates the sensitivity of optimal cellular growth to altered flux levels of these essential gene products. The results indicate that the E. coli metabolic network is robust with respect to the flux levels of these enzymes. The metabolic flux in the transketolase and the tricarboxylic acid cycle reactions can be reduced to 15% and 19%, respectively, of the optimal value without significantly influencing the optimal growth flux. The metabolic network also exhibited robustness with respect to the ribose-5-phosphate isomerase, and the ribose-5-phosephate isomerase flux was reduced to 28% of the optimal value without significantly effecting the optimal growth flux. The metabolic network exhibited limited robustness to the three-carbon glycolytic fluxes both increased and decreased. The development presented another dimension to the use of FBA to study the capabilities of metabolic networks.

Citric Acid Cycle↗
PubMedDate not supplied

Spatial sign preprocessing: a simple way to impart moderate robustness to multivariate estimators.

The spatial sign is a multivariate extension of the concept of sign. Recently multivariate estimators of covariance structures based on spatial signs have been examined by various authors. These new estimators are found to be robust to outlying observations. From a computational point of view, estimators based on spatial sign are very easy to implement as they boil down to a transformation of the data to their spatial signs, from which the classical estimator is then computed. Hence, one can also consider the transformation to spatial signs to be a preprocessing technique, which ensures that the calibration procedure as a whole is robust. In this paper, we examine the special case of spatial sign preprocessing in combination with partial least squares regression as the latter technique is frequently applied in the context of chemical data analysis. In a simulation study, we compare the performance of the spatial sign transformation to nontransformed data as well as to two robust counterparts of partial least squares regression. It turns out that the spatial sign transform is fairly efficient but has some undesirable bias properties. The method is applied to a recently published data set in the field of quantitative structure-activity relationships, where it is seen to perform equally well as the previously described best linear model for these data.

Journal Article↗
PubMed2000

Testing for robustness in Monte Carlo studies.

Monte Carlo studies provide the information needed to help researchers select appropriate analytical procedures under design conditions in which the underlying assumptions of the procedures are not met. In Monte Carlo studies, the 2 errors that one could commit involve (a) concluding that a statistical procedure is robust when it is not or (b) concluding that it is not robust when it is. In previous attempts to apply standard statistical design principles to Monte Carlo studies, the less severe of these errors has been wrongly designated the Type I error. In this article, a method is presented for controlling the appropriate Type I error rate; the determination of the number of iterations required in a Monte Carlo study to achieve desired power is described; and a confidence interval for a test's true Type I error rate is derived. A robustness criterion is also proposed that is a compromise between W. G. Cochran's (1952) and J. V. Bradley's (1978) criteria.

Humans↗
PubMed1999

Robustness in bacterial chemotaxis.

Networks of interacting proteins orchestrate the responses of living cells to a variety of external stimuli, but how sensitive is the functioning of these protein networks to variations in their biochemical parameters? One possibility is that to achieve appropriate function, the reaction rate constants and enzyme concentrations need to be adjusted in a precise manner, and any deviation from these 'fine-tuned' values ruins the network's performance. An alternative possibility is that key properties of biochemical networks are robust; that is, they are insensitive to the precise values of the biochemical parameters. Here we address this issue in experiments using chemotaxis of Escherichia coli, one of the best-characterized sensory systems. We focus on how response and adaptation to attractant signals vary with systematic changes in the intracellular concentration of the components of the chemotaxis network. We find that some properties, such as steady-state behaviour and adaptation time, show strong variations in response to varying protein concentrations. In contrast, the precision of adaptation is robust and does not vary with the protein concentrations. This is consistent with a recently proposed molecular mechanism for exact adaptation, where robustness is a direct consequence of the network's architecture.

Adaptation, Physiological↗
PubMed2006

Robustness trade-offs and host-microbial symbiosis in the immune system.

The immune system provides organisms with robustness against pathogen threats, yet it also often adversely affects the organism as in autoimmune diseases. Recently, the molecular interactions involved in the immune system have been uncovered. At the same time, the role of the bacterial flora and its interactions with the host immune system have been identified. In this article, we try to reconcile these findings to draw a consistent picture of the host defense system. Specifically, we first argue that the network of molecular interactions involved in immune functions has a bow-tie architecture that entails inherent trade-offs among robustness, fragility, resource limitation, and performance. Second, we discuss the possibility that commensal bacteria and the host immune system constitute an integrated defense system. This symbiotic association has evolved to optimize its robustness against pathogen attacks and nutrient perturbations by harboring a broad range of microorganisms. Owing to the inherent propensity of a host immune system toward hyperactivity, maintenance of bacterial flora homeostasis might be particularly important in the development of preventive strategies against immune disorders such as autoimmune diseases.

Bacteria↗
PubMed2006

Multiple knockout analysis of genetic robustness in the yeast metabolic network.

Genetic robustness characterizes the constancy of the phenotype in face of heritable perturbations. Previous investigations have used comprehensive single and double gene knockouts to study gene essentiality and pairwise gene interactions in the yeast Saccharomyces cerevisiae. Here we conduct an in silico multiple knockout investigation of a flux balance analysis model of the yeast's metabolic network. Cataloging gene sets that provide mutual functional backup, we identify sets of up to eight interacting genes and characterize the 'k robustness' (the depth of backup interactions) of each gene. We find that 74% (360) of the metabolic genes participate in processes that are essential to growth in a standard laboratory environment, compared with only 13% previously found to be essential using single knockouts. The genes' k robustness is shown to be a solid indicator of their biological buffering capacity and is correlated with both the genes' environmental specificity and their evolutionary retention.

Computer Simulation↗