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At least 1,135 records · Page 63Linked to original sources

Robust formation of morphogen gradients.

We discuss the formation of graded morphogen profiles in a cell layer by nonlinear transport phenomena, important for patterning developing organisms. We focus on a process termed transcytosis, where morphogen transport results from the binding of ligands to receptors on the cell surface, incorporation into the cell, and subsequent externalization. Starting from a microscopic model, we derive effective transport equations. We show that, in contrast to morphogen transport by extracellular diffusion, transcytosis leads to robust ligand profiles which are insensitive to the rate of ligand production.

Cell Communication↗

Robust one-step catalytic machine for high fidelity anticloning and W-state generation in a multiqubit system.

We propose a physically realizable machine which can either generate multiparticle W-like states, or implement high-fidelity 1-->M (M=1,2,...infinity) anticloning of an arbitrary qubit state, in a single step. This universal machine acts as a catalyst in that it is unchanged after either procedure, effectively resetting itself for its next operation. It possesses an inherent immunity to decoherence. Most importantly in terms of practical multiparty quantum communication, the machine's robustness in the presence of decoherence actually increases as the number of qubits M increases.

Journal Article↗

Robust one-dimensional metallic band structure of silicide nanowires.

Angle-resolved photoemission (ARP) is employed to investigate the electronic structure of an extremely anisotropic form of nanocrystals--GdSi(2-x) nanowires on Si(100). Using a stepped Si(100) surface, a well-ordered and uniformly oriented array of nanowires is formed along the step edges as confirmed by diffraction and microscopy. The ARP measurement discloses two distinct electronic bands near the Fermi level, which disperse one dimensionally along the nanowires. These bands are metallic with the electron filling of 1/4 and 2/5, respectively, and with the effective mass close to that of a free electron along the wires. The metallicity is robust down to 20 K, in contrast to metallic surface atomic chain systems, paving a way to further studies on one-dimensional physics of metallic nanowires.

Journal Article↗

Finding the center reliably: robust patterns of developmental gene expression.

We investigate a mechanism for the robust identification of the center of a developing biological system. We assume the existence of two morphogen gradients, an activator emanating from the anterior, and a corepressor from the posterior. The corepressor inhibits the action of the activator in switching on target genes. We apply this system to Drosophila embryos, where we predict the existence of a hitherto undetected posterior corepressor. Using mathematical modeling, we show that a symmetric activator-corepressor model can quantitatively explain the precise midembryo expression boundary of the hunchback gene, and the scaling of this pattern with embryo size.

Animals↗

Functional modes of proteins are among the most robust.

It is shown that a small subset of modes which are likely to be involved in protein functional motions of large amplitude can be determined by retaining the most robust normal modes obtained using different protein models. This result should prove helpful in the context of several applications proposed recently, like for solving difficult molecular replacement problems or for fitting atomic structures into low-resolution electron density maps. It may also pave the way for the development of methods allowing us to predict such motions accurately.

Computational Biology↗

A gene regulatory network model for cell-fate determination during Arabidopsis thaliana flower development that is robust and recovers experimental gene expression profiles.

Flowers are icons in developmental studies of complex structures. The vast majority of 250,000 angiosperm plant species have flowers with a conserved organ plan bearing sepals, petals, stamens, and carpels in the center. The combinatorial model for the activity of the so-called ABC homeotic floral genes has guided extensive experimental studies in Arabidopsis thaliana and many other plant species. However, a mechanistic and dynamical explanation for the ABC model and prevalence among flowering plants is lacking. Here, we put forward a simple discrete model that postulates logical rules that formally summarize published ABC and non-ABC gene interaction data for Arabidopsis floral organ cell fate determination and integrates this data into a dynamic network model. This model shows that all possible initial conditions converge to few steady gene activity states that match gene expression profiles observed experimentally in primordial floral organ cells of wild-type and mutant plants. Therefore, the network proposed here provides a dynamical explanation for the ABC model and shows that precise signaling pathways are not required to restrain cell types to those found in Arabidopsis, but these are rather determined by the overall gene network dynamics. Furthermore, we performed robustness analyses that clearly show that the cell types recovered depend on the network architecture rather than on specific values of the model's gene interaction parameters. These results support the hypothesis that such a network constitutes a developmental module, and hence provide a possible explanation for the overall conservation of the ABC model and overall floral plan among angiosperms. In addition, we have been able to predict the effects of differences in network architecture between Arabidopsis and Petunia hybrida.

Arabidopsis↗

A statistic for local intensity differences: robustness to anisotropy and pseudo-centering and utility for detecting twinning.

A new approach to analyzing macromolecular single-crystal X-ray diffraction intensity statistics is presented. Instead of considering reflections in resolution shells, differences between local pairs of reflection intensities are taken and normalized separately. When the two reflections to be compared (having intensities I(1) and I(2), respectively) are chosen appropriately, the behavior of the parameter L = (I(1) - I(2))/(I(1) + I(2)) is insensitive to phenomena that tend to confound traditional intensity statistics, such as anisotropic diffraction and pseudo-centering. The distributions and expected values for L take simple forms when the intensity data are from ordinary crystals or from perfectly twinned specimens. The robustness of the approach is demonstrated with examples using real proteins whose diffraction data appear aberrant by other methods of intensity analysis. The new statistic is better suited than other available methods for diagnosing perfect hemihedral twinning.

Anisotropy↗

A robust bulk-solvent correction and anisotropic scaling procedure.

A reliable method for the determination of bulk-solvent model parameters and an overall anisotropic scale factor is of increasing importance as structure determination becomes more automated. Current protocols require the manual inspection of refinement results in order to detect errors in the calculation of these parameters. Here, a robust method for determining bulk-solvent and anisotropic scaling parameters in macromolecular refinement is described. The implementation of a maximum-likelihood target function for determining the same parameters is also discussed. The formulas and corresponding derivatives of the likelihood function with respect to the solvent parameters and the components of anisotropic scale matrix are presented. These algorithms are implemented in the CCTBX bulk-solvent correction and scaling module.

Algorithms↗

A robust controller for insulin pumps based on H-infinity theory.

The feedback control of insulin pumps for diabetic patients is discussed. Because the parameters in the mathematical model of the blood glucose dynamics present a considerable amount of uncertainty, the H-infinity framework is well suited for the design of controllers that take into account a nice compromise between robust closed-loop regulation of a constant set point and performance expressed in terms of peak values of the plasma glucose concentration.

Blood Glucose↗

A robust parametric estimator for single-trial movement related brain potentials.

Current estimators for single-trial evoked potentials (EP's) require a signal-to-noise ratio (SNR) of 0 dB or better to obtain high quality estimations, yet many types of EP's suffer from substantially lower SNR's. This paper presents a robust-evoked-potential-estimator (REPE) facilitating high quality estimations of single movement related EP's with a relatively low SNR. The estimator is based on a standard ARX model, enhanced to support estimation under poor SNR conditions. The REPE was tested successfully on a computer simulated data set giving reliable single-trial estimations for the low SNR range of around -20 dB. THe REPE was also applied to experimental data, producing clear single-trial estimations of movement related brain signals recorded in a classic scenario of self-paced finger tapping experiment.

Algorithms↗

A robust ensemble data method for identification of human joint mechanical properties during movement.

This paper describes a perturbation method for the identification of linear time-varying systems with an unknown input (voluntary joint input) using ensemble data. The method separates the unknown input and the perturbation through high-pass filtering and recasts the multi-input single-output system identification into single-input single-output system identification. The method is robust to intertrial variation, and can track changes of system dynamics up to 5 Hz. Analysis and simulation are given for the conditions similar to those for the human arm experiments. Experiments show that mechanical properties of the human elbow joint change with voluntary movement speed and that the mean stiffness with voluntary movement is in the range of the posture and is higher than reported before.

Adult↗

Methods for robust clustering of epileptic EEG spikes.

We investigate algorithms for clustering of epileptic electroencephalogram (EEG) spikes. Such a method is useful prior to averaging and inverse computations since the spikes of a patient often belong to a few distinct classes. Data sets often contain outliers, which makes algorithms with robust performance desirable. We compare the fuzzy C-means (FCM) algorithm and a graph-theoretic algorithm. We give criteria for determination of the correct level of outlier contamination. The performance is then studied by aid of simulations, which show good results for a range of circumstances, for both algorithms. The graph-theoretic method gave better results than FCM for simulated signals. Also, when evaluating the methods on seven real-life data sets, the graph-theoretic method was the better method, in terms of closeness to the manual assessment by a neurophysiologist. However, there was some discrepancy between manual and automatic clustering and we suggest as an alternative method a human choice among a limited set of automatically obtained clusterings. Furthermore, we evaluate geometrically weighted feature extraction and conclude that it is useful as a supplementary dimension for clustering.

Algorithms↗

The boundary shift integral: an accurate and robust measure of cerebral volume changes from registered repeat MRI.

We propose the boundary shift integral (BSI) as a measure of cerebral volume changes derived from registered repeat three-dimensional (3-D) magnetic resonance (MR) [3D MR] scans. The BSI determines the total volume through which the boundaries of a given cerebral structure have moved and, hence, the volume change, directly from voxel intensities. We found brain and ventricular BSI's correlated tightly (r = 1.000 and r = 0.999) with simulated volumes of change. Applied to 21 control scan pairs and 11 scan pairs from Alzheimer's disease (AD) patients (mean interval 386 days) the BSI yielded mean brain volume loss of 1.8 cc (controls) and 34.7 cc (AD); the control group was tightly bunched (SD = 3.8 cc) and there was wide group separation, the group means differing by 8.7 control group standard deviations (SD's). A measure based on the same segmentation used by the BSI yielded similar group means, but wide spread in the control group (SD = 13.4 cc) and group overlap, the group means differing by 2.8 control group SD's. The BSI yielded mean ventricular volume losses of 0.4 cc (controls) and 10.1 cc (AD). Good linear correlation (r = 0.997) was obtained between the ventricular BSI and the difference in their segmented volumes. We conclude the BSI is an accurate and robust measure of regional and global cerebral volume changes.

Algorithms↗

Robust parameter estimation of intensity distributions for brain magnetic resonance images.

This paper presents two new methods for robust parameter estimation of mixtures in the context of magnetic resonance (MR) data segmentation. The head is constituted of different types of tissue that can be modeled by a finite mixture of multivariate Gaussian distributions. Our goal is to estimate accurately the statistics of desired tissues in presence of other ones of lesser interest. These latter can be considered as outliers and can severely bias the estimates of the former. For this purpose, we introduce a first method, which is an extension of the expectation-maximization (EM) algorithm, that estimates parameters of Gaussian mixtures but incorporates an outlier rejection scheme which allows to compute the properties of the desired tissues in presence of atypical data. The second method is based on genetic algorithms and is well suited for estimating the parameters of mixtures of different kind of distributions. We use this property by adding a uniform distribution to the Gaussian mixture for modeling the outliers. The proposed genetic algorithm can efficiently estimate the parameters of this extended mixture for various initial settings. Also, by changing the minimization criterion, estimates of the parameters can be obtained by histogram fitting which considerably reduces the computational cost. Experiments on synthetic and real MR data show that accurate estimates of the gray and white matters parameters are computed.

Adolescent↗

A robust numerical solution to reconstruct a globally relative shear modulus distribution from strain measurements.

To noninvasively quantify tissue elasticity for differentiating malignancy of soft tissue, we previously proposed a two-dimensional (2-D) mechanical inverse problem in which simultaneous partial differential equations (PDE's) represented the target distribution globally of relative shear moduli with respect to reference shear moduli such that the relative values could be determined from strain distributions obtained by conventional ultrasound (US) or nuclear magnetic resonance (NMR) imaging-based analysis. Here, we further consider the analytic solution in the region of interest, subsequently demonstrating that the problem is inevitably ill-conditioned in real-world applications, i.e., noise in measurement data and improper configurations of mechanical sources/reference regions make it impossible to guarantee the existence of a stable and unique target global distribution. Next, based on clarification of the inherent problematic conditions, we describe a newly developed numerical-based implicit-integration approach that novelly incorporates a computationally efficient regularization method designed to solve this differential inverse problem using just low-pass filtered spectra derived from strain measurements. To evaluate method effectiveness, reconstructions of the global distribution are carried out using intentionally created ill-conditioned models. The resultant reconstructions indicate the robust solution is highly suitable, while also showing it has high potential to be applied in the development of an effective yet versatile diagnostic tool for quantifying the distribution of elasticity in various soft tissues.

Biomechanical Phenomena↗

The real-time interactive 3-D-DVA for robust coronary MRA.

A graphical user interface (GUI) has been developed which enables interactive feedback and control to the real-time diminishing variance algorithm (DVA). This interactivity allows the user to set scan parameters, view scan statistics, and view image updates during the course of the scan. In addition, the DVA has been extended to simultaneously reduce motion artifacts in three dimensions using three orthogonal navigators. Preliminary in vivo studies indicate that these improvements to the standard DVA allow for significantly improved consistency and robustness in eliminating respiratory motion artifacts from MR images, particularly when imaging the coronary arteries.

Algorithms↗

Robust midsagittal plane extraction from normal and pathological 3-D neuroradiology images.

This paper focuses on extracting the ideal midsagittal plane (iMSP) from three-dimensional (3-D) normal and pathological neuroimages. The main challenges in this work are the structural asymmetry that may exist in pathological brains, and the anisotropic, unevenly sampled image data that is common in clinical practice. We present an edge-based, cross-correlation approach that decomposes the plane fitting problem into discovery of two-dimensional symmetry axes on each slice, followed by a robust estimation of plane parameters. The algorithm's tolerance to brain asymmetries, input image offsets and image noise is quantitatively evaluated. We find that the algorithm can extract the iMSP from input 3-D images with 1) large asymmetrical lesions; 2) arbitrary initial rotation offsets; 3) low signal-to-noise ratio or high bias field. The iMSP algorithm is compared with an approach based on maximization of mutual information registration, and is found to exhibit superior performance under adverse conditions. Finally, no statistically significant difference is found between the midsagittal plane computed by the iMSP algorithm and that estimated by two trained neuroradiologists.

Algorithms↗