Tracing water movement by means of Escherichia coli bacteriophages

Tiivistelmä: Escherichia coli bacteriofaagit merkkiaineena vesien kulkeutumistutkimuksissa

Interaction of Sesbania mosaic virus movement protein with the coat protein - implications for viral spread

Sesbania mosaic virus (SeMV) is a single-stranded positive-sense RNA plant virus belonging to the genus Sobemovirus. The movement protein (MP) encoded by SeMV ORF1 showed no significant sequence similarity with MPs of other genera, but showed 32% identity with the MP of Southern bean mosaic virus within the Sobemovirus genus. With a view to understanding the mechanism of cell-to-cell movement in sobemoviruses, the SeMV MP gene was cloned, over-expressed in Escherichia coli and purified. Interaction of the recombinant MP with the native virus (NV) was investigated by ELISA and pull-down assays. It was observed that SeMV MP interacted with NV in a concentration- and pH-dependent manner. Analysis of N- and C-terminal deletion mutants of the MP showed that SeMV MP interacts with the NV through the N- terminal 49 amino acid segment. Yeast two-hybrid assays confirmed the in vitro observations, and suggested that SeMV might belong to the class of viruses that require MP and NV/coat protein for cell-to-cell movement.

Interaction of Sesbania Mosaic Virus Movement Protein with VPg and P10: Implication to Specificity of Genome Recognition

Sesbania mosaic virus (SeMV) is a single strand positive-sense RNA plant virus that belongs to the genus Sobemovirus. The mechanism of cell-to-cell movement in sobemoviruses has not been well studied. With a view to identify the viral encoded ancillary proteins of SeMV that may assist in cell-to-cell movement of the virus, all the proteins encoded by SeMV genome were cloned into yeast Matchmaker system 3 and interaction studies were performed. Two proteins namely, viral protein genome linked (VPg) and a 10-kDa protein (P10) c v gft encoded by OFR 2a, were identified as possible interacting partners in addition to the viral coat protein (CP). Further characterization of these interactions revealed that the movement protein (MP) recognizes cognate RNA through interaction with VPg, which is covalently linked to the 59 end of the RNA. Analysis of the deletion mutants delineated the domains of MP involved in the interaction with VPg and P10. This study implicates for the first time that VPg might play an important role in specific recognition of viral genome by MP in SeMV and shed light on the possible role of P10 in the viral movement.

Crystal structure of Salmonella typhimurium 2-methylcitrate synthase: Insights on domain movement and substrate specificity

2-Methylcitric acid (2-MCA) cycle is one of the well studied pathways for the utilization of propionate as a source of carbon and energy in bacteria such as Salmonella typhimurium and Escherichia coli. 2-Methylcitrate synthase (2-MCS) catalyzes the conversion of oxaloacetate and propionyl-CoA to 2-methylcitrate and CoA in the second step of 2-MCA cycle. Here, we report the X-ray crystal structure of S. typhimurium 2-MCS (StPrpC) at 2.4 A resolution and its functional characterization. StPrpC was found to utilize propionyl-CoA more efficiently than acetyl-CoA or butyryl-CoA. The polypeptide fold and the catalytic residues of StPrpC are conserved in citrate synthases (CSs) suggesting similarities in their functional mechanisms. In the triclinic P1 cell, StPrpC molecules were organized as decamers composed of five identical dimer units. In solution, StPrpC was in a dimeric form at low concentrations and was converted to larger oligomers at higher concentrations. CSs are usually dimeric proteins. In Gram-negative bacteria, a hexameric form, believed to be important for regulation of activity by NADH, is also observed. Structural comparisons with hexameric E. coil CS suggested that the key residues involved in NADH binding are not conserved in StPrpC. Structural comparison with the ligand free and bound states of CSs showed that StPrpC is in a nearly closed conformation despite the absence of bound ligands. It was found that the Tyr197 and Leu324 of StPrpC are structurally equivalent to the ligand binding residues His and Val, respectively, of CSs. These substitutions might determine the specificities for acyl-CoAs of these enzymes. (C) 2010 Elsevier Inc. All rights reserved.

Singularity structure and chaotic behavior of the homopolar disk dynamo

We study in great detail a system of three first-order ordinary differential equations describing a homopolar disk dynamo (HDD). This system displays a large variety of behaviors, both regular and chaotic. Existence of periodic solutions is proved for certain ranges of parameters. Stability criteria for periodic solutions are given. The nonintegrability aspects of the HDD system are studied by investigating analytically the singularity structure of the system in the complex domain. Coexisting attractors (including period-doubling sequence) and coexisting strange attractors appear in some parametric regimes. The gluing of strange attractors and the ungluing of a strange attractor are also shown to occur. A period of bifurcation leading to chaos, not observed for other chaotic systems, is shown to characterize the chaotic behavior in some parametric ranges. The limiting case of the Lorenz system is also studied and is related to HDD.

On the integrability and chaotic behavior of an ecological model

A three-species food chain model is studied analytically as well as numerically. Integrability of the model is studied using Painleve analysis while chaotic behavior is studied using numerical techniques, such as calculation of Lyapunov exponents, plotting the bifurcation diagram and phase plots. We correct and critically comment on the wrong results reported recently on this ecological model, in a paper by Rai [1995].

Predictive uncertainty of chaotic daily streamflow using ensemble wavelet networks approach

Perfect or even mediocre weather predictions over a long period are almost impossible because of the ultimate growth of a small initial error into a significant one. Even though the sensitivity of initial conditions limits the predictability in chaotic systems, an ensemble of prediction from different possible initial conditions and also a prediction algorithm capable of resolving the fine structure of the chaotic attractor can reduce the prediction uncertainty to some extent. All of the traditional chaotic prediction methods in hydrology are based on single optimum initial condition local models which can model the sudden divergence of the trajectories with different local functions. Conceptually, global models are ineffective in modeling the highly unstable structure of the chaotic attractor. This paper focuses on an ensemble prediction approach by reconstructing the phase space using different combinations of chaotic parameters, i.e., embedding dimension and delay time to quantify the uncertainty in initial conditions. The ensemble approach is implemented through a local learning wavelet network model with a global feed-forward neural network structure for the phase space prediction of chaotic streamflow series. Quantification of uncertainties in future predictions are done by creating an ensemble of predictions with wavelet network using a range of plausible embedding dimensions and delay times. The ensemble approach is proved to be 50% more efficient than the single prediction for both local approximation and wavelet network approaches. The wavelet network approach has proved to be 30%-50% more superior to the local approximation approach. Compared to the traditional local approximation approach with single initial condition, the total predictive uncertainty in the streamflow is reduced when modeled with ensemble wavelet networks for different lead times. Localization property of wavelets, utilizing different dilation and translation parameters, helps in capturing most of the statistical properties of the observed data. The need for taking into account all plausible initial conditions and also bringing together the characteristics of both local and global approaches to model the unstable yet ordered chaotic attractor of a hydrologic series is clearly demonstrated.

Nonlinear dynamics and chaotic motions in feedback-controlled two- and three-degree-of-freedom robots

The dynamics of a feedback-controlled rigid robot is most commonly described by a set of nonlinear ordinary differential equations. In this paper we analyze these equations, representing the feedback-controlled motion of two- and three-degrees-of-freedom rigid robots with revolute (R) and prismatic (P) joints in the absence of compliance, friction, and potential energy, for the possibility of chaotic motions. We first study the unforced or inertial motions of the robots, and show that when the Gaussian or Riemannian curvature of the configuration space of a robot is negative, the robot equations can exhibit chaos. If the curvature is zero or positive, then the robot equations cannot exhibit chaos. We show that among the two-degrees-of-freedom robots, the PP and the PR robot have zero Gaussian curvature while the RP and RR robots have negative Gaussian curvatures. For the three-degrees-of-freedom robots, we analyze the two well-known RRP and RRR configurations of the Stanford arm and the PUMA manipulator respectively, and derive the conditions for negative curvature and possible chaotic motions. The criteria of negative curvature cannot be used for the forced or feedback-controlled motions. For the forced motion, we resort to the well-known numerical techniques and compute chaos maps, Poincare maps, and bifurcation diagrams. Numerical results are presented for the two-degrees-of-freedom RP and RR robots, and we show that these robot equations can exhibit chaos for low controller gains and for large underestimated models. From the bifurcation diagrams, the route to chaos appears to be through period doubling.

Chaotic and power law states in the Portevin-Le Chatelier effect

Recent studies on the Portevin-Le Chatelier effect report an intriguing crossover phenomenon from low-dimensional chaotic to an infinite-dimensional scale-invariant power law regime in experiments on CuAl single crystals and AlMg polycrystals, as function of strain rate. We devise fully dynamical model which reproduces these results. At low and medium strain rates, the model is chaotic with the structure of the attractor resembling the reconstructed experimental attractor. At high strain rates, power law statistics for the magnitudes and durations of the stress drops emerge as in experiments and concomitantly, the largest Lyapunov exponent is zero.

Multivariate nonlinear ensemble prediction of daily chaotic rainfall with climate inputs

The basic characteristic of a chaotic system is its sensitivity to the infinitesimal changes in its initial conditions. A limit to predictability in chaotic system arises mainly due to this sensitivity and also due to the ineffectiveness of the model to reveal the underlying dynamics of the system. In the present study, an attempt is made to quantify these uncertainties involved and thereby improve the predictability by adopting a multivariate nonlinear ensemble prediction. Daily rainfall data of Malaprabha basin, India for the period 1955-2000 is used for the study. It is found to exhibit a low dimensional chaotic nature with the dimension varying from 5 to 7. A multivariate phase space is generated, considering a climate data set of 16 variables. The chaotic nature of each of these variables is confirmed using false nearest neighbor method. The redundancy, if any, of this atmospheric data set is further removed by employing principal component analysis (PCA) method and thereby reducing it to eight principal components (PCs). This multivariate series (rainfall along with eight PCs) is found to exhibit a low dimensional chaotic nature with dimension 10. Nonlinear prediction employing local approximation method is done using univariate series (rainfall alone) and multivariate series for different combinations of embedding dimensions and delay times. The uncertainty in initial conditions is thus addressed by reconstructing the phase space using different combinations of parameters. The ensembles generated from multivariate predictions are found to be better than those from univariate predictions. The uncertainty in predictions is decreased or in other words predictability is increased by adopting multivariate nonlinear ensemble prediction. The restriction on predictability of a chaotic series can thus be altered by quantifying the uncertainty in the initial conditions and also by including other possible variables, which may influence the system. (C) 2011 Elsevier B.V. All rights reserved.

A Generalized Enthalpy Update Scheme for Solidification of a Binary Alloy with Solid Phase Movement

A generalized enthalpy update scheme is presented for evaluating solid and liquid fractions during the solidification of binary alloys, taking solid movement into consideration. A fixed-grid, enthalpy-based method is developed such that the scheme accounts for equilibrium as well as for nonequilibrium solidification phenomena, along with solid phase movement. The effect of solid movement on the solidification interface shape and macrosegregation is highlighted.

Is saccade averaging determined by visual processing or movement planning?

Bhutani N, Ray S, Murthy A. Is saccade averaging determined by visual processing or movement planning? J Neurophysiol 108: 3161-3171, 2012. First published September 26, 2012; doi:10.1152/jn.00344.2012.-Saccadic averaging that causes subjects' gaze to land between the location of two targets when faced with simultaneously or sequentially presented stimuli has been often used as a probe to investigate the nature of computations that transform sensory representations into an oculomotor plan. Since saccadic movements involve at least two processing stages-a visual stage that selects a target and a movement stage that prepares the response-saccade averaging can either occur due to interference in visual processing or movement planning. By having human subjects perform two versions of a saccadic double-step task, in which the stimuli remained the same, but different instructions were provided (REDIRECT gaze to the later-appearing target vs. FOLLOW the sequence of targets in their order of appearance), we tested two alternative hypotheses. If saccade averaging were due to visual processing alone, the pattern of saccade averaging is expected to remain the same across task conditions. However, whereas subjects produced averaged saccades between two targets in the FOLLOW condition, they produced hypometric saccades in the direction of the initial target in the REDIRECT condition, suggesting that the interaction between competing movement plans produces saccade averaging.