DNA Binding, Coprotease, and Strand Exchange Activities of Mycobacterial RecA Proteins: Implications for Functional Diversity among RecA Nucleoprotein Filaments

One of the fundamental questions concerning homologous recombination is how RecA or its homologues recognize several DNA sequences with high affinity and catalyze all the diverse biological activities. In this study, we show that the extent of single-stranded DNA binding and strand exchange (SE) promoted by mycobacterial RecA proteins with DNA substrates having various degrees of GC content was comparable with that observed for Escherichia coli RecA. However, the rate and extent of SE promoted by these recombinases showed a strong negative correlation with increasing amounts of sequence divergence embedded at random across the length of the donor strand. Conversely, a positive correlation was seen between SE efficiency and the degree of sequence divergence in the recipient duplex DNA. The extent of heteroduplex formation was not significantly affected when both the pairing partners contained various degrees of sequence divergence, although there was a moderate decrease in the case of mycobacterial RecA proteins with substrates containing larger amounts of sequence divergence. Whereas a high GC content had no discernible effect on E. coli RecA coprotease activity, a negative correlation was apparent between mycobacterial RecA proteins and GC content. We further show clear differences in the extent of SE promoted by E. coli and mycobacterial RecA proteins in the presence of a wide range of ATP:ADP ratios. Taken together, our findings disclose the existence of functional diversity among E. coli and mycobacterial RecA nucleoprotein filaments, and the milieu of sequence divergence (i.e., in the donor or recipient) exerts differential effects on heteroduplex formation, which has implications for the emergence of new genetic variants.

3-Dimensional laminar boundary-layer in a constant-pressure diverging

larity solution is obtained for laminar 3D constant pressure flow with lateral streamline divergence. The similarity solution is shown to reduce to a Blasius solution for 2D flow over a flat plate. Measurements of velocity profiles are made to compare the similarity solution and are found to be in excellent agreement with the prediction

Growing pseudo-eigenmodes and positive logarithmic norms in rotating shear flows

Rotating shear flows, when angular momentum increases and angular velocity decreases as functions of radiation coordinate, are hydrodynamically stable under linear perturbation. The Keplerian flow is an example of such a system, which appears in an astrophysical context. Although decaying eigenmodes exhibit large transient energy growth of perturbation which could govern nonlinearity in the system, the feedback of inherent instability to generate turbulence seems questionable. We show that such systems exhibiting growing pseudo-eigenmodes easily reach an upper bound of growth rate in terms of the logarithmic norm of the involved non-normal operators, thus exhibiting feedback of inherent instability. This supports the existence of turbulence of hydrodynamic origin in the Keplerian accretion disc in astrophysics. Hence, this answers the question of the mismatch between the linear theory and experimental/observed data and helps in resolving the outstanding question of the origin of turbulence therein.

Concise row-pruning algorithm to invert a matrix

Presented here, in a vector formulation, is an O(mn2) direct concise algorithm that prunes/identifies the linearly dependent (ld) rows of an arbitrary m X n matrix A and computes its reflexive type minimum norm inverse A(mr)-, which will be the true inverse A-1 if A is nonsingular and the Moore-Penrose inverse A+ if A is full row-rank. The algorithm, without any additional computation, produces the projection operator P = (I - A(mr)- A) that provides a means to compute any of the solutions of the consistent linear equation Ax = b since the general solution may be expressed as x = A(mr)+b + Pz, where z is an arbitrary vector. The rank r of A will also be produced in the process. Some of the salient features of this algorithm are that (i) the algorithm is concise, (ii) the minimum norm least squares solution for consistent/inconsistent equations is readily computable when A is full row-rank (else, a minimum norm solution for consistent equations is obtainable), (iii) the algorithm identifies ld rows, if any, and reduces concerned computation and improves accuracy of the result, (iv) error-bounds for the inverse as well as the solution x for Ax = b are readily computable, (v) error-free computation of the inverse, solution vector, rank, and projection operator and its inherent parallel implementation are straightforward, (vi) it is suitable for vector (pipeline) machines, and (vii) the inverse produced by the algorithm can be used to solve under-/overdetermined linear systems.

Decorrelation of broad band signals by toeplitz-block-toeplitz structuring

We propose F-norm of the cross-correlation part of the array covariance matrix as a measure of correlation between the impinging signals and study the performance of different decorrelation methods in the broadband case using this measure. We first show that dimensionality of the composite signal subspace, defined as the number of significant eigenvectors of the source sample covariance matrix, collapses in the presence of multipath and the spatial smoothing recovers this dimensionality. Using an upper bound on the proposed measure, we then study the decorrelation of the broadband signals with spatial smoothing and the effect of spacing and directions of the sources on the rate of decorrelation with progressive smoothing. Next, we introduce a weighted smoothing method based on Toeplitz-block-Toeplitz (TBT) structuring of the data covariance matrix which decorrelates the signals much faster than the spatial smoothing. Computer simulations are included to demonstrate the performance of the two methods.

Studies on molecular evolution and structural features of double-headed inhibitors of ?-amylase and trypsin in plants

Plant seeds usually have high concentrations of proteinase and amylase inhibitors. These inhibitors exhibit a wide range of specificity, stability and oligomeric structure. In this communication, we report analysis of sequences that show statistically significant similarity to the double-headed alpha-amylase/trypsin inhibitor of ragi (Eleusine coracana). Our aim is to understand their evolutionary and structural features. The 14 sequences of this family that are available in the SWISSPROT database form three evolutionarily distinct branches. The branches relate to enzyme specificities and also probably to the oligomeric state of the proteins and not to the botanical class of the plant from which the enzymes are derived. This suggests that the enzyme specificities of the inhibitors evolved before the divergence of commercially cultivated cereals. The inhibitor sequences have three regions that display periodicity in hydrophobicity. It is likely that this feature reflects extended secondary structure in these segments. One of the most variable regions of the polypeptide corresponds to a loop, which is most probably exposed in the native structure of the inhibitors and is responsible for the inhibitory property.

Testis-specific histone (H1t) is not phosphorylated and has a weak interaction with chromatin

Soluble chromatin was prepared from rat testes after a brief micrococcal nuclease digestion. After adsorption onto hydroxylapatite at low ionic strength, the histone Hl subtypes were eluted with a shallow salt gradient of 0.3 M NaCl to 0.7 M NaCl. Histone Hlt was eluted at 0.4 MNaCl, while histones H1a and Hlc were eluted at 0.43 M NaCl and 0.45 M respectively. The extreme divergence of the amino acid sequence of the C-terminal half of histone Hlt, the major DNA binding domain of histone Hl, from that of the somatic consensus sequence may contribute to the weaker interaction of histone Hlt with the rat testis chromatin. Further, histone Hlt was not phosphorylated in vivo in contrast to histone Hla and Hlc, as is evident from the observation that histone Hlt lacks the SPKK motif recognized by the CDC-2kinase or the RR/KXS motif recognized by protein kinase A.

The primary structure of sheep liver cytosolic serine hydroxymethyltransferase and an analysis of the evolutionary relationships among serine hydroxymethyltransferases

The complete amino-acid sequence of sheep liver cytosolic serine hydroxymethyltransferase was determined from an analysis of tryptic, chymotryptic, CNBr and hydroxylamine peptides. Each subunit of sheep liver serine hydroxymethyltransferase consisted of 483 amino-acid residues. A comparison of this sequence with 8 other serine hydroxymethyltransferases revealed that a possible gene duplication event could have occurred after the divergence of animals and fungi. This analysis also showed independent duplication of SHMT genes in Neurospora crassa. At the secondary structural level, all the serine hydroxymethyltransferases belong to the alpha/beta category of proteins. The predicted secondary structure of sheep liver serine hydroxymethyltransferase was similar to that of the observed structure of tryptophan synthase, another pyridoxal 5'-phosphate containing enzyme, suggesting that sheep liver serine hydroxymethyltransferase might have a similar pyridoxal 5'-phosphate binding domain. In addition, a conserved glycine rich region, G L Q G G P, was identified in all the serine hydroxymethyltransferases and could be important in pyridoxal 5'-phosphate binding. A comparison of the cytosolic serine hydroxymethyltransferases from rabbit and sheep liver with other proteins sequenced from both these sources showed that serine hydroxymethyltransferase was a highly conserved protein. It was slightly less conserved than cytochrome c but better conserved than myoglobin, both of which are well known evolutionary markers. C67 and C203 were specifically protected by pyridoxal 5'-phosphate against modification with [C-14]iodoacetic acid, while C247 and C261 were buried in the native serine hydroxymethyltransferase. However, the cysteines are not conserved among the various serine hydroxymethyltransferases. The exact role of the cysteines in the reaction catalyzed by serine hydroxymethyltransferase remains to be elucidated.

Effect of dynamical asymmetry on the viscosity of a random copolymer melt

The variation of the viscosity as a function of the sequence distribution in an A-B random copolymer melt is determined. The parameters that characterize the random copolymer are the fraction of A monomers f, the parameter lambda which determines the correlation in the monomer identities along a chain and the Flory chi parameter chi(F) which determines the strength of the enthalpic repulsion between monomers of type A and B. For lambda>0, there is a greater probability of finding like monomers at adjacent positions along the chain, and for lambda<0 unlike monomers are more likely to be adjacent to each other. The traditional Markov model for the random copolymer melt is altered to remove ultraviolet divergences in the equations for the renormalized viscosity, and the phase diagram for the modified model has a binary fluid type transition for lambda>0 and does not exhibit a phase transition for lambda<0. A mode coupling analysis is used to determine the renormalization of the viscosity due to the dependence of the bare viscosity on the local concentration field. Due to the dissipative nature of the coupling. there are nonlinearities both in the transport equation and in the noise correlation. The concentration dependence of the transport coefficient presents additional difficulties in the formulation due to the Ito-Stratonovich dilemma, and there is some ambiguity about the choice of the concentration to be used while calculating the noise correlation. In the Appendix, it is shown using a diagrammatic perturbation analysis that the Ito prescription for the calculation of the transport coefficient, when coupled with a causal discretization scheme, provides a consistent formulation that satisfies stationarity and the fluctuation dissipation theorem. This functional integral formalism is used in the present analysis, and consistency is verified for the present problem as well. The upper critical dimension for this type of renormaliaation is 2, and so there is no divergence in the viscosity in the vicinity of a critical point. The results indicate that there is a systematic dependence of the viscosity on lambda and chi(F). The fluctuations tend to increase the viscosity for lambda<0, and decrease the viscosity for lambda>0, and an increase in chi(F) tends to decrease the viscosity. (C) 1996 American Institute of Physics.

Variable Sparsity Kernel Learning

This paper(1) presents novel algorithms and applications for a particular class of mixed-norm regularization based Multiple Kernel Learning (MKL) formulations. The formulations assume that the given kernels are grouped and employ l(1) norm regularization for promoting sparsity within RKHS norms of each group and l(s), s >= 2 norm regularization for promoting non-sparse combinations across groups. Various sparsity levels in combining the kernels can be achieved by varying the grouping of kernels-hence we name the formulations as Variable Sparsity Kernel Learning (VSKL) formulations. While previous attempts have a non-convex formulation, here we present a convex formulation which admits efficient Mirror-Descent (MD) based solving techniques. The proposed MD based algorithm optimizes over product of simplices and has a computational complexity of O (m(2)n(tot) log n(max)/epsilon(2)) where m is no. training data points, n(max), n(tot) are the maximum no. kernels in any group, total no. kernels respectively and epsilon is the error in approximating the objective. A detailed proof of convergence of the algorithm is also presented. Experimental results show that the VSKL formulations are well-suited for multi-modal learning tasks like object categorization. Results also show that the MD based algorithm outperforms state-of-the-art MKL solvers in terms of computational efficiency.

Experimental studies of a distorted turbulent spot in a three-dimensional flow

We report here on the results of a series of experiments carried out on a turbulent spot in a distorted duct to study the effects of a divergence with straight streamlines preceded by a short stretch of transverse streamline curvature, both in the absence of any pressure gradient. It is found that the distortion produces substantial asymmetry in the spot: the angles at which the spot cuts across the local streamlines are altered dramatically (in contradiction of a hypothesis commonly made in transition zone modelling), and the Tollmien-Schlichting waves that accompany the wing tips of the spot are much stronger on the outside of the bend than on the inside. However there is no strong effect on the internal structure of the spot and the eddies therein, or on such propagation characteristics as overall spread rate and the celerities of the leading and trailing edges. Both lateral streamline curvature and non-homogeneity of the laminar boundary layer into which the spot propagates are shown to be strong factors responsible for the observed asymmetry. It is concluded that these factors produce chiefly a geometric distortion of the coherent structure in the spot, but do not otherwise affect its dynamics in any significant way.

Comparative Nucleotide and Amino Acid Sequence Analysis of the Sequence-Specific RNA-Binding Rotavirus Nonstructural Protein NSP3

NSP3, an acidic nonstructural protein, encoded by gene 7 has been implicated as the key player in the assembly of the 11 viral plus-strand RNAs into the early replication intermediates during rotavirus morphogenesis. To date, the sequence or NSP3 from only three animal rotaviruses (SA11, SA114F, and bovine UK) has been determined and that from a human strain has not been reported. To determine the genetic diversity among gene 7 alleles from group A rotaviruses, the nucleotide sequence of the NSP3 gene from 13 strains belonging to nine different G serotypes, from both humans and animals, has been determined. Based on the amino acid sequence identity as well as phylogenetic analysis, NSP3 from group A rotaviruses falls into three evolutionarily related groups, i.e., the SA11 group, the Wa group, and the S2 group. The SA 11/SA114F gene appears to have a distant ancestral origin from that of the others and codes for a polypeptide of 315 amino acids (aa) in length. NSP3 from all other group A rotaviruses is only 313 aa in length because of a 2-amino-acid deletion near the carboxy-terminus, While the SA114F gene has the longest 3' untranslated region (UTR) of 132 nucleotides, that from other strains suffered deletions of varying lengths at two positions downstream of the translational termination codon. In spite of the divergence of the nucleotide (nt) sequence in the protein coding region, a stretch of about 80 nt in the 3' UTR is highly conserved in the NSP3 gene from all the strains. This conserved sequence in the 3' UTR might play an important role in the regulation of expression of the NSP3 gene. (C) 1995 Academic Press, Inc.

Stability of the viscous flow of a fluid through a flexible tube

The stability of Hagen-Poiseuille flow of a Newtonian fluid of viscosity eta in a tube of radius R surrounded by a viscoelastic medium of elasticity G and viscosity eta(s) occupying the annulus R < r < HR is determined using a linear stability analysis. The inertia of the fluid and the medium are neglected, and the mass and momentum conservation equations for the fluid and wall are linear. The only coupling between the mean flow and fluctuations enters via an additional term in the boundary condition for the tangential velocity at the interface, due to the discontinuity in the strain rate in the mean flow at the surface. This additional term is responsible for destabilizing the surface when the mean velocity increases beyond a transition value, and the physical mechanism driving the instability is the transfer of energy from the mean flow to the fluctuations due to the work done by the mean flow at the interface. The transition velocity Gamma(t) for the presence of surface instabilities depends on the wavenumber k and three dimensionless parameters: the ratio of the solid and fluid viscosities eta(r) = (eta(s)/eta), the capillary number Lambda = (T/GR) and the ratio of radii H, where T is the surface tension of the interface. For eta(r) = 0 and Lambda = 0, the transition velocity Gamma(t) diverges in the limits k much less than 1 and k much greater than 1, and has a minimum for finite k. The qualitative behaviour of the transition velocity is the same for Lambda > 0 and eta(r) = 0, though there is an increase in Gamma(t) in the limit k much greater than 1. When the viscosity of the surface is non-zero (eta(r) > 0), however, there is a qualitative change in the Gamma(t) vs. k curves. For eta(r) < 1, the transition velocity Gamma(t) is finite only when k is greater than a minimum value k(min), while perturbations with wavenumber k < k(min) are stable even for Gamma--> infinity. For eta(r) > 1, Gamma(t) is finite only for k(min) < k < k(max), while perturbations with wavenumber k < k(min) or k > k(max) are stable in the limit Gamma--> infinity. As H decreases or eta(r) increases, the difference k(max)- k(min) decreases. At minimum value H = H-min, which is a function of eta(r), the difference k(max)-k(min) = 0, and for H < H-min, perturbations of all wavenumbers are stable even in the limit Gamma--> infinity. The calculations indicate that H-min shows a strong divergence proportional to exp (0.0832 eta(r)(2)) for eta(r) much greater than 1.

Nonequilibrium phase transition in the kinetic Ising model: Critical slowing down and the specific-heat singularity

The nonequilibrium dynamic phase transition, in the kinetic Ising model in the presence of an oscillating magnetic field has been studied both by Monte Carlo simulation and by solving numerically the mean-field dynamic equation of motion for the average magnetization. In both cases, the Debye ''relaxation'' behavior of the dynamic order parameter has been observed and the ''relaxation time'' is found to diverge near the dynamic transition point. The Debye relaxation of the dynamic order parameter and the power law divergence of the relaxation time have been obtained from a very approximate solution of the mean-field dynamic equation. The temperature variation of appropriately defined ''specific heat'' is studied by the Monte Carlo simulation near the transition point. The specific heat has been observed to diverge near the dynamic transition point.

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.