Andaman Postseismic Deformation Observations: Still Slipping after All These Years?

More than six years after the great (M-w 9.2) Sumatra-Andaman earthquake, postevent processes responsible for relaxation of the coseismic stress change remain controversial. Modeling of Andaman Islands Global Positioning System (GPS) displacements indicated early near-field motions were dominated by slip down-dip of the rupture, but various researchers ascribe elements of relaxation to dominantly poroelastic, dominantly viscoelastic, and dominantly fault slip processes, depending primarily on their measurement sampling and modeling tools used. After subtracting a pre-2004 interseismic velocity, significant transient motion during the 2008.5-2010.5 epoch confirms that postseismic relaxation processes continue in Andaman. Modeling three-component velocities as viscoelastic flow yields a weighted root-mean-square (wrms) misfit that always exceeds the wrms of the measured signal (26.3 mm/yr). The best-fitting models are those that yield negligible deformation, indicating the model parameters have no real physical meaning. GPS velocities are well fit (wrms 4.0 mm/yr) by combining a viscoelastic flow model that best fits the horizontal velocities with similar to 50 cm/yr thrust slip down-dip of the coseismic rupture. Both deep slip and flow respond to stress changes, and each can significantly change stress in the realm of the other; it therefore is reasonable to expect that both transient deep slip and viscoelastic flow will influence surface deformation long after a great earthquake.

Sequence-Structure Similarity: Do Sequentially Identical Peptide Fragments have Similar Three-Dimensional Structures?

The rapidly growing structure databases enhance the probability of finding identical sequences sharing structural similarity. Structure prediction methods are being used extensively to abridge the gap between known protein sequences and the solved structures which is essential to understand its specific biochemical and cellular functions. In this work, we plan to study the ambiguity between sequence-structure relationships and examine if sequentially identical peptide fragments adopt similar three-dimensional structures. Fragments of varying lengths (five to ten residues) were used to observe the behavior of sequence and its three-dimensional structures. The STAMP program was used to superpose the three-dimensional structures and the two parameters (Sequence Structure Similarity Score (Sc) and Root Mean Square Deviation value) were employed to classify them into three categories: similar, intermediate and dissimilar structures. Furthermore, the same approach was carried out on all the three-dimensional protein structures solved in the two organisms, Mycobacterium tuberculosis and Plasmodium falciparum to validate our results.

Glassy dynamics in a confined monatomic fluid

Molecular dynamic simulations of a strongly inhomogeneous system reveals that a single-component soft-sphere fluid can behave as a fragile glass former due to confinement. The self-intermediate scattering function, F-s(k,t), of a Lennard-Jones fluid confined in slit-shaped pores, which can accomodate two to four fluid layers, exhibits a two-step relaxation at moderate temperatures. The mean-squared displacement data are found to follow time-temperature superposition and both the self-diffusivity and late a relaxation times exhibit power-law divergences as the fluid is cooled. The system possesses a crossover temperature and follows the scalings of mode coupling theory for the glass transition. The temperature dependence of the self-diffusivity can be expressed using the Vogel-Fulcher-Tammann equation, and estimates of the fragility index of the system indicates a fragile glass former. At lower temperatures, signatures of additional relaxation processes are observed in the various dynamical quantities with a three-step relaxation observed in the F-s(k,t).

The Coil-to-Helix Transition in IlvN Regulates the Allosteric Control of Escherichia coli Acetohydroxyacid Synthase I

The solution structure of IlvN, the regulatory subunit of Escherichia coil acetohydroxyacid synthase I, in the valine-bound form has been determined using high-resolution multidimensional, multinuclear nuclear magnetic resonance (NMR) methods. IlvN in the presence or absence of the effector molecule is present as a 22.5 kDa dimeric molecule. The ensemble of 20 low-energy structures shows a backbone root-mean-square deviation of 0.73 +/- 0.13 angstrom and a root-mean-square deviation of 1.16 +/- 0.13 angstrom for all heavy atoms. Furthermore, more than 98% of the backbone phi and psi dihedral angles occupy the allowed and additionally allowed regions of the Ramachandran map, which is indicative of the fact that the structures are of high stereochemical quality. Each protomer exhibits a beta alpha beta beta alpha beta alpha topology that is a characteristic feature of the ACT domain seen in metabolic enzymes. In the valine-bound form, IlvN exists apparently as a single conformer. In the free form, IlvN exists as a mixture of conformational states that are in intermediate exchange on the NMR time scale. Thus, a large shift in the conformational equilibrium is observed upon going from the free form to the bound form. The structure of the valine-bound form of IlvN was found to be similar to that of the ACT domain of the unliganded form of IlvH. Comparisons of the structures of the unliganded forms of these proteins suggest significant differences. The structural and conformational properties of IlvN determined here have allowed a better understanding of the mechanism of regulation of branched chain amino acid biosynthesis.

Cramer-Rao-Type Bounds for Sparse Bayesian Learning

In this paper, we derive Hybrid, Bayesian and Marginalized Cramer-Rao lower bounds (HCRB, BCRB and MCRB) for the single and multiple measurement vector Sparse Bayesian Learning (SBL) problem of estimating compressible vectors and their prior distribution parameters. We assume the unknown vector to be drawn from a compressible Student-prior distribution. We derive CRBs that encompass the deterministic or random nature of the unknown parameters of the prior distribution and the regression noise variance. We extend the MCRB to the case where the compressible vector is distributed according to a general compressible prior distribution, of which the generalized Pareto distribution is a special case. We use the derived bounds to uncover the relationship between the compressibility and Mean Square Error (MSE) in the estimates. Further, we illustrate the tightness and utility of the bounds through simulations, by comparing them with the MSE performance of two popular SBL-based estimators. We find that the MCRB is generally the tightest among the bounds derived and that the MSE performance of the Expectation-Maximization (EM) algorithm coincides with the MCRB for the compressible vector. We also illustrate the dependence of the MSE performance of SBL based estimators on the compressibility of the vector for several values of the number of observations and at different signal powers.

Optimal MSE solution for a decision feedback equalizer

Due to the inherent feedback in a decision feedback equalizer (DFE) the minimum mean square error (MMSE) or Wiener solution is not known exactly. The main difficulty in such analysis is due to the propagation of the decision errors, which occur because of the feedback. Thus in literature, these errors are neglected while designing and/or analyzing the DFEs. Then a closed form expression is obtained for Wiener solution and we refer this as ideal DFE (IDFE). DFE has also been designed using an iterative and computationally efficient alternative called least mean square (LMS) algorithm. However, again due to the feedback involved, the analysis of an LMS-DFE is not known so far. In this paper we theoretically analyze a DFE taking into account the decision errors. We study its performance at steady state. We then study an LMS-DFE and show the proximity of LMS-DFE attractors to that of the optimal DFE Wiener filter (obtained after considering the decision errors) at high signal to noise ratios (SNR). Further, via simulations we demonstrate that, even at moderate SNRs, an LMS-DFE is close to the MSE optimal DFE. Finally, we compare the LMS DFE attractors with IDFE via simulations. We show that an LMS equalizer outperforms the IDFE. In fact, the performance improvement is very significant even at high SNRs (up to 33%), where an IDFE is believed to be closer to the optimal one. Towards the end, we briefly discuss the tracking properties of the LMS-DFE.

A risk-estimation-based formulation for speech enhancement and its relation to Wiener filtering

The goal of speech enhancement algorithms is to provide an estimate of clean speech starting from noisy observations. The often-employed cost function is the mean square error (MSE). However, the MSE can never be computed in practice. Therefore, it becomes necessary to find practical alternatives to the MSE. In image denoising problems, the cost function (also referred to as risk) is often replaced by an unbiased estimator. Motivated by this approach, we reformulate the problem of speech enhancement from the perspective of risk minimization. Some recent contributions in risk estimation have employed Stein's unbiased risk estimator (SURE) together with a parametric denoising function, which is a linear expansion of threshold/bases (LET). We show that the first-order case of SURE-LET results in a Wiener-filter type solution if the denoising function is made frequency-dependent. We also provide enhancement results obtained with both techniques and characterize the improvement by means of local as well as global SNR calculations.

Noise tolerance under risk minimization

In this paper, we explore noise-tolerant learning of classifiers. We formulate the problem as follows. We assume that there is an unobservable training set that is noise free. The actual training set given to the learning algorithm is obtained from this ideal data set by corrupting the class label of each example. The probability that the class label of an example is corrupted is a function of the feature vector of the example. This would account for most kinds of noisy data one encounters in practice. We say that a learning method is noise tolerant if the classifiers learnt with noise-free data and with noisy data, both have the same classification accuracy on the noise-free data. In this paper, we analyze the noise-tolerance properties of risk minimization (under different loss functions). We show that risk minimization under 0-1 loss function has impressive noise-tolerance properties and that under squared error loss is tolerant only to uniform noise; risk minimization under other loss functions is not noise tolerant. We conclude this paper with some discussion on the implications of these theoretical results.

Composition, structure and electrical properties of DC reactive magnetron sputtered Al2O3 thin films

Thin films of alumina (Al2O3) were deposited over Si < 1 0 0 > substrates at room temperature at an oxygen gas pressure of 0.03 Pa and sputtering power of 60 W using DC reactive magnetron sputtering. The composition of the as-deposited film was analyzed by X-ray photoelectron spectroscopy and the O/Al atomic ratio was found to be 1.72. The films were then annealed in vacuum to 350, 550 and 750 degrees C and X-ray diffraction results revealed that both as-deposited and post deposition annealed films were amorphous. The surface morphology and topography of the films was studied using scanning electron microscopy and atomic force microscopy, respectively. A progressive decrease in the root mean square (RMS) roughness of the films from 1.53 nm to 0.7 nm was observed with increase in the annealing temperature. Al-Al2O3-Al thin film capacitors were then fabricated on p-type Si < 1 0 0 > substrate to study the effect of temperature and frequency on the dielectric property of the films and the results are discussed.

Solution nuclear magnetic resonance structure of the GATase subunit and structural Basis of the interaction between GATase and ATPPase subunits in a two-subunit-type GMPS from methanocaldococcus jannaschii

The solution structure of the monomeric glutamine amidotransferase (GATase) subunit of the Methanocaldococcus janaschii (Mj) guanosine monophosphate synthetase (GMPS) has been determined using high-resolution nuclear magnetic resonance methods. Gel filtration chromatography and N-15 backbone relaxation studies have shown that the Mj GATase subunit is present in solution as a 21 kDa (188-residue) monomer. The ensemble of 20 lowest-energy structures showed root-mean-square deviations of 0.35 +/- 0.06 angstrom for backbone atoms and 0.8 +/- 0.06 angstrom for all heavy atoms. Furthermore, 99.4% of the backbone dihedral angles are present in the allowed region of the Ramachandran map, indicating the stereochemical quality of the structure. The core of the tertiary structure of the GATase is composed of a seven-stranded mixed beta-sheet that is fenced by five alpha-helices. The Mj GATase is similar in structure to the Pyrococcus horikoshi (Ph) GATase subunit. Nuclear magnetic resonance (NMR) chemical shift perturbations and changes in line width were monitored to identify residues on GATase that were responsible for interaction with magnesium and the ATPPase subunit, respectively. These interaction studies showed that a common surface exists for the metal ion binding as well as for the protein-protein interaction. The dissociation constant for the GATase-Mg2+ interaction has been found to be similar to 1 mM, which implies that interaction is very weak and falls in the fast chemical exchange regime. The GATase-ATPPase interaction, on the other hand, falls in the intermediate chemical exchange regime on the NMR time scale. The implication of this interaction in terms of the regulation of the GATase activity of holo GMPS is discussed.

Joint Self-Iterating Equalization and Detection for Two-Dimensional Intersymbol-Interference Channels

We develop several novel signal detection algorithms for two-dimensional intersymbol-interference channels. The contribution of the paper is two-fold: (1) We extend the one-dimensional maximum a-posteriori (MAP) detection algorithm to operate over multiple rows and columns in an iterative manner. We study the performance vs. complexity trade-offs for various algorithmic options ranging from single row/column non-iterative detection to a multi-row/column iterative scheme and analyze the performance of the algorithm. (2) We develop a self-iterating 2-D linear minimum mean-squared based equalizer by extending the 1-D linear equalizer framework, and present an analysis of the algorithm. The iterative multi-row/column detector and the self-iterating equalizer are further connected together within a turbo framework. We analyze the combined 2-D iterative equalization and detection engine through analysis and simulations. The performance of the overall equalizer and detector is near MAP estimate with tractable complexity, and beats the Marrow Wolf detector by about at least 0.8 dB over certain 2-D ISI channels. The coded performance indicates about 8 dB of significant SNR gain over the uncoded 2-D equalizer-detector system.

Cooperative Particle Swarm Optimization Based Receiver for Large-Dimension MIMO-ZPSC Systems

In this paper, we propose a cooperative particle swarm optimization (CPSO) based channel estimation/equalization scheme for multiple-input multiple-output zero-padded single-carrier (MIMO-ZPSC) systems with large dimensions in frequency selective channels. We estimate the channel state information at the receiver in time domain using a PSO based algorithm during training phase. Using the estimated channel, we perform information symbol detection in the frequency domain using FFT based processing. For this detection, we use a low complexity OLA (OverLap Add) likelihood ascent search equalizer which uses minimum mean square (MMSE) equalizer solution as the initial solution. Multiple iterations between channel estimation and data detection are carried out which significantly improves the mean square error and bit error rate performance of the receiver.

Power-Controlled Reverse Channel Training in a Multiuser TDD-MIMO Spatial Multiplexing System With CSIR

This paper considers the design of a power-controlled reverse channel training (RCT) scheme for spatial multiplexing (SM)-based data transmission along the dominant modes of the channel in a time-division duplex (TDD) multiple-input and multiple-output (MIMO) system, when channel knowledge is available at the receiver. A channel-dependent power-controlled RCT scheme is proposed, using which the transmitter estimates the beamforming (BF) vectors required for the forward-link SM data transmission. Tight approximate expressions for 1) the mean square error (MSE) in the estimate of the BF vectors, and 2) a capacity lower bound (CLB) for an SM system, are derived and used to optimize the parameters of the training sequence. Moreover, an extension of the channel-dependent training scheme and the data rate analysis to a multiuser scenario with M user terminals is presented. For the single-mode BF system, a closed-form expression for an upper bound on the average sum data rate is derived, which is shown to scale as ((L-c - L-B,L- tau)/L-c) log logM asymptotically in M, where L-c and L-B,L- tau are the channel coherence time and training duration, respectively. The significant performance gain offered by the proposed training sequence over the conventional constant-power orthogonal RCT sequence is demonstrated using Monte Carlo simulations.

Channel estimation at the transmitter in a reciprocal MIMO spatial multiplexing system

This paper considers the problem of channel estimation at the transmitter in a spatial multiplexing-based Time Division Duplex (TDD) Multiple Input Multiple Output (MIMO) system with perfect CSIR. A novel channel-dependent Reverse Channel Training (RCT) sequence is proposed, using which the transmitter estimates the beamforming vectors for forward link data transmission. This training sequence is designed based on the following two metrics: (i) a capacity lower bound, and (ii) the mean square error in the estimate. The performance of the proposed training scheme is analyzed and is shown to significantly outperform the conventional orthogonal RCT sequence. Also, in the case where the transmitter uses water-filling power allocation for data transmission, a novel RCT sequence is proposed and optimized with respect to the MSE in estimating the transmit covariance matrix.

Molecular Dynamics Perspective on the Protein Thermal Stability: A Case Study Using SAICAR Synthetase

The enzyme SAICAR synthetase ligates aspartate with CAIR (5'-phosphoribosyl-4-carboxy-5-aminoimidazole) forming SAICAR (5-amino-4-imidazole-N-succinocarboxamide ribonucleotide) in the presence of ATP. In continuation with our previous study on the thermostability of this enzyme in hyper-/thermophiles based on the structural aspects, here, we present the dynamic aspects that differentiate the mesophilic (E. coli, E. chaffeensis), thermophilic (G. kaustophilus), and hyperthermophilic (M. jannaschii, P. horikoshii) SAICAR synthetases by carrying out a total of 11 simulations. The five functional dimers from the above organisms were simulated using molecular dynamics for a period of 50 ns each at 300 K, 363 K, and an additional simulation at 333 K for the thermophilic protein. The basic features like root-mean-square deviations, root-mean-square fluctuations, surface accessibility, and radius of gyration revealed the instability of mesophiles at 363 K. Mean square displacements establish the reduced flexibility of hyper-/thermophiles at all temperatures. At the simulations time scale considered here, the long-distance networks are considerably affected in mesophilic structures at 363 K. In mesophiles, a comparatively higher number of short-lived (having less percent existence time) C alpha, hydrogen bonds, hydrophobic interactions are formed, and long-lived (with higher percentage existence time) contacts are lost. The number of time-averaged salt-bridges is at least 2-fold higher in hyperthermophiles at 363 K. The change in surface accessibility of salt-bridges at 363 K from 300 K is nearly doubled in mesophilic protein compared to proteins from other temperature classes.