Molecular Insights Revealing Interaction of Tim23 and Channel Subunits of Presequence Translocase

Tim23 is an essential channel-forming subunit of the presequence translocase recruiting multiple components for assembly of the core complex, thereby regulating the protein translocation process. However, understanding of the precise interaction of subunits associating with Tim23 remains largely elusive. Our findings highlight that transmembrane helix 1 (TM1) is required for homodimerization of Tim23, while, together with TM2, it is involved in preprotein binding within the channel. Based on our evidence, we predict that the TM1 and TM2 from each dimer are involved in the formation of the central translocation pore, aided by Tim17. Furthermore, TM2 is also involved in the recruitment of Tim21 and the presequence-associated motor (PAM) subcomplex to the Tim23 channel, while the matrix-exposed loop L1 generates specificity in their association with the core complex. Strikingly, our findings indicate that the C-terminal sequence of Tim23 is dispensable for growth and functions as an inhibitor for binding of Tim21. Our model conceptually explains the cooperative function between Tam41 and Pam17 subunits, while the antagonistic activity of Tim21 predominantly determines the bound and free forms of the PAM subcomplex during import.

Sparse Recovery Methods Hold Promise for Diffuse Optical Tomographic Image Reconstruction

The sparse recovery methods utilize the l(p)-normbased regularization in the estimation problem with 0 <= p <= 1. These methods have a better utility when the number of independent measurements are limited in nature, which is a typical case for diffuse optical tomographic image reconstruction problem. These sparse recovery methods, along with an approximation to utilize the l(0)-norm, have been deployed for the reconstruction of diffuse optical images. Their performancewas compared systematically using both numerical and gelatin phantom cases to show that these methods hold promise in improving the reconstructed image quality.

STOKES' SYSTEM IN A DOMAIN WITH OSCILLATING BOUNDARY: HOMOGENIZATION AND ERROR ANALYSIS OF AN INTERIOR OPTIMAL CONTROL PROBLEM

Homogenization and error analysis of an optimal interior control problem in the framework of Stokes' system, on a domain with rapidly oscillating boundary, are the subject matters of this article. We consider a three dimensional domain constituted of a parallelepiped with a large number of rectangular cylinders at the top of it. An interior control is applied in a proper subdomain of the parallelepiped, away from the oscillating volume. We consider two types of functionals, namely a functional involving the L-2-norm of the state variable and another one involving its H-1-norm. The asymptotic analysis of optimality systems for both cases, when the cross sectional area of the rectangular cylinders tends to zero, is done here. Our major contribution is to derive error estimates for the state, the co-state and the associated pressures, in appropriate functional spaces.

Scalable sequential alternating proximal methods for sparse structural SVMs and CRFs

Structural Support Vector Machines (SSVMs) and Conditional Random Fields (CRFs) are popular discriminative methods used for classifying structured and complex objects like parse trees, image segments and part-of-speech tags. The datasets involved are very large dimensional, and the models designed using typical training algorithms for SSVMs and CRFs are non-sparse. This non-sparse nature of models results in slow inference. Thus, there is a need to devise new algorithms for sparse SSVM and CRF classifier design. Use of elastic net and L1-regularizer has already been explored for solving primal CRF and SSVM problems, respectively, to design sparse classifiers. In this work, we focus on dual elastic net regularized SSVM and CRF. By exploiting the weakly coupled structure of these convex programming problems, we propose a new sequential alternating proximal (SAP) algorithm to solve these dual problems. This algorithm works by sequentially visiting each training set example and solving a simple subproblem restricted to a small subset of variables associated with that example. Numerical experiments on various benchmark sequence labeling datasets demonstrate that the proposed algorithm scales well. Further, the classifiers designed are sparser than those designed by solving the respective primal problems and demonstrate comparable generalization performance. Thus, the proposed SAP algorithm is a useful alternative for sparse SSVM and CRF classifier design.

Performance evaluation of typical approximation algorithms for nonconvex l(p)-minimization in diffuse optical tomography

The sparse estimation methods that utilize the l(p)-norm, with p being between 0 and 1, have shown better utility in providing optimal solutions to the inverse problem in diffuse optical tomography. These l(p)-norm-based regularizations make the optimization function nonconvex, and algorithms that implement l(p)-norm minimization utilize approximations to the original l(p)-norm function. In this work, three such typical methods for implementing the l(p)-norm were considered, namely, iteratively reweighted l(1)-minimization (IRL1), iteratively reweighted least squares (IRLS), and the iteratively thresholding method (ITM). These methods were deployed for performing diffuse optical tomographic image reconstruction, and a systematic comparison with the help of three numerical and gelatin phantom cases was executed. The results indicate that these three methods in the implementation of l(p)-minimization yields similar results, with IRL1 fairing marginally in cases considered here in terms of shape recovery and quantitative accuracy of the reconstructed diffuse optical tomographic images. (C) 2014 Optical Society of America

TIME VARYING LINEAR PREDICTION USING SPARSITY CONSTRAINTS

Time-varying linear prediction has been studied in the context of speech signals, in which the auto-regressive (AR) coefficients of the system function are modeled as a linear combination of a set of known bases. Traditionally, least squares minimization is used for the estimation of model parameters of the system. Motivated by the sparse nature of the excitation signal for voiced sounds, we explore the time-varying linear prediction modeling of speech signals using sparsity constraints. Parameter estimation is posed as a 0-norm minimization problem. The re-weighted 1-norm minimization technique is used to estimate the model parameters. We show that for sparsely excited time-varying systems, the formulation models the underlying system function better than the least squares error minimization approach. Evaluation with synthetic and real speech examples show that the estimated model parameters track the formant trajectories closer than the least squares approach.

Synthesis of a new tungsten-free gamma-gamma ` cobalt-based superalloy by tuning alloying additions

The paper presents the synthesis of a new class of gamma-gamma' cobalt-based superalloy that is free of tungsten as an alloying addition. It has much lower density and higher specific strength than the existing cobalt-based superalloys. The current superalloys have a base composition of Co-10Al and are further tuned by the addition of a binary combination of molybdenum and niobium, with the optimum composition of Co-10Al-5Mo-2Nb. The solvus temperature of the alloy (866 degrees C) can be further enhanced above 950 C by the addition of Ni to give the form Co-xNi-10Al-5Mo-2Nb, where x can be from 0 to 30 at.%. After heat treatment, these alloys exhibit a duplex microstructure with coherent cuboidal L1(2)-ordered precipitates (gamma') throughout the face-centred cubic matrix (gamma), yielding a microstructure that is very similar to nickel-based superalloys as well as recently developed Co-Al-W-based alloys. We show that the stability of the gamma' phase improves significantly with the nickel addition, which can be attributed to the increase in solvus temperature. A very high specific 0.2% proof stress of 94.3 MPa g(-1) cm(-3) at room temperature and 63.8 MPa g(-1) cm(-3) at 870 degrees C were obtained for alloy Co-30Ni-10Al-5Mo-2Nb. The remarkably high specific strength of these alloys makes this class of alloy a promising material for use at high temperature, including gas turbine applications. (C) 2014 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

First principles study of structural stability and site preference in Co-3 (W,X)

Since the discovery 1] of gamma' precipitate (L1(2) - Co-3 (Al, W)) in the Co-Al-W ternary system, there has been an increased interest in Co-based superalloys. Since these alloys have two phase microstructures (gamma + gamma') similar to Ni-based superalloys 2], they are viable candidates in high temperature applications, particularly in land-based turbines. The role of alloying on stability of the gamma' phase has been an active area of research. In this study, electronic structure calculations were done to probe the effect of alloying in Co3W with L1(2) structure. Compositions of type Co-3(W, X), (where X/Y = Mn, Fe, Ni, Pt, Cr, Al, Si, V, W, Ta, Ti, Nb, Hf, Zr and Mo) were studied. Effect of alloying on equilibrium lattice parameters and ground state energies was used to calculate Vegard's coefficients and site preference related data. The effect of alloying on the stability of the L1(2) structure vis a vis other geometrically close packed ordered structures was also studied for a range of Co3X compounds. Results suggest that the penchant of element for the W sublattice can be predicted by comparing heats of formation of Co3X in different structures.

Synthesis and characterization of monometallic rhenium(I) complexes and their application as selective sensors for copper(II) ions

Novel imine functionalized monometallic rhenium(I) polypyridine complexes (1-4) comprising two phenol moieties attached to 2,20-bipyridine ligands L1-L4 have been synthesized and characterized. These complexes exhibit selective and sensitive detection towards copper(II) ions and this is observed through changes in UV-visible absorption, luminescence and time-resolved spectroscopic techniques. An enormous enhancement is observed in emission intensity, quantum yield and luminescence lifetime with the addition of copper(II) ions, and this can be attributed to the restriction of C=N isomerization in the Re(I) complexes. The strong binding between copper(II) ions and these complexes reveals that the binding constant values are in the range of 1.1 x 10(3)-6.0 x 103 M-1. The absorption spectral behavior of the complexes is supported by DFT calculations.

L-infinity norms of holomorphic modular forms in the case of compact quotient

We prove a sub-convex estimate for the sup-norm of L-2-normalized holomorphic modular forms of weight k on the upper half plane, with respect to the unit group of a quaternion division algebra over Q. More precisely we show that when the L-2 norm of an eigenfunction f is one, parallel to f parallel to(infinity) <<(epsilon) k(1/2-1/33+epsilon) for any epsilon > 0 and for all k sufficiently large.

A new class of high strength high temperature Cobalt based gamma-gamma ` Co-Mo-Al alloys stabilized with Ta addition

The present paper reports a new class of Co based superalloys that has gamma-gamma' microstructure and exhibits much lower density compared to other commercially available Co superalloys including Co-Al-W based alloys. The basic composition is Co-10Al-5Mo (at%) with addition of 2 at% Ta for stabilization of gamma' phase. The gamma-gamma' microstructure evolves through solutionising and aging treatment. Using first principles calculations, we observe that Ta plays a crucial role in stabilizing gamma' phase. By addition of Ta in the basic stoichiometric composition Co-3(Al, Mo), the enthalpy of formation (Delta H-f) of L1(2) structure (gamma' phase) becomes more negative in comparison to DO19 structure. The All of the L12 structure becomes further more negative by the occupancy of Ni and Ti atoms in the lattice suggesting an increase in the stability of the gamma' precipitates. Among large number of alloys studied experimentally, the paper presents results of detailed investigations on Co-10Al-5Mo-2Ta, Co-30Ni-10Al-5Mo-2Ta and Co-30Ni-10Al-5Mo-2Ta-2Ti. To evaluate the role alloying elements, atom probe tomography investigations were carried out to obtain partition coefficients for the constituent elements. The results show strong partitioning of Ni, Al, Ta and Ti in ordered gamma' precipitates. 2015 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Global response sensitivity analysis using probability distance measures and generalization of Sobol's analysis

The study introduces two new alternatives for global response sensitivity analysis based on the application of the L-2-norm and Hellinger's metric for measuring distance between two probabilistic models. Both the procedures are shown to be capable of treating dependent non-Gaussian random variable models for the input variables. The sensitivity indices obtained based on the L2-norm involve second order moments of the response, and, when applied for the case of independent and identically distributed sequence of input random variables, it is shown to be related to the classical Sobol's response sensitivity indices. The analysis based on Hellinger's metric addresses variability across entire range or segments of the response probability density function. The measure is shown to be conceptually a more satisfying alternative to the Kullback-Leibler divergence based analysis which has been reported in the existing literature. Other issues addressed in the study cover Monte Carlo simulation based methods for computing the sensitivity indices and sensitivity analysis with respect to grouped variables. Illustrative examples consist of studies on global sensitivity analysis of natural frequencies of a random multi-degree of freedom system, response of a nonlinear frame, and safety margin associated with a nonlinear performance function. (C) 2015 Elsevier Ltd. All rights reserved.

An Alternating l(p) - l(2) Projections Algorithm (ALPA) for Speech Modeling using Sparsity Constraints

We address the problem of separating a speech signal into its excitation and vocal-tract filter components, which falls within the framework of blind deconvolution. Typically, the excitation in case of voiced speech is assumed to be sparse and the vocal-tract filter stable. We develop an alternating l(p) - l(2) projections algorithm (ALPA) to perform deconvolution taking into account these constraints. The algorithm is iterative, and alternates between two solution spaces. The initialization is based on the standard linear prediction decomposition of a speech signal into an autoregressive filter and prediction residue. In every iteration, a sparse excitation is estimated by optimizing an l(p)-norm-based cost and the vocal-tract filter is derived as a solution to a standard least-squares minimization problem. We validate the algorithm on voiced segments of natural speech signals and show applications to epoch estimation. We also present comparisons with state-of-the-art techniques and show that ALPA gives a sparser impulse-like excitation, where the impulses directly denote the epochs or instants of significant excitation.

Robust Savitzky-Golay Filters

Local polynomial approximation of data is an approach towards signal denoising. Savitzky-Golay (SG) filters are finite-impulse-response kernels, which convolve with the data to result in polynomial approximation for a chosen set of filter parameters. In the case of noise following Gaussian statistics, minimization of mean-squared error (MSE) between noisy signal and its polynomial approximation is optimum in the maximum-likelihood (ML) sense but the MSE criterion is not optimal for non-Gaussian noise conditions. In this paper, we robustify the SG filter for applications involving noise following a heavy-tailed distribution. The optimal filtering criterion is achieved by l(1) norm minimization of error through iteratively reweighted least-squares (IRLS) technique. It is interesting to note that at any stage of the iteration, we solve a weighted SG filter by minimizing l(2) norm but the process converges to l(1) minimized output. The results show consistent improvement over the standard SG filter performance.

A C-0 Interior Penalty Method for a Fourth-order Variational Inequality of the Second Kind

In this article, we propose a C-0 interior penalty ((CIP)-I-0) method for the frictional plate contact problem and derive both a priori and a posteriori error estimates. We derive an abstract error estimate in the energy norm without additional regularity assumption on the exact solution. The a priori error estimate is of optimal order whenever the solution is regular. Further, we derive a reliable and efficient a posteriori error estimator. Numerical experiments are presented to illustrate the theoretical results. (c) 2015Wiley Periodicals, Inc.