REPRODUCING KERNEL FOR A CLASS OF WEIGHTED BERGMAN SPACES ON THE SYMMETRIZED POLYDISC

A natural class of weighted Bergman spaces on the symmetrized polydisc is isometrically embedded as a subspace in the corresponding weighted Bergman space on the polydisc. We find an orthonormal basis for this subspace. It enables us to compute the kernel function for the weighted Bergman spaces on the symmetrized polydisc using the explicit nature of our embedding. This family of kernel functions includes the Szego and the Bergman kernel on the symmetrized polydisc.

Nanoindentation behavior of nanotwinned Cu: Influence of indenter angle on hardness, strain rate sensitivity and activation volume

The influence of strain on the mechanical properties and deformation kinetic parameters of nanotwinned (at) copper is investigated by a series of nanoindentation experiments, which were performed by employing sharp indenters with five varying centerline-to-face angles (psi). Comparison experiments were also conducted on (1 1 0) single crystalline Cu. Experimental results indicate that, unlike coarsegrained materials, nt-Cu is prone to plastic flow softening with large material pile-up around the indentation impression at high levels of strains. Localized detwinning becomes more significant with decreasing psi, concomitant with reduced strain-rate sensitivity (m) and enhanced activation volume (V*). The m of nt-Cu is found to depend sensitively on psi with a variation of more than a factor of 3, whereas V* exhibits a much less sensitive trend. This paper discusses the validation of the experimental techniques and the implications of various deformation kinetic parameters on the underlying deformation mechanisms of nt-Ca. 2013 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

On the images of Sobolev spaces under the heat kernel transform on the Heisenberg group

The aim of this paper is to obtain certain characterizations for the image of a Sobolev space on the Heisenberg group under the heat kernel transform. We give three types of characterizations for the image of a Sobolev space of positive order H-m (H-n), m is an element of N-n, under the heat kernel transform on H-n, using direct sum and direct integral of Bergmann spaces and certain unitary representations of H-n which can be realized on the Hilbert space of Hilbert-Schmidt operators on L-2 (R-n). We also show that the image of Sobolev space of negative order H-s (H-n), s(> 0) is an element of R is a direct sum of two weighted Bergman spaces. Finally, we try to obtain some pointwise estimates for the functions in the image of Schwartz class on H-n under the heat kernel transform. (C) 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Exploring novel methods to achieve sensitivity limits for high operating temperature infrared detectors

A review of high operating temperature (HOT) infrared (IR) photon detector technology vis-a-vis material requirements, device design and state of the art achieved is presented in this article. The HOT photon detector concept offers the promise of operation at temperatures above 120 K to near room temperature. Advantages are reduction in system size, weight, cost and increase in system reliability. A theoretical study of the thermal generation-recombination (g-r) processes such as Auger and defect related Shockley Read Hall (SRH) recombination responsible for increasing dark current in HgCdTe detectors is presented. Results of theoretical analysis are used to evaluate performance of long wavelength (LW) and mid wavelength (MW) IR detectors at high operating temperatures. (C) 2013 Elsevier B.V. All rights reserved.

Solvent Sensitivity of Protein Unfolding: Dynamical Study of Chicken Villin Headpiece Subdomain in Water Ethanol Binary Mixture

We carry out a series of long atomistic molecular dynamics simulations to study the unfolding of a small protein, chicken villin headpiece (HP-36), in water-ethanol (EtOH) binary mixture. The prime objective of this work is to explore the sensitivity of protein unfolding dynamics toward increasing concentration of the cosolvent and unravel essential features of intermediates formed in search of a dynamical pathway toward unfolding. In water ethanol binary mixtures, HP-36 is found to unfold partially, under ambient conditions, that otherwise requires temperature as high as similar to 600 K to denature in pure aqueous solvent. However, an interesting course of pathway is observed to be followed in the process, guided by the formation of unique intermediates. The first step of unfolding is essentially the separation of the cluster formed by three hydrophobic (phenylalanine) residues, namely, Phe-7, Phe-11, and Phe-18, which constitute the hydrophobic core, thereby initiating melting of helix-2 of the protein. The initial steps are similar to temperature-induced unfolding as well as chemical unfolding using DMSO as cosolvent. Subsequent unfolding steps follow a unique path. As water-ethanol shows composition-dependent anomalies, so do the details of unfolding dynamics. With an increase in cosolvent concentration, different partially unfolded intermediates are found to be formed. This is reflected in a remarkable nonmonotonic composition dependence of several order parameters, including fraction of native contacts and protein-solvent interaction energy. The emergence of such partially unfolded states can be attributed to the preferential solvation of the hydrophobic residues by the ethyl groups of ethanol. We further quantify the local dynamics of unfolding by using a Marcus-type theory.

CFT(4) partition functions and the heat kernel on AdS(5)

We consider four-dimensional CFTs which admit a large-N expansion, and whose spectrum contains states whose conformal dimensions do not scale with N. We explicitly reorganise the partition function obtained by exponentiating the one-particle partition function of these states into a heat kernel form for the dual string spectrum on AdS(5). On very general grounds, the heat kernel answer can be expressed in terms of a convolution of the one-particle partition function of the light states in the four-dimensional CFT. (C) 2013 Elsevier B.V. All rights reserved.

Bivariate frequency analysis of floods using a diffusion based kernel density estimator

Recent focus of flood frequency analysis (FFA) studies has been on development of methods to model joint distributions of variables such as peak flow, volume, and duration that characterize a flood event, as comprehensive knowledge of flood event is often necessary in hydrological applications. Diffusion process based adaptive kernel (D-kernel) is suggested in this paper for this purpose. It is data driven, flexible and unlike most kernel density estimators, always yields a bona fide probability density function. It overcomes shortcomings associated with the use of conventional kernel density estimators in FFA, such as boundary leakage problem and normal reference rule. The potential of the D-kernel is demonstrated by application to synthetic samples of various sizes drawn from known unimodal and bimodal populations, and five typical peak flow records from different parts of the world. It is shown to be effective when compared to conventional Gaussian kernel and the best of seven commonly used copulas (Gumbel-Hougaard, Frank, Clayton, Joe, Normal, Plackett, and Student's T) in estimating joint distribution of peak flow characteristics and extrapolating beyond historical maxima. Selection of optimum number of bins is found to be critical in modeling with D-kernel.

Spatially Adaptive Kernel Regression Using Risk Estimation

An important question in kernel regression is one of estimating the order and bandwidth parameters from available noisy data. We propose to solve the problem within a risk estimation framework. Considering an independent and identically distributed (i.i.d.) Gaussian observations model, we use Stein's unbiased risk estimator (SURE) to estimate a weighted mean-square error (MSE) risk, and optimize it with respect to the order and bandwidth parameters. The two parameters are thus spatially adapted in such a manner that noise smoothing and fine structure preservation are simultaneously achieved. On the application side, we consider the problem of image restoration from uniform/non-uniform data, and show that the SURE approach to spatially adaptive kernel regression results in better quality estimation compared with its spatially non-adaptive counterparts. The denoising results obtained are comparable to those obtained using other state-of-the-art techniques, and in some scenarios, superior.

Plasmonic Interactions at Close Proximity in Chiral Geometries: Route toward Broadband Chiroptical Response and Giant Enantiomeric Sensitivity

Chiral metamaterials can have diverse technological applications, such as engineering strongly twisted local electromagnetic fields for sensitive detection of chiral molecules, negative indices of refraction, broadband circular polarization devices, and many more. These are commonly achieved by arranging a group of noble-metal nanoparticles in a chiral geometry, which, for example, can be a helix, whose chiroptical response originates in the dynamic electromagnetic interactions between the localized plasmon modes of the individual nanoparticles. A key question relevant to the chiroptical response of such materials is the role of plasmon interactions as the constituent particles are brought closer, which is investigated in this paper through theoretical and experimental studies. The results of our theoretical analysis, when the particles are brought in close proximity are dramatic, showing a large red shift and enhancement of the spectral width and a near-exponential rise in the strength of the chiroptical response. These predictions were further confirmed with experimental studies of gold and silver nanoparticles arranged on a helical template, where the role of particle separation could be investigated in a systematic manner. The ``optical chirality'' of the electromagnetic fields in the vicinity of the nanoparticles was estimated to be orders of magnitude larger than what could be achieved in all other nanoplasmonic geometries considered so far, implying the suitability of the experimental system for sensitive detection of chiral molecules.

Structural basis for the redox sensitivity of the Mycobacterium tuberculosis SigK-RskA sigma-anti-sigma complex

The host-pathogen interactions in Mycobacterium tuberculosis infection are significantly influenced by redox stimuli and alterations in the levels of secreted antigens. The extracyto-plasmic function (ECF) sigma factor sigma(K) governs the transcription of the serodominant antigens MPT70 and MPT83. The cellular levels of sigma(K) are regulated by the membrane-associated anti-sigma(K) (RskA) that localizes sigma(K) in an inactive complex. The crystal structure of M. tuberculosis sigma(K) in complex with the cytosolic domain of RskA (RskAcyto) revealed a disulfide bridge in the -35 promoter-interaction region of sigma(K). Biochemical experiments reveal that the redox potential of the disulfide-forming cysteines in sigma(K) is consistent with its role as a sensor. The disulfide bond in sigma(K) influences the stability of the sigma(K)-RskA(cyto) complex but does not interfere with sigma(K)-promoter DNA interactions. It is noted that these disulfide-forming cysteines are conserved across homologues, suggesting that this could be a general mechanism for redox-sensitive transcription regulation.

Sensitivity of Water Dynamics to Biologically Significant Surfaces of Monomeric Insulin: Role of Topology and Electrostatic Interactions

In addition to the biologically active monomer of the protein insulin circulating in human blood, the molecule also exists in dimeric and hexameric forms that are used as storage. The insulin monomer contains two distinct surfaces, namely, the dimer forming surface (DFS) and the hexamer forming surface (HFS), that are specifically designed to facilitate the formation of the dimer and the hexamer, respectively. In order to characterize the structural and dynamical behavior of interfacial water molecules near these two surfaces (DFS and HFS), we performed atomistic molecular dynamics simulations of insulin with explicit water. Dynamical characterization reveals that the structural relaxation of the hydrogen bonds formed between the residues of DFS and the interfacial water molecules is faster than those formed between water and that of the HFS. Furthermore, the residence times of water molecules in the protein hydration layer for both the DFS and HFS are found to be significantly higher than those for some of the other proteins studied so far, such as HP-36 and lysozyme. In particular, we find that more structured water molecules, with higher residence times (similar to 300-500 ps), are present near HFS than those near DFS. A significant slowing down is observed in the decay of associated rotational auto time correlation functions of O-H bond vector of water in the vicinity of HFS. The surface topography and the arrangement of amino acid residues work together to organize the water molecules in the hydration layer in order to provide them with a preferred orientation. HFS having a large polar solvent accessible surface area and a convex extensive nonpolar region, drives the surrounding water molecules to acquire predominantly an outward H-atoms directed, clathrate-like structure. In contrast, near the DFS, the surrounding water molecules acquire an inward H-atoms directed orientation owing to the flat curvature of hydrophobic surface and the interrupted hydrophilic residual alignment. We have followed escape trajectory of several such quasi-bound water molecules from both the surfaces that reveal the significant differences between the two hydration layers.

Density Functional Theoretical Modeling, Electrostatic Surface Potential and Surface Enhanced Raman Spectroscopic Studies on Biosynthesized Silver Nanoparticles: Observation of 400 pM Sensitivity to Explosives

Interaction of adsorbate on charged surfaces, orientation of the analyte on the surface, and surface enhancement aspects have been studied. These aspects have been explored in details to explain the surface-enhanced Raman spectroscopic (SERS) spectra of 2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane (HNIW or CL-20), a well-known explosive, and 2,4,6-trinitrotoluene (TNT) using one-pot synthesis of silver nanoparticles via biosynthetic route using natural precursor extracts of clove and pepper. The biosynthesized silver nanoparticles (bio Ag Nps) have been characterized using UV-vis spectroscopy, scanning electron microscopy and atomic force microscopy. SERS studies conducted using bio Ag Nps on different water insoluble analytes, such as CL-20 and TNT, lead to SERS signals at concentration levels of 400 pM. The experimental findings have been corroborated with density functional computational results, electrostatic surface potential calculations, Fukui functions and potential measurements.

Regional flood frequency analysis using kernel- based fuzzy clustering approach

Regionalization approaches are widely used in water resources engineering to identify hydrologically homogeneous groups of watersheds that are referred to as regions. Pooled information from sites (depicting watersheds) in a region forms the basis to estimate quantiles associated with hydrological extreme events at ungauged/sparsely gauged sites in the region. Conventional regionalization approaches can be effective when watersheds (data points) corresponding to different regions can be separated using straight lines or linear planes in the space of watershed related attributes. In this paper, a kernel-based Fuzzy c-means (KFCM) clustering approach is presented for use in situations where such linear separation of regions cannot be accomplished. The approach uses kernel-based functions to map the data points from the attribute space to a higher-dimensional space where they can be separated into regions by linear planes. A procedure to determine optimal number of regions with the KFCM approach is suggested. Further, formulations to estimate flood quantiles at ungauged sites with the approach are developed. Effectiveness of the approach is demonstrated through Monte-Carlo simulation experiments and a case study on watersheds in United States. Comparison of results with those based on conventional Fuzzy c-means clustering, Region-of-influence approach and a prior study indicate that KFCM approach outperforms the other approaches in forming regions that are closer to being statistically homogeneous and in estimating flood quantiles at ungauged sites. Key Points Kernel-based regionalization approach is presented for flood frequency analysis Kernel procedure to estimate flood quantiles at ungauged sites is developed A set of fuzzy regions is delineated in Ohio, USA

Titania nanotube-modified screen printed carbon electrodes enhance the sensitivity in the electrochemical detection of proteins

The use of titania nanotubes (TiO2-NT) as the working electrode provides a substantial improvement in the electrochemical detection of proteins. A biosensor designed using this strategy provided a robust method to detect protein samples at very low concentrations (C-protein ca 1 ng/mu l). Reproducible measurements on protein samples at this concentration (I-p,I-a of 80 +/- 1.2 mu A) could be achieved using a sample volume of ca 30 mu l. We demonstrate the feasibility of this strategy for the accurate detection of penicillin binding protein, PBP2a, a marker for methicillin resistant Staphylococcus aureus (MRSA). The selectivity and efficiency of this sensor were also validated using other diverse protein preparations such as a recombinant protein tyrosine phosphatase (PTP10D) and bovine serum albumin (BSA). This electrochemical method also presents a substantial improvement in the time taken (few minutes) when compared to conventional enzyme-linked immunosorbent assay (ELISA) protocols. It is envisaged that this sensor could substantially aid in the rapid diagnosis of bacterial infections in resource strapped environments. (C) 2014 Elsevier B.V. All rights reserved.

Sensitivity enhancement in slice-selective NMR experiments through polarization sharing

The polarization sharing technique is utilized in gradient based slice selective experiments to transfer polarization from unutilized protons to selectively excited protons. This facilitates rapid data acquisition without any customary inter-scan relaxation delay, resulting in an average of 2-fold sensitivity enhancement per unit time.