Process Development for Functionalization of Cotton with Silver Nanoparticles Synthesized by Bio-based Approaches

Cotton is a widely used raw material for textiles but drawbacks regarding their poor mechanical properties often limit their applications as functional materials. The present investigation involved process development for one step coating of cotton with silver nanoparticles (SNP) synthesized using Azadirachta indica and Citrus limon extract to develop functional textiles. Addition of starch to functional textiles led to efficient binding of nanoparticles to fabric and led to drastic decrease in release of silver from fabricated textiles after ten washing cycles enhancing their environment friendliness. Differential scanning calorimetry, scanning electron microscopy, FT-IR analysis and mechanical studies demonstrated efficient binding of nanoparticles to fabric through bio-based processes. The functionalized textiles developed by the bio-based methods showed significant antibacterial activity against E. coli and S. aureus (with 99% microbial reduction). Present work offers a simple procedure for coating SNP using bio-based approaches with promising applications in specialized functions.

Automatic Tonic Identification in Indian Art Music: Approaches and Evaluation

The tonic is a fundamental concept in Indian art music. It is the base pitch, which an artist chooses in order to construct the melodies during a rg(a) rendition, and all accompanying instruments are tuned using the tonic pitch. Consequently, tonic identification is a fundamental task for most computational analyses of Indian art music, such as intonation analysis, melodic motif analysis and rg recognition. In this paper we review existing approaches for tonic identification in Indian art music and evaluate them on six diverse datasets for a thorough comparison and analysis. We study the performance of each method in different contexts such as the presence/absence of additional metadata, the quality of audio data, the duration of audio data, music tradition (Hindustani/Carnatic) and the gender of the singer (male/female). We show that the approaches that combine multi-pitch analysis with machine learning provide the best performance in most cases (90% identification accuracy on average), and are robust across the aforementioned contexts compared to the approaches based on expert knowledge. In addition, we also show that the performance of the latter can be improved when additional metadata is available to further constrain the problem. Finally, we present a detailed error analysis of each method, providing further insights into the advantages and limitations of the methods.

Approaches toward (eta(2)-HX) (X = H, SiR (R = Me-3 or Me2Ph), CH3) sigma-complexes of ruthenium

Two new Ru(II)-complexes RuH(Tpms)(PPh3)(2)] 1 (Tpms - (C3H3N2)(3)CSO3, tris-(pyrazolyl) methane sulfonate) and Ru(OTf)(Tpms)(PPh3)(2)] 2 (OTf = CF3SO3) have been synthesized and characterized wherein Ru-H and Ru-OTf are the key reactive centers. Reaction of 1 with HOTf results in the Ru(eta(2)-H-2)(Tpms)(PPh3)(2)]OTf] complex 3, whereas reaction of 1 with Me3SiOTf affords the dihydrogen complex 3 and complex 1 through an unobserved sigma-silane intermediate. In addition, an attempt to characterize the sigma methane complex via reaction of complex 1 with CH3OTf yields complex 2 and free methane. On the other hand, reaction of Ru(OTf)(Tpms)(PPh3)(2)] 2 with H-2 and PhMe2SiH at low temperature resulted in sigma-H-2, 3 and a probable sigma-silane complexes, respectively. However, no sigma-methane complex was observed for the reaction of complex 2 with methane even at low temperature. (C) 2014 Elsevier B. V. All rights reserved.

Exploring Transfer Learning Approaches for Head Pose Classification from Multi-view Surveillance Images

Head pose classification from surveillance images acquired with distant, large field-of-view cameras is difficult as faces are captured at low-resolution and have a blurred appearance. Domain adaptation approaches are useful for transferring knowledge from the training (source) to the test (target) data when they have different attributes, minimizing target data labeling efforts in the process. This paper examines the use of transfer learning for efficient multi-view head pose classification with minimal target training data under three challenging situations: (i) where the range of head poses in the source and target images is different, (ii) where source images capture a stationary person while target images capture a moving person whose facial appearance varies under motion due to changing perspective, scale and (iii) a combination of (i) and (ii). On the whole, the presented methods represent novel transfer learning solutions employed in the context of multi-view head pose classification. We demonstrate that the proposed solutions considerably outperform the state-of-the-art through extensive experimental validation. Finally, the DPOSE dataset compiled for benchmarking head pose classification performance with moving persons, and to aid behavioral understanding applications is presented in this work.

Catalytic asymmetric desymmetrization approaches to enantioenriched cyclopentanes

Catalytic asymmetric desymmetrization represents an excellent strategy for accessing highly functionalized chiral building blocks. However, the application of desymmetrization for the synthesis of enantio-enriched cyclopentane derivatives remained limited, when compared to chiral cyclohexanes. We have recently developed a desymmetrization protocol for prochiral 2,2-disubstituted cyclopentene-1,3-diones by direct catalytic asymmetric vinylogous nucleophilic addition of deconjugated butenolides. In this perspective, we give an overview of asymmetric desymmetrization reactions leading to enantioenriched cyclopentanes and their derivatives. The focus is kept confined to the diverse nature of reactions used for this purpose. A brief discussion on the potential future directions is also provided.

Enriching the annotation of Mycobacterium tuberculosis H37Rv proteome using remote homology detection approaches: Insights into structure and function

The availability of the genome sequence of Mycobacterium tuberculosis H37Rv has encouraged determination of large numbers of protein structures and detailed definition of the biological information encoded therein; yet, the functions of many proteins in M. tuberculosis remain unknown. The emergence of multidrug resistant strains makes it a priority to exploit recent advances in homology recognition and structure prediction to re-analyse its gene products. Here we report the structural and functional characterization of gene products encoded in the M. tuberculosis genome, with the help of sensitive profile-based remote homology search and fold recognition algorithms resulting in an enhanced annotation of the proteome where 95% of the M. tuberculosis proteins were identified wholly or partly with information on structure or function. New information includes association of 244 proteins with 205 domain families and a separate set of new association of folds to 64 proteins. Extending structural information across uncharacterized protein families represented in the M. tuberculosis proteome, by determining superfamily relationships between families of known and unknown structures, has contributed to an enhancement in the knowledge of structural content. In retrospect, such superfamily relationships have facilitated recognition of probable structure and/or function for several uncharacterized protein families, eventually aiding recognition of probable functions for homologous proteins corresponding to such families. Gene products unique to mycobacteria for which no functions could be identified are 183. Of these 18 were determined to be M. tuberculosis specific. Such pathogen-specific proteins are speculated to harbour virulence factors required for pathogenesis. A re-annotated proteome of M. tuberculosis, with greater completeness of annotated proteins and domain assigned regions, provides a valuable basis for experimental endeavours designed to obtain a better understanding of pathogenesis and to accelerate the process of drug target discovery. (C) 2014 Elsevier Ltd. All rights reserved.

Comparative evaluation of inversion approaches of the radiative transfer model for estimation of crop biophysical parameters

The inversion of canopy reflectance models is widely used for the retrieval of vegetation properties from remote sensing. This study evaluates the retrieval of soybean biophysical variables of leaf area index, leaf chlorophyll content, canopy chlorophyll content, and equivalent leaf water thickness from proximal reflectance data integrated broadbands corresponding to moderate resolution imaging spectroradiometer, thematic mapper, and linear imaging self scanning sensors through inversion of the canopy radiative transfer model, PROSAIL. Three different inversion approaches namely the look-up table, genetic algorithm, and artificial neural network were used and performances were evaluated. Application of the genetic algorithm for crop parameter retrieval is a new attempt among the variety of optimization problems in remote sensing which have been successfully demonstrated in the present study. Its performance was as good as that of the look-up table approach and the artificial neural network was a poor performer. The general order of estimation accuracy for para-meters irrespective of inversion approaches was leaf area index > canopy chlorophyll content > leaf chlorophyll content > equivalent leaf water thickness. Performance of inversion was comparable for broadband reflectances of all three sensors in the optical region with insignificant differences in estimation accuracy among them.

Reduction Approaches for Vibration Control of Repetitive Structures

The reduction approaches are presented for vibration control of symmetric, cyclic periodic and linking structures. The condensation of generalized coordinates, the locations of sensors and actuators, and the relation between system inputs and control forces are assumed to be set in a symmetric way so that the control system posses the same repetition as the structure considered. By employing proper transformations of condensed generalized coordinates and the system inputs, the vibration control of an entire system can be implemented by carrying out the control of a number of sub-structures, and thus the dimension of the control problem can be significantly reduced.

Feasibility of Protein a for the Oriented Immobilization of Immunoglobulin on Silicon Surface for a Biosensor with Imaging Ellipsometry

The feasibility of using protein A to immobilize antibody on silicon surface for a biosensor with imaging ellipsometry was presented in this study. The amount of human IgG bound with anti-IgG immobilized by the protein A on silicon surface was much more than that bound with anti-IgG immobilized by physical adsorption. The result indicated that the protein A could be used to immobilize antibody molecules in a highly oriented manner and maintain antibody molecular functional configuration on the silicon surface. High reproducibility of the amount of antibody immobilization and homogenous antibody adsorption layer on surfaces could be obtained by this immobilization method. Imaging ellipsometry has been proven to be a fast and reliable detection method and sensitive enough to detect small changes in a molecular monolayer level. The combination of imaging ellipsometry and surface modification with protein A has the potential to be further developed into an efficient immunoassay protein chip.

QM/MM and Classical Molecular Dynamics Simulation of Histidine-Tagged Peptide Immobilization on Nickel Surface

The hybrid quantum mechanics (QM) and molecular mechanics (MM) method is employed to simulate the His-tagged peptide adsorption to ionized region of nickel surface. Based on the previous experiments, the peptide interaction with one Ni ion is considered. In the QM/MM calculation, the imidazoles on the side chain of the peptide and the metal ion with several neighboring water molecules are treated as QM part calculated by “GAMESS”, and the rest atoms are treated as MM part calculated by “TINKER”. The integrated molecular orbital/molecular mechanics (IMOMM) method is used to deal with theQMpart with the transitional metal. By using the QM/MM method, we optimize the structure of the synthetic peptide chelating with a Ni ion. Different chelate structures are considered. The geometry parameters of the QM subsystem we obtained by QM/MM calculation are consistent with the available experimental results. We also perform a classical molecular dynamics (MD) simulation with the experimental parameters for the synthetic peptide adsorption on a neutral Ni(1 0 0) surface. We find that half of the His-tags are almost parallel with the substrate, which enhance the binding strength. Peeling of the peptide from the Ni substrate is simulated in the aqueous solvent and in vacuum, respectively. The critical peeling forces in the two environments are obtained. The results show that the imidazole rings are attached to the substrate more tightly than other bases in this peptide.

Quantitative approaches for characterising fibrillar protein nanostructures

Polypeptide sequences have an inherent tendency to self-assemble into filamentous nanostructures commonly known as amyloid fibrils. Such self-assembly is used in nature to generate a variety of functional materials ranging from protective coatings in bacteria to catalytic scaffolds in mammals. The aberrant self-assembly of misfolded peptides and proteins is also, however, implicated in a range of disease states including neurodegenerative conditions such as Alzheimer's and Parkinson's diseases. It is increasingly evident that the intrinsic material properties of these structures are crucial for understanding the thermodynamics and kinetics of the pathological deposition of proteins, particularly as the mechanical fragmentation of aggregates enhances the rate of protein deposition by exposing new fibril ends which can promote further growth. We discuss here recent advances in physical techniques that are able to characterise the hierarchical self-assembly of misfolded protein molecnles and define their properties. © 2010 Materials Research Society.