Negative sequence compensation within fundamental positive sequence reference frame for a stiff micro-grid generation in a wind power system using slip ring induction machine

An isolated wind power generation scheme using slip ring induction machine (SRIM) is proposed. The proposed scheme maintains constant load voltage and frequency irrespective of the wind speed or load variation. The power circuit consists of two back-to-back connected inverters with a common dc link, where one inverter is directly connected to the rotor side of SRIM and the other inverter is connected to the stator side of the SRIM through LC filter. Developing a negative sequence compensation method to ensure that, even under the presence of unbalanced load, the generator experiences almost balanced three-phase current and most of the unbalanced current is directed through the stator side converter is the focus here. The SRIM controller varies the speed of the generator with variation in the wind speed to extract maximum power. The difference of the generated power and the load power is either stored in or extracted from a battery bank, which is interfaced to the common dc link through a multiphase bidirectional fly-back dc-dc converter. The SRIM control scheme, maximum power point extraction algorithm and the fly-back converter topology are incorporated from available literature. The proposed scheme is both simulated and experimentally verified.

Prediction of protein-protein interactions between Helicobacter pylori and a human host

A lack of information on protein-protein interactions at the host-pathogen interface is impeding the understanding of the pathogenesis process. A recently developed, homology search-based method to predict protein-protein interactions is applied to the gastric pathogen, Helicobacter pylori to predict the interactions between proteins of H. pylori and human proteins in vitro. Many of the predicted interactions could potentially occur between the pathogen and its human host during pathogenesis as we focused mainly on the H. pylori proteins that have a transmembrane region or are encoded in the pathogenic island and those which are known to be secreted into the human host. By applying the homology search approach to protein-protein interaction databases DIP and iPfam, we could predict in vitro interactions for a total of 623 H. pylori proteins with 6559 human proteins. The predicted interactions include 549 hypothetical proteins of as yet unknown function encoded in the H. pylori genome and 13 experimentally verified secreted proteins. We have recognized 833 interactions involving the extracellular domains of transmembrane proteins of H. pylori. Structural analysis of some of the examples reveals that the interaction predicted by us is consistent with the structural compatibility of binding partners. Examples of interactions with discernible biological relevance are discussed.

Titration calorimetric studies to elucidate the specificity of the interactions of polymyxin B with lipopolysaccharides and lipid A

Lipopolysaccharide (LPS), the major cell wall constituent of Gram-negative bacteria, evokes a multitude of biological effects in mammals including pyrogenicity and toxic shock syndrome. Polymyxin B (PmB), a polycationic cyclic peptide, is known to neutralize most of its activities. The nature of the interaction of PmB with LPS and lipid A was investigated by isothermal titration calorimetry. PmB binds to LPS as well as lipid A stoichiometrically and non-co-operatively with micromolar affinity. These interactions are driven primarily by a favourable change in entropy (delta S) and are endothermic in nature. These positive changes in enthalpies decrease with increasing temperature, yielding a heat capacity change, delta Cp, of -2385 J.mol-1.degree-1 for PmB-LPS interactions while the binding of PmB to lipid A displays a delta Cp of -2259 J.mol-1.degree-1. The negative heat capacity changes provide strong evidence for the role of hydrophobic interactions as the driving force for the association of PmB with LPS and lipid A. A correlation of the energetics of these interactions with analyses of the molecular models of PmB suggests that a cluster of solvent-exposed non-polar amino acid side-chains that line one surface of the molecule, together with a ring of positively charged residues on its other surface, are responsible for its strong and stoichiometric binding to LPS.

Multivalent ligand presentation as a central concept to study intricate carbohydrate-protein interactions

Studying the weak binding affinities between carbohydrates and proteins has been a central theme in sustained efforts to uncover intricate details of this class of biomolecular interaction. The amphiphilic nature of most carbohydrates, the competing nature of the surrounding water molecules to a given protein receptor site and the receptor binding site characteristics led to the realization that carbohydrates are required to exert favorable interactions, primarily through clustering of the ligands. The clustering of sugar ligands has been augmented using many different innovative molecular scaffolds. The synthesis of clustered ligands also facilitates fine-tuning of the spatial and topological proximities between the ligands, so as to allow the identification of optimal molecular features for significant binding affinity enhancements. The kinetic and thermodynamic parameters have been delineated in many instances, thereby allowing an ability to correlate the multivalent presentation and the observed ligand-receptor interaction profiles. This critical review presents various multivalent ligands, synthetic and semisynthetic, and mechanisms by which the weak binding affinities are overcome, and the ligand-receptor complexation leads to significantly enhanced binding affinities (157 references).

Purification of RNA polymerase from mycobacteria for optimized promoter-polymerase interactions

In vitro transcription analysis is important to understand the mechanism of transcription. Various assays for the analysis of initiation, elongation and termination form the basis for better understanding of the process. Purified RNA polymerase (RNAP) with high specific activity is necessary to carry out variety of these specific reactions. The RNAP purified from Mycobacterium smegmatis from exponential phase showed low promoter specificity in promoter-polymerase interaction studies. This is due to the presence of a large number of sigma factors during exponential phase and under-representation of sigma(A) required for house-keeping transcription. We describe an in vivo reconstitution of RNAP holoenzyme with sigma(A) and its purification, which resulted in holoenzyme with stoichiometric sigma(A) content. The reconstituted holoenzyme showed enhanced promoter-specific binding and promoter-specific-transcription activity compared to the enzyme isolated using standard procedure. Such in vivo reconstitution of stoichiometric holoenzyme could facilitate promoter-specific transcription assays, especially in organisms which encode a large number of sigma factors.

Prediction of Candidate Primary Immunodeficiency Disease Genes Using a Support Vector Machine Learning Approach

Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein-protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes.

Theoretical Investigation on the Effect of Different Nitrogen Donors on Intramolecular Se center dot center dot center dot N Interactions

The effect of different donor nitrogen atoms on the strength and nature of intramolecular Se center dot center dot center dot N interactions is evaluated for organoselenium compounds having N,N-dimethylaminomethyl (dime), oxazoline (oxa) and pyridyl (py) substituents. Quantum chemical calculations on three series of compounds [2-(dime)C6H4SeX (1a-g), 2-(oxa)C6H4SeX (2a-g), 2- (py)C6H4SeX (3-ag); X=Cl, Br, OH, CN, SPh, SePh, CH3] at the B3LYP/6-31G(d) level show that the stability of different conformers depends on the strength of intramolecular nonbonded Se center dot center dot center dot N interactions. Natural bond orbital (NBO), NBO deletion and atoms in molecules (AIM) analyses suggest that the nature of the Se center dot center dot center dot N interaction is predominantly covalent and involves nN ->sigma*(Se-X) orbital interaction. In the three series of compounds, the strength of the Se center dot center dot center dot N interaction decreases in the order 3>2>1 for a particular X, and it decreases in the order Cl > Br > OH>SPh approximate to CN approximate to SePh>CH3 for all the three series 1-3. However, further analyses suggest that the differences in strength of Se center dot center dot center dot N interaction in 1-3 is predominantly determined by the distance between the Se and N atoms, which in turn is an outcome of specific structures of 1, 2 and 3, and the nature of the donor nitrogen atoms involved has very little effect on the strength of Se center dot center dot center dot N interaction. It is also observed that Se center dot center dot center dot N interaction becomes stronger in polar solvents such as CHCl3, as indicated by the shorter r(Se center dot center dot center dot N) and higher E-Se center dot center dot center dot N values in CHCl3 compared to those observed in the gas phase.

Complex solitons in Bose-Einstein condensates with two- and three-body interactions

For the first time, we find the complex solitons for a quasi-one-dimensional Bose-Einstein condensate with two-and three-body interactions. These localized solutions are characterized by a power law behaviour. Both dark and right solitons can be excited in the experimentally allowed parameter domain, when two-and three-body interactions are,respectively, repulsive and attractive. The dark solitons travel with a constant speed, which is quite different from the Lieb mode, where profiles with different speeds, bounded above by sound velocity, can exist for specified interaction strengths. We also study the properties of these solitons in the presence of harmonic confinement with time-dependent nonlinearity and loss. The modulational instability and the Vakhitov-Kolokolov criterion of stability are also studied.

MIPS: metal interactions in protein structures

MIPS (metal interactions in protein structures) is a database of metals in the three-dimensional acromolecular structures available in the Protein Data Bank. Bound metal ions in proteins have both catalytic and structural functions. The proposed database serves as an open resource for the analysis and visualization of all metals and their interactions with macromolecular (protein and nucleic acid) structures. MIPS can be searched via a user-friendly interface, and the interactions between metals and protein molecules, and the geometric parameters, can be viewed in both textual and graphical format using the freely available graphics plug-in Jmol. MIPS is updated regularly, by means of programmed scripts to find metal-containing proteins from newly released protein structures. The database is useful for studying the properties of coordination between metals and protein molecules. It also helps to improve understanding of the relationship between macromolecular structure and function. This database is intended to serve the scientific community working in the areas of chemical and structural biology, and is freely available to all users, around the clock, at http://dicsoft2.physics.iisc.ernet.in/mips/.

Inter-helical Interactions in Membrane Proteins: Analysis Based on the Local Backbone Geometry and the Side Chain Interactions

The availability of a significant number of the Structures of helical membrane proteins has prompted us to investigate the mode of helix-helix packing. In the present study, we have considered a dataset of alpha-helical membrane proteins representing Structures solved from all the known superfamilies. We have described the geometry of all the helical residues in terms of local coordinate axis at the backbone level. Significant inter-helical interactions have been considered as contacts by weighing the number of atom-atom contacts, including all the side-chain atoms. Such a definition of local axis and the contact criterion has allowed us to investigate the inter-helical interaction in a systematic and quantitative manner. We show that a single parameter (designated as alpha), which is derived from the parameters representing the Mutual orientation of local axes, is able to accurately Capture the details of helix-helix interaction. The analysis has been carried Out by dividing the dataset into parallel, anti-parallel, and perpendicular orientation of helices. The study indicates that a specific range of alpha value is preferred for interactions among the anti-parallel helices. Such a preference is also seen among interacting residues of parallel helices, however to a lesser extent. No such preference is seen in the case of perpendicular helices, the contacts that arise mainly due to the interaction Of Surface helices with the end of the trans-membrane helices. The Study Supports the prevailing view that the anti-parallel helices are well packed. However, the interactions between helices of parallel orientation are non-trivial. The packing in alpha-helical membrane proteins, which is systematically and rigorously investigated in this study, may prove to be useful in modeling of helical membrane proteins.

Investigation of inter-ion interactions in N,N,N',N'-tetramethylethylenediammonium dithiocyanate via experimental and theoretical charge density studies

The crystal structure of the N,N,N',N'-tetramethylethylenediammonium dithiocyanate salt has been examined by experimental charge density studies from high-resolution X-ray diffraction data. The corresponding results are compared with multipole refinements, using theoretical structure factors obtained from a periodic density functional theory calculation at the B3LYP level with a 6-31G** basis set. The salt crystallizes in space group P (1) over bar and contains only a single ion pair with an inversion center in the cation. The salt has thus one unique classical N+-H center dot center dot center dot(NCS)(-) hydrogen bond but also has six other weaker interactions: four C-H center dot center dot center dot S, one C-H center dot center dot center dot N, and one C-H center dot center dot center dot C-pi. The nature of all these interactions has been examined topologically using Bader's quantum theory of "atoms in molecules" and all eight of the Koch-Popelier criteria. The experimental and theoretical approaches agree well and both show that the inter-ion interactions, even in this simplest of systems, play an integrated and complex role in the packing of the ions in the crystal. Electrostatic potential maps are derived from experimental charge densities. This is the first time such a system has been examined in detail by these methods.

Electromechanical interactions in a carbon nanotube based thin film field emitting diode

Carbon nanotubes (CNTs) have emerged as promising candidates for biomedical x-ray devices and other applications of field emission. CNTs grown/deposited in a thin film are used as cathodes for field emission. In spite of the good performance of such cathodes, the procedure to estimate the device current is not straightforward and the required insight towards design optimization is not well developed. In this paper, we report an analysis aided by a computational model and experiments by which the process of evolution and self-assembly (reorientation) of CNTs is characterized and the device current is estimated. The modeling approach involves two steps: (i) a phenomenological description of the degradation and fragmentation of CNTs and (ii) a mechanics based modeling of electromechanical interaction among CNTs during field emission. A computational scheme is developed by which the states of CNTs are updated in a time incremental manner. Finally, the device current is obtained by using the Fowler–Nordheim equation for field emission and by integrating the current density over computational cells. A detailed analysis of the results reveals the deflected shapes of the CNTs in an ensemble and the extent to which the initial state of geometry and orientation angles affect the device current. Experimental results confirm these effects.

Do inventory management practices affect economic performance? An empirical evaluation of the machine tool SMEs in Bangalore

Inventory management (IM) has a decisive role in the enhancement of manufacturing industry's competitiveness. Therefore, major manufacturing industries are following IM practices with the intention of improving their performance. However, the effort to introduce IM in SMEs is very limited due to lack of initiation, expertise, and financial constraints. This paper aims to provide a guideline for entrepreneurs in enhancing their IM performance, as it presents the results of a survey based study carried out for machine tool Small and Medium Enterprises (SMEs) in Bangalore. Having established the significance of inventory as an input, we probed the relationship between IM performance and economic performance of these SMEs. To the extent possible all the factors of production and performance indicators were deliberately considered in pure economic terms. All economic performance indicators adopted seem to have a positive and significant association with IM performance in SMEs. On the whole, we found that SMEs which are IM efficient are likely to perform better on the economic front also and experience higher returns to scale.

OCR Prediction Using Support Vector Machine Based on Piezocone Data

The determination of the overconsolidation ratio (OCR) of clay deposits is an important task in geotechnical engineering practice. This paper examines the potential of a support vector machine (SVM) for predicting the OCR of clays from piezocone penetration test data. SVM is a statistical learning theory based on a structural risk minimization principle that minimizes both error and weight terms. The five input variables used for the SVM model for prediction of OCR are the corrected cone resistance (qt), vertical total stress (sigmav), hydrostatic pore pressure (u0), pore pressure at the cone tip (u1), and the pore pressure just above the cone base (u2). Sensitivity analysis has been performed to investigate the relative importance of each of the input parameters. From the sensitivity analysis, it is clear that qt=primary in situ data influenced by OCR followed by sigmav, u0, u2, and u1. Comparison between SVM and some of the traditional interpretation methods is also presented. The results of this study have shown that the SVM approach has the potential to be a practical tool for determination of OCR.

How Effective Is The Data On Co-Occurrence Of Domains In Multi-Domain Proteins In Prediction Of Protein-Protein Interactions?

We explore the fuse of information on co-occurrence of domains in multi-domain proteins in predicting protein-protein interactions. The basic premise of our work is the assumption that domains co-occurring in a polypeptide chain undergo either structural or functional interactions among themselves. In this study we use a template dataset of domains in multidomain proteins and predict protein-protein interactions in a target organism. We note that maximum number of correct predictions of interacting protein domain families (158) is made in S. cerevisiae when the dataset of closely related organisms is used as the template followed by the more diverse dataset of bacterial proteins (48) and a dataset of randomly chosen proteins (23). We conclude that use of multi-domain information from organisms closely-related to the target can aid prediction of interacting protein families.