From anomalies in neat liquid to structure, dynamics and function in the biological world

Water brings its remarkable thermodynamic and dynamic anomalies in the pure liquid state to biological world where water molecules face a multitude of additional interactions that frustrate its hydrogen bond network. Yet the water molecules participate and control enormous number of biological processes in manners which are yet to be understood at a molecular level. We discuss thermodynamics, structure, dynamics and properties of water around proteins and DNA, along with those in reverse micelles. We discuss the roles of water in enzyme kinetics, in drug-DNA intercalation and in kinetic-proof reading ( the theory of lack of errors in biosynthesis). We also discuss how water may play an important role in the natural selection of biomolecules. (C) 2011 Elsevier B. V. All rights reserved.

Construction of equivalent circuit of a transformer winding from driving-point impedance function - analytical approach

Analytical solution is presented to convert a given driving-point impedance function (in s-domain) into a physically realisable ladder network with inductive coupling between any two sections and losses considered. The number of sections in the ladder network can vary, but its topology is assumed fixed. A study of the coefficients of the numerator and denominator polynomials of the driving-point impedance function of the ladder network, for increasing number of sections, led to the identification of certain coefficients, which exhibit very special properties. Generalised expressions for these specific coefficients have also been derived. Exploiting their properties, it is demonstrated that the synthesis method essentially turns out to be an exercise of solving a set of linear, simultaneous, algebraic equations, whose solution directly yields the ladder network elements. The proposed solution is novel, simple and guarantees a unique network. Presently, the formulation can synthesise a unique ladder network up to six sections.

A robust three phase power flow algorithm for radial distribution systems

An efficient load flow solution technique is required as a part of the distribution automation (DA) system for taking various control and operations decisions. This paper presents an efficient and robust three phase power flow algorithm for application to radial distribution networks. This method exploits the radial nature of the network and uses forward and backward propagation to calculate branch currents and node voltages. The proposed method has been tested to analyse several practical distribution networks of various voltage levels and also having high R/X ratio. The results for a practical distribution feeder are presented for illustration purposes. The application of the proposed method is also extended to find optimum location for reactive power compensation and network reconfiguration for planning and day-to-day operation of distribution networks.

Application of UPFC for system security improvement under normal and network contingencies

Electric power systems are exposed to various contingencies. Network contingencies often contribute to over-loading of network branches, unsatisfactory voltages and also leading to problems of stability/voltage collapse. To maintain security of the systems, it is desirable to estimate the effect of contingencies and plan suitable measures to improve system security/stability. This paper presents an approach for selection of unified power flow controller (UPFC) suitable locations considering normal and network contingencies after evaluating the degree of severity of the contingencies. The ranking is evaluated using composite criteria based fuzzy logic for eliminating masking effect. The fuzzy approach, in addition to real power loadings and bus voltage violations, voltage stability indices at the load buses also used as the post-contingent quantities to evaluate the network contingency ranking. The selection of UPFC suitable locations uses the criteria on the basis of improved system security/stability. The proposed approach for selection of UPFC suitable locations has been tested under simulated conditions on a few power systems and the results for a 24-node real-life equivalent EHV power network and 39-node New England (modified) test system are presented for illustration purposes.

An Online Actor-Critic Algorithm with Function Approximation for Constrained Markov Decision Processes

We develop an online actor-critic reinforcement learning algorithm with function approximation for a problem of control under inequality constraints. We consider the long-run average cost Markov decision process (MDP) framework in which both the objective and the constraint functions are suitable policy-dependent long-run averages of certain sample path functions. The Lagrange multiplier method is used to handle the inequality constraints. We prove the asymptotic almost sure convergence of our algorithm to a locally optimal solution. We also provide the results of numerical experiments on a problem of routing in a multi-stage queueing network with constraints on long-run average queue lengths. We observe that our algorithm exhibits good performance on this setting and converges to a feasible point.

Network properties of protein-decoy structures

Convergence of the vast sequence space of proteins into a highly restricted fold/conformational space suggests a simple yet unique underlying mechanism of protein folding that has been the subject of much debate in the last several decades. One of the major challenges related to the understanding of protein folding or in silico protein structure prediction is the discrimination of non-native structures/decoys from the native structure. Applications of knowledge-based potentials to attain this goal have been extensively reported in the literature. Also, scoring functions based on accessible surface area and amino acid neighbourhood considerations were used in discriminating the decoys from native structures. In this article, we have explored the potential of protein structure network (PSN) parameters to validate the native proteins against a large number of decoy structures generated by diverse methods. We are guided by two principles: (a) the PSNs capture the local properties from a global perspective and (b) inclusion of non-covalent interactions, at all-atom level, including the side-chain atoms, in the network construction accommodates the sequence dependent features. Several network parameters such as the size of the largest cluster, community size, clustering coefficient are evaluated and scored on the basis of the rank of the native structures and the Z-scores. The network analysis of decoy structures highlights the importance of the global properties contributing to the uniqueness of native structures. The analysis also exhibits that the network parameters can be used as metrics to identify the native structures and filter out non-native structures/decoys in a large number of data-sets; thus also has a potential to be used in the protein `structure prediction' problem.

Thermal diffusivity measurements on ternary Ge-Te-Tl glasses using Photo-thermal Deflection method: Effect of thallium addition

Photo-thermal Deflection (PTD) technique is used to investigate the thermal diffusivity (alpha) of Ge17Te83 - xTlx (0 <= x <= 13) glasses as a function of composition. The thermal diffusivity of these glasses is found to lie in the range 0.020 to 0.048 cm(2)/s, which is consistent with the memory type of electrical switching exhibited by these samples. Further, it is found that alpha shows an initial increase with Tl addition, followed by a decrease. The observed composition dependence of thermal diffusivity has been understood on the basis that the thallium atoms are incorporated as a covalent species for lower values of x, increasing the network rigidity; however, they enter as ionic species for higher x values, fragmenting the network. The initial increase in a is due to the increasing network rigidity and the subsequent decrease is because of the fragmentation of the network. Also, there is a strong correlation between the composition dependence of switching voltages observed earlier and the variation with composition of electrical resistivity and thermal diffusivity of Ge17Te83 - xTlx glasses obtained in the present study. (C) 2012 Elsevier B.V. All rights reserved.

Thermal degradation kinetics of poly(trimethylol propane triacrylate)/poly(hexane diol diacrylate) interpenetrating polymer network

Interpenetrating polymer networks (IPNs) of trimethylol propane triacrylate (TMPTA) and 1,6-hexane diol diacrylate (HDDA) at different weight ratios were synthesized. Temperature modulated differential scanning calorimetry (TMDSC) was used to determine whether the formation resulted in a copolymer or interpenetrating polymer network (IPN). These polymers are used as binders for microstereolithography (MSL) based ceramic microfabrication. The kinetics of thermal degradation of these polymers are important to optimize the debinding process for fabricating 3D shaped ceramic objects by MSL based rapid prototyping technique. Therefore, thermal and thermo-oxidative degradation of these IPNs have been studied by dynamic and isothermal thermogravimetry (TGA). Non-isothermal model-free kinetic methods have been adopted (isoconversional differential and KAS) to calculate the apparent activation energy (E a) as a function of conversion (α) in N 2 and air. The degradation of these polymers in N 2 atmosphere occurs via two mechanisms. Chain end scission plays a dominant role at lower temperature while the kinetics is governed by random chain scission at higher temperature. Oxidative degradation shows multiple degradation steps having higher activation energy than in N 2. Isothermal degradation was also carried out to predict the reaction model which is found to be decelerating. It was shown that the degradation of PTMPTA follows a contracting sphere reaction model in N 2. However, as the HDDA content increases in the IPNs, the degradation reaction follows Avrami-Erofeev model and diffusion governed mechanisms. The intermediate IPN compositions show both type of mechanism. Based on the above study, debinding strategy for MSL based microfabricated ceramic structure has been proposed. © 2012 Elsevier B.V.

Network approaches to drug discovery

Introduction: Advances in genomics technologies are providing a very large amount of data on genome-wide gene expression profiles, protein molecules and their interactions with other macromolecules and metabolites. Molecular interaction networks provide a useful way to capture this complex data and comprehend it. Networks are beginning to be used in drug discovery, in many steps of the modern discovery pipeline, with large-scale molecular networks being particularly useful for the understanding of the molecular basis of the disease. Areas covered: The authors discuss network approaches used for drug target discovery and lead identification in the drug discovery pipeline. By reconstructing networks of targets, drugs and drug candidates as well as gene expression profiles under normal and disease conditions, the paper illustrates how it is possible to find relationships between different diseases, find biomarkers, explore drug repurposing and study emergence of drug resistance. Furthermore, the authors also look at networks which address particular important aspects such as off-target effects, combination-targets, mechanism of drug action and drug safety. Expert opinion: The network approach represents another paradigm shift in drug discovery science. A network approach provides a fresh perspective of understanding important proteins in the context of their cellular environments, providing a rational basis for deriving useful strategies in drug design. Besides drug target identification and inferring mechanism of action, networks enable us to address new ideas that could prove to be extremely useful for new drug discovery, such as drug repositioning, drug synergy, polypharmacology and personalized medicine.

Network congestion management by fuzzy inference using virtual flows

Effective network overload alleviation is very much essential in order to maintain security and integrity from the operational viewpoint of deregulated power systems. This paper aims at developing a methodology to reschedule the active power generation from the sources in order to manage the network congestion under normal/contingency conditions. An effective method has been proposed using fuzzy rule based inference system. Using virtual flows concept, which provides partial contributions/counter flows in the network elements is used as a basis in the proposed method to manage network congestions to the possible extent. The proposed method is illustrated on a sample 6 bus test system and on modified IEEE 39 bus system.

A novel Q-learning algorithm with function approximation for constrained Markov decision processes

We present a novel multi-timescale Q-learning algorithm for average cost control in a Markov decision process subject to multiple inequality constraints. We formulate a relaxed version of this problem through the Lagrange multiplier method. Our algorithm is different from Q-learning in that it updates two parameters - a Q-value parameter and a policy parameter. The Q-value parameter is updated on a slower time scale as compared to the policy parameter. Whereas Q-learning with function approximation can diverge in some cases, our algorithm is seen to be convergent as a result of the aforementioned timescale separation. We show the results of experiments on a problem of constrained routing in a multistage queueing network. Our algorithm is seen to exhibit good performance and the various inequality constraints are seen to be satisfied upon convergence of the algorithm.

Relation Between the Diffusivity, Viscosity, and Ionic Radius of LiCl in Water, Methanol, and Ethylene Glycol: A Molecular Dynamics Simulation

A molecular dynamics (MD) investigation of LiCl in water, methanol, and ethylene glycol (EG) at 298 K is reported. Several; structural and dynamical properties of the ions as well as the solvent such as self-diffusivity, radial distribution functions, void and neck distributions, velocity autocorrelation functions, and mean residence times of solvent in the first solvation shell have been computed. The results show that the reciprocal relationship between the self-diffusivity of the ions and the viscosity is valid in almost all solvents with the exception of water. From an analysis of radial distribution functions and coordination numbers the nature of hydrogen bonding within the solvent and its influence on the void and neck distribution becomes evident. It is seen that the solvent solvent interaction is important in EG while solute solvent interactions dominate in water and methanol. From Voronoi tessellation, it is seen that the voids and necks within methanol are larger as compared to those within water or EG. On the basis of the void and neck distributions obtained from MD simulations and literature experimental data of limiting ion conductivity for various ions of different sizes we show that there is a relation between the void and neck radius on e one hand and dependence of conductivity on the ionic radius on the other. It is shown that the presence of large diameter voids and necks in methanol is responsible for maximum in limiting ion conductivity (lambda(0)) of TMA(+), while in water in EG, the maximum is seen for Rb+. In the case of monovalent anions, maximum in lambda(0) as a function ionic radius is seen for Br- in water EG but for the larger ClO4- ion in methanol. The relation between the void and neck distribution and the variation in lambda(0) with ionic radius arises via the Levitation effect which is discussed. These studies show the importance of the solvent structure and the associated void structure.

Physical Layer Network Coding for the K-User Multiple Access Relay Channel

We propose a Physical layer Network Coding (PNC) scheme for the K-user wireless Multiple Access Relay Channel, in which K source nodes want to transmit messages to a destination node D with the help of a relay node R. The proposed scheme involves (i) Phase 1 during which the source nodes alone transmit and (ii) Phase 2 during which the source nodes and the relay node transmit. At the end of Phase 1, the relay node decodes the messages of the source nodes and during Phase 2 transmits a many-to-one function of the decoded messages. To counter the error propagation from the relay node, we propose a novel decoder which takes into account the possibility of error events at R. It is shown that if certain parameters are chosen properly and if the network coding map used at R forms a Latin Hypercube, the proposed decoder offers the maximum diversity order of two. Also, it is shown that for a proper choice of the parameters, the proposed decoder admits fast decoding, with the same decoding complexity order as that of the reference scheme based on Complex Field Network Coding (CFNC). Simulation results indicate that the proposed PNC scheme offers a large gain over the CFNC scheme.

Synconset Waves and Chains: Spiking Onsets in Synchronous Populations Predict and Are Predicted by Network Structure

Synfire waves are propagating spike packets in synfire chains, which are feedforward chains embedded in random networks. Although synfire waves have proved to be effective quantification for network activity with clear relations to network structure, their utilities are largely limited to feedforward networks with low background activity. To overcome these shortcomings, we describe a novel generalisation of synfire waves, and define `synconset wave' as a cascade of first spikes within a synchronisation event. Synconset waves would occur in `synconset chains', which are feedforward chains embedded in possibly heavily recurrent networks with heavy background activity. We probed the utility of synconset waves using simulation of single compartment neuron network models with biophysically realistic conductances, and demonstrated that the spread of synconset waves directly follows from the network connectivity matrix and is modulated by top-down inputs and the resultant oscillations. Such synconset profiles lend intuitive insights into network organisation in terms of connection probabilities between various network regions rather than an adjacency matrix. To test this intuition, we develop a Bayesian likelihood function that quantifies the probability that an observed synfire wave was caused by a given network. Further, we demonstrate it's utility in the inverse problem of identifying the network that caused a given synfire wave. This method was effective even in highly subsampled networks where only a small subset of neurons were accessible, thus showing it's utility in experimental estimation of connectomes in real neuronal-networks. Together, we propose synconset chains/waves as an effective framework for understanding the impact of network structure on function, and as a step towards developing physiology-driven network identification methods. Finally, as synconset chains extend the utilities of synfire chains to arbitrary networks, we suggest utilities of our framework to several aspects of network physiology including cell assemblies, population codes, and oscillatory synchrony.

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.