Stabilisation of unusual simultaneous binding of four cytosine nucleobases to copper(II) by a novel network of bifurcated hydrogen bonding

The crystal structure of tetrakis(cytosine)copper(II) perchlorate dihydrate has been determined. All the hydrogen atoms were obtained from Fourier-difference synthesis. The geometry around. copper is a bicapped octahedron (4 + 2 + 2*). The adjacent cytosine rings are oriented head-to-tail with respect to each other and are roughly at right angles to the co-ordination plane. The exocyclic oxo groups form an interligand, intracomplex hydrogen-bonding network above and below the co-ordination plane with the exocyclic amino groups of alternate cytosine bases. The EPR and electronic spectra are consistent with the retention of the solid-state structure in solution. The steric effect of the C(2)=O group of cytosine is offset by the presence of the intracomplex hydrogen-bonding network. The trend in Ei values of Cu-II-Cu-I couples for 1.4 complexes of cytosine, cytodine, pyridine, 2-methylpyridine and N-methylimidazole suggests that both steric effects and pi-delocalization in imidazole and pyridine ligands and the steric effect of C(2)=O in pyrimidine ligands are important in stabilising Cu-I relative to Cu-II.

Optimization of Helicopter Rotor Using Polynomial and Neural Network Metamodels

This study aims to determine optimal locations of dual trailing-edge flaps and blade stiffness to achieve minimum hub vibration levels in a helicopter, with low penalty in terms of required trailing-edge flap control power. An aeroelastic analysis based on finite elements in space and time is used in conjunction with an optimal control algorithm to determine the flap time history for vibration minimization. Using the aeroelastic analysis, it is found that the objective functions are highly nonlinear and polynomial response surface approximations cannot describe the objectives adequately. A neural network is then used for approximating the objective functions for optimization. Pareto-optimal points minimizing both helicopter vibration and flap power ale obtained using the response surface and neural network metamodels. The two metamodels give useful improved designs resulting in about 27% reduction in hub vibration and about 45% reduction in flap power. However, the design obtained using response surface is less sensitive to small perturbations in the design variables.

Network approach for capturing ligand-induced subtle global changes in protein structures

Ligand-induced conformational changes in proteins are of immense functional relevance. It is a major challenge to elucidate the network of amino acids that are responsible for the percolation of ligand-induced conformational changes to distal regions in the protein from a global perspective. Functionally important subtle conformational changes (at the level of side-chain noncovalent interactions) upon ligand binding or as a result of environmental variations are also elusive in conventional studies such as those using root-mean-square deviations (r.m.s.d.s). In this article, the network representation of protein structures and their analyses provides an efficient tool to capture these variations (both drastic and subtle) in atomistic detail in a global milieu. A generalized graph theoretical metric, using network parameters such as cliques and/or communities, is used to determine similarities or differences between structures in a rigorous manner. The ligand-induced global rewiring in the protein structures is also quantified in terms of network parameters. Thus, a judicious use of graph theory in the context of protein structures can provide meaningful insights into global structural reorganizations upon perturbation and can also be helpful for rigorous structural comparison. Data sets for the present study include high-resolution crystal structures of serine proteases from the S1A family and are probed to quantify the ligand-induced subtle structural variations.

Artificial intelligence approaches to network management: recent advances and a survey

With increased number of new services and users being added to the communication network, management of such networks becomes crucial to provide assured quality of service. Finding skilled managers is often a problem. To alleviate this problem and also to provide assistance to the available network managers, network management has to be automated. Many attempts have been made in this direction and it is a promising area of interest to researchers in both academia and industry. In this paper, a review of the management complexities in present day networks and artificial intelligence approaches to network management are presented. Published by Elsevier Science B.V.

Kinetics of copolyurethane network formation

Cure kinetics for the formation of copolyurethane networks of various compositions based on hydroxy-terminated polybutadiene(HTPB), poly(12-hydroxy stearic acid-co-TMP) ester polyol(PEP), and different isocyanates has been studied through viscosity build up during the cure reaction. The viscosity (N)-time (t) plots conform to the equation N = ae(bt), where a and b are empirical constants, dependent on the composition and the nature of the polyols and the isocyanates. The rate constants (b) for viscosity build up, evaluated from the slopes of dN/dt versus N plots at different temperatures, were found to vary significantly from 0.0073 to 0.25 min(-1); and the activation energies for gelation were found to be in the range 20 to 40 kJ mol(-1). The results have been interpreted in terms of the dependence of the rate constants on structural characteristics of the prepolymers. (C) 1997 John Wiley & Sons, Inc.

Quantum clustering and network analysis of MD simulation trajectories to probe the conformational ensembles of protein-ligand interactions

In this article, we present a novel application of a quantum clustering (QC) technique to objectively cluster the conformations, sampled by molecular dynamics simulations performed on different ligand bound structures of the protein. We further portray each conformational population in terms of dynamically stable network parameters which beautifully capture the ligand induced variations in the ensemble in atomistic detail. The conformational populations thus identified by the QC method and verified by network parameters are evaluated for different ligand bound states of the protein pyrrolysyl-tRNA synthetase (DhPylRS) from D. hafniense. The ligand/environment induced re-distribution of protein conformational ensembles forms the basis for understanding several important biological phenomena such as allostery and enzyme catalysis. The atomistic level characterization of each population in the conformational ensemble in terms of the re-orchestrated networks of amino acids is a challenging problem, especially when the changes are minimal at the backbone level. Here we demonstrate that the QC method is sensitive to such subtle changes and is able to cluster MD snapshots which are similar at the side-chain interaction level. Although we have applied these methods on simulation trajectories of a modest time scale (20 ns each), we emphasize that our methodology provides a general approach towards an objective clustering of large-scale MD simulation data and may be applied to probe multistate equilibria at higher time scales, and to problems related to protein folding for any protein or protein-protein/RNA/DNA complex of interest with a known structure.

Transitional intermittency detection by neural network

A neural network has been used to predict the flow intermittency from velocity signals in the transition zone in a boundary layer. Unlike many of the available intermittency detection methods requiring a proper threshold choice in order to distinguish between the turbulent and non-turbulent parts of a signal, a trained neural network does not involve any threshold decision. The intermittency prediction based on the neural network has been found to be very satisfactory.

Network Error Correction for Unit-Delay, Memory-Free Networks Using Convolutional Codes

A single source network is said to be memory-free if all of the internal nodes (those except the source and the sinks) do not employ memory but merely send linear combinations of the symbols received at their incoming edges on their outgoing edges. In this work, we introduce network-error correction for single source, acyclic, unit-delay, memory-free networks with coherent network coding for multicast. A convolutional code is designed at the source based on the network code in order to correct network- errors that correspond to any of a given set of error patterns, as long as consecutive errors are separated by a certain interval which depends on the convolutional code selected. Bounds on this interval and the field size required for constructing the convolutional code with the required free distance are also obtained. We illustrate the performance of convolutional network error correcting codes (CNECCs) designed for the unit-delay networks using simulations of CNECCs on an example network under a probabilistic error model.

Single-Generation Network Coding for Networks with Delay

A single-source network is said to be memory-free if all of the internal nodes (those except the source and the sinks) do not employ memory but merely send linear combinations of the incoming symbols (received at their incoming edges) on their outgoing edges. Memory-free networks with delay using network coding are forced to do inter-generation network coding, as a result of which the problem of some or all sinks requiring a large amount of memory for decoding is faced. In this work, we address this problem by utilizing memory elements at the internal nodes of the network also, which results in the reduction of the number of memory elements used at the sinks. We give an algorithm which employs memory at all the nodes of the network to achieve single- generation network coding. For fixed latency, our algorithm reduces the total number of memory elements used in the network to achieve single- generation network coding. We also discuss the advantages of employing single-generation network coding together with convolutional network-error correction codes (CNECCs) for networks with unit- delay and illustrate the performance gain of CNECCs by using memory at the intermediate nodes using simulations on an example network under a probabilistic network error model.

Bandwidth management in a hybrid wireless network: For superstore applications

The integration of different wireless networks, such as GSM and WiFi, as a two-tier hybrid wireless network is more popular and economical. Efficient bandwidth management, call admission control strategies and mobility management are important issues in supporting multiple types of services with different bandwidth requirements in hybrid networks. In particular, bandwidth is a critical commodity because of the type of transactions supported by these hybrid networks, which may have varying bandwidth and time requirements. In this paper, we consider such a problem in a hybrid wireless network installed in a superstore environment and design a bandwidth management algorithm based on the priority level, classification of the incoming transactions. Our scheme uses a downlink transaction scheduling algorithm, which decides how to schedule the outgoing transactions based on their priority level with efficient use of available bandwidth. The transaction scheduling algorithm is used to maximize the number of transaction-executions. The proposed scheme is simulated in a superstore environment with multi Rooms. The performance results describe that the proposed scheme can considerably improve the bandwidth utilization by reducing transaction blocking and accommodating more essential transactions at the peak time of the business.

Discovery of Application Workloads from Network File Traces

An understanding of application I/O access patterns is useful in several situations. First, gaining insight into what applications are doing with their data at a semantic level helps in designing efficient storage systems. Second, it helps create benchmarks that mimic realistic application behavior closely. Third, it enables autonomic systems as the information obtained can be used to adapt the system in a closed loop.All these use cases require the ability to extract the application-level semantics of I/O operations. Methods such as modifying application code to associate I/O operations with semantic tags are intrusive. It is well known that network file system traces are an important source of information that can be obtained non-intrusively and analyzed either online or offline. These traces are a sequence of primitive file system operations and their parameters. Simple counting, statistical analysis or deterministic search techniques are inadequate for discovering application-level semantics in the general case, because of the inherent variation and noise in realistic traces.In this paper, we describe a trace analysis methodology based on Profile Hidden Markov Models. We show that the methodology has powerful discriminatory capabilities that enable it to recognize applications based on the patterns in the traces, and to mark out regions in a long trace that encapsulate sets of primitive operations that represent higher-level application actions. It is robust enough that it can work around discrepancies between training and target traces such as in length and interleaving with other operations. We demonstrate the feasibility of recognizing patterns based on a small sampling of the trace, enabling faster trace analysis. Preliminary experiments show that the method is capable of learning accurate profile models on live traces in an online setting. We present a detailed evaluation of this methodology in a UNIX environment using NFS traces of selected commonly used applications such as compilations as well as on industrial strength benchmarks such as TPC-C and Postmark, and discuss its capabilities and limitations in the context of the use cases mentioned above.

Dehydrophenylalanine zippers: strong helix-helix clamping through a network of weak interactions

A decapeptide Boc-L-Ala-(DeltaPhe)(4)-L-Ala-(DeltaPhe)(3)-Gly-OMe (Peptide I) was synthesized to study the preferred screw sense of consecutive alpha,beta-dehydrophenylalanine (DeltaPhe) residues. Crystallographic and CD studies suggest that, despite the presence of two L-Ala residues in the sequence, the decapeptide does not have a preferred screw sense. The peptide crystallizes with two conformers per asymmetric unit, one of them a slightly distorted right-handed 3(10)-helix (X) and the other a left-handed 3(10)-helix (Y) with X and Y being antiparallel to each other. An unanticipated and interesting observation is that in the solid state, the two shape-complement molecules self-assemble and interact with an extensive network of C-H...O hydrogen bonds and pi-pi interactions, directed laterally to the helix axis with amazing regularity. Here, we present an atomic resolution picture of the weak interaction mediated mutual recognition of two secondary structural elements and its possible implication in understanding the specific folding of the hydrophobic core of globular proteins and exploitation in future work on de novo design.

Glassy behavior in neural network models of associative memory

Neural network models of associative memory exhibit a large number of spurious attractors of the network dynamics which are not correlated with any memory state. These spurious attractors, analogous to "glassy" local minima of the energy or free energy of a system of particles, degrade the performance of the network by trapping trajectories starting from states that are not close to one of the memory states. Different methods for reducing the adverse effects of spurious attractors are examined with emphasis on the role of synaptic asymmetry. (C) 2002 Elsevier Science B.V. All rights reserved.

Improving web server performance by network aware data buffering and caching

In this paper we propose a new method of data handling for web servers. We call this method Network Aware Buffering and Caching (NABC for short). NABC facilitates reduction of data copies in web server's data sending path, by doing three things: (1) Layout the data in main memory in a way that protocol processing can be done without data copies (2) Keep a unified cache of data in kernel and ensure safe access to it by various processes and kernel and (3) Pass only the necessary meta data between processes so that bulk data handling time spent during IPC can be reduced. We realize NABC by implementing a set of system calls and an user library. The end product of the implementation is a set of APIs specifically designed for use by the web servers. We port an in house web server called SWEET, to NABC APIs and evaluate performance using a range of workloads both simulated and real. The results show a very impressive gain of 12% to 21% in throughput for static file serving and 1.6 to 4 times gain in throughput for lightweight dynamic content serving for a server using NABC APIs over the one using UNIX APIs.

Neural network based optimal routing algorithm for communication networks

This paper presents the capability of the neural networks as a computational tool for solving constrained optimization problem, arising in routing algorithms for the present day communication networks. The application of neural networks in the optimum routing problem, in case of packet switched computer networks, where the goal is to minimize the average delays in the communication have been addressed. The effectiveness of neural network is shown by the results of simulation of a neural design to solve the shortest path problem. Simulation model of neural network is shown to be utilized in an optimum routing algorithm known as flow deviation algorithm. It is also shown that the model will enable the routing algorithm to be implemented in real time and also to be adaptive to changes in link costs and network topology. (C) 2002 Elsevier Science Ltd. All rights reserved.