Mycobacterium tuberculosis UvrD1 and UvrA Proteins Suppress DNA Strand Exchange Promoted by Cognate and Noncognate RecA Proteins

DNA helicases are present in all kingdoms of life and play crucial roles in processes of DNA metabolism such as replication, repair, recombination, and transcription. To date, however, the role of DNA helicases during homologous recombination in mycobacteria remains unknown. In this study, we show that Mycobacterium tuberculosis UvrD1 more efficiently inhibited the strand exchange promoted by its cognate RecA, compared to noncognate Mycobacterium smegmatis or Escherichia coli RecA proteins. The M. tuberculosis UvrD1(Q276R) mutant lacking the helicase and ATPase activities was able to block strand exchange promoted by mycobacterial RecA proteins but not of E. coil RecA. We observed that M. tuberculosis UvrA by itself has no discernible effect on strand exchange promoted by E. coli RecA but impedes the reaction catalyzed by the mycobacterial RecA proteins. Our data also show that M. tuberculosis UvrA and UvrD1 can act together to inhibit strand exchange promoted by mycobacterial RecA proteins. Taken together, these findings raise the possibility that UvrD1 and UvrA might act together in vivo to counter the deleterious effects of RecA nucleoprotein filaments and/or facilitate the dissolution of recombination intermediates. Finally, we provide direct experimental evidence for a physical interaction between M. tuberculosis UvrD1 and RecA on one hand and RecA and UvrA on the other hand. These observations are consistent with a molecular mechanism, whereby M. tuberculosis UvrA and UvrD1, acting together, block DNA strand exchange promoted by cognate and noncognate RecA proteins.

Lessons from the Sparse Sensor Network Deployment in Rural India

We discuss the key issues in the deployment of sparse sensor networks. The network monitors several environment parameters and is deployed in a semi-arid region for the benefit of small and marginal farmers. We begin by discussing the problems of an existing unreliable 1 sq km sparse network deployed in a village. The proposed solutions are implemented in a new cluster. The new cluster is a reliable 5 sq km network. Our contributions are two fold. Firstly, we describe a. novel methodology to deploy a sparse reliable data gathering sensor network and evaluate the ``safe distance'' or ``reliable'' distance between nodes using propagation models. Secondly, we address the problem of transporting data from rural aggregation servers to urban data centres. This paper tracks our steps in deploying a sensor network in a village,in India, trying to provide better diagnosis for better crop management. Keywords - Rural, Agriculture, CTRS, Sparse.

Optimal Blood Glucose Regulation using Single Network Adaptive Critics

Diabetes is a serious disease during which the body's production and use of insulin is impaired, causing glucose concentration level toincrease in the bloodstream. Regulating blood glucose levels as close to normal as possible, leads to a substantial decrease in long term complications of diabetes. In this paper, an intelligent neural network on-line optimal feedback treatment strategy based on nonlinear optimal control theory is presented for the disease using subcutaneous treatment strategy. A simple mathematical model of the nonlinear dynamics of glucose and insulin interaction in the blood system is considered based on the Bergman's minimal model. A glucose infusion term representing the effect of glucose intake resulting from a meal is introduced into the model equations. The efficiency of the proposed controllers is shown taking random parameters and random initial conditions in presence of physical disturbances like food intake. A comparison study with linear quadratic regulator theory brings Out the advantages of the nonlinear control synthesis approach. Simulation results show that unlike linear optimal control, the proposed on-line continuous infusion strategy never leads to severe hypoglycemia problems.

Asynchronous behaviour in some three-atom linear concerted radical-exchange reactions

A few simple three-atom thermoneutral radical exchange reactions (i.e. A + BC --> AB + C) are examined by ab initio SCF methods. Emphasis is laid on the detailed analysis of density matrices rather than on energetics. Results reveal that the sum of the bond orders of the breaking and forming bonds is not conserved to unity, due to development of free valence on the migrating atom 'B' in the transition state. Bond orders, free valence and spin densities on the atoms are calculated. The present analysis shows that the bond-cleavage process is always more advanced than the bond-formation process in the transition state. Further analysis shows a development of the negative spin density on the migrating atom 'B' in the transition state. The depletion of the alpha-spin density on the radical site "A" in the reactant during the reaction lags behind the growth of the alpha-spin density on the terminal atom "C" of the reactant bond, 'B-C' in the transition state. But all these processes are completed simultaneously at the end of the reaction. Hence, the reactions are asynchronous but kinetically concerted in most cases.

Effect of network connectivity on thermal properties of As30Te70-xTlx glasses

Alternating differential scanning calorimetry (ADSC) studies were undertaken to investigate the effect of Tl addition on the thermal properties of As30Te70-xTlx ( 6 <= x <= 22 at%) glasses. These include parameters such as glass-transition temperature (T-g), changes in specific heat capacity (Delta C-p) and relaxation enthalpy (Delta H-NR) at the glass transition. It was found that T-g of the glasses decreased with the addition of Tl, which is in contrast to the dependence of T-g in As - Te glasses on the addition of Al and In. The change in heat capacity Delta C-p through the glass transition was also found to decrease with increasing Tl content. The addition of Tl to the As - Te matrix may lead to a breaking of As - Te chains and the formation of Tl+Te- AsTe2/2 dipoles. There was no significant dependence of the change of relaxation enthalpy, through the glass transition, with composition.

Multiple contour extraction from graylevel images using an artificial neural network

For active contour modeling (ACM), we propose a novel self-organizing map (SOM)-based approach, called the batch-SOM (BSOM), that attempts to integrate the advantages of SOM- and snake-based ACMs in order to extract the desired contours from images. We employ feature points, in the form of ail edge-map (as obtained from a standard edge-detection operation), to guide the contour (as in the case of SOM-based ACMs) along with the gradient and intensity variations in a local region to ensure that the contour does not "leak" into the object boundary in case of faulty feature points (weak or broken edges). In contrast with the snake-based ACMs, however, we do not use an explicit energy functional (based on gradient or intensity) for controlling the contour movement. We extend the BSOM to handle extraction of contours of multiple objects, by splitting a single contour into as many subcontours as the objects in the image. The BSOM and its extended version are tested on synthetic binary and gray-level images with both single and multiple objects. We also demonstrate the efficacy of the BSOM on images of objects having both convex and nonconvex boundaries. The results demonstrate the superiority of the BSOM over others. Finally, we analyze the limitations of the BSOM.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.

Convolutional Codes for Network-Error Correction

In this work, we introduce convolutional codes for network-error correction in the context of coherent network coding. We give a construction of convolutional codes that correct a given set of error patterns, as long as consecutive errors are separated by a certain interval. We also give some bounds on the field size and the number of errors that can get corrected in a certain interval. Compared to previous network error correction schemes, using convolutional codes is seen to have advantages in field size and decoding technique. Some examples are discussed which illustrate the several possible situations that arise in this context.

Power System Stability Enhancement by Single Network Adaptive Critic Stabilizers

This paper proposes a Single Network Adaptive Critic (SNAC) based Power System Stabilizer (PSS) for enhancing the small-signal stability of power systems over a wide range of operating conditions. SNAC uses only a single critic neural network instead of the action-critic dual network architecture of typical adaptive critic designs. SNAC eliminates the iterative training loops between the action and critic networks and greatly simplifies the training procedure. The performance of the proposed PSS has been tested on a Single Machine Infinite Bus test system for various system and loading conditions. The proposed stabilizer, which is relatively easier to synthesize, consistently outperformed stabilizers based on conventional lead-lag and linear quadratic regulator designs.

Glioblastoma-Specific Protein Interaction Network Identifies PP1A and CSK21 as Connecting Molecules between Cell Cycle-Associated Genes

Glioblastoma (GBM; grade IV astrocytoma) is a very aggressive form of brain cancer with a poor survival and few qualified predictive markers. This study integrates experimentally validated genes that showed specific upregulation in GBM along with their protein-protein interaction information. A system level analysis was used to construct GBM-specific network. Computation of topological parameters of networks showed scale-free pattern and hierarchical organization. From the large network involving 1,447 proteins, we synthesized subnetworks and annotated them with highly enriched biological processes. A careful dissection of the functional modules, important nodes, and their connections identified two novel intermediary molecules CSK21 and protein phosphatase 1 alpha (PP1A) connecting the two subnetworks CDC2-PTEN-TOP2A-CAV1-P53 and CDC2-CAV1-RB-P53-PTEN, respectively. Real-time quantitative reverse transcription-PCR analysis revealed CSK21 to be moderately upregulated and PP1A to be overexpressed by 20-fold in GBM tumor samples. Immunohistochemical staining revealed nuclear expression of PP1A only in GBM samples. Thus, CSK21 and PP1A, whose functions are intimately associated with cell cycle regulation, might play key role in gliomagenesis. Cancer Res; 70(16); 6437-47. (C)2010 AACR.

Distributed nonlinear optimization algorithms for general network problems

There are a number of large networks which occur in many problems dealing with the flow of power, communication signals, water, gas, transportable goods, etc. Both design and planning of these networks involve optimization problems. The first part of this paper introduces the common characteristics of a nonlinear network (the network may be linear, the objective function may be non linear, or both may be nonlinear). The second part develops a mathematical model trying to put together some important constraints based on the abstraction for a general network. The third part deals with solution procedures; it converts the network to a matrix based system of equations, gives the characteristics of the matrix and suggests two solution procedures, one of them being a new one. The fourth part handles spatially distributed networks and evolves a number of decomposition techniques so that we can solve the problem with the help of a distributed computer system. Algorithms for parallel processors and spatially distributed systems have been described.There are a number of common features that pertain to networks. A network consists of a set of nodes and arcs. In addition at every node, there is a possibility of an input (like power, water, message, goods etc) or an output or none. Normally, the network equations describe the flows amoungst nodes through the arcs. These network equations couple variables associated with nodes. Invariably, variables pertaining to arcs are constants; the result required will be flows through the arcs. To solve the normal base problem, we are given input flows at nodes, output flows at nodes and certain physical constraints on other variables at nodes and we should find out the flows through the network (variables at nodes will be referred to as across variables).The optimization problem involves in selecting inputs at nodes so as to optimise an objective function; the objective may be a cost function based on the inputs to be minimised or a loss function or an efficiency function. The above mathematical model can be solved using Lagrange Multiplier technique since the equalities are strong compared to inequalities. The Lagrange multiplier technique divides the solution procedure into two stages per iteration. Stage one calculates the problem variables % and stage two the multipliers lambda. It is shown that the Jacobian matrix used in stage one (for solving a nonlinear system of necessary conditions) occurs in the stage two also.A second solution procedure has also been imbedded into the first one. This is called total residue approach. It changes the equality constraints so that we can get faster convergence of the iterations.Both solution procedures are found to coverge in 3 to 7 iterations for a sample network.The availability of distributed computer systems — both LAN and WAN — suggest the need for algorithms to solve the optimization problems. Two types of algorithms have been proposed — one based on the physics of the network and the other on the property of the Jacobian matrix. Three algorithms have been deviced, one of them for the local area case. These algorithms are called as regional distributed algorithm, hierarchical regional distributed algorithm (both using the physics properties of the network), and locally distributed algorithm (a multiprocessor based approach with a local area network configuration). The approach used was to define an algorithm that is faster and uses minimum communications. These algorithms are found to converge at the same rate as the non distributed (unitary) case.

PVA-SSA-HPA Mixed-Matrix-Membrane Electrolytes for DMFCs

Stabilized forms of heteropolyacids (HPAs), namely phosphomolybdic acid (PMA), phosphotungstic acid (PTA), and silicotungstic acid (STA), are incorporated into poly (vinyl alcohol) (PVA) cross-linked with sulfosuccinic acid (SSA) to form mixed-matrix membranes for application in direct methanol fuel cells (DMFCs). Bridging SSA between PVA molecules not only strengthens the network but also facilitates proton conduction in HPAs. The mixed-matrix membranes are characterized for their mechanical stability, sorption capability, ion-exchange capacity, and wetting in conjunction with their proton conductivity, methanol permeability, and DMFC performance. Methanol-release kinetics is studied ex situ by volume-localized NMR spectroscopy (employing point-resolved spectroscopy'') with the results clearly demonstrating that the incorporation of certain inorganic fillers in PVA-SSA viz., STA and PTA, retards the methanol-release kinetics under osmotic drag compared to Nafion, although PVA-SSA itself exhibits a still lower methanol permeability. The methanol crossover rate for PVA-SSA-HPA-bridged-mixed-matrix membranes decreases dramatically with increasing current density rendering higher DMFC performance in relation to a DMFC using a pristine PVA-SSA membrane. A peak power density of 150 mW/cm(2) at a load current density of 500 mA/cm(2) is achieved for the DMFC using a PVA-SSA-STA-bridged-mixed-matrix-membrane electrolyte. (C) 2010 The Electrochemical Society. [DOI: 10.1149/1.3465653] All rights reserved.

An Inexpensive Digital Network Analyzer

Frequency response analysis is critical in understanding the steady and transient state behavior of any electrical network. Network analyzeror frequency response analyzer is used to determine the frequency response of an electrical network. This paper deals with the design of an inexpensive digitally controlled Network Analyzer. The frequency range of the network analyzer is from 10Hz to 50kHz (suitable range for system studies on most power electronics apparatus). It is composed of a microcontroller (as central processing unit) and a personal computer (as analyzer and display). The communication between the microcontroller and personal computer is established through one of the USB ports. The testing and evaluation of the analyzer is done with RC, RLC and multi-resonant circuits. The design steps, basis of analysis, experimental results, limitation in bandwidth and possible techniques for improvement in performances are presented.

HNbWO6 and HTaWO6: Novel layered oxides related to the rutile structure. Synthesis and investigation of ion-exchange and intercalation behaviour

New protonated layered oxides, HMWO6·1.5H2O (M=Nb or Ta), have been synthesized by topotactic exchange of lithium in trirutile LiMWO6 with protons by treatment with dilute HNO3. The tetragonal cell constants are a=4.71 (2) and c=25.70 (8)Å for HNbWO6·1.5H2O and a=4.70 (2) and c=25.75 (9) Å for HTaWO6·1.5H2O. Partially hydrated compounds, HMWO6·0.5H2O and anhydrous compounds, HMWO6 retain the layered structure. The structure of these oxides consists of MWO6 sheets built up of M/W-oxygen octahedra with rutile type corner- and edge-sharing. Interlayer protons in HMWO6 are exchanged with Li+, Na+, K+ and Tl+. HMWO6 exhibit Brønsted acidity intercalating n-alkylamines and pyridine.

Bonding isomerism in the chelate-exchange reactions of isonitroso-β-keto-imino isomeric nickel(ii) mixed ligand complexes

A novel chelate exchange reaction, leading to the formation of a series of N-alkyl substituent dependent mixed ligand isomeric complexes of the type Ni(R-AB)(AC') and Ni(R-AC)(AB') (Figure 1) are discussed. Here, AB and AC denote two different N-bonded isonitroso-β-keto-imino ligand moieties, while AB' and AC' are the corresponding O-bonded ligand moieties and R is an N-alkyl substituent. The isomeric complexes are suggested to be monomeric, neutral and diamagnetic with an asymmetric square planar geometry. The bonding isomerism of the isonitroso group in these complexes is discussed on the basis of the infrared and proton magnetic resonance spectral studies. A probable mechanism for the preparative route is also proposed.