Functional Analysis of an Acid Adaptive DNA Adenine Methyltransferase from Helicobacter pylori 26695

HP0593 DNA-(N-6-adenine)-methyltransferase (HP0593 MTase) is a member of a Type III restriction-modification system in Helicobacter pylori strain 26695. HP0593 MTase has been cloned, overexpressed and purified heterologously in Escherichia coli. The recognition sequence of the purified MTase was determined as 5'-GCAG-3' and the site of methylation was found to be adenine. The activity of HP0593 MTase was found to be optimal at pH 5.5. This is a unique property in context of natural adaptation of H. pylori in its acidic niche. Dot-blot assay using antibodies that react specifically with DNA containing m6A modification confirmed that HP0593 MTase is an adenine-specific MTase. HP0593 MTase occurred as both monomer and dimer in solution as determined by gel-filtration chromatography and chemical-crosslinking studies. The nonlinear dependence of methylation activity on enzyme concentration indicated that more than one molecule of enzyme was required for its activity. Analysis of initial velocity with AdoMet as a substrate showed that two molecules of AdoMet bind to HP0593 MTase, which is the first example in case of Type III MTases. Interestingly, metal ion cofactors such as Co2+, Mn2+, and also Mg2+ stimulated the HP0593 MTase activity. Preincubation and isotope partitioning analyses clearly indicated that HP0593 MTase-DNA complex is catalytically competent, and suggested that DNA binds to the MTase first followed by AdoMet. HP0593 MTase shows a distributive mechanism of methylation on DNA having more than one recognition site. Considering the occurrence of GCAG sequence in the potential promoter regions of physiologically important genes in H. pylori, our results provide impetus for exploring the role of this DNA MTase in the cellular processes of H. pylori.

E. C. G. Sudarshan and Symmetry in Classical Dynamics, Optics and Quantum Mechanics

We review work initiated and inspired by Sudarshan in relativistic dynamics, beam optics, partial coherence theory, Wigner distribution methods, multimode quantum optical squeezing, and geometric phases. The 1963 No Interaction Theorem using Dirac's instant form and particle World Line Conditions is recalled. Later attempts to overcome this result exploiting constrained Hamiltonian theory, reformulation of the World Line Conditions and extending Dirac's formalism, are reviewed. Dirac's front form leads to a formulation of Fourier Optics for the Maxwell field, determining the actions of First Order Systems (corresponding to matrices of Sp(2,R) and Sp(4,R)) on polarization in a consistent manner. These groups also help characterize properties and propagation of partially coherent Gaussian Schell Model beams, leading to invariant quality parameters and the new Twist phase. The higher dimensional groups Sp(2n,R) appear in the theory of Wigner distributions and in quantum optics. Elegant criteria for a Gaussian phase space function to be a Wigner distribution, expressions for multimode uncertainty principles and squeezing are described. In geometric phase theory we highlight the use of invariance properties that lead to a kinematical formulation and the important role of Bargmann invariants. Special features of these phases arising from unitary Lie group representations, and a new formulation based on the idea of Null Phase Curves, are presented.

Assortative mating and the adaptive landscape

The ability of a population to shift from one adaptive peak to another was examined for a two-locus model with different degrees of assortative mating, selection, and linkage. As expected, if the proportion of the population that mates assortatively increases, so does its ability to shift to a new peak. Assortative mating affects this process by allowing the mean fitness of a population to increase monotonically as it passes through intermediate gene frequencies on the way to a new, higher, homozygotic peak. Similarly, if the height of the new peak increases or selection against intermediates becomes less severe, the population becomes more likely to shift to a new peak. Close linkage also helps the shift to a new adaptive peak and acts similarly to assortative mating, but it is not necessary for such a shift as was previously thought. When a population shifts to a new peak, the number of generations required is significantly less than that needed to return to the original peak when that happens. The short period of time required may be an explanation for rapid changes in the geological record. Under extremely high degrees of assortative mating, the shift takes longer, presumably because of the difficulty of breaking up less favored allelic combinations.

A single network adaptive critic (SNAC) architecture for optimal control synthesis for a class of nonlinear systems

Even though dynamic programming offers an optimal control solution in a state feedback form, the method is overwhelmed by computational and storage requirements. Approximate dynamic programming implemented with an Adaptive Critic (AC) neural network structure has evolved as a powerful alternative technique that obviates the need for excessive computations and storage requirements in solving optimal control problems. In this paper, an improvement to the AC architecture, called the �Single Network Adaptive Critic (SNAC)� is presented. This approach is applicable to a wide class of nonlinear systems where the optimal control (stationary) equation can be explicitly expressed in terms of the state and costate variables. The selection of this terminology is guided by the fact that it eliminates the use of one neural network (namely the action network) that is part of a typical dual network AC setup. As a consequence, the SNAC architecture offers three potential advantages: a simpler architecture, lesser computational load and elimination of the approximation error associated with the eliminated network. In order to demonstrate these benefits and the control synthesis technique using SNAC, two problems have been solved with the AC and SNAC approaches and their computational performances are compared. One of these problems is a real-life Micro-Electro-Mechanical-system (MEMS) problem, which demonstrates that the SNAC technique is applicable to complex engineering systems.

Spectral Sensing of Adaptive Frequency Hopping Signal for Cognitive Radio

The issue of dynamic spectrum scene analysis in any cognitive radio network becomes extremely complex when low probability of intercept, spread spectrum systems are present in environment. The detection and estimation become more complex if frequency hopping spread spectrum is adaptive in nature. In this paper, we propose two phase approach for detection and estimation of frequency hoping signals. Polyphase filter bank has been proposed as the architecture of choice for detection phase to efficiently detect the presence of frequency hopping signal. Based on the modeling of frequency hopping signal it can be shown that parametric methods of line spectral analysis are well suited for estimation of frequency hopping signals if the issues of order estimation and time localization are resolved. An algorithm using line spectra parameter estimation and wavelet based transient detection has been proposed which resolves above issues in computationally efficient manner suitable for implementation in cognitive radio. The simulations show promising results proving that adaptive frequency hopping signals can be detected and demodulated in a non cooperative context, even at a very low signal to noise ratio in real time.

Adaptive optimal tuning of a general class of stable LTI systems with restricted inputs

The problem addressed is one of model reference adaptive control (MRAC) of asymptotically stable plants of unknown order with zeros located anywhere in the s-plane except at the origin. The reference model is also asymptotically stable and lacking zero(s) at s = 0. The control law is to be specified only in terms of the inputs to and outputs of the plant and the reference model. For inputs from a class of functions that approach a non-zero constant, the problem is formulated in an optimal control framework. By successive refinements of the sub-optimal laws proposed here, two schemes are finally design-ed. These schemes are characterized by boundedness, convergence and optimality. Simplicity and total time-domain implementation are the additional striking features. Simulations to demonstrate the efficacy of the control schemes are presented.

Adaptive optimization of continuous bioreactor using neural network model

An adaptive optimization algorithm using backpropogation neural network model for dynamic identification is developed. The algorithm is applied to maximize the cellular productivity of a continuous culture of baker's yeast. The robustness of the algorithm is demonstrated in determining and maintaining the optimal dilution rate of the continuous bioreactor in presence of disturbances in environmental conditions and microbial culture characteristics. The simulation results show that a significant reduction in time required to reach optimal operating levels can be achieved using neural network model compared with the traditional dynamic linear input-output model. The extension of the algorithm for multivariable adaptive optimization of continuous bioreactor is briefly discussed.

Optimal management of beaver population using a reduced-order distributed parameter model and single network adaptive critics

Beavers are often found to be in conflict with human interests by creating nuisances like building dams on flowing water (leading to flooding), blocking irrigation canals, cutting down timbers, etc. At the same time they contribute to raising water tables, increased vegetation, etc. Consequently, maintaining an optimal beaver population is beneficial. Because of their diffusion externality (due to migratory nature), strategies based on lumped parameter models are often ineffective. Using a distributed parameter model for beaver population that accounts for their spatial and temporal behavior, an optimal control (trapping) strategy is presented in this paper that leads to a desired distribution of the animal density in a region in the long run. The optimal control solution presented, imbeds the solution for a large number of initial conditions (i.e., it has a feedback form), which is otherwise nontrivial to obtain. The solution obtained can be used in real-time by a nonexpert in control theory since it involves only using the neural networks trained offline. Proper orthogonal decomposition-based basis function design followed by their use in a Galerkin projection has been incorporated in the solution process as a model reduction technique. Optimal solutions are obtained through a "single network adaptive critic" (SNAC) neural-network architecture.

Integrated optics � technology and applications

The emergence of optoelectronics and photonics as viable alternatives to electronics in many key areas of engineering relevance is indeed significant. This paper presents a tutorial review of integrated optics � a technologically important development in photonics. Materials, processes, device technology and applications are highlighted.

Optimal blood glucose regulation of diabetic patients using single network adaptive critics

Diabetes is a long-term disease during which the body's production and use of insulin are impaired, causing glucose concentration level to increase 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 online feedback-treatment strategy is presented for the control of blood glucose levels in diabetic patients using single network adaptive critic (SNAC) neural networks (which is based on nonlinear optimal control theory). A recently developed mathematical model of the nonlinear dynamics of glucose and insulin interaction in the blood system has been revised and considered for synthesizing the neural network for feedback control. The idea is to replicate the function of pancreatic insulin, i.e. to have a fairly continuous measurement of blood glucose and a situation-dependent insulin injection to the body using an external device. Detailed studies are carried out to analyze the effectiveness of this adaptive critic-based feedback medication strategy. A comparison study with linear quadratic regulator (LQR) theory shows that the proposed nonlinear approach offers some important advantages such as quicker response, avoidance of hypoglycemia problems, etc. Robustness of the proposed approach is also demonstrated from a large number of simulations considering random initial conditions and parametric uncertainties. Copyright (C) 2009 John Wiley & Sons, Ltd.

Generation of novel materials for nonlinear optics: Analysis of hydrogen-bonded networks in haloaromatic and aliphatic amine-tartrate complexes

Five tartrate-amine complexes have been studied in terms of crystal packing and hydrogen bonding frameworks. The salts are 3-bromoanilinium-L-monohydrogen tartrate 1, 3-fluoroanilinium-D-dibenzoylmonohydrogen tartrate 2, 1-nonylium-D-dibenzoylmonohydrogen tartrate 3, 1 -decylium-D-dibenzoylmonohydrogen tartrate 4, and 1,4-diaminobutanium-D-dibenzoyl tartrate trihydrate 5. The results indicate that there are no halogen-halogen interactions in the haloaromatic-tartrate complexes. The anionic framework allows accomodation of ammonium ions that bear alkyl chain residues of variable lengths. The long chain amines in these structures remain disordered while the short chain amines form multidirectional hydrogen bonds on either side.

Systematic crystallographic investigation of hydrogen-bonded networks involving monohydrogen tartrate-amine complexes: Potential materials for nonlinear optics

Crystal structures of six binary salts involving aromatic amines as cations and hydrogen tartrates as anions are presented. The materials are 2,6-xylidinium-L-monohydrogen tartrate monohydrate, C12H18O6.5N, P22(1)2(1), a = 7.283(2) Angstrom, b = 17.030(2) Angstrom, c = 22.196(2) Angstrom, Z = 8; 2,6-xylidinium-D-dibenzoyl monohydrogen tartrate, C26H25O8N, P2(1), a = 7.906(1) Angstrom, b = 24.757(1) Angstrom, c = 13.166(1) Angstrom, beta = 105.01(1)degrees, Z = 4; 2,3-xylidinium-D-dibenzoyl monohydrogen tartrate monohydrate, C26H26O8.5N, P2(1), a = 7.837(1) Angstrom, b = 24.488(1) Angstrom, c = 13.763(1) Angstrom, beta = 105.69(1)degrees, Z = 4; 2-toluidinium-D-dibenzoyl monohydrogen tartrate, C25H23O8N, P2(1)2(1)2(1), a = 13.553(2) Angstrom, b = 15.869(3) Angstrom, c = 22.123(2) Angstrom, Z = 8; 3-toluidinium-D-dibenzoyl monohydrogen tartrate (1:1), C25H23O8N, P1, a = 7.916(3) Angstrom, b = 11.467(6) Angstrom, c = 14.203(8) Angstrom, alpha = 96.44(4)degrees, beta = 98.20(5)degrees, = 110.55(5)degrees, Z = 2; 3-toluidinium-D-dibenzoyl tartrate dihydrate (1:2), C32H36O10N, P1, a = 7.828(3) Angstrom, b = 8.233(1) Angstrom, c = 24.888(8) Angstrom, alpha = 93.98 degrees, beta = 94.58(3)degrees, = 89.99(2)degrees, Z = 2. An analysis of the hydrogen-bonding schemes in terms of crystal packing, stoichiometric variations, and substitutional variations in these materials provides insights to design hydrogen-bonded networks directed toward the engineering of crystalline nonlinear optical materials.

Low-power pipelined LMS adaptive filter architectures with minimal adaptation delay

The use of delayed coefficient adaptation in the least mean square (LMS) algorithm has enabled the design of pipelined architectures for real-time transversal adaptive filtering. However, the convergence speed of this delayed LMS (DLMS) algorithm, when compared with that of the standard LMS algorithm, is degraded and worsens with increase in the adaptation delay. Existing pipelined DLMS architectures have large adaptation delay and hence degraded convergence speed. We in this paper, first present a pipelined DLMS architecture with minimal adaptation delay for any given sampling rate. The architecture is synthesized by using a number of function preserving transformations on the signal flow graph representation of the DLMS algorithm. With the use of carry-save arithmetic, the pipelined architecture can support high sampling rates, limited only by the delay of a full adder and a 2-to-1 multiplexer. In the second part of this paper, we extend the synthesis methodology described in the first part, to synthesize pipelined DLMS architectures whose power dissipation meets a specified budget. This low-power architecture exploits the parallelism in the DLMS algorithm to meet the required computational throughput. The architecture exhibits a novel tradeoff between algorithmic performance (convergence speed) and power dissipation. (C) 1999 Elsevier Science B.V. All rights resented.

Mapping adaptive resonance theory onto ring and mesh architectures

In recent years, parallel computers have been attracting attention for simulating artificial neural networks (ANN). This is due to the inherent parallelism in ANN. This work is aimed at studying ways of parallelizing adaptive resonance theory (ART), a popular neural network algorithm. The core computations of ART are separated and different strategies of parallelizing ART are discussed. We present mapping strategies for ART 2-A neural network onto ring and mesh architectures. The required parallel architecture is simulated using a parallel architectural simulator, PROTEUS and parallel programs are written using a superset of C for the algorithms presented. A simulation-based scalability study of the algorithm-architecture match is carried out. The various overheads are identified in order to suggest ways of improving the performance. Our main objective is to find out the performance of the ART2-A network on different parallel architectures. (C) 1999 Elsevier Science B.V. All rights reserved.

A new class of three dimensional D-pi-A trigonal cryptand derivatives for second-order nonlinear optics

Synthesis, crystal structures, linear and nonlinear optical properties of tris D-pi-A cryptand derivatives with C-3 symmetry are reported. Three fold symmetry inherent in the cryptand molecules has been utilized for designing these molecules. Molecular nonlinearities have been measured by hyper-Rayleigh scattering (HRS) experiments. Among the compounds studied, L-1 adopts non-centrosymmetric crystal structure. Compounds L-1, L-2, L-3 and L-4 show a measurable SHG powder signal. These molecules are more isotropic and have significantly higher melting points than the classical p-nitroaniline based dipolar NLO compounds, making them useful for further device applications. Besides, different acceptor groups can be attached to the cryptand molecules to modulate their NLO properties.