Protein sequence design on the basis of topology optimization techniques -Using continuous modeling of discrete amino acid types

The notion of optimization is inherent in protein design. A long linear chain of twenty types of amino acid residues are known to fold to a 3-D conformation that minimizes the combined inter-residue energy interactions. There are two distinct protein design problems, viz. predicting the folded structure from a given sequence of amino acid monomers (folding problem) and determining a sequence for a given folded structure (inverse folding problem). These two problems have much similarity to engineering structural analysis and structural optimization problems respectively. In the folding problem, a protein chain with a given sequence folds to a conformation, called a native state, which has a unique global minimum energy value when compared to all other unfolded conformations. This involves a search in the conformation space. This is somewhat akin to the principle of minimum potential energy that determines the deformed static equilibrium configuration of an elastic structure of given topology, shape, and size that is subjected to certain boundary conditions. In the inverse-folding problem, one has to design a sequence with some objectives (having a specific feature of the folded structure, docking with another protein, etc.) and constraints (sequence being fixed in some portion, a particular composition of amino acid types, etc.) while obtaining a sequence that would fold to the desired conformation satisfying the criteria of folding. This requires a search in the sequence space. This is similar to structural optimization in the design-variable space wherein a certain feature of structural response is optimized subject to some constraints while satisfying the governing static or dynamic equilibrium equations. Based on this similarity, in this work we apply the topology optimization methods to protein design, discuss modeling issues and present some initial results.

Scalable non-linear Support Vector Machine using hierarchical clustering

This paper discusses a method for scaling SVM with Gaussian kernel function to handle large data sets by using a selective sampling strategy for the training set. It employs a scalable hierarchical clustering algorithm to construct cluster indexing structures of the training data in the kernel induced feature space. These are then used for selective sampling of the training data for SVM to impart scalability to the training process. Empirical studies made on real world data sets show that the proposed strategy performs well on large data sets.

Construction and use of linear regression models for processor performance analysis

Processor architects have a challenging task of evaluating a large design space consisting of several interacting parameters and optimizations. In order to assist architects in making crucial design decisions, we build linear regression models that relate Processor performance to micro-architecture parameters, using simulation based experiments. We obtain good approximate models using an iterative process in which Akaike's information criteria is used to extract a good linear model from a small set of simulations, and limited further simulation is guided by the model using D-optimal experimental designs. The iterative process is repeated until desired error bounds are achieved. We used this procedure to establish the relationship of the CPI performance response to 26 key micro-architectural parameters using a detailed cycle-by-cycle superscalar processor simulator The resulting models provide a significance ordering on all micro-architectural parameters and their interactions, and explain the performance variations of micro-architectural techniques.

Rare earth exchanged (Ce3+, La3+ and RE3+) H-Y zeolites as solid acid catalysts for the synthesis of linear alkyl benzenes

Rare earth exchanged H–Y zeolites were prepared by simple ion exchange methods at 353 K and have been characterized using different physicochemical techniques. A strong peak around 58 ppm in the 27Al{1H} MAS NMR spectra of these zeolites suggests a tetrahedral coordination for the framework aluminium. Small peak at or near 0 ppm is due to hexa-coordinated extra-framework aluminium and a shoulder peak near 30 ppm is a penta-coordinated aluminium species; [Al(OH)4]−. The vapor-phase benzene alkylation with 1-decene and 1-dodecene was investigated with these catalytic systems. Under the reaction conditions of 448 K, benzene/olefin molar ratio of 20 and time on stream 3 h, the most efficient catalyst was CeH–Y which showed more than 70% of olefin conversion with 48.5% 2-phenyldecane and 46.8%, 2-phenyldodecane selectivities with 1-decene and 1-dodecene respectively.

Analysis of solar radiation pressure induced coupled liberations of a gravity stabilized axisymmetric satellite

This paper presents an analysis of solar radiation pressure induced coupled librations of gravity stabilized cylindrical spacecraft with a special reference to geostationary communication satellites. The Lagrangian approach is used to obtain the corresponding equations of motion. The solar induced torques are assumed to be free of librational angles and are represented by their Fourier expansion. The response and periodic solutions are obtained through linear and nonlinear analyses, using the method of harmonic balance in the latter case. The stability conditions are obtained using Routh-Hurwitz criteria. To establish the ranges of validity the analytic response is compared with the numerical solution. Finally, values of the system parameters are suggested to make the satellite behave as desired. Among these is a possible approach to subdue the solar induced roll resonance. It is felt that the approximate analysis presented here should significantly reduce the computational efforts involved in the design and stability analysis of the systems.

Direct Synthesis of Terminally ``Clickable'' Linear and Hyperbranched Polyesters

1,3-Dipolar cycloaddition of an organic azide and an acetylenic unit,often referred to as the ``click reaction'', has become an important ligation tool both in the context of materials chemistry and biology. Thus, development of simple approaches to directly generate polymers that bear either an azide or an alkyne unit has gained considerable importance. We describe here a straightforward approach to directly prepare linear and hyperbranched polyesters that carry terminal propargyl groups. To achieve the former, we designed an AB-type monomer that carries a hydroxyl group and a propargyl ester, which upon self-condensation under standard transesterification conditions yielded a polyester that carries a single propargyl group at one of its chain-ends. Similarly, an AB(2) type monomer that carries one hydroxyl group and two propargyl ester groups, when polymerized under the same conditions yielded a hyperbranched polymer with numerous clickable'' propargyl groups at its molecular periphery. These propargyl groups can be readily clicked with different organic azides, such as benzyl azide, omega-azido heptaethyleneglycol monomethylether or 9-azidomethyl anthracene. When an anthracene chromophore is clicked, the molecular weight of the linear polyester could be readily estimated using both UV-visible and fluorescence spectroscopic measurements. Furthermore, the reactive propargyl end group could also provide an opportunity to prepare block copolymers in the case of linear polyesters and to generate nanodimensional scaffolds to anchor variety of functional units, in the case of the hyperbranched polymer. (C) 2010 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 48: 3200-3208, 2010.

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.

On the momentum balance in linear-combination models for the transition zone

The momentum balance of the linear-combination integral model for the transition zone is investigated for constant pressure flows. The imbalance is found to be small enough to be negligible for all practical purposes. [S0889-504X(00)00703-0].

On the L2 stability of linear multidimensional time varying systems

This paper analyzes the L2 stability of solutions of systems with time-varying coefficients of the form [A + C(t)]x′ = [B + D(t)]x + u, where A, B, C, D are matrices. Following proof of a lemma, the main result is derived, according to which the system is L2 stable if the eigenvalues of the coefficient matrices are related in a simple way. A corollary of the theorem dealing with small periodic perturbations of constant coefficient systems is then proved. The paper concludes with two illustrative examples, both of which deal with the attitude dynamics of a rigid, axisymmetric, spinning satellite in an eccentric orbit, subject to gravity gradient torques.

Bounded-input/bounded-output stability of linear multidimensional time- varying systems

The present study of the stability of systems governed by a linear multidimensional time-varying equation, which are encountered in spacecraft dynamics, economics, demographics, and biological systems, gives attention the lemma dealing with L(inf) stability of an integral equation that results from the differential equation of the system under consideration. Using the proof of this lemma, the main result on L(inf) stability is derived according; a corollary of the theorem deals with constant coefficient systems perturbed by small periodic terms. (O.C.)

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.

Reliability models for existing structures based on dynamic state estimation and data based asymptotic extreme value analysis

The problem of time variant reliability analysis of existing structures subjected to stationary random dynamic excitations is considered. The study assumes that samples of dynamic response of the structure, under the action of external excitations, have been measured at a set of sparse points on the structure. The utilization of these measurements m in updating reliability models, postulated prior to making any measurements, is considered. This is achieved by using dynamic state estimation methods which combine results from Markov process theory and Bayes' theorem. The uncertainties present in measurements as well as in the postulated model for the structural behaviour are accounted for. The samples of external excitations are taken to emanate from known stochastic models and allowance is made for ability (or lack of it) to measure the applied excitations. The future reliability of the structure is modeled using expected structural response conditioned on all the measurements made. This expected response is shown to have a time varying mean and a random component that can be treated as being weakly stationary. For linear systems, an approximate analytical solution for the problem of reliability model updating is obtained by combining theories of discrete Kalman filter and level crossing statistics. For the case of nonlinear systems, the problem is tackled by combining particle filtering strategies with data based extreme value analysis. In all these studies, the governing stochastic differential equations are discretized using the strong forms of Ito-Taylor's discretization schemes. The possibility of using conditional simulation strategies, when applied external actions are measured, is also considered. The proposed procedures are exemplifiedmby considering the reliability analysis of a few low-dimensional dynamical systems based on synthetically generated measurement data. The performance of the procedures developed is also assessed based on a limited amount of pertinent Monte Carlo simulations. (C) 2010 Elsevier Ltd. All rights reserved.

Modified linear prediction method for directions of arrival estimation of narrow-band plane waves

A modified linear prediction (MLP) method is proposed in which the reference sensor is optimally located on the extended line of the array. The criterion of optimality is the minimization of the prediction error power, where the prediction error is defined as the difference between the reference sensor and the weighted array outputs. It is shown that the L2-norm of the least-squares array weights attains a minimum value for the optimum spacing of the reference sensor, subject to some soft constraint on signal-to-noise ratio (SNR). How this minimum norm property can be used for finding the optimum spacing of the reference sensor is described. The performance of the MLP method is studied and compared with that of the linear prediction (LP) method using resolution, detection bias, and variance as the performance measures. The study reveals that the MLP method performs much better than the LP technique.