Cloning, overexpression, folding and purification of a biosynthetically derived three disulfide scorpion toxin (BTK-2) from Mesobuthus tamulus

BTK-2, a 32 residue scorpion toxin initially identified in the venom of red Indian scorpion Mesobuthus tamulus was cloned, overexpressed and purified using Cytochrome 155 fusion protein system developed in our laboratory. The synthetic gene coding for the peptide was designed taking into account optimal codon usage by Escherichia coli. High expression levels of the fusion protein enabled facile purification of this peptide. The presence of disulfide bonded isomers, occurring as distinctly populated states even in the fusion protein, were separated by gel filtration chromatography. The target peptide was liberated from the host protein by Tev protease cleavage and subsequent purification was achieved using RP-HPLC methods. Reverse phase HPLC clearly showed the presence of at least two isomeric forms of the peptide that were significantly populated. The oxidative folding of BTK-2 was achieved under ambient conditions during the course of purification. Structural characterization of the two forms, by solution homonuclear and heteronuclear NMR methods, has shown that these two forms exhibit significantly different structural properties, and represent the natively folded and a "misfolded" form of the peptide. The formation of properly folded BTK-2 as a major fraction without the use of in vitro oxidative refolding methods clearly indicate the versatility of the Cytochrome b(5) fusion protein system for the efficient production of peptides for high resolution NMR studies.

Cloning and development of pathotype-specific SCAR marker associated with Sclerospora graminicola isolates from pearl millet

Downy mildew pathogen of pearl millet in India is associated with the spread of the highly virulent Sclerospora graminicola pathotype-1. Twenty-seven S. graminicola isolates were screened using 20 intersimple sequence repeats (ISSR). Dinucleotide repeat primer [17898A-(CA)(6) AC] amplified a similar to 600 bp fragment specific to five isolates of pathotype-1 (Sg 048, Sg 153, Sg 212, DM-11 and DM-90). The ISSR fragment linked with pathotype-1 was cloned successfully and sequenced. To convert ISSR fragments into pathotype-specific sequence characterised amplified region (SCAR) markers, PCR primers were designed using a sequence of the cloned DNA fragment. PCR amplification using SCAR primer pair (UOM3-Sg-Path1-F/R) amplified a single 284 bp band only in isolates of S. graminicola pathotype-1. This SCAR primer pair did not amplify the 284 bp product from the other five S. graminicola pathotypes or a negative control, which demonstrates primer specificity for pathotype-1. The SCAR primer pair (UOM3-Sg-Path1-F/R) obtained in this study will provide a valuable tool for rapid identification and specific detection of S. graminicola pathotype-1.

Optimal barrier subdivision for Kramers' escape rate

We examine the effect of subdividing the potential barrier along the reaction coordinate on Kramers' escape rate for a model potential. Using the known supersymmetric potential approach, we show the existence of an optimal number of subdivisions that maximizes the rate.

Optimal scheduling of hydro-thermal power systems under shortage

The problem of optimal scheduling of the generation of a hydro-thermal power system that is faced with a shortage of energy is studied. The deterministic version of the problem is first analyzed, and the results are then extended to cases where the loads and the hydro inflows are random variables.

Optimal hydrothermal load flow: Formulation and a successive approximation solution for fixed head systems

This paper deals with the optimal load flow problem in a fixed-head hydrothermal electric power system. Equality constraints on the volume of water available for active power generation at the hydro plants as well as inequality constraints on the reactive power generation at the voltage controlled buses are imposed. Conditions for optimal load flow are derived and a successive approximation algorithm for solving the optimal generation schedule is developed. Computer implementation of the algorithm is discussed, and the results obtained from the computer solution of test systems are presented.

Cloning Of Rice Dna And Identification Of Transfer-Rna Gene Clones

THE rapid development of recombinant DNA technology has brought forth a revolution in biology'>", it aids us to have a closer look at the 'way genes are organized, eS11 ecially in the complex eucaryotic genornes'<", Although many animal and yeast genes have been studied in detail using recombinant DNA technology, plant genes have seldom been targets for such studie., Germination is an ideal process to study gene expression .because it effects a . shift in the metabolic status of seeds from a state of 'dormancy to an active one. AJ;l understanding of gene organization and regulation darin.g germination can be accomplblted by molecular cloning of DNA from seeds lik.e rice. To study the status of histone, rRNA tRNA and other genes in the rice genome, a general method was developed to clone eucarvotic DNA in a' plasmid vector pBR 322. This essentially ~ involves the following steps. The rice embryo and plasmid pBR 322 DNAs were cut witll restriction endonuclease Bam Hi to generate stick.Y ends, The plasmid DNA was puosphatased, the DNA~ ware a~·tnealed and joined 'by T4 phage DNA ligase. The recombinant DNA molecules thus produced were transjerred into E. coli and colonies containing them Were selected by their sensitivity to tetracycline and resistance to ampicillin, Two clones were identified . 2S haVing tRNA genes by hybridization of the DNA in the clones \vitl1 32P-la.belled rice tRNAs.

A hierarchical system of learning automata that can learn die globally optimal path

Systems of learning automata have been studied by various researchers to evolve useful strategies for decision making under uncertainity. Considered in this paper are a class of hierarchical systems of learning automata where the system gets responses from its environment at each level of the hierarchy. A classification of such sequential learning tasks based on the complexity of the learning problem is presented. It is shown that none of the existing algorithms can perform in the most general type of hierarchical problem. An algorithm for learning the globally optimal path in this general setting is presented, and its convergence is established. This algorithm needs information transfer from the lower levels to the higher levels. Using the methodology of estimator algorithms, this model can be generalized to accommodate other kinds of hierarchical learning tasks.

Optimal Parameter Trajectory Estimation in Parameterized SDEs: An Algorithmic Procedure

We consider the problem of estimating the optimal parameter trajectory over a finite time interval in a parameterized stochastic differential equation (SDE), and propose a simulation-based algorithm for this purpose. Towards this end, we consider a discretization of the SDE over finite time instants and reformulate the problem as one of finding an optimal parameter at each of these instants. A stochastic approximation algorithm based on the smoothed functional technique is adapted to this setting for finding the optimal parameter trajectory. A proof of convergence of the algorithm is presented and results of numerical experiments over two different settings are shown. The algorithm is seen to exhibit good performance. We also present extensions of our framework to the case of finding optimal parameterized feedback policies for controlled SDE and present numerical results in this scenario as well.

Bi-criteria single machine scheduling problem with a learning effect: Aneja-Nair method to obtain the set of optimal sequences

In this paper, we consider the bi-criteria single machine scheduling problem of n jobs with a learning effect. The two objectives considered are the total completion time (TC) and total absolute differences in completion times (TADC). The objective is to find a sequence that performs well with respect to both the objectives: the total completion time and the total absolute differences in completion times. In an earlier study, a method of solving bi-criteria transportation problem is presented. In this paper, we use the methodology of solvin bi-criteria transportation problem, to our bi-criteria single machine scheduling problem with a learning effect, and obtain the set of optimal sequences,. Numerical examples are presented for illustrating the applicability and ease of understanding.

Comment: Eigenvalue sensitivity method for optimal stabilisation of linear systems

In a letter RauA proposed a new method for designing statefeedback controllers using eigenvalue sensitivity matrices. However, there appears to be a conceptual mistake in the procedure, or else it is unduly restricted in its applicability. In particular the equation — BR~lBTK = A/.I, in which K is a positive-definite symmetric matrix.

D-MG Tradeoff and Optimal Codes for a Class of AF and DF Cooperative Communication Protocols

Cooperative relay communication in a fading channel environment under the orthogonal amplify-and-forward (OAF), nonorthogonal and orthogonal selection decode-and-forward (NSDF and OSDF) protocols is considered here. The diversity-multiplexing gain tradeoff (DMT) of the three protocols is determined and DMT-optimal distributed space-time (ST) code constructions are provided. The codes constructed are sphere decodable and in some instances incur minimum possible delay. Included in our results is the perhaps surprising finding that the orthogonal and the nonorthogonal amplify-and-forward (NAF) protocols have identical DMT when the time durations of the broadcast and cooperative phases are optimally chosen to suit the respective protocol. Moreover our code construction for the OAF protocol incurs less delay. Two variants of the NSDF protocol are considered: fixed-NSDF and variable-NSDF protocol. In the variable-NSDF protocol, the fraction of time occupied by the broadcast phase is allowed to vary with multiplexing gain. The variable-NSDF protocol is shown to improve on the DMT of the best previously known static protocol when the number of relays is greater than two. Also included is a DMT optimal code construction for the NAF protocol.

Learning Optimal Discriminant Functions through a Cooperative Game of Automata

The problem of learning correct decision rules to minimize the probability of misclassification is a long-standing problem of supervised learning in pattern recognition. The problem of learning such optimal discriminant functions is considered for the class of problems where the statistical properties of the pattern classes are completely unknown. The problem is posed as a game with common payoff played by a team of mutually cooperating learning automata. This essentially results in a probabilistic search through the space of classifiers. The approach is inherently capable of learning discriminant functions that are nonlinear in their parameters also. A learning algorithm is presented for the team and convergence is established. It is proved that the team can obtain the optimal classifier to an arbitrary approximation. Simulation results with a few examples are presented where the team learns the optimal classifier.

An Optimal g-Guidance Scheme for Satellite Launch Vehicles

A closed-loop steering logic based on an optimal (2-guidance is developed here. The guidance system drives the satellite launch vehicle along a two- or three- dimensional trajectory for placing the payload into a specified circular orbit. The modified g-guidance algorithm makes use of the optimal required velocity vector, which minimizes the total impulse needed for an equivalent two-impluse transfer from the present state to the final orbit. The required velocity vector is defined as velocity of the vehicle on the hypothetical transfer orbit immediately after the application of the first impulse. For this optimal transfer orbit, a simple and elegant expression for the Q-matrix is derived. A working principle for the guidance algorithm in terms of the major and minor cycles, and also for the generation of the steering command, is outlined.

An Online Implementable Differential Evolution Tuned Optimal Guidance Law

This paper proposes a novel application of differential evolution to solve a difficult dynamic optimisation or optimal control problem. The miss distance in a missile-target engagement is minimised using differential evolution. The difficulty of solving it by existing conventional techniques in optimal control theory is caused by the nonlinearity of the dynamic constraint equation, inequality constraint on the control input and inequality constraint on another parameter that enters problem indirectly. The optimal control problem of finding the minimum miss distance has an analytical solution subject to several simplifying assumptions. In the approach proposed in this paper, the initial population is generated around the seed value given by this analytical solution. Thereafter, the algorithm progresses to an acceptable final solution within a few generations, satisfying the constraints at every iteration. Since this solution or the control input has to be obtained in real time to be of any use in practice, the feasibility of online implementation is also illustrated.

Optimal dynamic inversion-based semi-active control of benchmark bridge using MR dampers

A new two-stage state feedback control design approach has been developed to monitor the voltage supplied to magnetorheological (MR) dampers for semi-active vibration control of the benchmark highway bridge. The first stage contains a primary controller, which provides the force required to obtain a desired closed-loop response of the system. In the second stage, an optimal dynamic inversion (ODI) approach has been developed to obtain the amount of voltage to be supplied to each of the MR dampers such that it provides the required force prescribed by the primary controller. ODI is formulated by optimization with dynamic inversion, such that an optimal voltage is supplied to each damper in a set. The proposed control design has been simulated for both phase-I and phase-II study of the recently developed benchmark highway bridge problem. The efficiency of the proposed controller is analyzed in terms of the performance indices defined in the benchmark problem definition. Simulation results demonstrate that the proposed approach generally reduces peak response quantities over those obtained from the sample semi-active controller, although some response quantities have been seen to be increasing. Overall, the proposed control approach is quite competitive as compared with the sample semi-active control approach.