Preparation of Potential Cell-Permeant Nucleoside-2',3'-Cyclic Phosphate Precursors

Uridine-3'-phosphorothiolate triesters bearing lipophilic moieties were prepared via Michaelis-Arbuzov chemistry. Subsequent deprotection of the S-cholesteryl phosphorothiolate triester afforded the corresponding diester which underwent spontaneous Cyclization to cleanly afford uridine 2',3'-cyclic phosphate. This transesterification reaction could be expedited by treatment with iodine under mild, neutral conditions.

Modelling the correlation between processing parameters and properties of maraging steels using artificial neural network

An artificial neural network (ANN) model is developed for the analysis and simulation of the correlation between the properties of maraging steels and composition, processing and working conditions. The input parameters of the model consist of alloy composition, processing parameters (including cold deformation degree, ageing temperature, and ageing time), and working temperature. The outputs of the ANN model include property parameters namely: ultimate tensile strength, yield strength, elongation, reduction in area, hardness, notched tensile strength, Charpy impact energy, fracture toughness, and martensitic transformation start temperature. Good performance of the ANN model is achieved. The model can be used to calculate properties of maraging steels as functions of alloy composition, processing parameters, and working condition. The combined influence of Co and Mo on the properties of maraging steels is simulated using the model. The results are in agreement with experimental data. Explanation of the calculated results from the metallurgical point of view is attempted. The model can be used as a guide for further alloy development.

Neural network controlled three-phase four-wire shunt active powerfilter

A three-phase four-wire shunt active power filter for harmonic mitigation and reactive power compensation in power systems supplying nonlinear loads is presented. Three adaptive linear neurons are used to tackle the desired three-phase filter current templates. Another feedforward three-layer neural network is adopted to control the output filter compensating currents online. This is accomplished by producing the appropriate switching patterns of the converter's legs IGBTs. Adequate tracking of the filter current references is obtained by this method. The active filter injects the current required to compensate for the harmonic and reactive components of the line currents, Simulation results of the proposed active filter indicate a remarkable improvement in the source current waveforms. This is reflected in the enhancement of the unified power quality index defined. Also, the filter has exhibited quite a high dynamic response for step variations in the load current, assuring its potential for real-time applications

Bradykinin-related peptides, including a novel structural variant, (Val1)-bradykinin, from the skin secretion of Guenther's frog, Hylarana guentheri and their molecular precursors.

Multiple bradykinin-related peptides including a novel bradykinin structural variant, (Val1)-bradykinin, have been identified from the defensive skin secretion of Guenther's frog, Hylarana guentheri by a tandem mass spectrometry method. Subsequently, four different preprobradykinin cDNAs, which encoded multiple bradykinin copies and its structural variants, were consistently cloned from a skin derived cDNA library. These preprobradykinin cDNAs showed little structural similarity with mammalian kininogens and the kininogens from the skin of toads, but have regions that are highly conserved in the kininogens from another ranid frog, Odorrana schmackeri. Alignment of these preprobradykinins revealed that preprobradykinin 1, 2 and 3 may derive from a single gene by alternative exon splicing.

Novel brevinins from Chinese piebald odorous frog (Huia schmackeri) skin deduced from cloned biosynthetic precursors

Antimicrobial peptides represent the most characterized and diverse class of peptides within the defensive skin secretions of anuran amphibians. With an ever expanding database of primary structures, the current accepted rules for nomenclature have become increasingly difficult to apply to peptides whose primary structural attributes are either unique or that fall between those that define existing groups. An additional factor that adds to the confusion is the regular re-classification or revision of existing taxa. In the present study, we have identified five new antimicrobial peptide homologs in the defensive skin secretion of the Chinese piebald odorous frog, Huia schmackeri (formerly Rana (Odorrana) schmackeri), by cloning of their respective biosynthetic precursors. As these peptides are obvious homologs of the brevinin-1 and brevinin-2 families we have named these in accordance: (1) brevinin-1HS1, (2) brevinin-2HS1, (3) brevinin-2HS2, (4) brevinin-2HS3 and (5) brevinin-1HS2. The reasons for adopting these names are discussed. It is clear that with an ever-increasing number of amphibian skin antimicrobial peptides appearing in the literature that a consistent nomenclature scheme needs to be established.

A taxonomy for wavelet neural networks applied to nonlinear modelling

This article presents a novel classification of wavelet neural networks based on the orthogonality/non-orthogonality of neurons and the type of nonlinearity employed. On the basis of this classification different network types are studied and their characteristics illustrated by means of simple one-dimensional nonlinear examples. For multidimensional problems, which are affected by the curse of dimensionality, the idea of spherical wavelet functions is considered. The behaviour of these networks is also studied for modelling of a low-dimension map.

A new Jacobian matrix for optimal learning of single-layer neural networks

This paper investigates the learning of a wide class of single-hidden-layer feedforward neural networks (SLFNs) with two sets of adjustable parameters, i.e., the nonlinear parameters in the hidden nodes and the linear output weights. The main objective is to both speed up the convergence of second-order learning algorithms such as Levenberg-Marquardt (LM), as well as to improve the network performance. This is achieved here by reducing the dimension of the solution space and by introducing a new Jacobian matrix. Unlike conventional supervised learning methods which optimize these two sets of parameters simultaneously, the linear output weights are first converted into dependent parameters, thereby removing the need for their explicit computation. Consequently, the neural network (NN) learning is performed over a solution space of reduced dimension. A new Jacobian matrix is then proposed for use with the popular second-order learning methods in order to achieve a more accurate approximation of the cost function. The efficacy of the proposed method is shown through an analysis of the computational complexity and by presenting simulation results from four different examples.

A novel continuous forward algorithm for RBF neural modelling

A continuous forward algorithm (CFA) is proposed for nonlinear modelling and identification using radial basis function (RBF) neural networks. The problem considered here is simultaneous network construction and parameter optimization, well-known to be a mixed integer hard one. The proposed algorithm performs these two tasks within an integrated analytic framework, and offers two important advantages. First, the model performance can be significantly improved through continuous parameter optimization. Secondly, the neural representation can be built without generating and storing all candidate regressors, leading to significantly reduced memory usage and computational complexity. Computational complexity analysis and simulation results confirm the effectiveness.