The comparative analysis of the energies of conjugated triple base triplets and their applications

We built 64 sets of 3D models of DNA triplex base triplets (TBT) and minimized their energies. The TBTs were divided into 32 pairs of conjugated ones on the basis of their sequence characteristic, and the energies of each pair of them were compared and analyzed, the results showed: (i) The duplex DNA of which any strand contains at least a couple of A or T, has a preference for selecting the oligodeoxyribonucleic acid (ODN) strand containing abundant T to form TBT. (ii) The duplex DNA of which any strand contains at least a couple of G or C has a preference for selecting ODN containing abundant G to form symmetric antiparallel TBT, but selecting ODN containing abundant C to form asymmetric parallel TBT. (iii) The duplex DNA of which any strand contains only one of A, T, G or C has a preference for selecting ODN containing abundant pyrimidines (T or C) to form antiparallel TBT. Additionally, two examples of TBTs applications, in designing ODN to form triplex with duplex were presented. The energy calculation result revealed that 15-TCG is the best ligand of the HIV PPT duplex. The comparative analysis of energies of the conjugated TBTs provides directive significance for designing ODN strand that is easy to form triplex in theory. (C) 2002 Elsevier Science B.V. All rights reserved.

Tentative study of the coding sequences without 3-base periodicity

Fourier spectra of 120 short coding sequences (<1 200 bp) show that not all coding sequences are characterized by 3-base periodicity. Statistical analysis suggests that whether a coding sequence has 3-base periodicity may be related to the composition and distribution of bases, the usage and the order of the amino acids of the encoded protein as well as the synonymous codon usage. Generally, the content of A+U is higher than that of G+C in non-period-3 sequences, inversely in period-3 sequences. In the three codon positions, the base distribution in the non-periodic-3 sequences is more uniform than in the periodic-3 sequences. The usage biases of the amino acids and the codons in non-period-3 sequences are weaker than that in period-3 sequences. All of these phenomena should be considered sufficiently in predicting the genes and exons of DNA sequences by Fourier analysis method.

PNA(T).DNA(AT) triplexes with Hoogsteen base pairing are more favorable

Peptide nucleic acids (PNAs) are nucleic acid analogs with the deoxyribose phosphate backbone replaced by pseudo-peptide polymers to which the nucleobases are linked. The achiral, uncharged and rather flexible properties of the peptide backbone permit peptide nucleic acids more potential than oligonucleotides in application to antisence and antigenic reagents. The process of PNA binding to DNA duplex and forming triplex is the first step of PNA interacting with PNA. But there are no PNA.2DNA triplex crystal data up to date and little has been reported on the structure features and the force of the PNA.2DNA triplex. In this work, PNA(T).DNA(AT) triplexes are successfully built and the structures and forces to stabilize the triplex after optimizations and molecule dynamics are systematically examined, which are expected to aid in the application of PNAs as anticense and antigene agents.

Derivative kernels for noise robust ASR

Recently there has been interest in combining generative and discriminative classifiers. In these classifiers features for the discriminative models are derived from the generative kernels. One advantage of using generative kernels is that systematic approaches exist to introduce complex dependencies into the feature-space. Furthermore, as the features are based on generative models standard model-based compensation and adaptation techniques can be applied to make discriminative models robust to noise and speaker conditions. This paper extends previous work in this framework in several directions. First, it introduces derivative kernels based on context-dependent generative models. Second, it describes how derivative kernels can be incorporated in structured discriminative models. Third, it addresses the issues associated with large number of classes and parameters when context-dependent models and high-dimensional feature-spaces of derivative kernels are used. The approach is evaluated on two noise-corrupted tasks: small vocabulary AURORA 2 and medium-to-large vocabulary AURORA 4 task. © 2011 IEEE.

Transducer and base compliance alter the in situ 6 dof force measured from muscle during an isometric contraction in a multi-joint limb.

Although musculoskeletal models are commonly used, validating the muscle actions predicted by such models is often difficult. In situ isometric measurements are a possible solution. The base of the skeleton is immobilized and the endpoint of the limb is rigidly attached to a 6-axis force transducer. Individual muscles are stimulated and the resulting forces and moments recorded. Such analyses generally assume idealized conditions. In this study we have developed an analysis taking into account the compliances due to imperfect fixation of the skeleton, imperfect attachment of the force transducer, and extra degrees of freedom (dof) in the joints that sometimes become necessary in fixed end contractions. We use simulations of the rat hindlimb to illustrate the consequences of such compliances. We show that when the limb is overconstrained, i.e., when there are fewer dof within the limb than are restrained by the skeletal fixation, the compliances of the skeletal fixation and of the transducer attachment can significantly affect measured forces and moments. When the limb dofs and restrained dofs are matched, however, the measured forces and moments are independent of these compliances. We also show that this framework can be used to model limb dofs, so that rather than simply omitting dofs in which a limb does not move (e.g., abduction at the knee), the limited motion of the limb in these dofs can be more realistically modeled as a very low compliance. Finally, we discuss the practical implications of these results to experimental measurements of muscle actions.

An example of the use of neural computing techniques in materials science - The modelling of fatigue thresholds in Ni-base superalloys

Two adaptive numerical modelling techniques have been applied to prediction of fatigue thresholds in Ni-base superalloys. A Bayesian neural network and a neurofuzzy network have been compared, both of which have the ability to automatically adjust the network's complexity to the current dataset. In both cases, despite inevitable data restrictions, threshold values have been modelled with some degree of success. However, it is argued in this paper that the neurofuzzy modelling approach offers real benefits over the use of a classical neural network as the mathematical complexity of the relationships can be restricted to allow for the paucity of data, and the linguistic fuzzy rules produced allow assessment of the model without extensive interrogation and examination using a hypothetical dataset. The additive neurofuzzy network structure means that redundant inputs can be excluded from the model and simple sub-networks produced which represent global output trends. Both of these aspects are important for final verification and validation of the information extracted from the numerical data. In some situations neurofuzzy networks may require less data to produce a stable solution, and may be easier to verify in the light of existing physical understanding because of the production of transparent linguistic rules. © 1999 Elsevier Science S.A.