Evolution of conserved secondary structures and their function in transcriptional regulation networks

Background: Many conserved secondary structures have been identified within conserved elements in the human genome, but only a small fraction of them are known to be functional RNAs. The evolutionary variations of these conserved secondary structures in h

THE MATRIX ANALYSIS FOR AN OPTICAL FREE-SPACE SWITCHING NETWORK AND AN OPTICAL CROSSOVER NETWORK WITH 4-FUNCTION INTERCHANGE NODES

A matrix analysis for free-space switching networks, such as perfect shuffle-exchange omega, crossover and Banyan is presented. On the basis of matrix analysis, the equivalence of these three switching networks and the route selection between input and output ports are simply explained. Furthermore, an optical crossover switching network, where MQW SEED arrays are used as electrically addressed four-function interchange nodes, is described and the optical crossover interconnection of 64 x 64, and high-speed four-function, interchange nodes is demonstrated in the experiment.

Intensity-moments characterization of general pulsed paraxial beams with the Wigner distribution function

On the basis of the space-time Wigner distribution function (STWDF), we use the matrix formalism to study the propagation laws for the intensity moments of quasi-monochromatic and polychromatic pulsed paraxial beams. The advantages of this approach are reviewed. Also, a least-squares fitting method for interpreting the physical meaning of the effective curvature matrix is described by means of the STWDF. Then the concept is extended to the higher-order situation, and what me believe is a novel technique for characterizing the beam phase is presented. (C) 1999 Optical Society of America [S0740-3232(99)001009-1].

Biomimetic pattern recognition theory and its applications

Biomimetic pattern recogntion (BPR), which is based on "cognition" instead of "classification", is much closer to the function of human being. The basis of BPR is the Principle of homology-continuity (PHC), which means the difference between two samples of the same class must be gradually changed. The aim of BPR is to find an optimal covering in the feature space, which emphasizes the "similarity" among homologous group members, rather than "division" in traditional pattern recognition. Some applications of BPR are surveyed, in which the results of BPR are much better than the results of Support Vector Machine. A novel neuron model, Hyper sausage neuron (HSN), is shown as a kind of covering units in BPR. The mathematical description of HSN is given and the 2-dimensional discriminant boundary of HSN is shown. In two special cases, in which samples are distributed in a line segment and a circle, both the HSN networks and RBF networks are used for covering. The results show that HSN networks act better than RBF networks in generalization, especially for small sample set, which are consonant with the results of the applications of BPR. And a brief explanation of the HSN networks' advantages in covering general distributed samples is also given.

Energy spectrum and persistent currents in finite-width mesoscopic ring with radial potential barrier threading a magnetic flux through its hole

The energy spectrum and the persistent currents are calculated for a finite-width mesoscopic annulus with radial potential barrier, threading a magnetic flux through the hole of the ring. Owing to the presence of tunneling barrier, the coupling effect leads to the splitting of each radial energy subband of individual concentrical rings into two one. Thus, total currents and currents carried by single high-lying eigenstate as a function of magnetic flux exhibit complicated patterns. However, periodicity and antisymmetry of current curves in the flux still preserve.

The structure and function of neurons with variable nonlinear transfer function

One novel neuron with variable nonlinear transfer function is firstly proposed, It could also be called as subsection transfer function neuron. With different transfer function components, by virtue of multi-thresholded, the variable transfer function neuron switch on among different nonlinear excitated state. And the comparison of output's transfer characteristics between it and single-thresholded neuron will be illustrated, with some practical application experiments on Bi-level logic operation, at last the simple comparison with conventional BP, RBF, and even DBF NN is taken to expect the development foreground on the variable neuron.. The novel nonlinear transfer function neuron could implement the random nonlinear mapping relationship between input layer and output layer, which could make variable transfer function neuron have one much wider applications on lots of reseach realm such as function approximation pattern recognition data compress and so on.

DISTRIBUTION FUNCTION OF A DRIFTING ELECTRON-GAS FOR GENERAL ENERGY-BAND STRUCTURES

The usual application of the Lei-Ting balance equation method for treating electron transport problems makes use of a Fermi distribution function for the electron motion relative to the center of mass. It is pointed out that this presumes the existence of a moving frame of reference that is dynamically equivalent to the rest frame of reference, and this is only true for electrons with a constant effective mass. The method is thus inapplicable to problems where electrons governed by a general energy-band dispersion E(k) are important (such as in miniband conduction). It is demonstrated that this difficulty can be overcome by introducing a distribution function for a drifting electron gas by maximizing the entropy subject to a prescribed average drift velocity. The distribution function reduces directly to the usual Fermi distribution for electron motion relative to the center of mass in the special case of E(k)=($) over bar h(2)\k\(2)/2m*. This maximum entropy treatment of a drifting electron gas provides a physically more direct as well as a more general basis for the application of the balance equation method.

Biomimetic (topological) pattern recognition - A new model of pattern recognition theory and its application

A new theoretical model of Pattern Recognition principles was proposed, which is based on "matter cognition" instead of "matter classification" in traditional statistical Pattern Recognition. This new model is closer to the function of human being, rather than traditional statistical Pattern Recognition using "optimal separating" as its main principle. So the new model of Pattern Recognition is called the Biomimetic Pattern Recognition (BPR)(1). Its mathematical basis is placed on topological analysis of the sample set in the high dimensional feature space. Therefore, it is also called the Topological Pattern Recognition (TPR). The fundamental idea of this model is based on the fact of the continuity in the feature space of any one of the certain kinds of samples. We experimented with the Biomimetic Pattern Recognition (BPR) by using artificial neural networks, which act through covering the high dimensional geometrical distribution of the sample set in the feature space. Onmidirectionally cognitive tests were done on various kinds of animal and vehicle models of rather similar shapes. For the total 8800 tests, the correct recognition rate is 99.87%. The rejection rate is 0.13% and on the condition of zero error rates, the correct rate of BPR was much better than that of RBF-SVM.

Global stability of Hopfield neural networks

This paper gives a condition for the global stability of a continuous-time hopfield neural network when its activation function maybe not monotonically increasing.

Range-based attacks on links in random scale-free networks

Range and load play key roles in the problem of attacks on links in random scale-free (RSF) networks. In this paper we obtain the approximate relation between range and load in RSF networks by the generating function theory, and then give an estimation about the impact of attacks on the efficiency of the network. The results show that short-range attacks are more destructive for RSF networks, and are confirmed numerically.

Enhancing robustness and immunization in geographical networks

We find that different geographical structures of networks lead to varied percolation thresholds, although these networks may have similar abstract topological structures. Thus, strategies for enhancing robustness and immunization of a geographical network are proposed. Using the generating function formalism, we obtain an explicit form of the percolation threshold q(c) for networks containing arbitrary order cycles. For three-cycles, the dependence of q(c) on the clustering coefficients is ascertained. The analysis substantiates the validity of the strategies with analytical evidence.

Analytical potential energy function and spectroscopic parameters for the ground and excited states of NaH

set of energies at different internuclear distances for the ground electronic state and two excited electronic states of NaH molecule have been calculated using valence internally contracted multireference configuration interaction(MRCI) including Davidson correction and three basis sets. Then, a potential energy curve (PEC) for each state was determined by extrapolating MRCI energies to the complete basis sets limit. Based on the PECs, accurate vibrational energy levels and rotational constants were determined. The computational PECs are were fitted to analytical potential energy functions using the Murrell-Sorbie potential function. Then, accurate spectroscopic parameters were calculated. Compared with experimental results, values obtained with the basis set extrapolation yield a potential energy curve that gives accurate vibrational energy levels, rotational constants and spectroscopic parameters for the NaH molecule. (C) 2004 Elsevier B.V. All rights reserved.

Theoretical study on analytical potential function and spectroscopic parameters for CaF molecule

The equilibrium properties and potential energy curves of the ground electronic state of CaF have been calculated using the Brueckner Doubles calculation with a triples contribution added [BD(T)] and the gradient-corrected density functional theory with three-parameter exact exchange mixing (B3LY-P) method, with 6-311 + G*,6-311 + G(2df,2pd) and 6-311 + G(3df,3pd) basis sets. All the computational PECs are fitted to analytical potential energy functions using Murrell-Sorbie, Huxley and Tang-Toennies potentials. Based on this, the spectroscopic parameters are calculated, and then compared with some other theoretical and experimental data. (C) 2004 Elsevier B.V. All rights reserved.

Retention modeling and simultaneous optimization of pH value and gradient steepness in RP-HPLC using feed-forward neural networks

A novel approach is proposed for the simultaneous optimization of mobile phase pH and gradient steepness in RP-HPLC using artificial neural networks. By presetting the initial and final concentration of the organic solvent, a limited number of experiments with different gradient time and pH value of mobile phase are arranged in the two-dimensional space of mobile phase parameters. The retention behavior of each solute is modeled using an individual artificial neural network. An "early stopping" strategy is adopted to ensure the predicting capability of neural networks. The trained neural networks can be used to predict the retention time of solutes under arbitrary mobile phase conditions in the optimization region. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for amino acids derivatised by a new fluorescent reagent.

Retention modeling and optimization of pH value and solvent composition in HPLC using back-propagation neural networks and uniform design

A novel method for the optimization of pH value and composition of mobile phase in HPLC using artificial neural networks and uniform design is proposed. As the first step. seven initial experiments were arranged and run according to uniform design. Then the retention behavior of the solutes is modeled using back-propagation neural networks. A trial method is used to ensure the predicting capability of neural networks. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for both basic and acidic samples.