From the first application of the Boolean model to the cell cycle regulation network of budding yeast, new regulative pathways have been discovered, particularly in the G1/S transition circuit. This discovery called for finer modeling to study the essential biology, and the resulting outcomes are first introduced in the article. A traditional Boolean network model set up for the new G1/S transition circuit shows that it cannot correctly simulate real biology unless the model parameters are fine tuned. The deficiency is caused by an overly coarse-grained description of the inhibitor binding process, which shall be overcome by a two-vector model proposed whose robustness is surveyed using random perturbations. Simulations show that the proposed two-vector model is much more robust in describing inhibitor binding processes within the Boolean framework.

The number and arrangement of subunits that form a protein are referred to as quaternary structure. Knowing the quaternary structure of an uncharacterized protein provides clues to finding its biological function and interaction process with other molecules in a biological system. With the explosion of protein sequences generated in the Post-Genomic Age, it is vital to develop an automated method to deal with such a challenge. To explore this problem, we adopted an approach based on the pseudo position-specific score matrix (Pse-PSSM) descriptor, proposed by Chou and Shen, representing a protein sample. The Pse-PSSM descriptor is advantageous in that it can combine the evolution information and sequence-correlated information. However, incorporating all these effects into a descriptor may cause ‘high dimension disaster’. To overcome such a problem, the fusion approach was adopted by Chou and Shen. A completely different approach, linear dimensionality reduction algorithm principal component analysis (PCA) is introduced to extract key features from the high-dimensional Pse-PSSM space. The obtained dimension-reduced descriptor vector is a compact representation of the original high dimensional vector. The jackknife test results indicate that the dimensionality reduction approach is efficient in coping with complicated problems in biological systems, such as predicting the quaternary structure of proteins.

Activating transcription factor 2 (ATF2) is a member of the ATF/cyclic AMP-responsive element binding protein family of transcription factors. However, the information concerning ATF2 ion-mediated DNA binding module and function of ATF2 in malignant pleural mesothelioma (MPM) has never been addressed. In this study, by using GRNInfer and GVedit based on linear programming and a decomposition procedure, with integrated analysis of the function cluster using Kappa statistics and fuzzy heuristic clustering in MPM, we identified one ATF2 ion-mediated DNA binding module involved in invasive function including ATF2 inhibition to target genes FALZ, C20orf31, NME2, PLOD2, RNF10, and RNASEH1, upstream RNF10 and PLOD2 activation to ATF2, upstream RNASEH1 and FALZ inhibition to ATF2 from 40 MPM tumors and 5 normal pleural tissues. Remarkably, our results showed that the predominant effect of ATF2 occupancy is to suppress the activation of target genes on MPM. Importantly, the ATF2 ion-mediated DNA binding module reflects ‘mutual’ positive and negative feedback regulation mechanism of ATF2 with up-and down-stream genes. It may be useful for developing novel prognostic markers and therapeutic targets in MPM.

The nucleosome is the fundamental unit of eukaryotic genomes. Its positioning in the promoter region plays a central role in regulating gene transcription. Experimental evidence suggests that the genomic DNA sequence is one impor

Based on the characteristics of colony emergence of artificial organisms, their dynamic interaction with the environment, and the food-chain crucial to the life system, the rules of local activities of artificial organisms at different levels are defined. The article proposes an artificial life-based algorithm, which is referred to as the food-chain algorithm. This algorithm optimizes computation by simulating the evolution of natural ecosystems and the information processing mechanism of natural organisms. The definition, idea and flow of the algorithm are introduced, and relevant rules on metabolic energy and change in the surroundings where artificial-life individuals live are depicted. Furthermore, key parameters of the algorithm are systematically analyzed. Test results show that the algorithm has quasi-life traits that include being autonomous, evolutionary, and self-adaptive. These traits are highly fit for optimization problems of life-like systems such as the location-allocation problem of a distribution network system.

All eight possible extended rough set models in incomplete information systems are proposed. By analyzing existing extended models and technical methods of rough set theory, the strategy of model extension is found to be suitable for processing incomplete information systems instead of filling possible values for missing attributes. After analyzing the definitions of existing extended models, a new general extended model is proposed. The new model is a generalization of indiscernibility relations, tolerance relations and non-symmetric similarity relations. Finally, suggestions for further study of rough set theory in incomplete information systems are put forward.

An adaptive background model based on maximum statistical probability and a shadow suppression scheme for indoor and outdoor people detection by exploiting hue saturation value (HSV) color information is proposed. To obtain the initial background scene, the frequency of R, G, and B component values for each pixel at the same position in the learning sequence are respectively calculated; the R, G, and B component values with the biggest ratios are incorporated to model the initial background. The background maintenance, or the so-called background re-initiation, is also proposed to adapt to scene changes such as illumination changes and scene geometry changes. Moving cast shadows generally exhibit a challenge for accurate moving target detection. Based on the observation that a shadow cast on a background region lowers its brightness but does not change its chromaticity significantly, we address this problem in the article by exploiting HSV color information. In addition, quantitative metrics is introduced to evaluate the algorithm on a benchmark suite of indoor and outdoor video sequences. The experimental results are given to show the performance of the algorithm.

To improve system performance and reduce the complexity and cost of receiver hardware, we investigated a new multiple-input multiple-output (MIMO) scheme combining maximal-ratio transmitting and receiver antenna selection (MRT/RAS). In this scheme, a single receiving antenna, which maximizes the signal-to-noise ratio (SNR) at the receiver, is selected for demodulation. The closed-form outage probability and the bit error rate (BER) of the MRT/RAS system are both presented. The simulation demonstrates that the MRT/RAS scheme can achieve a full diversity order as if all the receiving antennas were used. It is shown that the MRT/RAS scheme outperforms some more complex space-time codes of the same spectral efficiency. The analytical results are verified by simulation. In the end, we also analyze the MRT/RAS system based on partial channel information.

A new scheme is proposed to separate several compressed video signals transferred in multiple wireless channels using the blind source separation method. The scheme selects IEEE 802.11b and XVID compression as the wireless communications channel and MPEG-4 video compression, respectively. A simulation model is then made for the video communications under a wireless environment. The model can separate several video signals using blind source separation. Simulations show that the normalized whiten plus cyclic whiten based on cyclostationary (NWCW-CS) algorithm based upon the cyclostationary characteristics of signals has the best separation performance and fast convergence. Besides, the algorithm can solve the mixing of video signals. The image of the transferred video signals decompressed by XVID is nearly consistent with the source ones. The new method meets the requirement of real-time video communications.

The performance of ground moving target detection for distributed satellites will be affected significantly when there is an image registration error, clutter decorrelation and array error. In this paper, a new approach to moving target detection and relocation is proposed based on multi-channel and multi-pixel adaptive signal processing in an image domain. First, multi-channel and multi-pixel joint data are equated to a simple array model. Given that there is an image registration error, the real steering vector of the moving target can be estimated through a space projection approach. The optimal beam forming approach is used to cancel clutter, and at the same time the cross-track velocity of the moving target can be determined by searching for the peak value of the cost function. The moving target can then be relocated on the SAR image. The simulation results indicate that this method has a good robustness to image registration error, clutter decorrelation and array error. The detection performance and the estimation accuracy are significantly improved.

The performance of adaptive array beamforming algorithms substantially degrades in practice because of a slight mismatch between actual and presumed array responses to the desired signal. A novel robust adaptive beamforming algorithm based on Bayesian approach is therefore proposed. The algorithm responds to the current environment by estimating the direction of arrival (DOA) of the actual signal from observations. Computational complexity of the proposed algorithm can thus be reduced compared with other algorithms since the recursive method is used to obtain inverse matrix. In addition, it has strong robustness to the uncertainty of actual signal DOA and makes the mean output array signal-to-interference-plus-noise ratio (SINR) consistently approach the optimum. Simulation results show that the proposed algorithm is better in performance than conventional adaptive beamforming algorithms.

It has long been an outstanding conjecture that any (2n - 1)-stage shuffle exchange network (Omega network) is rearrangeable for 2ⁿ × 2ⁿ. Many researchers have failed to prove this conjecture, including a recent one established by Hasan. However, nobody has pointed out its fallacy. Therefore, as one of the objectives, this paper shall clarify this fact. Since the case of n = 3 has been proven by many researchers 12, this paper uses a constructive approach to prove that when n = 4, the 7-stage 16 × 16 shuffle exchange network is also rearrangeable. The paper also presents the model of a balanced tree to avoid internal conflict, the representation of permutations using a connection graph and loop graph, and the concepts of symmetry graph and identical transform. Based on graphic composition and bipartition, the permutations 16 × 16 are divided into five classes, with five assignment algorithms proposed. These algorithms are simpler, clearer and easier to program. The techniques used for n = 4 may provide hints for the general case of n > 4.

An online noise variance estimator for multi-sensor systems with unknown noise variances is proposed by using the correlation method. Based on the Riccati equation and optimal fusion rule weighted by scalars for state components, a self-tuning component decoupled information fusion Kalman filter is presented. It is proved that the filter converges to the optimal fusion Kalman filter in a realization by dynamic error system analysis method, so that it has asymptotic optimality. Its effectiveness is demonstrated by simulation for a tracking system with 3 sensors.

The backstepping method is applied to a certain class of switched nonlinear systems to design state feedback controllers and a switching law based on multi-Lyapunov functions. The state feedback controllers and the switching law that can stabilize the system are developed. The switched nonlinear systems with uncertainties can be stabilized robustly by using the proposed method. Finally, simulation results show the effectiveness of the method.

The problem of synthesizing a planar four-bar linkage with two given fixed pivots such that the coupler curve passes through five given points is considered with the Groebner-Sylvester hybrid approach. First, closed-form equations of a single point are constructed. The reduced Groebner basis in degree lexicographic ordering for the closed-form equations is then obtained using computer algebra. A 23 × 23 Sylvester’s matrix can be constructed by selecting 23 out of 89 Groebner bases. A 36th degree univariate equation is obtained directly from the determinate of the matrix. The same result can be obtained with a continuation method. A numerical example is given and verifies that the problem has at most 36 solutions in the complex field.

The back-propagation (BP) neural network is proposed to correct nonlinearity and optimize the force measurement and calibration of an optical tweezer system. Considering the low convergence rate of the BP algorithm, the Levenberg-Marquardt (LM) algorithm is used to improve the BP network. The proposed method is experimentally studied for force calibration in a typical optical tweezer system using hydromechanics. The result shows that with the nonlinear correction using BP networks, the range of force measurement of an optical tweezer system is enlarged by 30% and the precision is also improved compared with the polynomial fitting method. It is demonstrated that nonlinear correction by the neural network method effectively improves the performance of optical tweezers without adding or changing the measuring system.

The modern optics industry demands rigorous surface quality with minimum defects, which presents challenges to optics machining technologies. There are always certain defects on the final surfaces of the components formed in convent