Kangshun Li

Molecular Dynamics Simulations of DNA-Free and DNA-Bound TAL Effectors

TAL (transcriptional activator-like) effectors (TALEs) are DNA-binding proteins, containing a modular central domain that recognizes specific DNA sequences. Recently, the crystallographic studies of TALEs revealed the structure of DNA-recognition domain. In this article, molecular dynamics (MD) simulations are employed to study two crystal structures of an 11.5-repeat TALE, in the presence and absence of DNA, respectively. The simulated results indicate that the specific binding of RVDs (repeat-variable diresidues) with DNA leads to the markedly reduced fluctuations of tandem repeats, especially at the two ends. In the DNA-bound TALE system, the base-specific interaction is formed mainly by the residue at position 13 within a TAL repeat. Tandem repeats with weak RVDs are unfavorable for the TALE-DNA binding. These observations are consistent with experimental studies. By using principal component analysis (PCA), the dominant motions are open-close movements between the two ends of the superhelical structure in both DNA-free and DNA-bound TALE systems. The open-close movements are found to be critical for the recognition and binding of TALE-DNA based on the analysis of free energy landscape (FEL). The conformational analysis of DNA indicates that the 5′ end of DNA target sequence has more remarkable structural deformability than the other sites. Meanwhile, the conformational change of DNA is likely associated with the specific interaction of TALE-DNA. We further suggest that the arrangement of N-terminal repeats with strong RVDs may help in the design of efficient TALEs. This study provides some new insights into the understanding of the TALE-DNA recognition mechanism.

A hybrid particle swarm optimization algorithm using adaptive learning strategy

Abstract Many optimization problems in reality have become more and more complex, which promote the research on the improvement of different optimization algorithms. The particle swarm optimization (PSO) algorithm has been proved to be an effective tool to solve various kinds of optimization problems. However, for the basic PSO, the updating strategy is mainly aims to learn the global best, and it often suffers premature convergence as well as performs poorly on many complex optimization problems, especially for multimodal problems. A hybrid PSO algorithm which employs an adaptive learning strategy (ALPSO) is developed in this paper. In ALPSO, we employ a self-learning based candidate generation strategy to ensure the exploration ability, and a competitive learning based prediction strategy to guarantee exploitation of the algorithm. To balance the exploration ability and the exploitation ability well, we design a tolerance based search direction adjustment mechanism. The experimental results on 40 benchmark test functions demonstrate that, compared with five representative PSO algorithms, ALPSO performs much better than the others in more cases, on both convergence accuracy and convergence speed.

Comparative Analysis of Genetic Algorithm and Ant Colony Algorithm on Solving Traveling Salesman Problem

Ant Colony Algorithm and Genetic Algorithm (GA), two bionic-inspired optimization algorithms, have great potentials to solve the combination optimization problems, respectively used in solving traveling salesman problem, but there are some shortcomings if only one of them is used to solve TSP. Performance comparative analysis have been done by using ACA and GA respectively in solving TSP in this paper. The experiments show the advantages and disadvantages used only ACA or GA, we can overcome the shortcomings if GA and ACA are combined to solve TSP and get faster convergent speed and more accurate results compared with only using ACA or GA.

Exploring mutual information-based sentimental analysis with kernel-based extreme learning machine for stock prediction

Stock price volatility prediction is regarded as one of the most attractive and meaningful research issues in financial market. Some existing researches have pointed out that both the prediction accuracy and the prediction speed are the most important factors in the process of stock prediction. In this paper, we focus on the problem of how to design a methodology which can improve prediction accuracy as well as speed up prediction process, and propose a new prediction model which employs mutual information- based sentimental analysis methodology with extreme learning machine to enhance the prediction performance. The two major contributions of our work are (1) as the words in the news documents are not absolutely negative or positive, and the lengths of the financial news documents are various; here, we propose a new sentimental analysis methodology based on mutual information to improve the efficiency of feature selection, which is different from the traditional sentimental analysis algorithm, and a new weighting scheme is also used in the feature weighting process; (2) since ELM is a fast learning model and has been successfully applied in many research fields, we propose a prediction model which combined mutual information-based sentimental analysis with kernel-based ELM named as MISA-K-ELM. This model has the benefits of both statistical sentimental analysis and ELM, which can well balance the requirements of both prediction accuracy and prediction speed. We take experiments on HKEx 2001 stock market datasets to validate the performance of the proposed MISA-K-ELM. The market historical price and the market news are implemented in our MISA-K-ELM. To test the efficiency of MISA, we first compare the prediction accuracy of ELM model using MISA with ELM model using traditional sentimental analysis. Then, we compare our proposed MISA-K-ELM with existing state-of-the-art learning algorithms, such as Back-Propagation Neural Network (BP-NN), and Support Vector Machine (SVM). Our experimental results show that (1) MISA model can help get higher prediction accuracy than traditional sentimental analysis models; (2) MISA-K-ELM and MISA-SVM have a higher prediction accuracy than MISA-BP-NN and MISA-B-ELM; (3) both MISA-K-ELM and MISA-B-ELM can achieve faster prediction speed than MISA-SVM and MISA-BP-NN in most cases; (4) in most cases, MISA-K-ELM has higher prediction accuracy than the other three methodologies.

Thermal noise random number generator based on SHA-2 (512)

Couple to the rapid development of cryptography, the strength of security protocols and encryption algorithms consumingly relies on the quality of random number. This paper presents a new and security random number generator architecture. The philosophy architecture is based on SHA-2 (512) hash function whose security strength ensures the unpredictability of the produced random numbers. Furthermore, an FPGA-based implementation of architecture is described. The proposed architecture is a flexible solution in many applications taking into account the performance, power consumption, flexibility, cost and area.

Automated analog circuit design using two-layer genetic programming

Analog circuits are very important in many high-speed applications such as communications. Since the size of analog circuit is becoming larger and more complex, the design is becoming more and more difficult. This paper proposes a two-layer evolutionary scheme based on genetic programming (GP), which uses a divide-and-conquer approach to evolve the analog circuits. Corresponding to the two-layer GP, a new representation of circuit has been proposed here and it is more helpful to generate expectant circuit graphs. This algorithm can evolve the circuits with dynamical size, circuit topology, and component values. The experimental results on the designs of the voltage amplifier and the low-pass filter show that this algorithm is efficient.

Approximation and Parameterized Runtime Analysis of Evolutionary Algorithms for the Maximum Cut Problem

The maximum cut (MAX-CUT) problem is to find a bipartition of the vertices in a given graph such that the number of edges with ends in different sets reaches the largest. Though, several experimental investigations have shown that evolutionary algorithms (EAs) are efficient for this NP-complete problem, there is little theoretical work about EAs on the problem. In this paper, we theoretically investigate the performance of EAs on the MAX-CUT problem. We find that both the (1+1) EA and the solutions of (m/2) + (1/4)s(G) and (m/2) + (1/2)(√8m + 1 - 1), (1+1) EA*, two simple EAs, efficiently achieve approximation where m and s(G) are respectively the number of edges and the number of odd degree vertices in the input graph. We also reveal that for a given integer k the (1+1) EA* finds a cut of size at least k in expected runtime O(nm + 1/δ4k) and a cut of size at least (m/2) + k in expected runtime O(n2m + 1/δ(64/3)k2), where δ is a constant mutation probability and n is the number of vertices in the input graph. Finally, we show that the (1+1) EA and the (1+1) EA* are better than some local search algorithms in one instance, and we also show that these two simple EAs may not be efficient in another instance.

Allosteric and transport behavior analyses of a fucose transporter with network models

The major facilitator superfamily (MFS) is an important and widespread family of secondary membrane transporters. Recently, an outward-open structure of MFS, the fucose/H+ symporter FucP was determined by X-ray crystallography. In this article, the outward-open form of FucP is analyzed by elastic network models. It is found that the periplasmic half region has remarkable fluctuation, and the closure of the periplasmic half is the most dominant conformational change for outward-open conformation of FucP. To ascertain the process of transport, an adaptive anisotropic network model is applied to explore the allosteric transitions of FucP. In particular, our simulation not only yields the intermediate states similar to that seen in the EmrD crystal structure, but also exhibits the whole transport process of FucP. On the basis of the coarse-grained analyses, we propose a new working model of how FucP mediates the symport of L-fucose and a proton. The allosteric and transport knowledge of FucP revealed in this work can provide some insights into the mechanism studies of MFS and other transport proteins.

Design and Implement of Digital Modulator Based on Improved DDS Technology and DSP Builder

In this paper, according to the basic theory of digital modulator, the design model of digital modulator is built by using the improved Direct Digital Synthesizer (DDS) technology through running the Matlab/DSP Builder environment. Firstly,analyse the function of each module in this model and simulate the grade of algorithms to it and also produce VHDL language, then carry on Register Transport Level (RTL) grade simulation to the model in Modelsim. In the end, it also introduces the way to realize digital modulator in Field Programmable Gate Array(FPGA) chip, which verify the correctness and validity of the model. This design can not only produce many kinds of the digital signal modulation, but can also simplify the hardware circuit of the modern system greatly and save plenty of the system resources and improve the reliability and flexibility. what is more, compared with other designs, the performance price ratio of the design has advantage in modulated signal device and possess certain practicability and usefulness.

A privacy-preserving join on outsourced database

Outsourced database provides a solution for data owners who want to delegate the task of answering database queries to service provider. Of essential concern in such framework is data privacy. The data owner may want to keep the database hidden from service provider. Simultaneously, potential clients may desire a mean of privacypreserving computations on outsourced encrypted database. We present a solution of privacy-preserving join query with computational privacy and low overheads. The primary goal of this paper is to provide a set of security notion for such a system as well as a construction which is secure under the newly introduced security notions.