Community Detection in Disease-Gene Network Based on Principal Component Analysis

The identification of communities is imperative in the understanding of network structures and functions.Using community detection algorithms in biological networks, the community structure of biological networks can be determined, which is helpful in analyzing the topological structures and predicting the behaviors of biological networks. In this paper, we analyze the diseasome network using a new method called disease-gene network detecting algorithm based on principal component analysis, which can be used to investigate the connection between nodes within the same group. Experimental results on real-world networks have demonstrated that our algorithm is more efficient in detecting community structures when compared with other well-known results.     

Deterministic Teleportation of an Arbitrary Two-Qubit State Via a One-Dimensional Four-Qubit Cluster State

This paper presents an approach to deterministically teleport an arbitrary two-qubit state through a one-dimensional four-qubit cluster state serving as a probabilistic quantum channel. The channel is modulated in advance to avoid damage to the original states in this scheme, which is caused by the inevitable failure of constructing a channel between the sender and the receiver. The scheme is flexible because the channel can be modulated either by the sender or by the receiver, with the option of deciding whether the sender or the receiver modulates the channel, according to the distribution of the available particle resources. The efficiency can be improved by reusing previously discarded results that may lead to a faithful channel. The scheme can be uniformly performed, so the design process can be greatly simplified to realize a reliable deterministic teleportation. Finally, the scheme is extended to deterministic teleportation of an arbitrary n-qubit state in a generalized form.     

Discovering the cellular-localized functional modules and modular interactions in response to liver cancer

In this paper, we firstly identify the functional modules enriched with differentially expressed genes （DEGs） and characterized by biological processes in specific cellular locations, based on gene ontology （GO） and microarray data. Then, we further define and filter disease relevant signature modules according to the ranking of the disease discriminating abilities of the pre-selected functional modules. At last, we analyze the potential way by which they cooperate towards human disease. Application of the proposed method to the analysis of a liver cancer dataset shows that, using the same false discovery rate （ FDR ） threshold, we can find more biologically meaningful and detailed processes by using the cellular localization information. Some biological evidences support the relevancy of our biological modules to the disease mechanism.     

Dynamic Analysis of Cable Roofs Under Transient Wind:A Comparison Between Time Domain and Frequency Domain Approaches

At present, high-speed computing capabilities and advanced nonlinear dynamic finite element procedures enable detailed dynamic analysis of cable structures. Although deterministic approaches require considerable analysis time and effort in relation to modeling, running, and data processing, they seem to be the only alternative to obtain high accuracy. Detailed dynamic analysis of cable roof networks is sophisticated and requires advanced modeling expertise. This paper presents a comparison between detailed nonlinear dynamic analysis and a simplified frequency domain approach to estimate the maximum probable response of weakly nonlinear cable roofs. The approach can be considered as alternative to detailed time-domain analysis in the preliminary design phase, or can be used to validate results obtained from more elaborated numerical models. The proposed method is illustrated with two examples of cable net roofs that were also analysed in the time domain.     

Channel noise-induced phase transition of spiral wave in networks of Hodgkin-Huxley neurons

The phase transition of spiral waves in networks of Hodgkin-Huxley neurons induced by channel noise is investigated in detail.All neurons in the networks are coupled with small-world connections,and the results are compared with the case for regular networks,in which all neurons are completely coupled with nearest-neighbor connections.A statistical variable is defined to study the collective behavior and phase transition of the spiral wave due to the channel noise and topology of the network.The effect of small-world connection networks is described by local regular networks and long-range connection with certain probability p.The numerical results confirm that (1) a stable rotating spiral wave can be developed and maintain robust with low p,where the breakup of the spiral wave and turbulence result from increasing the probability p to a certain threshold;(2) appropriate intensity of the optimized channel noise can develop a spiral wave among turbulent states in small-world connection networks of H-H neurons;and (3) regular connection networks are more robust to channel noise than small-world connection networks.A spiral wave in a small-world network encounters instability more easily as the membrane temperature is increased to a certain high threshold.     

The Studying of Virtual Token Passing Approach in ControlNet

MAC protocol of ControlNet uses Virtual Token Passing (VTP) to solve the medium access contention, to assure the deterministic and repeatability of message transmission. This paper presents the basic method that VTP uses to transmit real-time I/O message, explicit message and maintenance message in scheduled part, unscheduled part and guardband part respectively. On the basis of analyzing the factors of message transmission delay, the maximum message transmission delay in scheduled part is studied, and the result shows it can be determined, so the deterministic and repeatability of message transmission in ControlNet is validated.     

Biodegradation of Straw Stalk and Experiment of Ethanol Fermentation

Biological preprocessing of lignin degradation of straw stalk with white-rot fungi and ethanol fermentation experiment are carried out. The result shows that after biological preprocessing, lignin content in straw stalk reduces significantly. The higher the lignin degradation rate, the higher the ethanol productivity. When the technique of solid state culturing to degrade lignin is adopted to produce ethanol, the productivity is 15.8%, which is equal to 374.4% of that produced without preprocessing process.     

LBLP: Link-Clustering-Based Approach for Overlapping Community Detection

Recently, complex networks have attracted considerable research attention. Community detection is an important problem in the field of complex networks and is useful in a variety of applications such as information propagation, link prediction, recommendation, and marketing. In this study, we focus on discovering overlapping community structures by using link partitions. We propose a Latent Dirichlet Allocation (LDA)-Based Link Partition (LBLP) method, which can find communities with an adjustable range of overlapping. This method employs the LDA model to detect link partitions, which can calculate the community belonging factor for each link. On the basis of this factor, link partitions with bridge links can be found efficiently. We validate the effectiveness of the proposed solution by using both real-world and synthesized networks. The experimental results demonstrate that the approach can find a meaningful and relevant link community structure.     

Randomness in the Hybrid Modeling and Simulation of Insulin Secretion Pathways in Pancreatic Islets

Insulin secreted by pancreatic islet ˇ-cells is the principal regulating hormone of glucose metabolism.Disruption of insulin secretion may cause glucose to accumulate in the blood, and result in diabetes mellitus.Although deterministic models of the insulin secretion pathway have been developed, the stochastic aspect of this biological pathway has not been explored. The first step in this direction presented here is a hybrid model of the insulin secretion pathway, in which the delayed rectifying KCchannels are treated as stochastic events. This hybrid model can not only reproduce the oscillation dynamics as the deterministic model does, but can also capture stochastic dynamics that the deterministic model does not. To measure the insulin oscillation system behavior, a probability-based measure is proposed and applied to test the effectiveness of a new remedy.     

A nearest neighbor search algorithm of high-dimensional data based on sequential NPsim matrix

Problems existin similarity measurement and index tree construction which affect the perform-ance of nearest neighbor search of high-dimensional data .The equidistance problem is solved using NPsim function to calculate similarity .And a sequential NPsim matrix is built to improve indexing performance .To sum up the above innovations , a nearest neighbor search algorithm of high-dimen-sional data based on sequential NPsim matrix is proposed in comparison with the nearest neighbor search algorithms based on KD-tree or SR-tree on Munsell spectral data set .Experimental results show that the proposed algorithm similarity is better than that of other algorithms and searching speed is more than thousands times of others .In addition , the slow construction speed of sequential NPsim matrix can be increased by using parallel computing .     

A feature ensemble technology to identify molecular mechanisms for distinction between multiple subtypes of lymphoma

Due to complexities and genetic heterogeneities of biological phenotypes, robust computational approaches are desirable to achieve high generalization performance with multiple classifiers, perturbations of the data structures, and biological interpretations. The purpose of this study is to extend our developed ensemble decision approach to distinguish multiple heterogeneous phenotypes and to elucidate the underlying molecular bridges that intertwine the subtypes. Our work identifies the significant molecular mechanisms （disease-relevant genes and functions） that underpin the complex molecular mechanisms for distinction between multiple phenotypes. Feature genes and hierarchical gene cores identified by our method have achieved high accuracy in the classification of multiple phenotypes. The results show that the proposed analysis strategy is feasible and powerful in the classification of biological subtypes and in the explanation of the molecular connections between clinical phenotypes. Biological interpretations with Gene Ontology revealed concerted genetic pathways for some lymphoma subtypes.     

A novel evolutionary algorithm for global numerical optimization with continuous variables

Evolutionary algorithms （EAs） are a class of general optimization algorithms which are applicable to functions that are multimodal, non-differentiable, or even discontinuous. In this paper, a novel evolutionary algorithm is proposed to solve global numerical optimiza- tion with continuous variables. In order to make the algorithm more robust, the initial population is generated by combining determinate factors with random ones, and a decent scale function is designed to tailor the crossover operator so that it can not only find the decent direction quickly but also keep scanning evenly in the whole feasible space. In addition, to improve the performance of the algorithm, a mutation operator which increases the convergence-rate and ensures the convergence of the proposed algorithm is designed. Then, the global convergence of the presented algorithm is proved in detail. Finally, the presented algorithm is executed to solve 24 benchmark problems, and the results show that the convergence-rate of the proposed algorithm is much faster than that of the compared algorithms.     

Integrated semantic similarity model based on ontology

To solve the problem of the inadequacy of semantic processing in the intelligent question answering system. an integrated semantic similarity model which calculates the semantic similarity using the geometric distance and informarion content is presented in this paper. With the help of interrelationship between concepts, the information content ofconcepts and the strength of the edges in the ontology network. we can calculate the semantic similarity between two concepts and provide information for the further calculation of the semantic similarity between user‘s question and answers in knowlegdge base. The results of the experiments on the prototype have shown that the semantic problem in natural language processing can also be solved with the help of the knowledge and the abundant semantic information in ontology. More than 90% accuracy with less than 50 ms average searching time in the intelligent question answering prototype system based on ontology has been reached. The result is vety satisfied.     

Online Distributed Fault Detection of Sensor Measurements

In wireless sensor networks (WSNs), a faulty sensor may produce incorrect data and transmit them to the other sensors. This would consume the limited energy and bandwidth of WSNs. Furthermore, the base station may make inappropriate decisions when it receives the incorrect data sent by the faulty sensors. To solve these problems, this paper develops an online distributed algorithm to detect such faults by exploring the weighted majority vote scheme. Considering the spatial correlations in WSNs, a faulty sensor can diagnose itself through utilizing the spatial and time information provided by its neighbor sensors. Simulation results show that even when as many as 30% of the sensors are faulty, over 95% of faults can be correctly detected with our algorithm. These results indicate that the proposed algorithm has excellent performance in detecting fault of sensor measurements in WSNs.     

On the Edge of Web-Based Multiple Sequence Alignment Services

There are many web-based multiple sequence alignment services accessible around the world. However, many researchers working on biological sequence analysis still struggle with inefficient, unfriendly user interface, and limited capability multiple sequence alignment software. In this study, we provide a comprehensive survey of regional and continental facilities that provide web-based alignment services. We also analyze and identify much needed services that are not available through these existing service providers. We then implement a web-based model to address these needs. From that perspective, our web-based multiple sequence alignment server, SeqAna, provides a unique set of services that none of these studied facilities have. For example, SeqAna provides a multiple sequence alignment scoring and ranking service. This service, the only of its kind, allows SeqAna’s users to perform multiple sequence alignment with several alignment tools and rank the results of these alignments in the order of quality. With this service, SeqAna’s users will be able to identify which alignment tools are more appropriate for their specific set of sequences. In addition, SeqAna’s users can customize a small alignment sample as a reference for SeqAna to automatically identify the best tool to align their large set of sequences.     

An Aggregation Cache Replacement Algorithm Based on Ontology Clustering

This paper describes the theory, implementation, and experimental evaluation of an Aggregation Cache Replacement （ ACR ） algorithm. By considering application background, carefully choosing weight values, using a special formula to calculate the similarity, and clustering ontologies by similarity for getting more embedded deep relations, ACR combines the ontology similarity with the value of object and decides which object is to be replaced. We demonstrate the usefulness of ACR through experiments. （a） It is found that the aggregation tree is created wholly differently according to the application cases. Therefore, clustering can direct the content adaptation more accurately according to the user perception and can satisfy the user with different preferences. （b） After comparing this new method with widely-used algorithm Last-Recently-Used （LRU） and First-in-First-out （FIFO） method, it is found that ACR outperforms the later two in accuracy and usability. （c） It has a better semantic explanation and makes adaptation more personalized and more precise.     

Wavelet Neural Networks for Adaptive Equalization by Using the Orthogonal Least Square Algorithm

Equalizers are widely used in digital communication systems for corrupted or time varying channels. To overcome performance decline for noisy and nonlinear channels, many kinds of neural network models have been used in nonlinear equalization. In this paper, we propose a new nonlinear channel equalization, which is structured by wavelet neural networks. The orthogonal least square algorithm is applied to update the weighting matrix of wavelet networks to form a more compact wavelet basis unit, thus obtaining good equalization performance. The experimental results show that performance of the proposed equalizer based on wavelet networks can significantly improve the neural modeling accuracy and outperform conventional neural network equalization in signal to noise ratio and channel non-linearity.     

Interference-Aware Probabilistic Routing for Wireless Sensor Networks

Wireless communications are prone to the interference, so the data transmission among the nodes in wireless sensor networks deployed in complex environments has the obvious uncertainty. This paper adopts probability theory to extend the existed interference model, and gives an interference analysis model and implements it through the cross-layer method. In addition, the isotonic property of the interference-aware routing metric is proved. Then, a probabilistic routing algorithm is proposed and its correctness and time-space complexity are analyzed. Simulation results show that the proposed algorithm can achieve better packet delivery ratio, throughput, jitter and average delay in dense deployment under the different loads at the expense of the comparable average length of paths compared with the Adhoc On-Demand Distance Vector (AODV) algorithm.     

Network Visualization in Cell Biology

Many of the processes known to take place in biological cells are analyzed in the form of different types of network.The complexity of these networks increases along with our knowledge of these processes,making their analysis more difficult.Network visualization is a powerful analysis method that will have to be developed further to deal with this complexity.This survey provides a brief overview of network visualization in general,followed by an in-depth discussion of its application to three network types specific to cell biology,namely gene regulatory,protein interaction,and metabolic networks.Finally,we discuss the difficulty of visually integrating these network types and trying to compare networks of cells that belong to different organisms.     

A novel method for solving the multiple traveling salesmen problem with multiple depots

Multi-traveling salesman problem(MTSP) is an extension of traveling salesman problem,which is a famous NP hard problem,and can be used to solve many real world problems,such as railway transportation,routing and pipeline laying.In this paper,we analyze the general properties of MTSP,and find that the multiple depots and closed paths in the graph is a big issue for MTSP.Thus,a novel method is presented to solve it.We transform a complicated graph into a simplified one firstly,then an effective algorithm is proposed to solve the MTSP based on the simplified results.In addition,we also propose a method to optimize the general results by using 2-OPT.Simulation results show that our method can find the global solution for MTSP efficiently.