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Breast cancer is one of the leading causes of deaths from cancer for the female population in both developed and developing countries. The average microelement absorption can affect death rate of breast cancer. Artificial neural networks have been successfully applied to problems in the prediction of death rate of breast cancer induced from average microelement absorption. To predict the death rate of breast cancer induced from average microelement absorption using artificial neural network is feasible and a well trained artificial neural network by Levenberg-Marquardt algorithm reveals an extremely fast convergence and a high degree of accuracy. The investigation demonstrates that the proposed training and forecasting procedure is almost 100 times faster than that of classical BP algorithm and poses higher forecasting precision. With the growth of the database, more and more cases will be collected and used as training set. [ABSTRACT FROM AUTHOR]

An adaptive fuzzy sliding mode controller for highly nonlinear biological system is proposed by considering the model of aneurysms of the circle of willis. The fuzzy logic system approximates and adaptively cancels an unknown plant nonlinearity using the state variables. A control law and adaptive laws for unknown parameters and bounding constant are established so that the whole closed-loop system is stable in the sense of Lyapunov. This paper discussed the nonlinear dynamic behavior and control of blood speed in aneurysm. The simulation for the aneurysms model demonstrates that the proposed controller provide good tracking and estimation performances. [ABSTRACT FROM AUTHOR]

Oilseed would display the characteristics of viscous-elastic-plasticity during pressing. The apparatus and method were successfully developed to measure the rheological properties of rapeseed and dehulled rapeseed, in which creep test was used under different stress. By using of artificial neural networks, the identification model of nonlinear rheological characteristics of rapeseed and dehulled rapeseed were developed on the basis of the creep test. Results indicated that the model simulated the nonlinear rheological characteristics very well. Compared to date fitting method and theoretical analysis method, the method of identification of rheological characteristic for oilseeds by using artificial neural networks is both simple and effective. The critical pressing time of rapeseed and dehulled rapeseed were determined by using of simulated creep curves. [ABSTRACT FROM AUTHOR]

This article introduced an effective design method of robot called remote-brain, which is made the brain and body separated. It leaves the brain in the mother environment, by which we mean the environment in which the brain's software is developed, and talks with its body by wireless links. It also presents a real robot TUT06-B based on this method which has human-machine interaction, vision systems, manipulator etc. Then it discussed the path planning method for the robot based on ant colony algorithm in details, especially the Ant-cycle model. And it also analyzed the parameter of the algorithm which can affect the convergence. Finally, it gives the program flow chat of this algorithm. [ABSTRACT FROM AUTHOR]

A novel in vitro mock circulatory system, which enables to reproduce an inflow restriction, simulating blood volume pooling due to heart failure, was developed to evaluate a hydrodynamic performance of a pulsatile ventricular assist device (VAD) in practical conditions. The concept of this development was motivated by a difference of an inflow restriction between in vitro and in vivo environments. The major idea of this study is to reproduce an inflow restriction by using a centrifugal pump placed at an inflow side of a left ventricular model instead of a constant head reservoir in a conventional circuit. In the novel circuit, the maximum flow rate was obtained at lower systolic fraction as compared with a conventional circuit. This similar tendency by the novel one was observed in an acute animal experiment in sheep. This result suggests that a new mock circuit is effective to confirm a practical drive strategy of the VAD for various diseased conditions. [ABSTRACT FROM AUTHOR]

A pneumatic driven ventricular assist device (VAD) is not only designed for short-term usage of a "bridge to recovery" (BTR) in cardiac function recovery, but also developed to apply as a "bridge to transplantation" (BTT) for cardiac transplant. However, in the latter, the VAD must be exchanged before its expiries of guarantee. In this research, the authors have investigated the connector's strength between connector and cannula, and safety during the VAD exchange. [ABSTRACT FROM AUTHOR]

A primal-dual neural network based on linear variational inequalities (LVI) is presented in this paper, which is used to solve the repetitive motion planning of redundant robots. To do so, a drift-free criterion is exploited. In addition, the physical constraints such as joint limits and joint velocity limits are incorporated into the problem formulation of such a scheme. The scheme is finally reformulated as a quadratic programming (QP) problem and resolved at the velocity-level. Compared to other computational strategies on inverse kinematics, the LVI-based primal-dual neural network is designed based on the QP-LVI conversion and Karush-Kuhn-Tucker (KKT) conditions. With simple piecewise-linear dynamics and global (exponential) convergence to optimal solutions, it can handle general QP and linear programming (LP) problems in the same inverse-free manner. The repetitive motion planning scheme and the LVI-based primal-dual neural network are simulated based on PUMA560 robot manipulator with effectiveness demonstrated. [ABSTRACT FROM AUTHOR]

Due to the complex and time-consuming of the reconstructed medical model, a novel method was proposed to rebuild an apparent and complete topological structure, which is necessary for mesh simplification based on a STL mode1. Firstly the sequence of rebuilding the topology information for various geometric elements was determined. Then, the Red-Black-Tree structure was conducted to get high efficiency for both vertex equivalence testing and element searching when deleting a large number of redundant vertexes. This structure can also handle disorderly and unsystematic data well. Finally, the relation of a vertex and faces was fully exploited to build the edge records, which are used in mesh simplification. This method can improve the mesh simplification procedure more effectively. [ABSTRACT FROM AUTHOR]

A multi-view face detection method for Video surveillance is proposed in this paper. It provides the ability to extract high-level features in terms of human activities rather than low-level features like color, texture and shape. The method is capable of locating human faces over a broad range of views in color image sequences or videos with complex scenes. Firstly, an improved frame difference is used to acquire promising regions of the image. Then it uses the presence of skin-tone pixels to locate faces. Finally, an improved method based on wavelet invariant moment and BPNN is used to verify the candidate face regions. The experimental results show that the proposed algorithm has high speed and low error-detection rate, so it can be used in the real-time video surveillance system. The main distinguishing contribution of this work is being able to detect faces irrespective of their poses by using the wavelet invariant moments as input of the BPNN, whereas contemporary systems deal with frontal-view faces only. The other novel aspect of the work lies in its accuracy of acquiring the candidate area to segment objects from background with the help of motion information and skin information. [ABSTRACT FROM AUTHOR]

Codebook design of VQ (Vector Quantization) is a global optimization problem. The LBG algorithm depends upon the initial codebook and is prone to converge to a local optimal solution. To solve the problem, adopt PSO (Particle Swarm Optimization) to design the optimal codebook of image vector quantization and present PSO-VQ (PSO Vector Quantization) algorithm. According to PSO-VQ, a particle indicates a codebook and the optimal codebook is obtained from iterations of the initial codebooks by method of the particle evolvement. To ensure the solution converge to the global optimal codebook, the authors presented the PCO (Particle Coherent Operation), by which the code vectors of each initial codebook are sorted in ascending order based on the average gray value of the pixels in the code vector, and so that the inner structures of all the particles are essentially identical. The experimental results show that the PSO-VQ algorithm is feasible and effective, as well as develops the application of the PSO. [ABSTRACT FROM AUTHOR]

For various reasons, many people suffer from the bone defects, and how to solve this problem has become a hot topic internationally in the field of tissue engineering. Nowadays, the doctor diagnoses the state of the bone defect illness mainly depending on observing the CT images of patient; it mainly depends on the technique and experience of the doctor. In order to solve this problem, this paper focuses on using the Reverse Engineering technology to make the Skull-Repair-Technology efficient. During this process, we can construct the Repair-Sheet that fits the skull surface and then mold it. This method can shorten the time of operation, reduce the risk of operation. [ABSTRACT FROM AUTHOR]

Signal transduction pathways describe the dynamics of cellular response to input signalling molecules at receptors on the cell membrane. The Mitogen-Activated Protein Kinase (MAPK) cascade is one of such pathways that are involved in many important cellular processes including cell growth and proliferation. This paper describes a black-box model of this pathway created using an advanced two-stage identification algorithm. Identification allows us to capture the unique features and dynamics of the pathway and also opens up the possibility of regulatory control design. In the approach described, an optimal model is obtained by performing model subset selection in two stages, where the terms are first determined by a forward selection method and then modified using a backward selection model refinement. The simulation results demonstrate that the model selected using the two-stage algorithm performs better than with the forward selection method alone. [ABSTRACT FROM AUTHOR]

Understanding how the retina encodes visual information is a key issue for the development of a retinal prosthesis. To study this issue, the neural retina is modeled as a retina module (RM) consisted of an ensemble of spatial-temporal (ST) filters and each ST filter simulates the input-output property of an individual ganglion cell (GC). Two receptive field (RF) models of retinal GC, the difference of Gaussians (DOG) model and the disinhibition (DIS) model, are employed to implement these ST filters respectively. RM performs the encoding operation from an input optical pattern to a group of parallel action potential (AP) trains. To assess the encoding efficiency of RF models, a central visual system module (VM) consisted of a group of artificial neural networks is employed to perform the decoding operation from AP trains to an output perceptual pattern. A matching error is defined as an index to quantify the similarity between the input optical pattern and the output perceptual pattern generated by VM. The simulation results suggest that the matching error declines dramatically when the DOG model is replaced by the DIS model, which implies that the encoding mechanism of the DIS model might be more effective than that of the DOG model. [ABSTRACT FROM AUTHOR]

Among HIV, immune cell and drug, exhibit interactions that are usually not well understood and as a result, cannot be accurately modeled. In this paper, Modeling by AOC is to understand the dynamics of HIV infection and treatment. The use of AOC-by-self-discovery modeling was investigated. AOC-by-self-discovery methods try to adjust the system parameters automatically. To demonstrate the effects of therapies, we design and implement a HIV Computational Lab prototype. HIV Computational Lab is an AOC-based simulation of HIV immune dynamics that is currently being developed in NetLogo. It allows researches to investigate dependencies various immune responses to HIV. The HIV Computational Lab provides a good tool to characterize the process of HIV infection and study HIV drug treatment. [ABSTRACT FROM AUTHOR]

The quantitative analysis of viral infection dynamical model can't be processed easily due to the lack of complex quantitative knowledge in such biological system; therefore, the methods based on qualitative analysis become an alternative solution to researches in the complicated biological process. In this paper the qualitative technology is introduced to model and analyze the process of virus entry. A rough model is proposed first to be the foundation of further research. With more knowledge in the process, the framework is expanded by inserting the qualitative description of different kinds of factors that have interactive influence on the process of virus entry. The factors are described qualitatively in influencing degree, and the qualitative model is built based on the interaction among these influencing factors and the viruses and cells. The judging matrices are constructed according to the qualitative model and the coherence of these matrices is verified. A qualitative analysis about the process is given finally. [ABSTRACT FROM AUTHOR]

In this article, we study a series of Combretastatin compounds which undergo B ring transformation. First of all, Genetic function analysis(GFA) is adopted to study two-dimensional quantitative structure activity relationship(QSAR). The results demonstrate that Apol, PMI-mag, Dipole-mag, Hbond donor, RadOfGyration descriptors make the most significant contributions to the activities of this series of inhibitors; Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis(CoMSIA) are adopted to study three-dimensional quantitative structure activity relationship, both of which demonstrate strong predictive abilities. The tri-dimensional contour maps of CoMFA and CoMSIA provide explanations for the structure-activity relationship of Combretastatin compounds, and elucidate the effects of different substituents of B ring on inhibiting the activities of tubulin polymerization. And molecular docking was used to analyze and validate quantitative structure activity relationship models. The results of this study provide evidence for further designing and synthesizing tubulin inhibitors and conducting structure optimization. [ABSTRACT FROM AUTHOR]

In this paper, biologic properties and physical properties of the bioscaffold are studied. The requirements of the model of defective bone repair bioscaffold are proposed. Then, a new modeling method is presented, which can construct a defective bone repair bioscaffold 3d digital model that has the macro-shape and macro-pores. This method combines the image processing technology, 3d-reconstructing technology, and a new hole filling method, in which a mapping method is developed. It can be used in both symmetrical and unsymmetrical defective bone. By programming, this method was successfully implemented and the repair bioscaffold 3d digital model was constructed. Through RP process, using polymeric blends, the physical model was obtained, which meets the requirements of the bioscaffold. [ABSTRACT FROM AUTHOR]

A long-term durability of a ventricular assist device (VAD) is required due to a shortage of donor hearts for cardiac transplantation, but there is no analyzed pump data after long-term use. This study aimed to perform a comparative study between new VAD and VADs after long-term use. The hydrodynamic performance of the used Toyobo VADs (mean period of 5 months with the maximum of 12 months) was evaluated in a mock circulatory system, where a new VAD was used as a control. Although a remarkable difference was not observed in terms of mean flow rate, flow and pressure waveforms varied significantly. Then, the pressure-volume relationship of each pump was measured. It was found that the capacity of long-term VADs was reduced. Although a further study is required, these results suggested that a long-term use of VAD may cause a change in mechanical properties of polymer materials. [ABSTRACT FROM AUTHOR]

The central problem in training a radial basis function neural network is the selection of hidden layer neurons, which includes the selection of the center and width of those neurons. In this paper, we propose a new method to construct an adaptive RBF neural network classifier based on artificial immune network algorithm. A multiple granularities immune network (MGIN) algorithm is employed to get the candidate hidden neurons and construct an original RBF network including all candidate neurons, and a removing redundant neurons procedure is used to simplify the classifier finally. Some experimental results show that the network obtained tends to generalize well. [ABSTRACT FROM AUTHOR]

It is very important to construct the three-dimensional finite element (FE) model of the human skeleton for research on the mechanism of skeleton blunt-impact injury and on the assessment of injury severity. A feasible and efficient method of the skeleton finite element modeling using CT images was proposed in the paper. There are many problems during the FEA processing of complex models such as distortions of the element, numerical divergence. Three methods of mesh optimization based on stereolithography (STL) were provided. According to the analysis on the complexity of the ANSYS processing and the precision of the result, the optimizing process of developing a finite element model of phalange was proposed. The described method can generate detailed and valid three dimensional finite element model of phalange with different inner constructions and complex geometry. This method is rapid and can readily be used for other medical applications. [ABSTRACT FROM AUTHOR]

Cerebrovascular diseases are one of the three major mortalities in Japan, such as the rupture of cerebral aneurysm and cerebral infarction caused by carotid stenosis. The growth mechanism of the cerebral aneurysm and carotid stenosis has not been clearly understood. In this research, we are introducing a numerical simulation tool; Computational Fluid Dynamics (CFD) technique, to simulate and predict the hemodynamics of blood passing through the cerebral aneurysms and stenosed carotid arteries. The results of a ruptured and an unruptured cerebral aneurysm were compared. Energy losses were calculated in ruptured and unruptured cerebral aneurysms, the results were 167 Pa and 6.3 Pa respectively. The results also indicated that the blood flows took longer residence inside of bleb of the ruptured aneurysm. The maximum wall shear stress was observed at 70% stenosis from the simulation results of stenosed carotid bifurcation. The result qualitatively agrees with classical treatments in carotid bifurcation therapy. [ABSTRACT FROM AUTHOR]

In this paper, a novel kind of algorithm, multiagent quantum evolutionary algorithm(MAQEA), is proposed based on multiagent, evolutionary programming and quantum computation. An agent represents a candidate solution for optimization problem. All agents are presented by quantum chromosome, whose core lies on the concept and principles of quantum computing, live in table environment. Each agent competes and cooperates with its neighbors in order to increase its competitive ability. Quantum computation mechanics is employed to accelerate evolution process. The result of experiments shows that MAQEA has a strong ability of global optimization and high convergence speed. [ABSTRACT FROM AUTHOR]

Image registration is playing a more and more important role in facilitating the smart use of widely available and complementary information from multiple imaging resources. To improve the efficiency and accuracy of medical image registration, in this paper, a bi-hierarchy registration method is presented. Firstly, on the basis of steerable pyramid transform with property of transformation-invariance, the images will be decomposed into multi-scale and multi-band representation. Then, to avoid transformation error accumulation and magnification during the parameter transmission, the registration will be performed only in the lowest-resolution hierarchy and the highest-resolution hierarchy. The experiments on medical images demonstrate that the proposed registration is of high performance and is robust to noise. [ABSTRACT FROM AUTHOR]

In this paper, a novel and robust verification approach using iris features is presented. Contrasting with conventional approaches, only two iris sub-regions instead of entire iris, where are nearly not occluded by useless parts such as eyelash and eyelid, are segmented for verification. Gabor filtering and wavelet moments methods are used to extract the iris texture features. In the verification stage, the principal component analysis (PCA) technique and one-class-one-network (Back-Propagation Neural Network (BPNN)) classification structure are employed for dimensionality reduction and classification, respectively. The experimental results show that the correct verification rate can reach 98.65% using our proposed approach. [ABSTRACT FROM AUTHOR]

Human mental state is related to outer stimuli and inner cognitive appraisal, and it mainly includes emotion, motivation, personality and social norm. Modeling mental state of virtual humans is very important in many fields. Simulating virtual humans with mental state is a challenging branch of computer animation, where virtual humans are regarded as agents with sense, perception, emotion, personality, motivation, behavior and action. 3D virtual humans are constructed with sensors for perceiving external stimuli and are able to express emotions autonomously. Mental state-based animation is demonstrated in a prototype system. [ABSTRACT FROM AUTHOR]

An evaluation formula of varying-time Hurst index is established by wavelet and the algorithm of varying-time index is presented, which is applied to extract the characteristics of the atrial fibrillation in this paper. The diagnosis of atrial fibrillation curve figure can be done at some resolution ratio level. The results show that the time-varying fractal dimension rises when atrial fibrillation begins, while it falls when atrial fibrillation ends. The begin and the end characteristics of atrial fibrillation can be successfully detected by means of the change of the time-varying fractal dimension. The result also indicates that the complexity of heart rate variability (HRV) decreases at the beginning of atrial fibrillation. [ABSTRACT FROM AUTHOR]

Protein-protein interactions play a crucial role in the cellular process. In recent years, yet new experimental techniques have been developed to discover the protein-protein interaction networks, the accuracy and coverage has still proven to be limited. Computational approaches come into being essential both to assist in the design and validation of experimental studies and for the prediction of interacted proteins. This paper presents a survey of the major computational methods for detecting protein-protein interactions, and expatiates on their key contribution by introducing the experimental methods, the typical computational methods and the improvement over them in turn. [ABSTRACT FROM AUTHOR]

The author proposed a novel approach for evolving the architecture of a multi-layer neural network based on neural network and fuzzy logic technologies. The model is front-network which comprised with five layers architecture which composed of dynamic inference of fuzzy rules where the consequent sub-models are implemented by recurrent neural networks with internal feedback paths and dynamic neuron synapses. An optimal learning scheme with the evaluation guide line which error data embed is applied for training of LF-DFNN models. The results of experiment demonstrate that new model have superior performance. [ABSTRACT FROM AUTHOR]

A novel feature extraction method based on cloud model for EEG classification is proposed in the paper. The cloud model can transform numerical data to qualitative concept described by a group of characteristics. It provides a new way for concept induction in machine learning. Classification of single trial EEG recorded in a ‘self-paced key typing' experiment is made through feature extraction based on cloud model and linear regression method for the classification of feature vectors. Results of up to 90% classification accuracy on test data set were obtained. The results show that compared with other methods, the feature extraction and translation method for EEG classification in this paper is simple and effective. [ABSTRACT FROM AUTHOR]

This study first created a model to predict the content of free calcium oxide (fCaO) of the calcined clinker in the rotary kiln by adopting the technologies of rough sets, neural networks and data fusion. And then it was used to predict the quality of the calcined clinker in the rotary kiln and pleasant simulation results were obtained, indicating that the model is valid and has attained the goal of increasing the training speed and precision. Besides, it has solved many problems in the course of cement production, such as big inertia, lagging, time variation, serious nonlinearity, multiple parameters, serious coupling, and difficulty in creating systematic models. [ABSTRACT FROM AUTHOR]

Locally linear embedding heavily depends on whether the neighborhood graph represents the underlying geometry structure of the data manifolds. Inspired from the cognitive relativity, this paper proposes a relative transformation that can be applied to build the relative space from the original space of data. In relative space, the noise and outliers will become further away from the normal points, while the near points will become relative closer. Accordingly we determine the neighborhood in the relative space for Hessian locally linear embedding, while the embedding is still performed in the original space. The conducted experiments on both synthetic and real data sets validate the approach. [ABSTRACT FROM AUTHOR]

Aiming at the recognition problem of EEG signals in brain-computer interfaces (BCIs), we present a pattern recognition method. The method combines an adaptive genetic algorithm (GA) with the support vector machine (SVM). It integrates the following three key techniques: (1) the feature selection and model parameters of the SVM are optimized synchronously, which constitutes a hybrid optimization; (2) the aim of the hybrid optimization is to improve the classification performance of the SVM; and (3) the hybrid optimization is solved by using the adaptive GA. The method is used to classify three types of EEG signals produced during motor imaginations. It yields 72% classification accuracy, which is higher 8% than the one obtained with the individual optimization of the feature selection and SVM parameters. [ABSTRACT FROM AUTHOR]

During the memorizing process, knowledge is memorized closely related to the emotional state. The emotional factor also effects on the decision making process as well as memorization. Accordingly in this paper Reticular Activating System was designed and the knowledge management strategy considering the emotional switching was proposed. We applied this system to the virtual memory and tested the results. [ABSTRACT FROM AUTHOR]

Until now, GPRS/GPS/GIS based on vehicle navigation and monitoring systems have been popularly developed to satisfy the demand for the intelligent transportation system (ITS). Such systems provide the large traffic convenience to drivers, but at the same time attach more burdens to drivers for learning about map information. Hence it is worth further verifying the negative effect of vehicle navigation and monitoring systems on drivers. Considering that human driving behavior is strongly relevant to cognitive characteristics, this study will address to the effect of vehicle navigation systems on drivers by means of measuring and analyzing the cognitive state inside brain. In this paper, a relatively new method of multi-channel near-infrared spectroscopy (NIRS) was used to investigate the brain activation by independently manipulating the cognitive demand in the different cases of a driving simulator. Experimental results indicated that, compared with the case of no map information available, there is no more obvious priority of activation for left brain and right brain in the case of map information available. Meanwhile, there seems to be a complete activation for the prefrontal cortex of left and right brain, suggesting that GPRS/GPS/GIS based vehicle navigation systems may exhaust drivers more easily so as to bring about more danger than traffic convenience under driving environment. [ABSTRACT FROM AUTHOR]

DirectShow based network living broadcast scheme of MPEG-4 medical video stream is presented. In this scheme, Digital Subtraction Angiography (DSA) video stream are captured by an advanced image capture board. MPEG-4 and DirectShow framework technique are used for data's compression and transmission. Data of DSA video stream can be transported easily from server sender Filter to client receiver Filter according to TCP and UDP protocols. Experimental results show that the image is clear in 100M LAN, and the delay is less than one second. High performances of definition and real-time of the DSA video are both achieved in this scheme. [ABSTRACT FROM AUTHOR]

The purpose of this study was to propose the real time face recognition system using multiple image sequences for network users. The algorithm used in this study aimed to optimize the overall time required for recognition process by reducing transmission delay and image processing by image compression and minification. At the same time, this study proposed a method that can improve recognition performance of the system by exploring the correlation between image compression and size and recognition capability of the face recognition system. The performance of the system and algorithm proposed in this study were evaluated through testing. [ABSTRACT FROM AUTHOR]

As well-known, the science of acupuncture and moxibustion is an important component of Traditional Chinese Medicine with a long history. Although there are a number of different acupuncture manipulations, the method for distinguishing them is rarely investigated. With the idea of the interspike interval (ISI), we study the electrical signal time series at the spinal dorsal horn produced by three different acupuncture manipulations in Zusanli point and present an effective way to distinguish them. Comparing with the traditional analysis methods, like phase space reconstruction and largest Lyapunov exponents, this new method is more efficiently and effective. [ABSTRACT FROM AUTHOR]

Life began hundred million years ago; it started from simple inorganic substances to intricate multi-cellular organics. Gradually, the brains of higher animals developed emotions and intelligence. These are well illustrated by the learning abilities and social behaviors of man. Those intelligent activities, progress from simple to complicate, primitive to sophisticated processes from incarnate to abstract. Man started to create artificial intelligent to enhance their brain capabilities sixty years ago. Here we are making a comparison between the natural and artificial intelligent, and see what we can emulate more from Nature. And disclose the author's point of view about the creation of natural lives. [ABSTRACT FROM AUTHOR]

We study the stochastic dynamics of three FitzHugh-Nagumo neurons with chemical coupling and electrical coupling (gap junction) respectively. For both of the coupling cases, optimal coherence resonance and weak signal propagation can be achieved with intermediate noise intensity. Through comparisons and analysis, we can make conclusions that chemical synaptic coupling is more efficient than the well known linear electrical coupling for both coherence resonance and weak signal propagation. We also find that neurons with parameters locate near the bifurcation point (canard regime) can exhibit the best response of coherence resonance and weak signal propagation. [ABSTRACT FROM AUTHOR]

It is very important and difficult to determine whether the person who is undertaking physical exercise is safe. A unique approach based on both analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) is presented to evaluate exercise risk. First, a new evaluation model combining the above two methods, AHP-FCE, is established by applying the fuzzy mathematics into hierarchical assessment. Then, various physical exercises relevant factors are analyzed and represented. In addition, steps of the AHP-FCE based physical exercise evaluation are set up. Finally, an actual calculation example is used to verify the feasibility of the method. [ABSTRACT FROM AUTHOR]

The comparative methods for predicting RNA secondary structure can be facilitated by taking structural alignments of homologous sequences as input. However, it is very difficult to construct a well structural alignment of RNA sequences without knowing the secondary structures. In this paper, we present a stem-based method for constructing structural alignment of RNA sequences with unknown structures. The method can be summarized by: 1) we detect possible stems in the RNA sequence using the so-called position matrix with which some possibly paired positions are uncovered; 2) we detect conserved stems across multiple sequences by multiplying the position matrices; 3) we assess the conserved stems using the Signal-to-Noise and the new SCFG model; 4) we construct structural alignment of RNA sequences by incorporating conserved stems with Clustal W which is a popular program for multiple sequence alignment. We tested our method on data sets composed of known structural alignments which are downloaded from the Rfam database. The accuracy, measured as sensitivity and true positive rate, of our method is much greater than alignments by Clustal W. [ABSTRACT FROM AUTHOR]

Constructive features of tissue engineering scaffold with defect bone are defined. And based on the definitions and medical image 3D reconstructions as well as CAD technology, the constructing method for macrostructure of its interior features of tissue engineering scaffold with defect bone is presented, which is based on parametric expressions. And demonstrations of two constructing methods are given. Finally from calculations of the porosity, the structures of interior features are proven to meet the demand of porosity of tissue engineering scaffold with defect bone. [ABSTRACT FROM AUTHOR]

Identifying the binding sites and promoters in the genomes remains one of the most research topics in computational biology in past ten years. In the upstream region of the start codon, there exist transcription and translation motifs whose distances and patterns vary among different genomic sequences. However, the existing computational approaches for detecting them are mostly general-purpose. For archaea, binding motifs are almost undiscovered as they are more hidden than bacteria or eukaryotes. In this report, an improved algorithm based on PWM (position-weight matrix) and Gibbs Sampler was proposed for finding any number of patterns in a given range. Experiments using this algorithm were done to detect the potential binding motifs in archaeal genomic sequences, and the results were analyzed among different settings and species. The comparison with biological experiments result shows that the improved algorithm is feasible to find more significant patterns for archaea than the traditional approaches. [ABSTRACT FROM AUTHOR]

RNA sequences detection is time-consuming because of its huge data set size. Although SVM has been proved to be useful, normal SVM is not suitable for classification of large data sets because of its high training complexity. A two-stage SVM classification approach is introduced for fast classifying large data sets. Experimental results on several RNA sequences detection demonstrate that the proposed approach is promising for such applications. [ABSTRACT FROM AUTHOR]

Mass spectrometry is being used to generate protein profiles from human serum, and proteomic data obtained from mass spectrometry have attracted great interest for the detection of early-stage cancer. However, high dimensional mass spectrometry data cause considerable challenges. In this paper a set of wavelet detail coefficients at different levels is used to characterize the localized changes of mass spectrometry data and reduce dimensionality of mass spectra. The experiments are performed on high resolution ovarian dataset. A highly competitive accuracy compared to the best performance of other kinds of classification models is achieved. [ABSTRACT FROM AUTHOR]

In this study, we present a classifier which takes an amino acid sequence as input and predicts potential DNA-binding domains with support vector machines (SVMs). We got amino acid sequences with known DNA-binding domains from the Protein Data Bank (PDB), and SVM models were designed integrating with four normalized sequence features(the side chain pKa value, hydrophobicity index, molecular mass of the amino acid and the number of isolated electron pairs) and a normalized feature on evolutionary information of amino acid sequences. The results show that DNA-binding domains can be predicted at 74.28% accuracy, 68.39% sensitivity and 79.76% specificity, in addition, at 0.822 ROC AUC value and 0.549 Pearson's correlation coefficient. [ABSTRACT FROM AUTHOR]

The rapidly increasing number of sequence entering into the genome databank has created the need for fully automated methods to analyze them. Knowing the cellular location of a protein is a key step towards understanding its function. The development in statistical prediction of protein attributes generally consists of two cores: one is to construct a training dataset and the other is to formulate a predictive algorithm. The latter can be further separated into two subcores: one is how to give a mathematical expression to effectively represent a protein and the other is how to find a powerful algorithm to accurately perform the prediction. Here, an improved evolutionary conservation algorithm was proposed to calculate per residue conservation score. Then, each protein can be represented as a feature vector created with multi-scale energy (MSE). In addition, the protein can be represented as other feature vectors based on amino acid composition (AAC), weighted auto-correlation function and Moment descriptor methods. Finally, a novel hybrid approach was developed by fusing the four kinds of feature classifiers through a product rule system to predict 12 subcellular locations. Compared with existing methods, this new approach provides better predictive performance. High success accuracies were obtained in both jackknife cross-validation test and independent dataset test, suggesting that introducing protein evolutionary information and the concept of fusing multi-features classifiers are quite promising, and might also hold a great potential as a useful vehicle for the other areas of molecular biology. [ABSTRACT FROM AUTHOR]

In this paper, kernel-based feature extraction method from gene expression data is proposed for cancer classification. The performances of four kernel algorithms, namely, kernel Fisher discriminant analysis (KFDA), kernel principal component analysis (KPCA), kernel partial least squares (KPLS), and kernel independent component analysis (KICA), are compared on three benchmarked datasets: breast cancer, leukemia and colon cancer. Experimental results show that the proposed kernel-based feature extraction methods work well for three benchmark gene dataset. Overall, the KPLS and KFDA show the best performance, and KPCA and KICA follow them. [ABSTRACT FROM AUTHOR]

Base-base correlation (BBC) method, based on information theory, translates a DNA sequence into a 16-dimensional vector. It has proven quite effective in distinguishing various functional regions on one chromosome. In this study, we explore the potential use of distinguishing different chromosomes within one species, with particular emphasis on Agrobacterium tumefaciens strain C58. Our findings show that BBC method could effectively distinguish informational structure of Agrobacterium tumefaciens strain C58 genomes. In conclusion, BBC provides a new methodology in post-genome informatics and its applications could be further explored in the further. [ABSTRACT FROM AUTHOR]

Performance of bone tissue depends on porous scaffold microstructures with specific porosity characteristics that influence the behavior of the ingrown cells. The mechanical properties of porous tissue scaffolds are important for their biomechanical tissue engineering application. In this study, the composite materials powder was developed for the selective laser sintering process, and the parameters of selective laser sintering were optimized. With the aim of evaluating the influence of porosity on mechanical properties, we have studied the load limits for three specimens of scaffolds which have different porosity. Young's modulus was computed by determining the slope of the stress - strain curve along the elastic portion of the deformation. In addition, the final element analysis (FEA) module of UG NX4 was used to analyze these scaffolds. The results showed that the bone tissue engineering scaffolds were fabricated by SLS technology have good mechanical properties, which have good potential for tissue engineering applications. [ABSTRACT FROM AUTHOR]