Computational and Mathematical Methods in Medicine The latest articles from Hindawi Publishing Corporation © 2014 , Hindawi Publishing Corporation . All rights reserved. Sparse-View Ultrasound Diffraction Tomography Using Compressed Sensing with Nonuniform FFT Thu, 24 Apr 2014 12:05:41 +0000 Accurate reconstruction of the object from sparse-view sampling data is an appealing issue for ultrasound diffraction tomography (UDT). In this paper, we present a reconstruction method based on compressed sensing framework for sparse-view UDT. Due to the piecewise uniform characteristics of anatomy structures, the total variation is introduced into the cost function to find a more faithful sparse representation of the object. The inverse problem of UDT is iteratively resolved by conjugate gradient with nonuniform fast Fourier transform. Simulation results show the effectiveness of the proposed method that the main characteristics of the object can be properly presented with only 16 views. Compared to interpolation and multiband method, the proposed method can provide higher resolution and lower artifacts with the same view number. The robustness to noise and the computation complexity are also discussed. Shaoyan Hua, Mingyue Ding, and Ming Yuchi Copyright © 2014 Shaoyan Hua et al. All rights reserved. Mixed-Norm Regularization for Brain Decoding Thu, 17 Apr 2014 16:46:51 +0000 This work investigates the use of mixed-norm regularization for sensor selection in event-related potential (ERP) based brain-computer interfaces (BCI). The classification problem is cast as a discriminative optimization framework where sensor selection is induced through the use of mixed-norms. This framework is extended to the multitask learning situation where several similar classification tasks related to different subjects are learned simultaneously. In this case, multitask learning helps in leveraging data scarcity issue yielding to more robust classifiers. For this purpose, we have introduced a regularizer that induces both sensor selection and classifier similarities. The different regularization approaches are compared on three ERP datasets showing the interest of mixed-norm regularization in terms of sensor selection. The multitask approaches are evaluated when a small number of learning examples are available yielding to significant performance improvements especially for subjects performing poorly. R. Flamary, N. Jrad, R. Phlypo, M. Congedo, and A. Rakotomamonjy Copyright © 2014 R. Flamary et al. All rights reserved. A CT Reconstruction Algorithm Based on L1/2 Regularization Wed, 16 Apr 2014 07:20:32 +0000 Computed tomography (CT) reconstruction with low radiation dose is a significant research point in current medical CT field. Compressed sensing has shown great potential reconstruct high-quality CT images from few-view or sparse-view data. In this paper, we use the sparser L1/2 regularization operator to replace the traditional L1 regularization and combine the Split Bregman method to reconstruct CT images, which has good unbiasedness and can accelerate iterative convergence. In the reconstruction experiments with simulation and real projection data, we analyze the quality of reconstructed images using different reconstruction methods in different projection angles and iteration numbers. Compared with algebraic reconstruction technique (ART) and total variance (TV) based approaches, the proposed reconstruction algorithm can not only get better images with higher quality from few-view data but also need less iteration numbers. Mianyi Chen, Deling Mi, Peng He, Luzhen Deng, and Biao Wei Copyright © 2014 Mianyi Chen et al. All rights reserved. Structural Equation Modeling for Analyzing Erythrocyte Fatty Acids in Framingham Tue, 15 Apr 2014 12:37:27 +0000 Research has shown that several types of erythrocyte fatty acids (i.e., omega-3, omega-6, and trans) are associated with risk for cardiovascular diseases. However, there are complex metabolic and dietary relations among fatty acids, which induce correlations that are typically ignored when using them as risk predictors. A latent variable approach could summarize these complex relations into a few latent variable scores for use in statistical models. Twenty-two red blood cell (RBC) fatty acids were measured in Framingham (N = 3196). The correlation matrix of the fatty acids was modeled using structural equation modeling; the model was tested for goodness-of-fit and gender invariance. Thirteen fatty acids were summarized by three latent variables, and gender invariance was rejected so separate models were developed for men and women. A score was developed for the polyunsaturated fatty acid (PUFA) latent variable, which explained about 30% of the variance in the data. The PUFA score included loadings in opposing directions among three omega-3 and three omega-6 fatty acids, and incorporated the biosynthetic and dietary relations among them. Whether the PUFA factor score can improve the performance of risk prediction in cardiovascular diseases remains to be tested. James V. Pottala, Gemechis D. Djira, Mark A. Espeland, Jun Ye, Martin G. Larson, and William S. Harris Copyright © 2014 James V. Pottala et al. All rights reserved. Improving the Performance of the Prony Method Using a Wavelet Domain Filter for MRI Denoising Mon, 14 Apr 2014 13:49:27 +0000 The Prony methods are used for exponential fitting. We use a variant of the Prony method for abnormal brain tissue detection in sequences of weighted magnetic resonance images. Here, MR images are considered to be affected only by Rician noise, and a new wavelet domain bilateral filtering process is implemented to reduce the noise in the images. This filter is a modification of Kazubek’s algorithm and we use synthetic images to show the ability of the new procedure to suppress noise and compare its performance with respect to the original filter, using quantitative and qualitative criteria. The tissue classification process is illustrated using a real sequence of MR images, and the filter is applied to each image before using the variant of the Prony method. Rodney Jaramillo, Marianela Lentini, and Marco Paluszny Copyright © 2014 Rodney Jaramillo et al. All rights reserved. Modeling and Visualizing Cell Type Switching Mon, 14 Apr 2014 13:47:15 +0000 Understanding cellular differentiation is critical in explaining development and for taming diseases such as cancer. Differentiation is conventionally represented using bifurcating lineage trees. However, these lineage trees cannot readily capture or quantify all the types of transitions now known to occur between cell types, including transdifferentiation or differentiation off standard paths. This work introduces a new analysis and visualization technique that is capable of representing all possible transitions between cell states compactly, quantitatively, and intuitively. This method considers the regulatory network of transcription factors that control cell type determination and then performs an analysis of network dynamics to identify stable expression profiles and the potential cell types that they represent. A visualization tool called CellDiff3D creates an intuitive three-dimensional graph that shows the overall direction and probability of transitions between all pairs of cell types within a lineage. In this study, the influence of gene expression noise and mutational changes during myeloid cell differentiation are presented as a demonstration of the CellDiff3D technique, a new approach to quantify and envision all possible cell state transitions in any lineage network. Ahmadreza Ghaffarizadeh, Gregory J. Podgorski, and Nicholas S. Flann Copyright © 2014 Ahmadreza Ghaffarizadeh et al. All rights reserved. Conflicts of Interest during Contact Investigations: A Game-Theoretic Analysis Mon, 14 Apr 2014 08:18:40 +0000 The goal of contact tracing is to reduce the likelihood of transmission, particularly to individuals who are at greatest risk for developing complications of infection, as well as identifying individuals who are in need of medical treatment of other interventions. In this paper, we develop a simple mathematical model of contact investigations among a small group of individuals and apply game theory to explore conflicts of interest that may arise in the context of perceived costs of disclosure. Using analytic Kolmogorov equations, we determine whether or not it is possible for individual incentives to drive noncooperation, even though cooperation would yield a better group outcome. We found that if all individuals have a cost of disclosure, then the optimal individual decision is to simply not disclose each other. With further analysis of (1) completely offsetting the costs of disclosure and (2) partially offsetting the costs of disclosure, we found that all individuals disclose all contacts, resulting in a smaller basic reproductive number and an alignment of individual and group optimality. More data are needed to understand decision making during outbreak investigations and what the real and perceived costs are. Nicolas Sippl-Swezey, Wayne T. Enanoria, and Travis C. Porco Copyright © 2014 Nicolas Sippl-Swezey et al. All rights reserved. Use of CHAID Decision Trees to Formulate Pathways for the Early Detection of Metabolic Syndrome in Young Adults Thu, 10 Apr 2014 11:49:51 +0000 Metabolic syndrome (MetS) in young adults (age 20–39) is often undiagnosed. A simple screening tool using a surrogate measure might be invaluable in the early detection of MetS. Methods. A chi-squared automatic interaction detection (CHAID) decision tree analysis with waist circumference user-specified as the first level was used to detect MetS in young adults using data from the National Health and Nutrition Examination Survey (NHANES) 2009-2010 Cohort as a representative sample of the United States population . Results. Twenty percent of the sample met the National Cholesterol Education Program Adult Treatment Panel III (NCEP) classification criteria for MetS. The user-specified CHAID model was compared to both CHAID model with no user-specified first level and logistic regression based model. This analysis identified waist circumference as a strong predictor in the MetS diagnosis. The accuracy of the final model with waist circumference user-specified as the first level was 92.3% with its ability to detect MetS at 71.8% which outperformed comparison models. Conclusions. Preliminary findings suggest that young adults at risk for MetS could be identified for further followup based on their waist circumference. Decision tree methods show promise for the development of a preliminary detection algorithm for MetS. Brian Miller, Mark Fridline, Pei-Yang Liu, and Deborah Marino Copyright © 2014 Brian Miller et al. All rights reserved. Establishing Reliable miRNA-Cancer Association Network Based on Text-Mining Method Thu, 10 Apr 2014 06:45:22 +0000 Associating microRNAs (miRNAs) with cancers is an important step of understanding the mechanisms of cancer pathogenesis and finding novel biomarkers for cancer therapies. In this study, we constructed a miRNA-cancer association network (miCancerna) based on more than 1,000 miRNA-cancer associations detected from millions of abstracts with the text-mining method, including 226 miRNA families and 20 common cancers. We further prioritized cancer-related miRNAs at the network level with the random-walk algorithm, achieving a relatively higher performance than previous miRNA disease networks. Finally, we examined the top 5 candidate miRNAs for each kind of cancer and found that 71% of them are confirmed experimentally. miCancerna would be an alternative resource for the cancer-related miRNA identification. Lun Li, Xingchi Hu, Zhaowan Yang, Zhenyu Jia, Ming Fang, Libin Zhang, and Yanhong Zhou Copyright © 2014 Lun Li et al. All rights reserved. Integrating Gene Expression and Protein Interaction Data for Signaling Pathway Prediction of Alzheimer’s Disease Wed, 09 Apr 2014 07:46:54 +0000 Discovering the signaling pathway and regulatory network would provide significant advance in genome-wide understanding of pathogenesis of human diseases. Despite the rich transcriptome data, the limitation for microarray data is unable to detect changes beyond transcriptional level and insufficient in reconstructing pathways and regulatory networks. In our study, protein-protein interaction (PPI) data is introduced to add molecular biological information for predicting signaling pathway of Alzheimer’s disease (AD). Combining PPI with gene expression data, significant genes are selected by modified linear regression model firstly. Then, according to the biological researches that inflammation reaction plays an important role in the generation and deterioration of AD, NF-κB (nuclear factor-kappa B), as a significant inflammatory factor, has been selected as the beginning gene of the predicting signaling pathway. Based on that, integer linear programming (ILP) model is proposed to reconstruct the signaling pathway between NF-κB and AD virulence gene APP (amyloid precursor protein). The results identify 6 AD virulence genes included in the predicted inflammatory signaling pathway, and a large amount of molecular biological analysis shows the great understanding of the underlying biological process of AD. Wei Kong, Jingmao Zhang, Xiaoyang Mou, and Yang Yang Copyright © 2014 Wei Kong et al. All rights reserved. Mathematical Modeling of Transmission Dynamics and Optimal Control of Vaccination and Treatment for Hepatitis B Virus Wed, 09 Apr 2014 07:18:13 +0000 Hepatitis B virus (HBV) infection is a worldwide public health problem. In this paper, we study the dynamics of hepatitis B virus (HBV) infection which can be controlled by vaccination as well as treatment. Initially we consider constant controls for both vaccination and treatment. In the constant controls case, by determining the basic reproduction number, we study the existence and stability of the disease-free and endemic steady-state solutions of the model. Next, we take the controls as time and formulate the appropriate optimal control problem and obtain the optimal control strategy to minimize both the number of infectious humans and the associated costs. Finally at the end numerical simulation results show that optimal combination of vaccination and treatment is the most effective way to control hepatitis B virus infection. Ali Vahidian Kamyad, Reza Akbari, Ali Akbar Heydari, and Aghileh Heydari Copyright © 2014 Ali Vahidian Kamyad et al. All rights reserved. Structure-Functional Prediction and Analysis of Cancer Mutation Effects in Protein Kinases Tue, 08 Apr 2014 07:18:44 +0000 A central goal of cancer research is to discover and characterize the functional effects of mutated genes that contribute to tumorigenesis. In this study, we provide a detailed structural classification and analysis of functional dynamics for members of protein kinase families that are known to harbor cancer mutations. We also present a systematic computational analysis that combines sequence and structure-based prediction models to characterize the effect of cancer mutations in protein kinases. We focus on the differential effects of activating point mutations that increase protein kinase activity and kinase-inactivating mutations that decrease activity. Mapping of cancer mutations onto the conformational mobility profiles of known crystal structures demonstrated that activating mutations could reduce a steric barrier for the movement from the basal “low” activity state to the “active” state. According to our analysis, the mechanism of activating mutations reflects a combined effect of partial destabilization of the kinase in its inactive state and a concomitant stabilization of its active-like form, which is likely to drive tumorigenesis at some level. Ultimately, the analysis of the evolutionary and structural features of the major cancer-causing mutational hotspot in kinases can also aid in the correlation of kinase mutation effects with clinical outcomes. Anshuman Dixit and Gennady M. Verkhivker Copyright © 2014 Anshuman Dixit and Gennady M. Verkhivker. All rights reserved. Global Hopf Bifurcation on Two-Delays Leslie-Gower Predator-Prey System with a Prey Refuge Mon, 07 Apr 2014 00:00:00 +0000 A modified Leslie-Gower predator-prey system with two delays is investigated. By choosing and as bifurcation parameters, we show that the Hopf bifurcations occur when time delay crosses some critical values. Moreover, we derive the equation describing the flow on the center manifold; then we give the formula for determining the direction of the Hopf bifurcation and the stability of bifurcating periodic solutions. Numerical simulations are carried out to illustrate the theoretical results and chaotic behaviors are observed. Finally, using a global Hopf bifurcation theorem for functional differential equations, we show the global existence of the periodic solutions. Qingsong Liu, Yiping Lin, and Jingnan Cao Copyright © 2014 Qingsong Liu et al. All rights reserved. Dynamics of High-Risk Nonvaccine Human Papillomavirus Types after Actual Vaccination Scheme Thu, 03 Apr 2014 11:52:25 +0000 Human papillomavirus (HPV) has been identified as the main etiological factor in the developing of cervical cancer (CC). This finding has propitiated the development of vaccines that help to prevent the HPVs 16 and 18 infection. Both genotypes are associated with 70% of CC worldwide. In the present study, we aimed to determine the emergence of high-risk nonvaccine HPV after actual vaccination scheme to estimate the impact of the current HPV vaccines. A SIR-type model was used to study the HPV dynamics after vaccination. According to the results, our model indicates that the application of the vaccine reduces infection by target or vaccine genotypes as expected. However, numerical simulations of the model suggest the presence of the phenomenon called vaccine—induced pathogen strain replacement. Here, we report the following replacement mechanism: if the effectiveness of cross-protective immunity is not larger than the effectiveness of the vaccine, then the high-risk nonvaccine genotypes emerge. In this scenario, further studies of infection dispersion by HPV are necessary to ascertain the real impact of the current vaccines, primarily because of the different high-risk HPV types that are found in CC. Raúl Peralta, Cruz Vargas-De-León, Augusto Cabrera, and Pedro Miramontes Copyright © 2014 Raúl Peralta et al. All rights reserved. Multimodal Spatial Calibration for Accurately Registering EEG Sensor Positions Thu, 03 Apr 2014 11:41:04 +0000 This paper proposes a fast and accurate calibration method to calibrate multiple multimodal sensors using a novel photogrammetry system for fast localization of EEG sensors. The EEG sensors are placed on human head and multimodal sensors are installed around the head to simultaneously obtain all EEG sensor positions. A multiple views’ calibration process is implemented to obtain the transformations of multiple views. We first develop an efficient local repair algorithm to improve the depth map, and then a special calibration body is designed. Based on them, accurate and robust calibration results can be achieved. We evaluate the proposed method by corners of a chessboard calibration plate. Experimental results demonstrate that the proposed method can achieve good performance, which can be further applied to EEG source localization applications on human brain. Jianhua Zhang, Jian Chen, Shengyong Chen, Gang Xiao, and Xiaoli Li Copyright © 2014 Jianhua Zhang et al. All rights reserved. Automatic Detection and Quantification of WBCs and RBCs Using Iterative Structured Circle Detection Algorithm Thu, 03 Apr 2014 08:55:19 +0000 Segmentation and counting of blood cells are considered as an important step that helps to extract features to diagnose some specific diseases like malaria or leukemia. The manual counting of white blood cells (WBCs) and red blood cells (RBCs) in microscopic images is an extremely tedious, time consuming, and inaccurate process. Automatic analysis will allow hematologist experts to perform faster and more accurately. The proposed method uses an iterative structured circle detection algorithm for the segmentation and counting of WBCs and RBCs. The separation of WBCs from RBCs was achieved by thresholding, and specific preprocessing steps were developed for each cell type. Counting was performed for each image using the proposed method based on modified circle detection, which automatically counted the cells. Several modifications were made to the basic (RCD) algorithm to solve the initialization problem, detecting irregular circles (cells), selecting the optimal circle from the candidate circles, determining the number of iterations in a fully dynamic way to enhance algorithm detection, and running time. The validation method used to determine segmentation accuracy was a quantitative analysis that included Precision, Recall, and F-measurement tests. The average accuracy of the proposed method was 95.3% for RBCs and 98.4% for WBCs. Yazan M. Alomari, Siti Norul Huda Sheikh Abdullah, Raja Zaharatul Azma, and Khairuddin Omar Copyright © 2014 Yazan M. Alomari et al. All rights reserved. A Time-Domain Hybrid Analysis Method for Detecting and Quantifying T-Wave Alternans Thu, 03 Apr 2014 08:34:03 +0000 T-wave alternans (TWA) in surface electrocardiograph (ECG) signals has been recognized as a marker of cardiac electrical instability and is hypothesized to be associated with increased risk for ventricular arrhythmias among patients. A novel time-domain TWA hybrid analysis method (HAM) utilizing the correlation method and least squares regression technique is described in this paper. Simulated ECGs containing artificial TWA (cases of absence of TWA and presence of stationary or time-varying or phase-reversal TWA) under different baseline wanderings are used to test the method, and the results show that HAM has a better ability of quantifying TWA amplitude compared with the correlation method (CM) and adapting match filter method (AMFM). The HAM is subsequently used to analyze the clinical ECGs, and results produced by the HAM have, in general, demonstrated consistency with those produced by the CM and the AMFM, while the quantifying TWA amplitudes by the HAM are universally higher than those by the other two methods. Xiangkui Wan, Kanghui Yan, Linlin Zhang, and Yanjun Zeng Copyright © 2014 Xiangkui Wan et al. All rights reserved. A Nonparametric Shape Prior Constrained Active Contour Model for Segmentation of Coronaries in CTA Images Tue, 01 Apr 2014 16:11:37 +0000 We present a nonparametric shape constrained algorithm for segmentation of coronary arteries in computed tomography images within the framework of active contours. An adaptive scale selection scheme, based on the global histogram information of the image data, is employed to determine the appropriate window size for each point on the active contour, which improves the performance of the active contour model in the low contrast local image regions. The possible leakage, which cannot be identified by using intensity features alone, is reduced through the application of the proposed shape constraint, where the shape of circular sampled intensity profile is used to evaluate the likelihood of current segmentation being considered vascular structures. Experiments on both synthetic and clinical datasets have demonstrated the efficiency and robustness of the proposed method. The results on clinical datasets have shown that the proposed approach is capable of extracting more detailed coronary vessels with subvoxel accuracy. Yin Wang and Han Jiang Copyright © 2014 Yin Wang and Han Jiang. All rights reserved. A Semiparametric Bivariate Probit Model for Joint Modeling of Outcomes in STEMI Patients Tue, 01 Apr 2014 09:23:50 +0000 In this work we analyse the relationship among in-hospital mortality and a treatment effectiveness outcome in patients affected by ST-Elevation myocardial infarction. The main idea is to carry out a joint modeling of the two outcomes applying a Semiparametric Bivariate Probit Model to data arising from a clinical registry called STEMI Archive. A realistic quantification of the relationship between outcomes can be problematic for several reasons. First, latent factors associated with hospitals organization can affect the treatment efficacy and/or interact with patient’s condition at admission time. Moreover, they can also directly influence the mortality outcome. Such factors can be hardly measurable. Thus, the use of classical estimation methods will clearly result in inconsistent or biased parameter estimates. Secondly, covariate-outcomes relationships can exhibit nonlinear patterns. Provided that proper statistical methods for model fitting in such framework are available, it is possible to employ a simultaneous estimation approach to account for unobservable confounders. Such a framework can also provide flexible covariate structures and model the whole conditional distribution of the response. Francesca Ieva, Giampiero Marra, Anna Maria Paganoni, and Rosalba Radice Copyright © 2014 Francesca Ieva et al. All rights reserved. Weighted Lin-Wang Tests for Crossing Hazards Sun, 30 Mar 2014 12:42:11 +0000 Lin and Wang have introduced a quadratic version of the logrank test, appropriate for situations in which the underlying survival distributions may cross. In this note, we generalize the Lin-Wang procedure to incorporate weights and investigate the performance of Lin and Wang’s test and weighted versions in various scenarios. We find that weighting does increase statistical power in certain situations; however, none of the procedures was dominant under every scenario. James A. Koziol and Zhenyu Jia Copyright © 2014 James A. Koziol and Zhenyu Jia. All rights reserved. Modeling the Impact of Climate Change on the Dynamics of Rift Valley Fever Sun, 30 Mar 2014 07:42:34 +0000 A deterministic SEIR model of rift valley fever (RVF) with climate change parameters was considered to compute the basic reproduction number and investigate the impact of temperature and precipitation on . To study the effect of model parameters to , sensitivity and elasticity analysis of were performed. When temperature and precipitation effects are not considered, is more sensitive to the expected number of infected Aedes spp. due to one infected livestock and more elastic to the expected number of infected livestock due to one infected Aedes spp. When climatic data are used, is found to be more sensitive and elastic to the expected number of infected eggs laid by Aedes spp. via transovarial transmission, followed by the expected number of infected livestock due to one infected Aedes spp. and the expected number of infected Aedes spp. due to one infected livestock for both regions Arusha and Dodoma. These results call for attention to parameters regarding incubation period, the adequate contact rate of Aedes spp. and livestock, the infective periods of livestock and Aedes spp., and the vertical transmission in Aedes species. Saul C. Mpeshe, Livingstone S. Luboobi, and Yaw Nkansah-Gyekye Copyright © 2014 Saul C. Mpeshe et al. All rights reserved. Logic Regression for Provider Effects on Kidney Cancer Treatment Delivery Thu, 27 Mar 2014 16:09:51 +0000 In the delivery of medical and surgical care, often times complex interactions between patient, physician, and hospital factors influence practice patterns. This paper presents a novel application of logic regression in the context of kidney cancer treatment delivery. Using linked data from the National Cancer Institute’s (NCI) Surveillance, Epidemiology, and End Results (SEER) program and Medicare we identified patients diagnosed with kidney cancer from 1995 to 2005. The primary endpoints in the study were use of innovative treatment modalities, namely, partial nephrectomy and laparoscopy. Logic regression allowed us to uncover the interplay between patient, provider, and practice environment variables, which would not be possible using standard regression approaches. We found that surgeons who graduated in or prior to 1980 despite having some academic affiliation, low volume surgeons in a non-NCI hospital, or surgeons in rural environment were significantly less likely to use laparoscopy. Surgeons with major academic affiliation and practising in HMO, hospital, or medical school based setting were significantly more likely to use partial nephrectomy. Results from our study can show efforts towards dismantling the barriers to adoption of innovative treatment modalities, ultimately improving the quality of care provided to patients with kidney cancer. Mousumi Banerjee, Christopher Filson, Rong Xia, and David C. Miller Copyright © 2014 Mousumi Banerjee et al. All rights reserved. Sparse-Representation-Based Direct Minimum -Norm Algorithm for MRI Phase Unwrapping Wed, 26 Mar 2014 00:00:00 +0000 A sparse-representation-based direct minimum -norm algorithm is proposed for a two-dimensional MRI phase unwrapping. First, the algorithm converts the weighted--norm-minimization-based phase unwrapping problem into a linear system problem whose system (coefficient) matrix is a large, symmetric one. Then, the coefficient-matrix is represented in the sparse structure. Finally, standard direct solvers are employed to solve this linear system. Several wrapped phase datasets, including simulated and MR data, were used to evaluate this algorithm’s performance. The results demonstrated that the proposed algorithm for unwrapping MRI phase data is reliable and robust. Wei He, Ling Xia, and Feng Liu Copyright © 2014 Wei He et al. All rights reserved. A Two-Stage Exon Recognition Model Based on Synergetic Neural Network Tue, 25 Mar 2014 14:21:50 +0000 Exon recognition is a fundamental task in bioinformatics to identify the exons of DNA sequence. Currently, exon recognition algorithms based on digital signal processing techniques have been widely used. Unfortunately, these methods require many calculations, resulting in low recognition efficiency. In order to overcome this limitation, a two-stage exon recognition model is proposed and implemented in this paper. There are three main works. Firstly, we use synergetic neural network to rapidly determine initial exon intervals. Secondly, adaptive sliding window is used to accurately discriminate the final exon intervals. Finally, parameter optimization based on artificial fish swarm algorithm is used to determine different species thresholds and corresponding adjustment parameters of adaptive windows. Experimental results show that the proposed model has better performance for exon recognition and provides a practical solution and a promising future for other recognition tasks. Zhehuang Huang and Yidong Chen Copyright © 2014 Zhehuang Huang and Yidong Chen. All rights reserved. Fitting Continuous Parametric Surfaces to Frontiers Delimiting Physiologic Structures Mon, 24 Mar 2014 06:27:15 +0000 We present a technique to fit continuous parametric surfaces to scattered geometric data points forming frontiers delimiting physiologic structures in segmented images. Such mathematical representation is interesting because it facilitates a large number of operations in modeling. While the fitting of continuous parametric curves to scattered geometric data points is quite trivial, the fitting of continuous parametric surfaces is not. The difficulty comes from the fact that each scattered data point should be assigned a unique parametric coordinate, and the fit is quite sensitive to their distribution on the parametric plane. We present a new approach where a polygonal (quadrilateral or triangular) surface is extracted from the segmented image. This surface is subsequently projected onto a parametric plane in a manner to ensure a one-to-one mapping. The resulting polygonal mesh is then regularized for area and edge length. Finally, from this point, surface fitting is relatively trivial. The novelty of our approach lies in the regularization of the polygonal mesh. Process performance is assessed with the reconstruction of a geometric model of mouse heart ventricles from a computerized tomography scan. Our results show an excellent reproduction of the geometric data with surfaces that are continuous. Jason D. Bayer, Matthew Epstein, and Jacques Beaumont Copyright © 2014 Jason D. Bayer et al. All rights reserved. Dynamics Analysis and Simulation of a Modified HIV Infection Model with a Saturated Infection Rate Sun, 23 Mar 2014 09:53:25 +0000 This paper studies a modified human immunodeficiency virus (HIV) infection differential equation model with a saturated infection rate. It is proved that if the basic virus reproductive number of the model is less than one, then the infection-free equilibrium point of the model is globally asymptotically stable; if of the model is more than one, then the endemic infection equilibrium point of the model is globally asymptotically stable. Based on the clinical data from HIV drug resistance database of Stanford University, using the proposed model simulates the dynamics of the two groups of patients’ anti-HIV infection treatment. The numerical simulation results are in agreement with the evolutions of the patients’ HIV RNA levels. It can be assumed that if an HIV infected individual’s basic virus reproductive number then this person will recover automatically; if an antiretroviral therapy makes an HIV infected individual’s , this person will be cured eventually; if an antiretroviral therapy fails to suppress an HIV infected individual’s HIV RNA load to be of unpredictable level, the time that the patient’s HIV RNA level has achieved the minimum value may be the starting time that drug resistance has appeared. Qilin Sun and Lequan Min Copyright © 2014 Qilin Sun and Lequan Min. All rights reserved. Screening for Cervical Cancer Using Automated Analysis of PAP-Smears Thu, 20 Mar 2014 13:01:23 +0000 Cervical cancer is one of the most deadly and common forms of cancer among women if no action is taken to prevent it, yet it is preventable through a simple screening test, the so-called PAP-smear. This is the most effective cancer prevention measure developed so far. But the visual examination of the smears is time consuming and expensive and there have been numerous attempts at automating the analysis ever since the test was introduced more than 60 years ago. The first commercial systems for automated analysis of the cell samples appeared around the turn of the millennium but they have had limited impact on the screening costs. In this paper we examine the key issues that need to be addressed when an automated analysis system is developed and discuss how these challenges have been met over the years. The lessons learned may be useful in the efforts to create a cost-effective screening system that could make affordable screening for cervical cancer available for all women globally, thus preventing most of the quarter million annual unnecessary deaths still caused by this disease. Ewert Bengtsson and Patrik Malm Copyright © 2014 Ewert Bengtsson and Patrik Malm. All rights reserved. Dynamics of a New Strain of the H1N1 Influenza A Virus Incorporating the Effects of Repetitive Contacts Thu, 13 Mar 2014 12:45:14 +0000 The respiratory disease caused by the Influenza A Virus is occurring worldwide. The transmission for new strain of the H1N1 Influenza A virus is studied by formulating a SEIQR (susceptible, exposed, infected, quarantine, and recovered) model to describe its spread. In the present model, we have assumed that a fraction of the infected population will die from the disease. This changes the mathematical equations governing the transmission. The effect of repetitive contact is also included in the model. Analysis of the model by using standard dynamical modeling method is given. Conditions for the stability of equilibrium state are given. Numerical solutions are presented for different values of parameters. It is found that increasing the amount of repetitive contacts leads to a decrease in the peak numbers of exposed and infectious humans. A stability analysis shows that the solutions are robust. Puntani Pongsumpun and I-Ming Tang Copyright © 2014 Puntani Pongsumpun and I-Ming Tang. All rights reserved. BioTCM-SE: A Semantic Search Engine for the Information Retrieval of Modern Biology and Traditional Chinese Medicine Wed, 12 Mar 2014 08:19:51 +0000 Understanding the functional mechanisms of the complex biological system as a whole is drawing more and more attention in global health care management. Traditional Chinese Medicine (TCM), essentially different from Western Medicine (WM), is gaining increasing attention due to its emphasis on individual wellness and natural herbal medicine, which satisfies the goal of integrative medicine. However, with the explosive growth of biomedical data on the Web, biomedical researchers are now confronted with the problem of large-scale data analysis and data query. Besides that, biomedical data also has a wide coverage which usually comes from multiple heterogeneous data sources and has different taxonomies, making it hard to integrate and query the big biomedical data. Embedded with domain knowledge from different disciplines all regarding human biological systems, the heterogeneous data repositories are implicitly connected by human expert knowledge. Traditional search engines cannot provide accurate and comprehensive search results for the semantically associated knowledge since they only support keywords-based searches. In this paper, we present BioTCM-SE, a semantic search engine for the information retrieval of modern biology and TCM, which provides biologists with a comprehensive and accurate associated knowledge query platform to greatly facilitate the implicit knowledge discovery between WM and TCM. Xi Chen, Huajun Chen, Xuan Bi, Peiqin Gu, Jiaoyan Chen, and Zhaohui Wu Copyright © 2014 Xi Chen et al. All rights reserved. Controllable Edge Feature Sharpening for Dental Applications Tue, 11 Mar 2014 09:24:02 +0000 This paper presents a new approach to sharpen blurred edge features in scanned tooth preparation surfaces generated by structured-light scanners. It aims to efficiently enhance the edge features so that the embedded feature lines can be easily identified in dental CAD systems, and to avoid unnatural oversharpening geometry. We first separate the feature regions using graph-cut segmentation, which does not require a user-defined threshold. Then, we filter the face normal vectors to propagate the geometry from the smooth region to the feature region. In order to control the degree of the sharpness, we propose a feature distance measure which is based on normal tensor voting. Finally, the vertex positions are updated according to the modified face normal vectors. We have applied the approach to scanned tooth preparation models. The results show that the blurred edge features are enhanced without unnatural oversharpening geometry. Ran Fan and Xiaogang Jin Copyright © 2014 Ran Fan and Xiaogang Jin. All rights reserved.