Computational and Mathematical Methods in Medicine The latest articles from Hindawi Publishing Corporation © 2014 , Hindawi Publishing Corporation . All rights reserved. Computerized Neuropsychological Assessment in Aging: Testing Efficacy and Clinical Ecology of Different Interfaces Thu, 24 Jul 2014 08:30:43 +0000 Digital technologies have opened new opportunities for psychological testing, allowing new computerized testing tools to be developed and/or paper and pencil testing tools to be translated to new computerized devices. The question that rises is whether these implementations may introduce some technology-specific effects to be considered in neuropsychological evaluations. Two core aspects have been investigated in this work: the efficacy of tests and the clinical ecology of their administration (the ability to measure real-world test performance), specifically (1) the testing efficacy of a computerized test when response to stimuli is measured using a touch-screen compared to a conventional mouse-control response device; (2) the testing efficacy of a computerized test with respect to different input modalities (visual versus verbal); and (3) the ecology of two computerized assessment modalities (touch-screen and mouse-control), including preference measurements of participants. Our results suggest that (1) touch-screen devices are suitable for administering experimental tasks requiring precise timings for detection, (2) intrinsic nature of neuropsychological tests should always be respected in terms of stimuli presentation when translated to new digitalized environment, and (3) touch-screen devices result in ecological instruments being proposed for the computerized administration of neuropsychological tests with a high level of preference from elderly people. Matteo Canini, Petronilla Battista, Pasquale Anthony Della Rosa, Eleonora Catricalà, Christian Salvatore, Maria Carla Gilardi, and Isabella Castiglioni Copyright © 2014 Matteo Canini et al. All rights reserved. PLI Cancellation in ECG Signal Based on Adaptive Filter by Using Wiener-Hopf Equation for Providing Initial Condition Wed, 23 Jul 2014 10:57:34 +0000 This paper presents a technique for finding the optimal initial weight for adaptive filter by using difference equation. The obtained analytical response of the system identifies the appropriate weights for the system and shows that the MSE depends on the initial weight. The proposed technique is applied to eliminate the known frequency power line interference (PLI) signal in the electrocardiogram (ECG) signal. The PLI signal is considered as a combination of cosine and sine signals. The adaptive filter, therefore, attempts to adjust the amplitude of cosine and sine signals to synthesize a reference signal very similar to the contaminated PLI signal. To compare the potential of the proposed technique to other techniques, the system is simulated by using the Matlab program and the TMS320C6713 digital board. The simulation results demonstrate that the proposed technique enables the system to eliminate the PLI signal with the fastest time and gains the superior results of the recovered ECG signal. Anchalee Manosueb, Jeerasuda Koseeyaporn, and Paramote Wardkein Copyright © 2014 Anchalee Manosueb et al. All rights reserved. Neurophysiological Tools to Investigate Consumer’s Gender Differences during the Observation of TV Commercials Wed, 23 Jul 2014 08:35:52 +0000 Neuromarketing is a multidisciplinary field of research whose aim is to investigate the consumers’ reaction to advertisements from a neuroscientific perspective. In particular, the neuroscience field is thought to be able to reveal information about consumer preferences which are unobtainable through conventional methods, including submitting questionnaires to large samples of consumers or performing psychological personal or group interviews. In this scenario, we performed an experiment in order to investigate cognitive and emotional changes of cerebral activity evaluated by neurophysiologic indices during the observation of TV commercials. In particular, we recorded the electroencephalographic (EEG), galvanic skin response (GSR), and heart rate (HR) in a group of 28 healthy subjects during the observation of a series of TV advertisements that have been grouped by commercial categories. Comparisons of cerebral indices have been performed to highlight gender differences between commercial categories and scenes of interest of two specific commercials. Findings show how EEG methodologies, along with the measurements of autonomic variables, could be used to obtain hidden information to marketers not obtainable otherwise. Most importantly, it was suggested how these tools could help to analyse the perception of TV advertisements and differentiate their production according to the consumer’s gender. Giovanni Vecchiato, Anton Giulio Maglione, Patrizia Cherubino, Barbara Wasikowska, Agata Wawrzyniak, Anna Latuszynska, Malgorzata Latuszynska, Kesra Nermend, Ilenia Graziani, Maria Rita Leucci, Arianna Trettel, and Fabio Babiloni Copyright © 2014 Giovanni Vecchiato et al. All rights reserved. Analysis of Epileptic Seizures with Complex Network Wed, 23 Jul 2014 07:25:05 +0000 Epilepsy is a disease of abnormal neural activities involving large area of brain networks. Until now the nature of functional brain network associated with epilepsy is still unclear. Recent researches indicate that the small world or scale-free attributes and the occurrence of highly clustered connection patterns could represent a general organizational principle in the human brain functional network. In this paper, we seek to find whether the small world or scale-free property of brain network is correlated with epilepsy seizure formation. A mass neural model was adopted to generate multiple channel EEG recordings based on regular, small world, random, and scale-free network models. Whether the connection patterns of cortical networks are directly associated with the epileptic seizures was investigated. The results showed that small world and scale-free cortical networks are highly correlated with the occurrence of epileptic seizures. In particular, the property of small world network is more significant during the epileptic seizures. Yan Ni, Yinghua Wang, Tao Yu, and Xiaoli Li Copyright © 2014 Yan Ni et al. All rights reserved. Optimal Treatment Strategy for a Tumor Model under Immune Suppression Wed, 23 Jul 2014 00:00:00 +0000 We propose a mathematical model describing tumor-immune interactions under immune suppression. These days evidences indicate that the immune suppression related to cancer contributes to its progression. The mathematical model for tumor-immune interactions would provide a new methodology for more sophisticated treatment options of cancer. To do this we have developed a system of 11 ordinary differential equations including the movement, interaction, and activation of NK cells, CD8+T-cells, CD4+T cells, regulatory T cells, and dendritic cells under the presence of tumor and cytokines and the immune interactions. In addition, we apply two control therapies, immunotherapy and chemotherapy to the model in order to control growth of tumor. Using optimal control theory and numerical simulations, we obtain appropriate treatment strategies according to the ratio of the cost for two therapies, which suggest an optimal timing of each administration for the two types of models, without and with immunosuppressive effects. These results mean that the immune suppression can have an influence on treatment strategies for cancer. Kwang Su Kim, Giphil Cho, and Il Hyo Jung Copyright © 2014 Kwang Su Kim et al. All rights reserved. A Biological Hierarchical Model Based Underwater Moving Object Detection Tue, 22 Jul 2014 09:59:09 +0000 Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results. Jie Shen, Tanghuai Fan, Min Tang, Qian Zhang, Zhen Sun, and Fengchen Huang Copyright © 2014 Jie Shen et al. All rights reserved. Effects of First-Time Overnight CPAP Therapy for Increasing the Complexity of the Patient’s Physiological System Mon, 21 Jul 2014 09:45:04 +0000 Studies regarding the effects of short-term continuous positive airway pressure (CPAP) therapy are not sufficient. A total of 35 patients with moderate to severe untreated OSA were divided into 2 groups. Group 1 comprised 22 patients who underwent polysomnography (PSG) for one night, and Group 2 comprised 13 patients who received PSG combined with CPAP therapy. To evaluate the influence of receiving CPAP therapy for one night, we measured 5 min wrist pulse signals before and after the experiment to assess heart rate variability, as well as novel short time multiscale entropy (sMSE) indicator that examines complexity in physiological signals. The results show that the participants in Group 1 exhibited significant changes in normalized low-frequency power/normalized high-frequency power (nLF/nHF) (0.72 ± 0.09 versus 1.11 ± 0.11, ) values before and after the PSG study. By contrast, the participants in Group 2 showed no significant changes in the 3 indicators. Regarding the sMSE indicator, Group 2 patients exhibited significant increases in the sMSE. CPAP therapy administered for one night can reduce the sympathovagal imbalance in patients with moderate to severe untreated OSA and increase the complexity of the patient’s physiological system, thereby reflecting their overall improved health. Hsien-Tsai Wu, Hong-Ruei Chen, Wen-Yao Pan, Cyuan-Cin Liu, Mao-Chang Su, and Meng-Chih Lin Copyright © 2014 Hsien-Tsai Wu et al. All rights reserved. Path-Counting Formulas for Generalized Kinship Coefficients and Condensed Identity Coefficients Mon, 21 Jul 2014 08:32:51 +0000 An important computation on pedigree data is the calculation of condensed identity coefficients, which provide a complete description of the degree of relatedness of two individuals. The applications of condensed identity coefficients range from genetic counseling to disease tracking. Condensed identity coefficients can be computed using linear combinations of generalized kinship coefficients for two, three, four individuals, and two pairs of individuals and there are recursive formulas for computing those generalized kinship coefficients (Karigl, 1981). Path-counting formulas have been proposed for the (generalized) kinship coefficients for two (three) individuals but there have been no path-counting formulas for the other generalized kinship coefficients. It has also been shown that the computation of the (generalized) kinship coefficients for two (three) individuals using path-counting formulas is efficient for large pedigrees, together with path encoding schemes tailored for pedigree graphs. In this paper, we propose a framework for deriving path-counting formulas for generalized kinship coefficients. Then, we present the path-counting formulas for all generalized kinship coefficients for which there are recursive formulas and which are sufficient for computing condensed identity coefficients. We also perform experiments to compare the efficiency of our method with the recursive method for computing condensed identity coefficients on large pedigrees. En Cheng and Z. Meral Ozsoyoglu Copyright © 2014 En Cheng and Z. Meral Ozsoyoglu. All rights reserved. Image Tracking for the High Similarity Drug Tablets Based on Light Intensity Reflective Energy and Artificial Neural Network Thu, 17 Jul 2014 11:55:44 +0000 It is obvious that tablet image tracking exerts a notable influence on the efficiency and reliability of high-speed drug mass production, and, simultaneously, it also emerges as a big difficult problem and targeted focus during production monitoring in recent years, due to the high similarity shape and random position distribution of those objectives to be searched for. For the purpose of tracking tablets accurately in random distribution, through using surface fitting approach and transitional vector determination, the calibrated surface of light intensity reflective energy can be established, describing the shape topology and topography details of objective tablet. On this basis, the mathematical properties of these established surfaces have been proposed, and thereafter artificial neural network (ANN) has been employed for classifying those moving targeted tablets by recognizing their different surface properties; therefore, the instantaneous coordinate positions of those drug tablets on one image frame can then be determined. By repeating identical pattern recognition on the next image frame, the real-time movements of objective tablet templates were successfully tracked in sequence. This paper provides reliable references and new research ideas for the real-time objective tracking in the case of drug production practices. Zhongwei Liang, Liang Zhou, Xiaochu Liu, and Xiaogang Wang Copyright © 2014 Zhongwei Liang et al. All rights reserved. Segmentation of Intensity Inhomogeneous Brain MR Images Using Active Contours Wed, 16 Jul 2014 09:13:11 +0000 Segmentation of intensity inhomogeneous regions is a well-known problem in image analysis applications. This paper presents a region-based active contour method for image segmentation, which properly works in the context of intensity inhomogeneity problem. The proposed region-based active contour method embeds both region and gradient information unlike traditional methods. It contains mainly two terms, area and length, in which the area term practices a new region-based signed pressure force (SPF) function, which utilizes mean values from a certain neighborhood using the local binary fitted (LBF) energy model. In turn, the length term uses gradient information. The novelty of our method is to locally compute new SPF function, which uses local mean values and is able to detect boundaries of the homogenous regions. Finally, a truncated Gaussian kernel is used to regularize the level set function, which not only regularizes it but also removes the need of computationally expensive reinitialization. The proposed method targets the segmentation problem of intensity inhomogeneous images and reduces the time complexity among locally computed active contour methods. The experimental results show that the proposed method yields better segmentation result as well as less time complexity compared with the state-of-the-art active contour methods. Farhan Akram, Jeong Heon Kim, Han Ul Lim, and Kwang Nam Choi Copyright © 2014 Farhan Akram et al. All rights reserved. Screening for Prediabetes Using Machine Learning Models Wed, 16 Jul 2014 00:00:00 +0000 The global prevalence of diabetes is rapidly increasing. Studies support the necessity of screening and interventions for prediabetes, which could result in serious complications and diabetes. This study aimed at developing an intelligence-based screening model for prediabetes. Data from the Korean National Health and Nutrition Examination Survey (KNHANES) were used, excluding subjects with diabetes. The KNHANES 2010 data were used for training and internal validation, while data from KNHANES 2011 were used for external validation. We developed two models to screen for prediabetes using an artificial neural network (ANN) and support vector machine (SVM) and performed a systematic evaluation of the models using internal and external validation. We compared the performance of our models with that of a screening score model based on logistic regression analysis for prediabetes that had been developed previously. The SVM model showed the areas under the curve of 0.731 in the external datasets, which is higher than those of the ANN model (0.729) and the screening score model (0.712), respectively. The prescreening methods developed in this study performed better than the screening score model that had been developed previously and may be more effective method for prediabetes screening. Soo Beom Choi, Won Jae Kim, Tae Keun Yoo, Jee Soo Park, Jai Won Chung, Yong-ho Lee, Eun Seok Kang, and Deok Won Kim Copyright © 2014 Soo Beom Choi et al. All rights reserved. A Note regarding Problems with Interaction and Varying Block Sizes in a Comparison of Endotracheal Tubes Tue, 15 Jul 2014 07:57:26 +0000 A randomized clinical experiment to compare two types of endotracheal tubes utilized a block design where each of the six participating anesthesiologists performed tube insertions for an equal number of patients for each type of tube. Five anesthesiologists intubated at least three patients with each tube type, but one anesthesiologist intubated only one patient per tube type. Overall, one type of tube outperformed the other on all three effectiveness measures. However, analysis of the data using an interaction model gave conflicting and misleading results, making the tube with the better performance appear to perform worse. This surprising result was caused by the undue influence of the data for the anesthesiologist who intubated only two patients. We therefore urge caution in interpreting results from interaction models with designs containing small blocks. Richard L. Einsporn and Zhenyu Jia Copyright © 2014 Richard L. Einsporn and Zhenyu Jia. All rights reserved. Automatic Blastomere Recognition from a Single Embryo Image Mon, 14 Jul 2014 14:23:12 +0000 The number of blastomeres of human day 3 embryos is one of the most important criteria for evaluating embryo viability. However, due to the transparency and overlap of blastomeres, it is a challenge to recognize blastomeres automatically using a single embryo image. This study proposes an approach based on least square curve fitting (LSCF) for automatic blastomere recognition from a single image. First, combining edge detection, deletion of multiple connected points, and dilation and erosion, an effective preprocessing method was designed to obtain part of blastomere edges that were singly connected. Next, an automatic recognition method for blastomeres was proposed using least square circle fitting. This algorithm was tested on 381 embryo microscopic images obtained from the eight-cell period, and the results were compared with those provided by experts. Embryos were recognized with a 0 error rate occupancy of 21.59%, and the ratio of embryos in which the false recognition number was less than or equal to 2 was 83.16%. This experiment demonstrated that our method could efficiently and rapidly recognize the number of blastomeres from a single embryo image without the need to reconstruct the three-dimensional model of the blastomeres first; this method is simple and efficient. Yun Tian, Ya-bo Yin, Fu-qing Duan, Wei-zhou Wang, Wei Wang, and Ming-quan Zhou Copyright © 2014 Yun Tian et al. All rights reserved. ProBLM Web Server: Protein and Membrane Placement and Orientation Package Mon, 14 Jul 2014 12:37:50 +0000 The 3D structures of membrane proteins are typically determined without the presence of a lipid bilayer. For the purpose of studying the role of membranes on the wild type characteristics of the corresponding protein, determining the position and orientation of transmembrane proteins within a membrane environment is highly desirable. Here we report a geometry-based approach to automatically insert a membrane protein with a known 3D structure into pregenerated lipid bilayer membranes with various dimensions and lipid compositions or into a pseudomembrane. The pseudomembrane is built using the Protein Nano-Object Integrator which generates a parallelepiped of user-specified dimensions made up of pseudoatoms. The pseudomembrane allows for modeling the desolvation effects while avoiding plausible errors associated with wrongly assigned protein-lipid contacts. The method is implemented into a web server, the ProBLM server, which is freely available to the biophysical community. The web server allows the user to upload a protein coordinate file and any missing residues or heavy atoms are regenerated. ProBLM then creates a combined protein-membrane complex from the given membrane protein and bilayer lipid membrane or pseudomembrane. The user is given an option to manually refine the model by manipulating the position and orientation of the protein with respect to the membrane. Taylor Kimmett, Nicholas Smith, Shawn Witham, Marharyta Petukh, Subhra Sarkar, and Emil Alexov Copyright © 2014 Taylor Kimmett et al. All rights reserved. Privacy-Preserving Self-Helped Medical Diagnosis Scheme Based on Secure Two-Party Computation in Wireless Sensor Networks Mon, 14 Jul 2014 09:54:38 +0000 With the continuing growth of wireless sensor networks in pervasive medical care, people pay more and more attention to privacy in medical monitoring, diagnosis, treatment, and patient care. On one hand, we expect the public health institutions to provide us with better service. On the other hand, we would not like to leak our personal health information to them. In order to balance this contradiction, in this paper we design a privacy-preserving self-helped medical diagnosis scheme based on secure two-party computation in wireless sensor networks so that patients can privately diagnose themselves by inputting a health card into a self-helped medical diagnosis ATM to obtain a diagnostic report just like drawing money from a bank ATM without revealing patients’ health information and doctors’ diagnostic skill. It makes secure self-helped disease diagnosis feasible and greatly benefits patients as well as relieving the heavy pressure of public health institutions. Yi Sun, Qiaoyan Wen, Yudong Zhang, and Wenmin Li Copyright © 2014 Yi Sun et al. All rights reserved. Computational and Control Methods in Rehabilitation Medicine Mon, 14 Jul 2014 00:00:00 +0000 Imre Cikajlo, Takashi Watanabe, and Strahinja Dosen Copyright © 2014 Imre Cikajlo et al. All rights reserved. Mathematical Modelling of Cerebral Blood Circulation and Cerebral Autoregulation: Towards Preventing Intracranial Hemorrhages in Preterm Newborns Sun, 13 Jul 2014 11:54:49 +0000 Impaired cerebral autoregulation leads to fluctuations in cerebral blood flow, which can be especially dangerous for immature brain of preterm newborns. In this paper, two mathematical models of cerebral autoregulation are discussed. The first one is an enhancement of a vascular model proposed by Piechnik et al. We extend this model by adding a polynomial dependence of the vascular radius on the arterial blood pressure and adjusting the polynomial coefficients to experimental data to gain the autoregulation behavior. Moreover, the inclusion of a Preisach hysteresis operator, simulating a hysteretic dependence of the cerebral blood flow on the arterial pressure, is tested. The second model couples the blood vessel system model by Piechnik et al. with an ordinary differential equation model of cerebral autoregulation by Ursino and Lodi. An optimal control setting is proposed for a simplified variant of this coupled model. The objective of the control is the maintenance of the autoregulatory function for a wider range of the arterial pressure. The control can be interpreted as the effect of a medicament changing the cerebral blood flow by, for example, dilation of blood vessels. Advanced numerical methods developed by the authors are applied for the numerical treatment of the control problem. Renée Lampe, Nikolai Botkin, Varvara Turova, Tobias Blumenstein, and Ana Alves-Pinto Copyright © 2014 Renée Lampe et al. All rights reserved. Predicting Tooth Surface Loss Using Genetic Algorithms-Optimized Artificial Neural Networks Thu, 10 Jul 2014 11:37:43 +0000 Our aim was to predict tooth surface loss in individuals without the need to conduct clinical examinations. Artificial neural networks (ANNs) were used to construct a mathematical model. Input data consisted of age, smoker status, type of tooth brush, brushing, and consumption of pickled food, fizzy drinks, orange, apple, lemon, and dried seeds. Output data were the sum of tooth surface loss scores for selected teeth. The optimized constructed ANN consisted of 2-layer network with 15 neurons in the first layer and one neuron in the second layer. The data of 46 subjects were used to build the model, while the data of 15 subjects were used to test the model. Accepting an error of ±5 scores for all chosen teeth, the accuracy of the network becomes more than 80%. In conclusion, this study shows that modeling tooth surface loss using ANNs is possible and can be achieved with a high degree of accuracy. Ali Al Haidan, Osama Abu-Hammad, and Najla Dar-Odeh Copyright © 2014 Ali Al Haidan et al. All rights reserved. Three-Dimensional Simulation of Scalp Soft Tissue Expansion Using Finite Element Method Wed, 09 Jul 2014 00:00:00 +0000 Scalp soft tissue expansion is one of the key medical techniques to generate new skin tissue for correcting various abnormalities and defects of skin in plastic surgery. Therefore, it is very important to work out the appropriate approach to evaluate the increase of expanded scalp area and to predict the shape, size, number, and placement of the expander. A novel method using finite element model is proposed to solve large deformation of scalp expansion in this paper. And the procedure to implement the scalp tissue expansion with finite element method is also described in detail. The three-dimensional simulation results show that the proposed method is effective, and the analysis of simulation experiment shows that the volume and area of the expansion scalp can be accurately calculated and the quantity, location, and size of the expander can also be predicted successfully with the proposed model. Qiu Guan, Xiaochen Du, Yan Shao, Lili Lin, and Shengyong Chen Copyright © 2014 Qiu Guan et al. All rights reserved. Preprocessing by a Bayesian Single-Trial Event-Related Potential Estimation Technique Allows Feasibility of an Assistive Single-Channel P300-Based Brain-Computer Interface Mon, 07 Jul 2014 08:01:14 +0000 A major clinical goal of brain-computer interfaces (BCIs) is to allow severely paralyzed patients to communicate their needs and thoughts during their everyday lives. Among others, P300-based BCIs, which resort to EEG measurements, have been successfully operated by people with severe neuromuscular disabilities. Besides reducing the number of stimuli repetitions needed to detect the P300, a current challenge in P300-based BCI research is the simplification of system’s setup and maintenance by lowering the number of recording channels. By using offline data collected in 30 subjects (21 amyotrophic lateral sclerosis patients and 9 controls) through a clinical BCI with channels, in the present paper we show that a preprocessing approach based on a Bayesian single-trial ERP estimation technique allows reducing to 1 without affecting the system’s accuracy. The potentially great benefit for the practical usability of BCI devices (including patient acceptance) that would be given by the reduction of the number of channels encourages further development of the present study, for example, in an online setting. Anahita Goljahani, Costanza D’Avanzo, Stefano Silvoni, Paolo Tonin, Francesco Piccione, and Giovanni Sparacino Copyright © 2014 Anahita Goljahani et al. All rights reserved. New Region-Scalable Discriminant and Fitting Energy Functional for Driving Geometric Active Contours in Medical Image Segmentation Mon, 07 Jul 2014 00:00:00 +0000 We propose a novel region-based geometric active contour model that uses region-scalable discriminant and fitting energy functional for handling the intensity inhomogeneity and weak boundary problems in medical image segmentation. The region-scalable discriminant and fitting energy functional is defined to capture the image intensity characteristics in local and global regions for driving the evolution of active contour. The discriminant term in the model aims at separating background and foreground in scalable regions while the fitting term tends to fit the intensity in these regions. This model is then transformed into a variational level set formulation with a level set regularization term for accurate computation. The new model utilizes intensity information in the local and global regions as much as possible; so it not only handles better intensity inhomogeneity, but also allows more robustness to noise and more flexible initialization in comparison to the original global region and regional-scalable based models. Experimental results for synthetic and real medical image segmentation show the advantages of the proposed method in terms of accuracy and robustness. Xuchu Wang, Yanmin Niu, Liwen Tan, and Shao-Xiang Zhang Copyright © 2014 Xuchu Wang et al. All rights reserved. Effects of Maximal Sodium and Potassium Conductance on the Stability of Hodgkin-Huxley Model Thu, 03 Jul 2014 09:53:25 +0000 Hodgkin-Huxley (HH) equation is the first cell computing model in the world and pioneered the use of model to study electrophysiological problems. The model consists of four differential equations which are based on the experimental data of ion channels. Maximal conductance is an important characteristic of different channels. In this study, mathematical method is used to investigate the importance of maximal sodium conductance and maximal potassium conductance . Applying stability theory, and taking and as variables, we analyze the stability and bifurcations of the model. Bifurcations are found when the variables change, and bifurcation points and boundary are also calculated. There is only one bifurcation point when is the variable, while there are two points when is variable. The (,  ) plane is partitioned into two regions and the upper bifurcation boundary is similar to a line when both and are variables. Numerical simulations illustrate the validity of the analysis. The results obtained could be helpful in studying relevant diseases caused by maximal conductance anomaly. Yue Zhang, Kuanquan Wang, Yongfeng Yuan, Dong Sui, and Henggui Zhang Copyright © 2014 Yue Zhang et al. All rights reserved. Advanced Computer Vision Approaches in Biomedical Image Analysis Thu, 03 Jul 2014 06:36:55 +0000 Anke Meyer-Baese, Claudia Plant, and Juan Manuel Gorriz Saez Copyright © 2014 Anke Meyer-Baese et al. All rights reserved. A Pipeline for Neuron Reconstruction Based on Spatial Sliding Volume Filter Seeding Wed, 02 Jul 2014 11:54:55 +0000 Neuron’s shape and dendritic architecture are important for biosignal transduction in neuron networks. And the anatomy architecture reconstruction of neuron cell is one of the foremost challenges and important issues in neuroscience. Accurate reconstruction results can facilitate the subsequent neuron system simulation. With the development of confocal microscopy technology, researchers can scan neurons at submicron resolution for experiments. These make the reconstruction of complex dendritic trees become more feasible; however, it is still a tedious, time consuming, and labor intensity task. For decades, computer aided methods have been playing an important role in this task, but none of the prevalent algorithms can reconstruct full anatomy structure automatically. All of these make it essential for developing new method for reconstruction. This paper proposes a pipeline with a novel seeding method for reconstructing neuron structures from 3D microscopy images stacks. The pipeline is initialized with a set of seeds detected by sliding volume filter (SVF), and then the open curve snake is applied to the detected seeds for reconstructing the full structure of neuron cells. The experimental results demonstrate that the proposed pipeline exhibits excellent performance in terms of accuracy compared with traditional method, which is clearly a benefit for 3D neuron detection and reconstruction. Dong Sui, Kuanquan Wang, Jinseok Chae, Yue Zhang, and Henggui Zhang Copyright © 2014 Dong Sui et al. All rights reserved. Advanced Medical Image Analysis Tue, 01 Jul 2014 09:49:58 +0000 Rong Chen, Zhongqiu Wang, and Yuanjie Zheng Copyright © 2014 Rong Chen et al. All rights reserved. Image Segmentation and Identification of Paired Antibodies in Breast Tissue Tue, 01 Jul 2014 08:11:47 +0000 Comparing staining patterns of paired antibodies designed towards a specific protein but toward different epitopes of the protein provides quality control over the binding and the antibodies’ ability to identify the target protein correctly and exclusively. We present a method for automated quantification of immunostaining patterns for antibodies in breast tissue using the Human Protein Atlas database. In such tissue, dark brown dye 3,3′-diaminobenzidine is used as an antibody-specific stain whereas the blue dye hematoxylin is used as a counterstain. The proposed method is based on clustering and relative scaling of features following principal component analysis. Our method is able (1) to accurately segment and identify staining patterns and quantify the amount of staining and (2) to detect paired antibodies by correlating the segmentation results among different cases. Moreover, the method is simple, operating in a low-dimensional feature space, and computationally efficient which makes it suitable for high-throughput processing of tissue microarrays. Jimmy C. Azar, Martin Simonsson, Ewert Bengtsson, and Anders Hast Copyright © 2014 Jimmy C. Azar et al. All rights reserved. Synchronization of Coupled Different Chaotic FitzHugh-Nagumo Neurons with Unknown Parameters under Communication-Direction-Dependent Coupling Mon, 30 Jun 2014 09:35:07 +0000 This paper investigates the chaotic behavior and synchronization of two different coupled chaotic FitzHugh-Nagumo (FHN) neurons with unknown parameters under external electrical stimulation (EES). The coupled FHN neurons of different parameters admit unidirectional and bidirectional gap junctions in the medium between them. Dynamical properties, such as the increase in synchronization error as a consequence of the deviation of neuronal parameters for unlike neurons, the effect of difference in coupling strengths caused by the unidirectional gap junctions, and the impact of large time-delay due to separation of neurons, are studied in exploring the behavior of the coupled system. A novel integral-based nonlinear adaptive control scheme, to cope with the infeasibility of the recovery variable, for synchronization of two coupled delayed chaotic FHN neurons of different and unknown parameters under uncertain EES is derived. Further, to guarantee robust synchronization of different neurons against disturbances, the proposed control methodology is modified to achieve the uniformly ultimately bounded synchronization. The parametric estimation errors can be reduced by selecting suitable control parameters. The effectiveness of the proposed control scheme is illustrated via numerical simulations. Muhammad Iqbal, Muhammad Rehan, Abdul Khaliq, Saeed-ur- Rehman, and Keum-Shik Hong Copyright © 2014 Muhammad Iqbal et al. All rights reserved. A CT Reconstruction Algorithm Based on Non-Aliasing Contourlet Transform and Compressive Sensing Mon, 30 Jun 2014 07:26:06 +0000 Compressive sensing (CS) theory has great potential for reconstructing CT images from sparse-views projection data. Currently, total variation (TV-) based CT reconstruction method is a hot research point in medical CT field, which uses the gradient operator as the sparse representation approach during the iteration process. However, the images reconstructed by this method often suffer the smoothing problem; to improve the quality of reconstructed images, this paper proposed a hybrid reconstruction method combining TV and non-aliasing Contourlet transform (NACT) and using the Split-Bregman method to solve the optimization problem. Finally, the simulation results show that the proposed algorithm can reconstruct high-quality CT images from few-views projection using less iteration numbers, which is more effective in suppressing noise and artefacts than algebraic reconstruction technique (ART) and TV-based reconstruction method. Lu-zhen Deng, Peng Feng, Mian-yi Chen, Peng He, Quang-sang Vo, and Biao Wei Copyright © 2014 Lu-zhen Deng et al. All rights reserved. Optimal Control of HIV/AIDS in the Workplace in the Presence of Careless Individuals Thu, 26 Jun 2014 00:00:00 +0000 A nonlinear dynamical system is proposed and qualitatively analyzed to study the dynamics of HIV/AIDS in the workplace. The disease-free equilibrium point of the model is shown to be locally asymptotically stable if the basic reproductive number, , is less than unity and the model is shown to exhibit a unique endemic equilibrium when the basic reproductive number is greater than unity. It is shown that, in the absence of recruitment of infectives, the disease is eradicated when , whiles the disease is shown to persist in the presence of recruitment of infected persons. The basic model is extended to include control efforts aimed at reducing infection, irresponsibility, and nonproductivity at the workplace. This leads to an optimal control problem which is qualitatively analyzed using Pontryagin’s Maximum Principle (PMP). Numerical simulation of the resulting optimal control problem is carried out to gain quantitative insights into the implications of the model. The simulation reveals that a multifaceted approach to the fight against the disease is more effective than single control strategies. Baba Seidu and Oluwole D. Makinde Copyright © 2014 Baba Seidu and Oluwole D. Makinde. All rights reserved. A Mixture Modeling Framework for Differential Analysis of High-Throughput Data Wed, 25 Jun 2014 00:00:00 +0000 The inventions of microarray and next generation sequencing technologies have revolutionized research in genomics; platforms have led to massive amount of data in gene expression, methylation, and protein-DNA interactions. A common theme among a number of biological problems using high-throughput technologies is differential analysis. Despite the common theme, different data types have their own unique features, creating a “moving target” scenario. As such, methods specifically designed for one data type may not lead to satisfactory results when applied to another data type. To meet this challenge so that not only currently existing data types but also data from future problems, platforms, or experiments can be analyzed, we propose a mixture modeling framework that is flexible enough to automatically adapt to any moving target. More specifically, the approach considers several classes of mixture models and essentially provides a model-based procedure whose model is adaptive to the particular data being analyzed. We demonstrate the utility of the methodology by applying it to three types of real data: gene expression, methylation, and ChIP-seq. We also carried out simulations to gauge the performance and showed that the approach can be more efficient than any individual model without inflating type I error. Cenny Taslim and Shili Lin Copyright © 2014 Cenny Taslim and Shili Lin. All rights reserved.