Computational and Mathematical Methods in Medicine The latest articles from Hindawi Publishing Corporation © 2016 , Hindawi Publishing Corporation . All rights reserved. A Mathematical Model of Regenerative Axon Growing along Glial Scar after Spinal Cord Injury Wed, 04 May 2016 14:30:41 +0000 A major factor in the failure of central nervous system (CNS) axon regeneration is the formation of glial scar after the injury of CNS. Glial scar generates a dense barrier which the regenerative axons cannot easily pass through or by. In this paper, a mathematical model was established to explore how the regenerative axons grow along the surface of glial scar or bypass the glial scar. This mathematical model was constructed based on the spinal cord injury (SCI) repair experiments by transplanting Schwann cells as bridge over the glial scar. The Lattice Boltzmann Method (LBM) was used in this model for three-dimensional numerical simulation. The advantage of this model is that it provides a parallel and easily implemented algorithm and has the capability of handling complicated boundaries. Using the simulated data, two significant conclusions were made in this study: the levels of inhibitory factors on the surface of the glial scar are the main factors affecting axon elongation and when the inhibitory factor levels on the surface of the glial scar remain constant, the longitudinal size of the glial scar has greater influence on the average rate of axon growth than the transverse size. These results will provide theoretical guidance and reference for researchers to design efficient experiments. Xuning Chen and Weiping Zhu Copyright © 2016 Xuning Chen and Weiping Zhu. All rights reserved. Semantic Signature: Comparative Interpretation of Gene Expression on a Semantic Space Tue, 03 May 2016 06:55:02 +0000 Background. Interpretation of microarray data remains challenging because biological meaning should be extracted from enormous numeric matrices and be presented explicitly. Moreover, huge public repositories of microarray dataset are ready to be exploited for comparative analysis. This study aimed to provide a platform where essential implication of a microarray experiment could be visually expressed and various microarray datasets could be intuitively compared. Results. On the semantic space, gene sets from Molecular Signature Database (MSigDB) were plotted as landmarks and their relative distances were calculated by Lin’s semantic similarity measure. By formal concept analysis, a microarray dataset was transformed into a concept lattice with gene clusters as objects and Gene Ontology terms as attributes. Concepts of a lattice were located on the semantic space reflecting semantic distance from landmarks and edges between concepts were drawn; consequently, a specific geographic pattern could be observed from a microarray dataset. We termed a distinctive geography shared by microarray datasets of the same category as “semantic signature.” Conclusions. “Semantic space,” a map of biological entities, could serve as a universal platform for comparative microarray analysis. When microarray data were displayed on the semantic space as concept lattices, “semantic signature,” characteristic geography for a microarray experiment, could be discovered. Jihun Kim, Keewon Kim, and Ju Han Kim Copyright © 2016 Jihun Kim et al. All rights reserved. Lattice Boltzmann Model of 3D Multiphase Flow in Artery Bifurcation Aneurysm Problem Thu, 28 Apr 2016 08:45:32 +0000 This paper simulates and predicts the laminar flow inside the 3D aneurysm geometry, since the hemodynamic situation in the blood vessels is difficult to determine and visualize using standard imaging techniques, for example, magnetic resonance imaging (MRI). Three different types of Lattice Boltzmann (LB) models are computed, namely, single relaxation time (SRT), multiple relaxation time (MRT), and regularized BGK models. The results obtained using these different versions of the LB-based code will then be validated with ANSYS FLUENT, a commercially available finite volume- (FV-) based CFD solver. The simulated flow profiles that include velocity, pressure, and wall shear stress (WSS) are then compared between the two solvers. The predicted outcomes show that all the LB models are comparable and in good agreement with the FVM solver for complex blood flow simulation. The findings also show minor differences in their WSS profiles. The performance of the parallel implementation for each solver is also included and discussed in this paper. In terms of parallelization, it was shown that LBM-based code performed better in terms of the computation time required. Aizat Abas, N. Hafizah Mokhtar, M. H. H. Ishak, M. Z. Abdullah, and Ang Ho Tian Copyright © 2016 Aizat Abas et al. All rights reserved. Detecting the Intention to Move Upper Limbs from Electroencephalographic Brain Signals Wed, 27 Apr 2016 09:29:14 +0000 Early decoding of motor states directly from the brain activity is essential to develop brain-machine interfaces (BMI) for natural motor control of neuroprosthetic devices. Hence, this study aimed to investigate the detection of movement information before the actual movement occurs. This information piece could be useful to provide early control signals to drive BMI-based rehabilitation and motor assisted devices, thus providing a natural and active rehabilitation therapy. In this work, electroencephalographic (EEG) brain signals from six healthy right-handed participants were recorded during self-initiated reaching movements of the upper limbs. The analysis of these EEG traces showed that significant event-related desynchronization is present before and during the execution of the movements, predominantly in the motor-related and frequency bands and in electrodes placed above the motor cortex. This oscillatory brain activity was used to continuously detect the intention to move the limbs, that is, to identify the motor phase prior to the actual execution of the reaching movement. The results showed, first, significant classification between relax and movement intention and, second, significant detection of movement intention prior to the onset of the executed movement. On the basis of these results, detection of movement intention could be used in BMI settings to reduce the gap between mental motor processes and the actual movement performed by an assisted or rehabilitation robotic device. Berenice Gudiño-Mendoza, Gildardo Sanchez-Ante, and Javier M. Antelis Copyright © 2016 Berenice Gudiño-Mendoza et al. All rights reserved. Control Law Design for Propofol Infusion to Regulate Depth of Hypnosis: A Nonlinear Control Strategy Wed, 27 Apr 2016 07:25:35 +0000 Maintaining the depth of hypnosis (DOH) during surgery is one of the major objectives of anesthesia infusion system. Continuous administration of Propofol infusion during surgical procedures is essential but increases the undue load of an anesthetist in operating room working in a multitasking setup. Manual and target controlled infusion (TCI) systems are not good at handling instabilities like blood pressure changes and heart rate variability arising due to interpatient variability. Patient safety, large interindividual variability, and less postoperative effects are the main factors to motivate automation in anesthesia. The idea of automated system for Propofol infusion excites the control engineers to come up with a more sophisticated and safe system that handles optimum delivery of drug during surgery and avoids postoperative effects. In contrast to most of the investigations with linear control strategies, the originality of this research work lies in employing a nonlinear control technique, backstepping, to track the desired hypnosis level of patients during surgery. This effort is envisioned to unleash the true capabilities of this nonlinear control technique for anesthesia systems used today in biomedical field. The working of the designed controller is studied on the real dataset of five patients undergoing surgery. The controller tracks the desired hypnosis level within the acceptable range for surgery. Ali Khaqan, Muhammad Bilal, Muhammad Ilyas, Bilal Ijaz, and Raja Ali Riaz Copyright © 2016 Ali Khaqan et al. All rights reserved. Detection of Periodic Leg Movements by Machine Learning Methods Using Polysomnographic Parameters Other Than Leg Electromyography Sun, 24 Apr 2016 14:23:59 +0000 The number of channels used for polysomnographic recording frequently causes difficulties for patients because of the many cables connected. Also, it increases the risk of having troubles during recording process and increases the storage volume. In this study, it is intended to detect periodic leg movement (PLM) in sleep with the use of the channels except leg electromyography (EMG) by analysing polysomnography (PSG) data with digital signal processing (DSP) and machine learning methods. PSG records of 153 patients of different ages and genders with PLM disorder diagnosis were examined retrospectively. A novel software was developed for the analysis of PSG records. The software utilizes the machine learning algorithms, statistical methods, and DSP methods. In order to classify PLM, popular machine learning methods (multilayer perceptron, -nearest neighbour, and random forests) and logistic regression were used. Comparison of classified results showed that while -nearest neighbour classification algorithm had higher average classification rate (91.87%) and lower average classification error value (RMSE = 0.2850), multilayer perceptron algorithm had the lowest average classification rate (83.29%) and the highest average classification error value (RMSE = 0.3705). Results showed that PLM can be classified with high accuracy (91.87%) without leg EMG record being present. İlhan Umut and Güven Çentik Copyright © 2016 İlhan Umut and Güven Çentik. All rights reserved. Frequency and Time Domain Analysis of Foetal Heart Rate Variability with Traditional Indexes: A Critical Survey Mon, 18 Apr 2016 07:09:44 +0000 Monitoring of foetal heart rate and its variability (FHRV) covers an important role in assessing health of foetus. Many analysis methods have been used to get quantitative measures of FHRV. FHRV has been studied in time and in frequency domain and interesting clinical results have been obtained. Nevertheless, a standardized definition of FHRV and a precise methodology to be used for its evaluation are lacking. We carried out a literature overview about both frequency domain analysis (FDA) and time domain analysis (TDA). Then, by using simulated FHR signals, we defined the methodology for FDA. Further, employing more than 400 real FHR signals, we analysed some of the most common indexes, Short Term Variability for TDA and power content of the spectrum bands and sympathovagal balance for FDA, and evaluated their ranges of values, which in many cases are a novelty. Finally, we verified the relationship between these indexes and two important parameters: week of gestation, indicator of foetal growth, and foetal state, classified as active or at rest. Our results indicate that, according to literature, it is necessary to standardize the procedure for FHRV evaluation and to consider week of gestation and foetal state before FHR analysis. Maria Romano, Luigi Iuppariello, Alfonso Maria Ponsiglione, Giovanni Improta, Paolo Bifulco, and Mario Cesarelli Copyright © 2016 Maria Romano et al. All rights reserved. A Novel Automatic Rapid Diagnostic Test Reader Platform Thu, 14 Apr 2016 16:05:29 +0000 A novel automatic Rapid Diagnostic Test (RDT) reader platform is designed to analyze and diagnose target disease by using existing consumer cameras of a laptop-computer or a tablet. The RDT reader is useable with numerous lateral immunochromatographic assays and similar biomedical tests. The system has two different components, which are 3D-printed, low-cost, tiny, and compact stand and a decision program named RDT-AutoReader 2.0. The program takes the image of RDT, crops the region of interest (ROI), and extracts the features from the control end test lines to classify the results as invalid, positive, or negative. All related patient’s personal information, image of ROI, and the e-report are digitally saved and transferred to the related clinician. Condition of the patient and the progress of the disease can be monitored by using the saved data. The reader platform has been tested by taking image from used cassette RDTs of rotavirus (RtV)/adenovirus (AdV) and lateral flow strip RDTs of Helicobacter pylori (H. pylori) before discarding them. The created RDT reader can also supply real-time statistics of various illnesses by using databases and Internet. This can help to inhibit propagation of contagious diseases and to increase readiness against epidemic diseases worldwide. Haydar Ozkan and Osman Semih Kayhan Copyright © 2016 Haydar Ozkan and Osman Semih Kayhan. All rights reserved. Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis Mon, 11 Apr 2016 14:09:21 +0000 Dementia is a growing problem that affects elderly people worldwide. More accurate evaluation of dementia diagnosis can help during the medical examination. Several methods for computer-aided dementia diagnosis have been proposed using resonance imaging scans to discriminate between patients with Alzheimer’s disease (AD) or mild cognitive impairment (MCI) and healthy controls (NC). Nonetheless, the computer-aided diagnosis is especially challenging because of the heterogeneous and intermediate nature of MCI. We address the automated dementia diagnosis by introducing a novel supervised pretraining approach that takes advantage of the artificial neural network (ANN) for complex classification tasks. The proposal initializes an ANN based on linear projections to achieve more discriminating spaces. Such projections are estimated by maximizing the centered kernel alignment criterion that assesses the affinity between the resonance imaging data kernel matrix and the label target matrix. As a result, the performed linear embedding allows accounting for features that contribute the most to the MCI class discrimination. We compare the supervised pretraining approach to two unsupervised initialization methods (autoencoders and Principal Component Analysis) and against the best four performing classification methods of the 2014 CADDementia challenge. As a result, our proposal outperforms all the baselines (7% of classification accuracy and area under the receiver-operating-characteristic curve) at the time it reduces the class biasing. David Cárdenas-Peña, Diego Collazos-Huertas, and German Castellanos-Dominguez Copyright © 2016 David Cárdenas-Peña et al. All rights reserved. Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-Based Graph Cuts Tue, 05 Apr 2016 09:48:36 +0000 Accurate segmentation of liver from abdominal CT scans is critical for computer-assisted diagnosis and therapy. Despite many years of research, automatic liver segmentation remains a challenging task. In this paper, a novel method was proposed for automatic delineation of liver on CT volume images using supervoxel-based graph cuts. To extract the liver volume of interest (VOI), the region of abdomen was firstly determined based on maximum intensity projection (MIP) and thresholding methods. Then, the patient-specific liver VOI was extracted from the region of abdomen by using a histogram-based adaptive thresholding method and morphological operations. The supervoxels of the liver VOI were generated using the simple linear iterative clustering (SLIC) method. The foreground/background seeds for graph cuts were generated on the largest liver slice, and the graph cuts algorithm was applied to the VOI supervoxels. Thirty abdominal CT images were used to evaluate the accuracy and efficiency of the proposed algorithm. Experimental results show that the proposed method can detect the liver accurately with significant reduction of processing time, especially when dealing with diseased liver cases. Weiwei Wu, Zhuhuang Zhou, Shuicai Wu, and Yanhua Zhang Copyright © 2016 Weiwei Wu et al. All rights reserved. Mathematical Model of Three Age-Structured Transmission Dynamics of Chikungunya Virus Tue, 05 Apr 2016 06:13:29 +0000 We developed a new age-structured deterministic model for the transmission dynamics of chikungunya virus. The model is analyzed to gain insights into the qualitative features of its associated equilibria. Some of the theoretical and epidemiological findings indicate that the stable disease-free equilibrium is globally asymptotically stable when the associated reproduction number is less than unity. Furthermore, the model undergoes, in the presence of disease induced mortality, the phenomenon of backward bifurcation, where the stable disease-free equilibrium of the model coexists with a stable endemic equilibrium when the associated reproduction number is less than unity. Further analysis of the model indicates that the qualitative dynamics of the model are not altered by the inclusion of age structure. This is further emphasized by the sensitivity analysis results, which shows that the dominant parameters of the model are not altered by the inclusion of age structure. However, the numerical simulations show the flaw of the exclusion of age in the transmission dynamics of chikungunya with regard to control implementations. The exclusion of age structure fails to show the age distribution needed for an effective age based control strategy, leading to a one size fits all blanket control for the entire population. Folashade B. Agusto, Shamise Easley, Kenneth Freeman, and Madison Thomas Copyright © 2016 Folashade B. Agusto et al. All rights reserved. The Virtual Screening of the Drug Protein with a Few Crystal Structures Based on the Adaboost-SVM Sun, 03 Apr 2016 12:42:09 +0000 Using the theory of machine learning to assist the virtual screening (VS) has been an effective plan. However, the quality of the training set may reduce because of mixing with the wrong docking poses and it will affect the screening efficiencies. To solve this problem, we present a method using the ensemble learning to improve the support vector machine to process the generated protein-ligand interaction fingerprint (IFP). By combining multiple classifiers, ensemble learning is able to avoid the limitations of the single classifier’s performance and obtain better generalization. According to the research of virtual screening experiment with SRC and Cathepsin K as the target, the results show that the ensemble learning method can effectively reduce the error because the sample quality is not high and improve the effect of the whole virtual screening process. Meng-yu Wang, Peng Li, and Pei-li Qiao Copyright © 2016 Meng-yu Wang et al. All rights reserved. Fluid Structural Analysis of Urine Flow in a Stented Ureter Thu, 31 Mar 2016 13:08:20 +0000 Many urologists are currently studying new designs of ureteral stents to improve the quality of their operations and the subsequent recovery of the patient. In order to help during this design process, many computational models have been developed to simulate the behaviour of different biological tissues and provide a realistic computational environment to evaluate the stents. However, due to the high complexity of the involved tissues, they usually introduce simplifications to make these models less computationally demanding. In this study, the interaction between urine flow and a double-J stented ureter with a simplified geometry has been analysed. The Fluid-Structure Interaction (FSI) of urine and the ureteral wall was studied using three models for the solid domain: Mooney-Rivlin, Yeoh, and Ogden. The ureter was assumed to be quasi-incompressible and isotropic. Data obtained in previous studies from ex vivo and in vivo mechanical characterization of different ureters were used to fit the mentioned models. The results show that the interaction between the stented ureter and urine is negligible. Therefore, we can conclude that this type of models does not need to include the FSI and could be solved quite accurately assuming that the ureter is a rigid body and, thus, using the more simple Computational Fluid Dynamics (CFD) approach. J. Carlos Gómez-Blanco, F. Javier Martínez-Reina, Domingo Cruz, J. Blas Pagador, Francisco M. Sánchez-Margallo, and Federico Soria Copyright © 2016 J. Carlos Gómez-Blanco et al. All rights reserved. A Bayesian Approach for Evaluation of Determinants of Health System Efficiency Using Stochastic Frontier Analysis and Beta Regression Tue, 29 Mar 2016 07:35:00 +0000 In today’s world, Public expenditures on health are one of the most important issues for governments. These increased expenditures are putting pressure on public budgets. Therefore, health policy makers have focused on the performance of their health systems and many countries have introduced reforms to improve the performance of their health systems. This study investigates the most important determinants of healthcare efficiency for OECD countries using second stage approach for Bayesian Stochastic Frontier Analysis (BSFA). There are two steps in this study. First we measure 29 OECD countries’ healthcare efficiency by BSFA using the data from the OECD Health Database. At second stage, we expose the multiple relationships between the healthcare efficiency and characteristics of healthcare systems across OECD countries using Bayesian beta regression. Talat Şenel and Mehmet Ali Cengiz Copyright © 2016 Talat Şenel and Mehmet Ali Cengiz. All rights reserved. Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques Sun, 27 Mar 2016 09:02:31 +0000 With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC. Kemal Akyol, Baha Şen, and Şafak Bayır Copyright © 2016 Kemal Akyol et al. All rights reserved. The Dynamics of Avian Influenza: Individual-Based Model with Intervention Strategies in Traditional Trade Networks in Phitsanulok Province, Thailand Wed, 23 Mar 2016 09:58:10 +0000 Avian influenza virus subtype H5N1 is endemic to Southeast Asia. In Thailand, avian influenza viruses continue to cause large poultry stock losses. The spread of the disease has a serious impact on poultry production especially among rural households with backyard chickens. The movements and activities of chicken traders result in the spread of the disease through traditional trade networks. In this study, we investigate the dynamics of avian influenza in the traditional trade network in Phitsanulok Province, Thailand. We also propose an individual-based model with intervention strategies to control the spread of the disease. We found that the dynamics of the disease mainly depend on the transmission probability and the virus inactivation period. This study also illustrates the appropriate virus disinfection period and the target for intervention strategies on traditional trade network. The results suggest that good hygiene and cleanliness among household traders and trader of trader areas and ensuring that any equipment used is clean can lead to a decrease in transmission and final epidemic size. These results may be useful to epidemiologists, researchers, and relevant authorities in understanding the spread of avian influenza through traditional trade networks. Chaiwat Wilasang, Anuwat Wiratsudakul, and Sudarat Chadsuthi Copyright © 2016 Chaiwat Wilasang et al. All rights reserved. Signal and Image Processing of Physiological Data: Methods for Diagnosis and Treatment Purposes Thu, 17 Mar 2016 14:15:47 +0000 Anne Humeau-Heurtier, Edite Figueiras, and Joao Cardoso Copyright © 2016 Anne Humeau-Heurtier et al. All rights reserved. Establishment of Relational Model of Congenital Heart Disease Markers and GO Functional Analysis of the Association between Its Serum Markers and Susceptibility Genes Wed, 16 Mar 2016 08:45:00 +0000 Purpose. The purpose of present study was to construct the best screening model of congenital heart disease serum markers and to provide reference for further prevention and treatment of the disease. Methods. Documents from 2006 to 2014 were collected and meta-analysis was used for screening susceptibility genes and serum markers closely related to the diagnosis of congenital heart disease. Data of serum markers were extracted from 80 congenital heart disease patients and 80 healthy controls, respectively, and then logistic regression analysis and support vector machine were utilized to establish prediction models of serum markers and Gene Ontology (GO) functional annotation. Results. Results showed that NKX2.5, GATA4, and FOG2 were susceptibility genes of congenital heart disease. CRP, BNP, and cTnI were risk factors of congenital heart disease (); cTnI, hs-CRP, BNP, and Lp(a) were significantly close to congenital heart disease (). ROC curve indicated that the accuracy rate of Lp(a) and cTnI, Lp(a) and BNP, and BNP and cTnI joint prediction was 93.4%, 87.1%, and 97.2%, respectively. But the detection accuracy rate of the markers’ relational model established by support vector machine was only 85%. GO analysis suggested that NKX2.5, GATA4, and FOG2 were functionally related to Lp(a) and BNP. Conclusions. The combined markers model of BNP and cTnI had the highest accuracy rate, providing a theoretical basis for the diagnosis of congenital heart disease. Min Liu, Luosha Zhao, and Jiaying Yuan Copyright © 2016 Min Liu et al. All rights reserved. Multiscale CNNs for Brain Tumor Segmentation and Diagnosis Wed, 16 Mar 2016 08:38:25 +0000 Early brain tumor detection and diagnosis are critical to clinics. Thus segmentation of focused tumor area needs to be accurate, efficient, and robust. In this paper, we propose an automatic brain tumor segmentation method based on Convolutional Neural Networks (CNNs). Traditional CNNs focus only on local features and ignore global region features, which are both important for pixel classification and recognition. Besides, brain tumor can appear in any place of the brain and be any size and shape in patients. We design a three-stream framework named as multiscale CNNs which could automatically detect the optimum top-three scales of the image sizes and combine information from different scales of the regions around that pixel. Datasets provided by Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized by MICCAI 2013 are utilized for both training and testing. The designed multiscale CNNs framework also combines multimodal features from T1, T1-enhanced, T2, and FLAIR MRI images. By comparison with traditional CNNs and the best two methods in BRATS 2012 and 2013, our framework shows advances in brain tumor segmentation accuracy and robustness. Liya Zhao and Kebin Jia Copyright © 2016 Liya Zhao and Kebin Jia. All rights reserved. Modelling of the Electric Field Distribution in Deep Transcranial Magnetic Stimulation in the Adolescence, in the Adulthood, and in the Old Age Wed, 16 Mar 2016 07:29:44 +0000 In the last few years, deep transcranial magnetic stimulation (dTMS) has been used for the treatment of depressive disorders, which affect a broad category of people, from adolescents to aging people. To facilitate its clinical application, particular shapes of coils, including the so-called Hesed coils, were designed. Given their increasing demand and the lack of studies which accurately characterize their use, this paper aims to provide a picture of the distribution of the induced electric field in four realistic human models of different ages and gender. In detail, the electric field distributions were calculated by using numerical techniques in the brain structures potentially involved in the progression of the disease and were quantified in terms of both amplitude levels and focusing power of the distribution. The results highlight how the chosen Hesed coil (H7 coil) is able to induce the maxima levels of E mainly in the prefrontal cortex, particularly for the younger model. Moreover, growing levels of induced electric fields with age were found by going in deep in the brain, as well as a major capability to penetrate in the deepest brain structures with an electric field higher than 50%, 70%, and 90% of the peak found in the cortex. Serena Fiocchi, Michela Longhi, Paolo Ravazzani, Yiftach Roth, Abraham Zangen, and Marta Parazzini Copyright © 2016 Serena Fiocchi et al. All rights reserved. Evaluation of Analytical Modeling Functions for the Phonation Onset Process Tue, 15 Mar 2016 16:59:18 +0000 The human voice originates from oscillations of the vocal folds in the larynx. The duration of the voice onset (VO), called the voice onset time (VOT), is currently under investigation as a clinical indicator for correct laryngeal functionality. Different analytical approaches for computing the VOT based on endoscopic imaging were compared to determine the most reliable method to quantify automatically the transient vocal fold oscillations during VO. Transnasal endoscopic imaging in combination with a high-speed camera (8000 fps) was applied to visualize the phonation onset process. Two different definitions of VO interval were investigated. Six analytical functions were tested that approximate the envelope of the filtered or unfiltered glottal area waveform (GAW) during phonation onset. A total of 126 recordings from nine healthy males and 210 recordings from 15 healthy females were evaluated. Three criteria were analyzed to determine the most appropriate computation approach: (1) reliability of the fit function for a correct approximation of VO; (2) consistency represented by the standard deviation of VOT; and (3) accuracy of the approximation of VO. The results suggest the computation of VOT by a fourth-order polynomial approximation in the interval between 32.2 and 67.8% of the saturation amplitude of the filtered GAW. Simon Petermann, Stefan Kniesburges, Anke Ziethe, Anne Schützenberger, and Michael Döllinger Copyright © 2016 Simon Petermann et al. All rights reserved. Influence of PEEK Coating on Hip Implant Stress Shielding: A Finite Element Analysis Mon, 14 Mar 2016 07:01:11 +0000 Stress shielding is a well-known failure factor in hip implants. This work proposes a design concept for hip implants, using a combination of metallic stem with a polymer coating (polyether ether ketone (PEEK)). The proposed design concept is simulated using titanium alloy stems and PEEK coatings with thicknesses varying from 100 to 400 μm. The Finite Element analysis of the cancellous bone surrounding the implant shows promising results. The effective von Mises stress increases between 81 and 92% for the complete volume of cancellous bone. When focusing on the proximal zone of the implant, the increased stress transmission to the cancellous bone reaches between 47 and 60%. This increment in load transferred to the bone can influence mineral bone loss due to stress shielding, minimizing such effect, and thus prolonging implant lifespan. Jesica Anguiano-Sanchez, Oscar Martinez-Romero, Hector R. Siller, Jose A. Diaz-Elizondo, Eduardo Flores-Villalba, and Ciro A. Rodriguez Copyright © 2016 Jesica Anguiano-Sanchez et al. All rights reserved. A Novel Method for the Separation of Overlapping Pollen Species for Automated Detection and Classification Thu, 10 Mar 2016 13:09:58 +0000 The identification of pollen in an automated way will accelerate different tasks and applications of palynology to aid in, among others, climate change studies, medical allergies calendar, and forensic science. The aim of this paper is to develop a system that automatically captures a hundred microscopic images of pollen and classifies them into the 12 different species from Lagunera Region, Mexico. Many times, the pollen is overlapping on the microscopic images, which increases the difficulty for its automated identification and classification. This paper focuses on a method to segment the overlapping pollen. First, the proposed method segments the overlapping pollen. Second, the method separates the pollen based on the mean shift process (100% segmentation) and erosion by H-minima based on the Fibonacci series. Thus, pollen is characterized by its shape, color, and texture for training and evaluating the performance of three classification techniques: random tree forest, multilayer perceptron, and Bayes net. Using the newly developed system, we obtained segmentation results of 100% and classification on top of 96.2% and 96.1% in recall and precision using multilayer perceptron in twofold cross validation. Santiago Tello-Mijares and Francisco Flores Copyright © 2016 Santiago Tello-Mijares and Francisco Flores. All rights reserved. Use of a Novel Grammatical Inference Approach in Classification of Amyloidogenic Hexapeptides Wed, 09 Mar 2016 10:02:01 +0000 The present paper is a novel contribution to the field of bioinformatics by using grammatical inference in the analysis of data. We developed an algorithm for generating star-free regular expressions which turned out to be good recommendation tools, as they are characterized by a relatively high correlation coefficient between the observed and predicted binary classifications. The experiments have been performed for three datasets of amyloidogenic hexapeptides, and our results are compared with those obtained using the graph approaches, the current state-of-the-art methods in heuristic automata induction, and the support vector machine. The results showed the superior performance of the new grammatical inference algorithm on fixed-length amyloid datasets. Wojciech Wieczorek and Olgierd Unold Copyright © 2016 Wojciech Wieczorek and Olgierd Unold. All rights reserved. A Stochastic Differential Equation Model for the Spread of HIV amongst People Who Inject Drugs Wed, 09 Mar 2016 09:31:32 +0000 We introduce stochasticity into the deterministic differential equation model for the spread of HIV amongst people who inject drugs (PWIDs) studied by Greenhalgh and Hay (1997). This was based on the original model constructed by Kaplan (1989) which analyses the behaviour of HIV/AIDS amongst a population of PWIDs. We derive a stochastic differential equation (SDE) for the fraction of PWIDs who are infected with HIV at time. The stochasticity is introduced using the well-known standard technique of parameter perturbation. We first prove that the resulting SDE for the fraction of infected PWIDs has a unique solution in (0, 1) provided that some infected PWIDs are initially present and next construct the conditions required for extinction and persistence. Furthermore, we show that there exists a stationary distribution for the persistence case. Simulations using realistic parameter values are then constructed to illustrate and support our theoretical results. Our results provide new insight into the spread of HIV amongst PWIDs. The results show that the introduction of stochastic noise into a model for the spread of HIV amongst PWIDs can cause the disease to die out in scenarios where deterministic models predict disease persistence. Yanfeng Liang, David Greenhalgh, and Xuerong Mao Copyright © 2016 Yanfeng Liang et al. All rights reserved. Computer Aided Detection System for Prediction of the Malaise during Hemodialysis Sun, 06 Mar 2016 12:42:33 +0000 Monitoring of dialysis sessions is crucial as different stress factors can yield suffering or critical situations. Specialized personnel is usually required for the administration of this medical treatment; nevertheless, subjects whose clinical status can be considered stable require different monitoring strategies when compared with subjects with critical clinical conditions. In this case domiciliary treatment or monitoring can substantially improve the quality of life of patients undergoing dialysis. In this work, we present a Computer Aided Detection (CAD) system for the telemonitoring of patients’ clinical parameters. The CAD was mainly designed to predict the insurgence of critical events; it consisted of two Random Forest (RF) classifiers: the first one () predicting the onset of any malaise one hour after the treatment start and the second one () again two hours later. The developed system shows an accurate classification performance in terms of both sensitivity and specificity. The specificity in the identification of nonsymptomatic sessions and the sensitivity in the identification of symptomatic sessions for are equal to 86.60% and 71.40%, respectively, thus suggesting the CAD as an effective tool to support expert nephrologists in telemonitoring the patients. Sabina Tangaro, Annarita Fanizzi, Nicola Amoroso, Roberto Corciulo, Elena Garuccio, Loreto Gesualdo, Giuliana Loizzo, Deni Aldo Procaccini, Lucia Vernò, and Roberto Bellotti Copyright © 2016 Sabina Tangaro et al. All rights reserved. Seasonality Impact on the Transmission Dynamics of Tuberculosis Wed, 02 Mar 2016 08:55:15 +0000 The statistical data of monthly pulmonary tuberculosis (TB) incidence cases from January 2004 to December 2012 show the seasonality fluctuations in Shaanxi of China. A seasonality TB epidemic model with periodic varying contact rate, reactivation rate, and disease-induced death rate is proposed to explore the impact of seasonality on the transmission dynamics of TB. Simulations show that the basic reproduction number of time-averaged autonomous systems may underestimate or overestimate infection risks in some cases, which may be up to the value of period. The basic reproduction number of the seasonality model is appropriately given, which determines the extinction and uniform persistence of TB disease. If it is less than one, then the disease-free equilibrium is globally asymptotically stable; if it is greater than one, the system at least has a positive periodic solution and the disease will persist. Moreover, numerical simulations demonstrate these theorem results. Yali Yang, Chenping Guo, Luju Liu, Tianhua Zhang, and Weiping Liu Copyright © 2016 Yali Yang et al. All rights reserved. Bayesian Inference for Functional Dynamics Exploring in fMRI Data Tue, 01 Mar 2016 10:50:28 +0000 This paper aims to review state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI) data. Particularly, we focus on one specific long-standing challenge in the computational modeling of fMRI datasets: how to effectively explore typical functional interactions from fMRI time series and the corresponding boundaries of temporal segments. Bayesian inference is a method of statistical inference which has been shown to be a powerful tool to encode dependence relationships among the variables with uncertainty. Here we provide an introduction to a group of Bayesian-inference-based methods for fMRI data analysis, which were designed to detect magnitude or functional connectivity change points and to infer their functional interaction patterns based on corresponding temporal boundaries. We also provide a comparison of three popular Bayesian models, that is, Bayesian Magnitude Change Point Model (BMCPM), Bayesian Connectivity Change Point Model (BCCPM), and Dynamic Bayesian Variable Partition Model (DBVPM), and give a summary of their applications. We envision that more delicate Bayesian inference models will be emerging and play increasingly important roles in modeling brain functions in the years to come. Xuan Guo, Bing Liu, Le Chen, Guantao Chen, Yi Pan, and Jing Zhang Copyright © 2016 Xuan Guo et al. All rights reserved. Evaluation of Round Window Stimulation Performance in Otosclerosis Using Finite Element Modeling Mon, 29 Feb 2016 11:07:57 +0000 Round window (RW) stimulation is a new type of middle ear implant’s application for treating patients with middle ear disease, such as otosclerosis. However, clinical outcomes show a substantial degree of variability. One source of variability is the variation in the material properties of the ear components caused by the disease. To investigate the influence of the otosclerosis on the performance of the RW stimulation, a human ear finite element model including middle ear and cochlea was established based on a set of microcomputerized tomography section images of a human temporal bone. Three characteristic changes of the otosclerosis in the auditory system were simulated in the FE model: stapedial annular ligament stiffness enlargement, stapedial abnormal bone growth, and partial fixation of the malleus. The FE model was verified by comparing the model-predicted results with published experimental measurements. The equivalent sound pressure (ESP) of RW stimulation was calculated via comparing the differential intracochlear pressure produced by the RW stimulation and the normal eardrum sound stimulation. The results show that the increase of stapedial annular ligament and partial fixation of the malleus decreases RW stimulation’s ESP prominently at lower frequencies. In contrast, the stapedial abnormal bone growth deteriorates RW stimulation’s ESP severely at higher frequencies. Shanguo Yang, Dan Xu, and Xiaole Liu Copyright © 2016 Shanguo Yang et al. All rights reserved. Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer Mon, 29 Feb 2016 09:20:47 +0000 Purpose. The objective of our study was to predicate candidate genes in cervical cancer (CC) using a network-based strategy and to understand the pathogenic process of CC. Methods. A pathogenic network of CC was extracted based on known pathogenic genes (seed genes) and differentially expressed genes (DEGs) between CC and normal controls. Subsequently, cluster analysis was performed to identify the subnetworks in the pathogenic network using ClusterONE. Each gene in the pathogenic network was assigned a weight value, and then candidate genes were obtained based on the weight distribution. Eventually, pathway enrichment analysis for candidate genes was performed. Results. In this work, a total of 330 DEGs were identified between CC and normal controls. From the pathogenic network, 2 intensely connected clusters were extracted, and a total of 52 candidate genes were detected under the weight values greater than 0.10. Among these candidate genes, VIM had the highest weight value. Moreover, candidate genes MMP1, CDC45, and CAT were, respectively, enriched in pathway in cancer, cell cycle, and methane metabolism. Conclusion. Candidate pathogenic genes including MMP1, CDC45, CAT, and VIM might be involved in the pathogenesis of CC. We believe that our results can provide theoretical guidelines for future clinical application. Yun-Xia Zhang and Yan-Li Zhao Copyright © 2016 Yun-Xia Zhang and Yan-Li Zhao. All rights reserved.