Computational and Mathematical Methods in Medicine The latest articles from Hindawi © 2017 , Hindawi Limited . All rights reserved. Fast Parabola Detection Using Estimation of Distribution Algorithms Tue, 21 Feb 2017 00:00:00 +0000 This paper presents a new method based on Estimation of Distribution Algorithms (EDAs) to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function. This proposed method is evaluated in terms of computational time and compared with two implementations of the generalized Hough transform and RANSAC method for parabola detection. Experimental results show that the proposed method outperforms the comparative methods in terms of execution time about on synthetic images and on retinal fundus and human plantar arch images. In addition, experimental results have also shown that the proposed method can be highly suitable for different medical applications. Jose de Jesus Guerrero-Turrubiates, Ivan Cruz-Aceves, Sergio Ledesma, Juan Manuel Sierra-Hernandez, Jonas Velasco, Juan Gabriel Avina-Cervantes, Maria Susana Avila-Garcia, Horacio Rostro-Gonzalez, and Roberto Rojas-Laguna Copyright © 2017 Jose de Jesus Guerrero-Turrubiates et al. All rights reserved. Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine Sun, 19 Feb 2017 13:53:23 +0000 Preterm birth (PTB) is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG) related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT) and extreme learning machine (ELM). For each sample, each channel was decomposed into a set of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). Then, the Hilbert transform was applied to IMF to obtain analytic function. The maximum amplitude of analytic function was extracted as feature. The identification model was constructed based on ELM. Experimental results reveal that the best classification performance of the proposed method can reach an accuracy of 88.00%, a sensitivity of 91.30%, and a specificity of 85.19%. The area under receiver operating characteristic (ROC) curve is 0.88. Finally, experimental results indicate that the method developed in this work could be effective in the classification of EHG between pregnancy and labour group. Lili Chen and Yaru Hao Copyright © 2017 Lili Chen and Yaru Hao. All rights reserved. Automated Classification of Severity in Cardiac Dyssynchrony Merging Clinical Data and Mechanical Descriptors Sun, 19 Feb 2017 07:15:47 +0000 Cardiac resynchronization therapy (CRT) improves functional classification among patients with left ventricle malfunction and ventricular electric conduction disorders. However, a high percentage of subjects under CRT (20%–30%) do not show any improvement. Nonetheless the presence of mechanical contraction dyssynchrony in ventricles has been proposed as an indicator of CRT response. This work proposes an automated classification model of severity in ventricular contraction dyssynchrony. The model includes clinical data such as left ventricular ejection fraction (LVEF), QRS and P-R intervals, and the 3 most significant factors extracted from the factor analysis of dynamic structures applied to a set of equilibrium radionuclide angiography images representing the mechanical behavior of cardiac contraction. A control group of 33 normal volunteers ( years, LVEF of ) and a HF group of 42 subjects ( years, LVEF < 35%) were studied. The proposed classifiers had hit rates of 90%, 50%, and 80% to distinguish between absent, mild, and moderate-severe interventricular dyssynchrony, respectively. For intraventricular dyssynchrony, hit rates of 100%, 50%, and 90% were observed distinguishing between absent, mild, and moderate-severe, respectively. These results seem promising in using this automated method for clinical follow-up of patients undergoing CRT. Alejandro Santos-Díaz, Raquel Valdés-Cristerna, Enrique Vallejo, Salvador Hernández, and Luis Jiménez-Ángeles Copyright © 2017 Alejandro Santos-Díaz et al. All rights reserved. Noise Attenuation Estimation for Maximum Length Sequences in Deconvolution Process of Auditory Evoked Potentials Sun, 19 Feb 2017 00:00:00 +0000 The use of maximum length sequence (m-sequence) has been found beneficial for recovering both linear and nonlinear components at rapid stimulation. Since m-sequence is fully characterized by a primitive polynomial of different orders, the selection of polynomial order can be problematic in practice. Usually, the m-sequence is repetitively delivered in a looped fashion. Ensemble averaging is carried out as the first step and followed by the cross-correlation analysis to deconvolve linear/nonlinear responses. According to the classical noise reduction property based on additive noise model, theoretical equations have been derived in measuring noise attenuation ratios (NARs) after the averaging and correlation processes in the present study. A computer simulation experiment was conducted to test the derived equations, and a nonlinear deconvolution experiment was also conducted using order 7 and 9 m-sequences to address this issue with real data. Both theoretical and experimental results show that the NAR is essentially independent of the m-sequence order and is decided by the total length of valid data, as well as stimulation rate. The present study offers a guideline for m-sequence selections, which can be used to estimate required recording time and signal-to-noise ratio in designing m-sequence experiments. Xian Peng, Yun’er Chen, Tao Wang, Lei Ding, and Xiaodan Tan Copyright © 2017 Xian Peng et al. All rights reserved. Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach Sun, 19 Feb 2017 00:00:00 +0000 In recent years, some methods of sentiment analysis have been developed for the health domain; however, the diabetes domain has not been explored yet. In addition, there is a lack of approaches that analyze the positive or negative orientation of each aspect contained in a document (a review, a piece of news, and a tweet, among others). Based on this understanding, we propose an aspect-level sentiment analysis method based on ontologies in the diabetes domain. The sentiment of the aspects is calculated by considering the words around the aspect which are obtained through N-gram methods (N-gram after, N-gram before, and N-gram around). To evaluate the effectiveness of our method, we obtained a corpus from Twitter, which has been manually labelled at aspect level as positive, negative, or neutral. The experimental results show that the best result was obtained through the N-gram around method with a precision of 81.93%, a recall of 81.13%, and an -measure of 81.24%. María del Pilar Salas-Zárate, José Medina-Moreira, Katty Lagos-Ortiz, Harry Luna-Aveiga, Miguel Ángel Rodríguez-García, and Rafael Valencia-García Copyright © 2017 María del Pilar Salas-Zárate et al. All rights reserved. Mathematical Modelling of Immune Parameters in the Evolution of Severe Dengue Wed, 15 Feb 2017 00:00:00 +0000 Aims. Predicting the risk of severity at an early stage in an individual patient will be invaluable in preventing morbidity and mortality caused by dengue. We hypothesized that such predictions are possible by analyzing multiple parameters using mathematical modeling. Methodology. Data from 11 adult patients with dengue fever (DF) and 25 patients with dengue hemorrhagic fever (DHF) were analyzed. Multivariate statistical analysis was performed to study the characteristics and interactions of parameters using dengue NS1 antigen levels, dengue IgG antibody levels, platelet counts, and lymphocyte counts. Fuzzy logic fundamentals were used to map the risk of developing severe forms of dengue. The cumulative effects of the parameters were incorporated using the Hamacher and the OWA operators. Results. The operator classified the patients according to the severity level during the time period of 96 hours to 120 hours after the onset of fever. The accuracy ranged from 53% to 89%. Conclusion. The results show a robust mathematical model that explains the evolution from dengue to its serious forms in individual patients. The model allows prediction of severe cases of dengue which could be useful for optimal management of patients during a dengue outbreak. Further analysis of the model may also deepen our understanding of the pathways towards severe illness. M. K. Premaratne, S. S. N. Perera, G. N. Malavige, and Saroj Jayasinghe Copyright © 2017 M. K. Premaratne et al. All rights reserved. Optimally Repeatable Kinetic Model Variant for Myocardial Blood Flow Measurements with 82Rb PET Mon, 13 Feb 2017 06:39:52 +0000 Purpose. Myocardial blood flow (MBF) quantification with positron emission tomography (PET) is gaining clinical adoption, but improvements in precision are desired. This study aims to identify analysis variants producing the most repeatable MBF measures. Methods. 12 volunteers underwent same-day test-retest rest and dipyridamole stress imaging with dynamic PET, from which MBF was quantified using 1-tissue-compartment kinetic model variants: () blood-pool versus uptake region sampled input function (Blood/Uptake-ROI), () dual spillover correction (SOC-On/Off), () right blood correction (RBC-On/Off), () arterial blood transit delay (Delay-On/Off), and () distribution volume (DV) constraint (Global/Regional-DV). Repeatability of MBF, stress/rest myocardial flow reserve (MFR), and stress/rest MBF difference (ΔMBF) was assessed using nonparametric reproducibility coefficients ( = 1.45 × interquartile range). Results. MBF using SOC-On, RVBC-Off, Blood-ROI, Global-DV, and Delay-Off was most repeatable for combined rest and stress: = 0.21 mL/min/g (15.8%). Corresponding MFR and ΔMBF were 0.42 (20.2%) and 0.24 mL/min/g (23.5%). MBF repeatability improved with SOC-On at stress () and tended to improve with RBC-Off at both rest and stress (). DV and ROI did not significantly influence repeatability. The Delay-On model was overdetermined and did not reliably converge. Conclusion. MBF and MFR test-retest repeatability were the best with dual spillover correction, left atrium blood input function, and global DV. Adrian F. Ocneanu, Robert A. deKemp, Jennifer M. Renaud, Andy Adler, Rob S. B. Beanlands, and Ran Klein Copyright © 2017 Adrian F. Ocneanu et al. All rights reserved. A Psychometric Tool for a Virtual Reality Rehabilitation Approach for Dyslexia Mon, 13 Feb 2017 00:00:00 +0000 Dyslexia is a chronic problem that affects the life of subjects and often influences their life choices. The standard rehabilitation methods all use a classic paper and pencil training format but these exercises are boring and demanding for children who may have difficulty in completing the treatments. It is important to develop a new rehabilitation program that would help children in a funny and engaging way. A Wii-based game was developed to demonstrate that a short treatment with an action video game, rather than phonological or orthographic training, may improve the reading abilities in dyslexic children. According to the results, an approach using cues in the context of a virtual environment may represent a promising tool to improve attentional skills. On the other hand, our results do not demonstrate an immediate effect on reading performance, suggesting that a more prolonged protocol may be a future direction. Elisa Pedroli, Patrizia Padula, Andrea Guala, Maria Teresa Meardi, Giuseppe Riva, and Giovanni Albani Copyright © 2017 Elisa Pedroli et al. All rights reserved. Mixed Total Variation and Regularization Method for Optical Tomography Based on Radiative Transfer Equation Thu, 09 Feb 2017 14:15:16 +0000 Optical tomography is an emerging and important molecular imaging modality. The aim of optical tomography is to reconstruct optical properties of human tissues. In this paper, we focus on reconstructing the absorption coefficient based on the radiative transfer equation (RTE). It is an ill-posed parameter identification problem. Regularization methods have been broadly applied to reconstruct the optical coefficients, such as the total variation (TV) regularization and the regularization. In order to better reconstruct the piecewise constant and sparse coefficient distributions, TV and norms are combined as the regularization. The forward problem is discretized with the discontinuous Galerkin method on the spatial space and the finite element method on the angular space. The minimization problem is solved by a Jacobian-based Levenberg-Marquardt type method which is equipped with a split Bregman algorithms for the regularization. We use the adjoint method to compute the Jacobian matrix which dramatically improves the computation efficiency. By comparing with the other imaging reconstruction methods based on TV and regularizations, the simulation results show the validity and efficiency of the proposed method. Jinping Tang, Bo Han, Weimin Han, Bo Bi, and Li Li Copyright © 2017 Jinping Tang et al. All rights reserved. 3D Kidney Segmentation from Abdominal Images Using Spatial-Appearance Models Thu, 09 Feb 2017 00:00:00 +0000 Kidney segmentation is an essential step in developing any noninvasive computer-assisted diagnostic system for renal function assessment. This paper introduces an automated framework for 3D kidney segmentation from dynamic computed tomography (CT) images that integrates discriminative features from the current and prior CT appearances into a random forest classification approach. To account for CT images’ inhomogeneities, we employ discriminate features that are extracted from a higher-order spatial model and an adaptive shape model in addition to the first-order CT appearance. To model the interactions between CT data voxels, we employed a higher-order spatial model, which adds the triple and quad clique families to the traditional pairwise clique family. The kidney shape prior model is built using a set of training CT data and is updated during segmentation using not only region labels but also voxels’ appearances in neighboring spatial voxel locations. Our framework performance has been evaluated on in vivo dynamic CT data collected from 20 subjects and comprises multiple 3D scans acquired before and after contrast medium administration. Quantitative evaluation between manually and automatically segmented kidney contours using Dice similarity, percentage volume differences, and 95th-percentile bidirectional Hausdorff distances confirms the high accuracy of our approach. Fahmi Khalifa, Ahmed Soliman, Adel Elmaghraby, Georgy Gimel’farb, and Ayman El-Baz Copyright © 2017 Fahmi Khalifa et al. All rights reserved. Modeling the Parasitic Filariasis Spread by Mosquito in Periodic Environment Wed, 08 Feb 2017 09:20:13 +0000 In this paper a mosquito-borne parasitic infection model in periodic environment is considered. Threshold parameter is given by linear next infection operator, which determined the dynamic behaviors of system. We obtain that when , the disease-free periodic solution is globally asymptotically stable and when by Poincaré map we obtain that disease is uniformly persistent. Numerical simulations support the results and sensitivity analysis shows effects of parameters on , which provided references to seek optimal measures to control the transmission of lymphatic filariasis. Yan Cheng, Xiaoyun Wang, Qiuhui Pan, and Mingfeng He Copyright © 2017 Yan Cheng et al. All rights reserved. Complementary Keratoconus Indices Based on Topographical Interpretation of Biomechanical Waveform Parameters: A Supplement to Established Keratoconus Indices Tue, 07 Feb 2017 09:10:30 +0000 Purpose. To build new models with the Ocular Response Analyzer (ORA) waveform parameters to create new indices analogous to established topographic keratoconus indices. Method. Biomechanical, tomographic, and topographic measurements of 505 eyes from the Homburger Keratoconus Centre were included. Thirty-seven waveform parameters (WF) were derived from the biomechanical measurement with the ORA. Area under curve (ROC, receiver operating characteristic) was used to quantify the screening performance. A logistic regression analysis was used to create two new keratoconus prediction models based on these waveform parameters to resample the clinically established keratoconus indices from Pentacam and TMS-5. Results. ROC curves show the best results for the waveform parameters p1area, p2area, , , dive1, mslew1, aspect1, aplhf, and dslope1. The new keratoconus prediction model to resample the Pentacam topographic keratoconus index (TKC) was = −4.068 + 0.002 × p2area − 0.005 × dive1 − 0.01 × h1 − 2.501 × aplhf, which achieves a sensitivity of 90.3% and specificity of 89.4%; to resample the TMS-5 keratoconus classification index (KCI) it was = −3.606 + 0.002 × p2area, which achieves a sensitivity of 75.4% and a specificity of 81.8%. Conclusion. In addition to the biomechanically provided Keratoconus Index two new indices which were based on the topographic gold standards (either Pentacam or TMS-5) were created. Of course, these do not replace the original topographic measurement. Susanne Goebels, Timo Eppig, Stefan Wagenpfeil, Alan Cayless, Berthold Seitz, and Achim Langenbucher Copyright © 2017 Susanne Goebels et al. All rights reserved. Automatic Lumen Segmentation in Intravascular Optical Coherence Tomography Images Using Level Set Tue, 07 Feb 2017 00:00:00 +0000 Automatic lumen segmentation from intravascular optical coherence tomography (IVOCT) images is an important and fundamental work for diagnosis and treatment of coronary artery disease. However, it is a very challenging task due to irregular lumen caused by unstable plaque and bifurcation vessel, guide wire shadow, and blood artifacts. To address these problems, this paper presents a novel automatic level set based segmentation algorithm which is very competent for irregular lumen challenge. Before applying the level set model, a narrow image smooth filter is proposed to reduce the effect of artifacts and prevent the leakage of level set meanwhile. Moreover, a divide-and-conquer strategy is proposed to deal with the guide wire shadow. With our proposed method, the influence of irregular lumen, guide wire shadow, and blood artifacts can be appreciably reduced. Finally, the experimental results showed that the proposed method is robust and accurate by evaluating 880 images from 5 different patients and the average DSC value was . Yihui Cao, Kang Cheng, Xianjing Qin, Qinye Yin, Jianan Li, Rui Zhu, and Wei Zhao Copyright © 2017 Yihui Cao et al. All rights reserved. Retinal Image Denoising via Bilateral Filter with a Spatial Kernel of Optimally Oriented Line Spread Function Sun, 05 Feb 2017 14:22:45 +0000 Filtering belongs to the most fundamental operations of retinal image processing and for which the value of the filtered image at a given location is a function of the values in a local window centered at this location. However, preserving thin retinal vessels during the filtering process is challenging due to vessels’ small area and weak contrast compared to background, caused by the limited resolution of imaging and less blood flow in the vessel. In this paper, we present a novel retinal image denoising approach which is able to preserve the details of retinal vessels while effectively eliminating image noise. Specifically, our approach is carried out by determining an optimal spatial kernel for the bilateral filter, which is represented by a line spread function with an orientation and scale adjusted adaptively to the local vessel structure. Moreover, this approach can also be served as a preprocessing tool for improving the accuracy of the vessel detection technique. Experimental results show the superiority of our approach over state-of-the-art image denoising techniques such as the bilateral filter. Yunlong He, Yuanjie Zheng, Yanna Zhao, Yanju Ren, Jian Lian, and James Gee Copyright © 2017 Yunlong He et al. All rights reserved. Development of a Patient-Specific Finite Element Model for Predicting Implant Failure in Pelvic Ring Fracture Fixation Wed, 01 Feb 2017 00:00:00 +0000 Introduction. The main purpose of this study is to develop an efficient technique for generating FE models of pelvic ring fractures that is capable of predicting possible failure regions of osteosynthesis with acceptable accuracy. Methods. Patient-specific FE models of two patients with osteoporotic pelvic fractures were generated. A validated FE model of an uninjured pelvis from our previous study was used as a master model. Then, fracture morphologies and implant positions defined by a trauma surgeon in the preoperative CT were manually introduced as 3D splines to the master model. Four loading cases were used as boundary conditions. Regions of high stresses in the models were compared with actual locations of implant breakages and loosening identified from follow-up X-rays. Results. Model predictions and the actual clinical outcomes matched well. For Patient A, zones of increased tension and maximum stress coincided well with the actual locations of implant loosening. For Patient B, the model predicted accurately the loosening of the implant in the anterior region. Conclusion. Since a significant reduction in time and labour was achieved in our mesh generation technique, it can be considered as a viable option to be implemented as a part of the clinical routine to aid presurgical planning and postsurgical management of pelvic ring fracture patients. Vickie Shim, Andreas Höch, Ronny Grunert, Steffen Peldschus, and Jörg Böhme Copyright © 2017 Vickie Shim et al. All rights reserved. Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel Tue, 31 Jan 2017 13:33:03 +0000 Driver fatigue has become an important factor to traffic accidents worldwide, and effective detection of driver fatigue has major significance for public health. The purpose method employs entropy measures for feature extraction from a single electroencephalogram (EEG) channel. Four types of entropies measures, sample entropy (SE), fuzzy entropy (FE), approximate entropy (AE), and spectral entropy (PE), were deployed for the analysis of original EEG signal and compared by ten state-of-the-art classifiers. Results indicate that optimal performance of single channel is achieved using a combination of channel CP4, feature FE, and classifier Random Forest (RF). The highest accuracy can be up to 96.6%, which has been able to meet the needs of real applications. The best combination of channel + features + classifier is subject-specific. In this work, the accuracy of FE as the feature is far greater than the Acc of other features. The accuracy using classifier RF is the best, while that of classifier SVM with linear kernel is the worst. The impact of channel selection on the Acc is larger. The performance of various channels is very different. Jianfeng Hu Copyright © 2017 Jianfeng Hu. All rights reserved. Detection of Impaired Cerebral Autoregulation Using Selected Correlation Analysis: A Validation Study Tue, 31 Jan 2017 00:00:00 +0000 Multimodal brain monitoring has been utilized to optimize treatment of patients with critical neurological diseases. However, the amount of data requires an integrative tool set to unmask pathological events in a timely fashion. Recently we have introduced a mathematical model allowing the simulation of pathophysiological conditions such as reduced intracranial compliance and impaired autoregulation. Utilizing a mathematical tool set called selected correlation analysis (sca), correlation patterns, which indicate impaired autoregulation, can be detected in patient data sets (scp). In this study we compared the results of the sca with the pressure reactivity index (PRx), an established marker for impaired autoregulation. Mean PRx values were significantly higher in time segments identified as scp compared to segments showing no selected correlations (nsc). The sca based approach predicted cerebral autoregulation failure with a sensitivity of 78.8% and a specificity of 62.6%. Autoregulation failure, as detected by the results of both analysis methods, was significantly correlated with poor outcome. Sca of brain monitoring data detects impaired autoregulation with high sensitivity and sufficient specificity. Since the sca approach allows the simultaneous detection of both major pathological conditions, disturbed autoregulation and reduced compliance, it may become a useful analysis tool for brain multimodal monitoring data. Martin A. Proescholdt, Rupert Faltermeier, Sylvia Bele, and Alexander Brawanski Copyright © 2017 Martin A. Proescholdt et al. All rights reserved. Predictive Behavior of a Computational Foot/Ankle Model through Artificial Neural Networks Mon, 30 Jan 2017 00:00:00 +0000 Computational models are useful tools to study the biomechanics of human joints. Their predictive performance is heavily dependent on bony anatomy and soft tissue properties. Imaging data provides anatomical requirements while approximate tissue properties are implemented from literature data, when available. We sought to improve the predictive capability of a computational foot/ankle model by optimizing its ligament stiffness inputs using feedforward and radial basis function neural networks. While the former demonstrated better performance than the latter per mean square error, both networks provided reasonable stiffness predictions for implementation into the computational model. Ruchi D. Chande, Rosalyn Hobson Hargraves, Norma Ortiz-Robinson, and Jennifer S. Wayne Copyright © 2017 Ruchi D. Chande et al. All rights reserved. An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis Thu, 26 Jan 2017 14:19:30 +0000 In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA) was firstly adopted to generate the diversified initial positions, and then grey wolf optimization (GWO) was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on KELM. The proposed approach is compared against the original GA and GWO on the two common disease diagnosis problems in terms of a set of performance metrics, including classification accuracy, sensitivity, specificity, precision, -mean, -measure, and the size of selected features. The simulation results have proven the superiority of the proposed method over the other two competitive counterparts. Qiang Li, Huiling Chen, Hui Huang, Xuehua Zhao, ZhenNao Cai, Changfei Tong, Wenbin Liu, and Xin Tian Copyright © 2017 Qiang Li et al. All rights reserved. Are Health Videos from Hospitals, Health Organizations, and Active Users Available to Health Consumers? An Analysis of Diabetes Health Video Ranking in YouTube Tue, 24 Jan 2017 00:00:00 +0000 Health consumers are increasingly using the Internet to search for health information. The existence of overloaded, inaccurate, obsolete, or simply incorrect health information available on the Internet is a serious obstacle for finding relevant and good-quality data that actually helps patients. Search engines of multimedia Internet platforms are thought to help users to find relevant information according to their search. But, is the information recovered by those search engines from quality sources? Is the health information uploaded from reliable sources, such as hospitals and health organizations, easily available to patients? The availability of videos is directly related to the ranking position in YouTube search. The higher the ranking of the information is, the more accessible it is. The aim of this study is to analyze the ranking evolution of diabetes health videos on YouTube in order to discover how videos from reliable channels, such as hospitals and health organizations, are evolving in the ranking. The analysis was done by tracking the ranking of 2372 videos on a daily basis during a 30-day period using 20 diabetes-related queries. Our conclusions are that the current YouTube algorithm favors the presence of reliable videos in upper rank positions in diabetes-related searches. Carlos Fernandez-Llatas, Vicente Traver, Jose-Enrique Borras-Morell, Antonio Martinez-Millana, and Randi Karlsen Copyright © 2017 Carlos Fernandez-Llatas et al. All rights reserved. Utilizing Selected Di- and Trinucleotides of siRNA to Predict RNAi Activity Tue, 24 Jan 2017 00:00:00 +0000 Small interfering RNAs (siRNAs) induce posttranscriptional gene silencing in various organisms. siRNAs targeted to different positions of the same gene show different effectiveness; hence, predicting siRNA activity is a crucial step. In this paper, we developed and evaluated a powerful tool named “siRNApred” with a new mixed feature set to predict siRNA activity. To improve the prediction accuracy, we proposed 2-3NTs as our new features. A Random Forest siRNA activity prediction model was constructed using the feature set selected by our proposed Binary Search Feature Selection (BSFS) algorithm. Experimental data demonstrated that the binding site of the Argonaute protein correlates with siRNA activity. “siRNApred” is effective for selecting active siRNAs, and the prediction results demonstrate that our method can outperform other current siRNA activity prediction methods in terms of prediction accuracy. Ye Han, Yuanning Liu, Hao Zhang, Fei He, Chonghe Shu, and Liyan Dong Copyright © 2017 Ye Han et al. All rights reserved. A Novel Computer-Aided Approach for Parametric Investigation of Custom Design of Fracture Fixation Plates Thu, 19 Jan 2017 14:14:02 +0000 The present study proposes an integrated computer-aided approach combining femur surface modeling, fracture evidence recover plate creation, and plate modification in order to conduct a parametric investigation of the design of custom plate for a specific patient. The study allows for improving the design efficiency of specific plates on the patients’ femur parameters and the fracture information. Furthermore, the present approach will lead to exploration of plate modification and optimization. The three-dimensional (3D) surface model of a detailed femur and the corresponding fixation plate were represented with high-level feature parameters, and the shape of the specific plate was recursively modified in order to obtain the optimal plate for a specific patient. The proposed approach was tested and verified on a case study, and it could be helpful for orthopedic surgeons to design and modify the plate in order to fit the specific femur anatomy and the fracture information. Xiaozhong Chen, Kunjin He, and Zhengming Chen Copyright © 2017 Xiaozhong Chen et al. All rights reserved. Bionic Vision-Based Intelligent Power Line Inspection System Thu, 19 Jan 2017 10:58:04 +0000 Detecting the threats of the external obstacles to the power lines can ensure the stability of the power system. Inspired by the attention mechanism and binocular vision of human visual system, an intelligent power line inspection system is presented in this paper. Human visual attention mechanism in this intelligent inspection system is used to detect and track power lines in image sequences according to the shape information of power lines, and the binocular visual model is used to calculate the 3D coordinate information of obstacles and power lines. In order to improve the real time and accuracy of the system, we propose a new matching strategy based on the traditional SURF algorithm. The experimental results show that the system is able to accurately locate the position of the obstacles around power lines automatically, and the designed power line inspection system is effective in complex backgrounds, and there are no missing detection instances under different conditions. Qingwu Li, Yunpeng Ma, Feijia He, Shuya Xi, and Jinxin Xu Copyright © 2017 Qingwu Li et al. All rights reserved. Steady-State-Preserving Simulation of Genetic Regulatory Systems Thu, 19 Jan 2017 10:22:27 +0000 A novel family of exponential Runge-Kutta (expRK) methods are designed incorporating the stable steady-state structure of genetic regulatory systems. A natural and convenient approach to constructing new expRK methods on the base of traditional RK methods is provided. In the numerical integration of the one-gene, two-gene, and p53-mdm2 regulatory systems, the new expRK methods are shown to be more accurate than their prototype RK methods. Moreover, for nonstiff genetic regulatory systems, the expRK methods are more efficient than some traditional exponential RK integrators in the scientific literature. Ruqiang Zhang, Julius Osato Ehigie, Xilin Hou, Xiong You, and Chunlu Yuan Copyright © 2017 Ruqiang Zhang et al. All rights reserved. Shannon’s Energy Based Algorithm in ECG Signal Processing Wed, 18 Jan 2017 00:00:00 +0000 Physikalisch-Technische Bundesanstalt (PTB) database is electrocardiograms (ECGs) set from healthy volunteers and patients with different heart diseases. PTB is provided for research and teaching purposes by National Metrology Institute of Germany. The analysis method of complex QRS in ECG signals for diagnosis of heart disease is extremely important. In this article, a method on Shannon energy (SE) in order to detect QRS complex in 12 leads of ECG signal is provided. At first, this algorithm computes the Shannon energy (SE) and then makes an envelope of Shannon energy (SE) by using the defined threshold. Then, the signal peaks are determined. The efficiency of the algorithm is tested on 70 cases. Of all 12 standard leads, ECG signals include 840 leads of the PTB Diagnostic ECG Database (PTBDB). The algorithm shows that the Shannon energy (SE) sensitivity is equal to 99.924%, the detection error rate (DER) is equal to 0.155%, Positive Predictivity (+P) is equal to 99.922%, and Classification Accuracy (Acc) is equal to 99.846%. Hamed Beyramienanlou and Nasser Lotfivand Copyright © 2017 Hamed Beyramienanlou and Nasser Lotfivand. All rights reserved. A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis Tue, 17 Jan 2017 06:44:46 +0000 EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA), which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies. Balbir Singh and Hiroaki Wagatsuma Copyright © 2017 Balbir Singh and Hiroaki Wagatsuma. All rights reserved. Intelligibility Evaluation of Pathological Speech through Multigranularity Feature Extraction and Optimization Tue, 17 Jan 2017 00:00:00 +0000 Pathological speech usually refers to speech distortion resulting from illness or other biological insults. The assessment of pathological speech plays an important role in assisting the experts, while automatic evaluation of speech intelligibility is difficult because it is usually nonstationary and mutational. In this paper, we carry out an independent innovation of feature extraction and reduction, and we describe a multigranularity combined feature scheme which is optimized by the hierarchical visual method. A novel method of generating feature set based on -transform and chaotic analysis is proposed. There are BAFS (430, basic acoustics feature), local spectral characteristics MSCC (84, Mel -transform cepstrum coefficients), and chaotic features (). Finally, radar chart and -score are proposed to optimize the features by the hierarchical visual fusion. The feature set could be optimized from 526 to 96 dimensions based on NKI-CCRT corpus and 104 dimensions based on SVD corpus. The experimental results denote that new features by support vector machine (SVM) have the best performance, with a recognition rate of 84.4% on NKI-CCRT corpus and 78.7% on SVD corpus. The proposed method is thus approved to be effective and reliable for pathological speech intelligibility evaluation. Chunying Fang, Haifeng Li, Lin Ma, and Mancai Zhang Copyright © 2017 Chunying Fang et al. All rights reserved. Comparison of Functional Connectivity Estimated from Concatenated Task-State Data from Block-Design Paradigm with That of Continuous Task Mon, 16 Jan 2017 00:00:00 +0000 Functional connectivity (FC) analysis with data collected as continuous tasks and activation analysis using data from block-design paradigms are two main methods to investigate the task-induced brain activation. If the concatenated data of task blocks extracted from the block-design paradigm could provide equivalent FC information to that derived from continuous task data, it would shorten the data collection time and simplify experimental procedures, and the already collected data of block-design paradigms could be reanalyzed from the perspective of FC. Despite being used in many studies, such a hypothesis of equivalence has not yet been tested from multiple perspectives. In this study, we collected fMRI blood-oxygen-level-dependent signals from 24 healthy subjects during a continuous task session as well as in block-design task sessions. We compared concatenated task blocks and continuous task data in terms of region of interest- (ROI-) based FC, seed-based FC, and brain network topology during a short motor task. According to our results, the concatenated data was not significantly different from the continuous data in multiple aspects, indicating the potential of using concatenated data to estimate task-state FC in short motor tasks. However, even under appropriate experimental conditions, the interpretation of FC results based on concatenated data should be cautious and take the influence due to inherent information loss during concatenation into account. Yang Zhu, Lin Cheng, Naying He, Yang Yang, Huawei Ling, Hasan Ayaz, Shanbao Tong, Junfeng Sun, and Yi Fu Copyright © 2017 Yang Zhu et al. All rights reserved. Threshold Dynamics in Stochastic SIRS Epidemic Models with Nonlinear Incidence and Vaccination Mon, 16 Jan 2017 00:00:00 +0000 In this paper, the dynamical behaviors for a stochastic SIRS epidemic model with nonlinear incidence and vaccination are investigated. In the models, the disease transmission coefficient and the removal rates are all affected by noise. Some new basic properties of the models are found. Applying these properties, we establish a series of new threshold conditions on the stochastically exponential extinction, stochastic persistence, and permanence in the mean of the disease with probability one for the models. Furthermore, we obtain a sufficient condition on the existence of unique stationary distribution for the model. Finally, a series of numerical examples are introduced to illustrate our main theoretical results and some conjectures are further proposed. Lei Wang, Zhidong Teng, Tingting Tang, and Zhiming Li Copyright © 2017 Lei Wang et al. All rights reserved. Level Set Based Hippocampus Segmentation in MR Images with Improved Initialization Using Region Growing Sun, 15 Jan 2017 00:00:00 +0000 The hippocampus has been known as one of the most important structures referred to as Alzheimer’s disease and other neurological disorders. However, segmentation of the hippocampus from MR images is still a challenging task due to its small size, complex shape, low contrast, and discontinuous boundaries. For the accurate and efficient detection of the hippocampus, a new image segmentation method based on adaptive region growing and level set algorithm is proposed. Firstly, adaptive region growing and morphological operations are performed in the target regions and its output is used for the initial contour of level set evolution method. Then, an improved edge-based level set method utilizing global Gaussian distributions with different means and variances is developed to implement the accurate segmentation. Finally, gradient descent method is adopted to get the minimization of the energy equation. As proved by experiment results, the proposed method can ideally extract the contours of the hippocampus that are very close to manual segmentation drawn by specialists. Xiaoliang Jiang, Zhaozhong Zhou, Xiaokang Ding, Xiaolei Deng, Ling Zou, and Bailin Li Copyright © 2017 Xiaoliang Jiang et al. All rights reserved.