Computational and Mathematical Methods in Medicine The latest articles from Hindawi Publishing Corporation © 2016 , Hindawi Publishing Corporation . All rights reserved. Wavelet Based Method for Congestive Heart Failure Recognition by Three Confirmation Functions Tue, 02 Feb 2016 14:00:40 +0000 An investigation of the electrocardiogram (ECG) signals and arrhythmia characterization by wavelet energy is proposed. This study employs a wavelet based feature extraction method for congestive heart failure (CHF) obtained from the percentage energy (PE) of terminal wavelet packet transform (WPT) subsignals. In addition, the average framing percentage energy (AFE) technique is proposed, termed WAFE. A new classification method is introduced by three confirmation functions. The confirmation methods are based on three concepts: percentage root mean square difference error (PRD), logarithmic difference signal ratio (LDSR), and correlation coefficient (CC). The proposed method showed to be a potential effective discriminator in recognizing such clinical syndrome. ECG signals taken from MIT-BIH arrhythmia dataset and other databases are utilized to analyze different arrhythmias and normal ECGs. Several known methods were studied for comparison. The best recognition rate selection obtained was for WAFE. The recognition performance was accomplished as 92.60% accurate. The Receiver Operating Characteristic curve as a common tool for evaluating the diagnostic accuracy was illustrated, which indicated that the tests are reliable. The performance of the presented system was investigated in additive white Gaussian noise (AWGN) environment, where the recognition rate was 81.48% for 5 dB. K. Daqrouq and A. Dobaie Copyright © 2016 K. Daqrouq and A. Dobaie. All rights reserved. Predicting Renal Failure Progression in Chronic Kidney Disease Using Integrated Intelligent Fuzzy Expert System Tue, 02 Feb 2016 11:01:47 +0000 Background. Chronic kidney disease (CKD) is a covert disease. Accurate prediction of CKD progression over time is necessary for reducing its costs and mortality rates. The present study proposes an adaptive neurofuzzy inference system (ANFIS) for predicting the renal failure timeframe of CKD based on real clinical data. Methods. This study used 10-year clinical records of newly diagnosed CKD patients. The threshold value of 15 cc/kg/min/1.73 m2 of glomerular filtration rate (GFR) was used as the marker of renal failure. A Takagi-Sugeno type ANFIS model was used to predict GFR values. Variables of age, sex, weight, underlying diseases, diastolic blood pressure, creatinine, calcium, phosphorus, uric acid, and GFR were initially selected for the predicting model. Results. Weight, diastolic blood pressure, diabetes mellitus as underlying disease, and current showed significant correlation with GFRs and were selected as the inputs of model. The comparisons of the predicted values with the real data showed that the ANFIS model could accurately estimate GFR variations in all sequential periods (Normalized Mean Absolute Error lower than 5%). Conclusions. Despite the high uncertainties of human body and dynamic nature of CKD progression, our model can accurately predict the GFR variations at long future periods. Jamshid Norouzi, Ali Yadollahpour, Seyed Ahmad Mirbagheri, Mitra Mahdavi Mazdeh, and Seyed Ahmad Hosseini Copyright © 2016 Jamshid Norouzi et al. All rights reserved. Computer Vision Based Automatic Extraction and Thickness Measurement of Deep Cervical Flexor from Ultrasonic Images Mon, 01 Feb 2016 08:34:39 +0000 Deep Cervical Flexor (DCF) muscles are important in monitoring and controlling neck pain. While ultrasonographic analysis is useful in this area, it has intrinsic subjectivity problem. In this paper, we propose automatic DCF extractor/analyzer software based on computer vision. One of the major difficulties in developing such an automatic analyzer is to detect important organs and their boundaries under very low brightness contrast environment. Our fuzzy sigma binarization process is one of the answers for that problem. Another difficulty is to compensate information loss that happened during such image processing procedures. Many morphologically motivated image processing algorithms are applied for that purpose. The proposed method is verified as successful in extracting DCFs and measuring thicknesses in experiment using two hundred 800 × 600 DICOM ultrasonography images with 98.5% extraction rate. Also, the thickness of DCFs automatically measured by this software has small difference (less than 0.3 cm) for 89.8% of extracted DCFs. Kwang Baek Kim, Doo Heon Song, and Hyun Jun Park Copyright © 2016 Kwang Baek Kim et al. All rights reserved. Drug-Drug Interaction Extraction via Convolutional Neural Networks Sun, 31 Jan 2016 08:04:56 +0000 Drug-drug interaction (DDI) extraction as a typical relation extraction task in natural language processing (NLP) has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM) with a large number of manually defined features. Recently, convolutional neural networks (CNN), a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an -score of 69.75%, which outperforms the existing best performing method by 2.75%. Shengyu Liu, Buzhou Tang, Qingcai Chen, and Xiaolong Wang Copyright © 2016 Shengyu Liu et al. All rights reserved. Comparison of Support-Vector Machine and Sparse Representation Using a Modified Rule-Based Method for Automated Myocardial Ischemia Detection Tue, 26 Jan 2016 16:19:20 +0000 An automatic method is presented for detecting myocardial ischemia, which can be considered as the early symptom of acute coronary events. Myocardial ischemia commonly manifests as ST- and T-wave changes on ECG signals. The methods in this study are proposed to detect abnormal ECG beats using knowledge-based features and classification methods. A novel classification method, sparse representation-based classification (SRC), is involved to improve the performance of the existing algorithms. A comparison was made between two classification methods, SRC and support-vector machine (SVM), using rule-based vectors as input feature space. The two methods are proposed with quantitative evaluation to validate their performances. The results of SRC method encompassed with rule-based features demonstrate higher sensitivity than that of SVM. However, the specificity and precision are a trade-off. Moreover, SRC method is less dependent on the selection of rule-based features and can achieve high performance using fewer features. The overall performances of the two methods proposed in this study are better than the previous methods. Yi-Li Tseng, Keng-Sheng Lin, and Fu-Shan Jaw Copyright © 2016 Yi-Li Tseng et al. All rights reserved. A Fetal Electrocardiogram Signal Extraction Algorithm Based on the Temporal Structure and the Non-Gaussianity Tue, 26 Jan 2016 13:37:21 +0000 Fetal electrocardiogram (FECG) extraction is an important issue in biomedical signal processing. In this paper, we develop an objective function for extraction of FECG. The objective function is based on the non-Gaussianity and the temporal structure of source signals. Maximizing the objective function, we can extract the desired FECG. Combining with the solution vector obtained by maximizing the objective function, we further improve the accuracy of the extracted FECG. In addition, the feasibility of the innovative methods is analyzed by mathematical derivation theoretically and the efficiency of the proposed approaches is illustrated with the computer simulations experimentally. Yibing Li, Wei Nie, Fang Ye, and Ao Li Copyright © 2016 Yibing Li et al. All rights reserved. Iterative Image Reconstruction for Limited-Angle CT Using Optimized Initial Image Tue, 26 Jan 2016 13:06:39 +0000 Limited-angle computed tomography (CT) has great impact in some clinical applications. Existing iterative reconstruction algorithms could not reconstruct high-quality images, leading to severe artifacts nearby edges. Optimal selection of initial image would influence the iterative reconstruction performance but has not been studied deeply yet. In this work, we proposed to generate optimized initial image followed by total variation (TV) based iterative reconstruction considering the feature of image symmetry. The simulated data and real data reconstruction results indicate that the proposed method effectively removes the artifacts nearby edges. Jingyu Guo, Hongliang Qi, Yuan Xu, Zijia Chen, Shulong Li, and Linghong Zhou Copyright © 2016 Jingyu Guo et al. All rights reserved. Population Dynamics of Patients with Bacterial Resistance in Hospital Environment Sun, 24 Jan 2016 07:19:39 +0000 During the past decades, the increase of antibiotic resistance has become a major concern worldwide. The researchers found that superbugs with new type of resistance genes (NDM-1) have two aspects of transmission characteristics; the first is that the antibiotic resistance genes can horizontally transfer among bacteria, and the other is that the superbugs can spread between humans through direct contact. Based on these two transmission mechanisms, we study the dynamics of population in hospital environment where superbugs exist. In this paper, we build three mathematic models to illustrate the dynamics of patients with bacterial resistance in hospital environment. The models are analyzed using stability theory of differential equations. Positive equilibrium points of the system are investigated and their stability analysis is carried out. Moreover, the numerical simulation of the proposed model is also performed which supports the theoretical findings. Leilei Qu, Qiuhui Pan, Xubin Gao, and Mingfeng He Copyright © 2016 Leilei Qu et al. All rights reserved. Geodesic Distance Algorithm for Extracting the Ascending Aorta from 3D CT Images Wed, 20 Jan 2016 11:08:38 +0000 This paper presents a method for the automatic 3D segmentation of the ascending aorta from coronary computed tomography angiography (CCTA). The segmentation is performed in three steps. First, the initial seed points are selected by minimizing a newly proposed energy function across the Hough circles. Second, the ascending aorta is segmented by geodesic distance transformation. Third, the seed points are effectively transferred through the next axial slice by a novel transfer function. Experiments are performed using a database composed of 10 patients’ CCTA images. For the experiment, the ground truths are annotated manually on the axial image slices by a medical expert. A comparative evaluation with state-of-the-art commercial aorta segmentation algorithms shows that our approach is computationally more efficient and accurate under the DSC (Dice Similarity Coefficient) measurements. Yeonggul Jang, Ho Yub Jung, Youngtaek Hong, Iksung Cho, Hackjoon Shim, and Hyuk-Jae Chang Copyright © 2016 Yeonggul Jang et al. All rights reserved. Lateral Penumbra Modelling Based Leaf End Shape Optimization for Multileaf Collimator in Radiotherapy Tue, 19 Jan 2016 14:26:20 +0000 Lateral penumbra of multileaf collimator plays an important role in radiotherapy treatment planning. Growing evidence has revealed that, for a single-focused multileaf collimator, lateral penumbra width is leaf position dependent and largely attributed to the leaf end shape. In our study, an analytical method for leaf end induced lateral penumbra modelling is formulated using Tangent Secant Theory. Compared with Monte Carlo simulation and ray tracing algorithm, our model serves well the purpose of cost-efficient penumbra evaluation. Leaf ends represented in parametric forms of circular arc, elliptical arc, Bézier curve, and B-spline are implemented. With biobjective function of penumbra mean and variance introduced, genetic algorithm is carried out for approximating the Pareto frontier. Results show that for circular arc leaf end objective function is convex and convergence to optimal solution is guaranteed using gradient based iterative method. It is found that optimal leaf end in the shape of Bézier curve achieves minimal standard deviation, while using B-spline minimum of penumbra mean is obtained. For treatment modalities in clinical application, optimized leaf ends are in close agreement with actual shapes. Taken together, the method that we propose can provide insight into leaf end shape design of multileaf collimator. Dong Zhou, Hui Zhang, and Peiqing Ye Copyright © 2016 Dong Zhou et al. All rights reserved. Modeling the Treatment of Glioblastoma Multiforme and Cancer Stem Cells with Ordinary Differential Equations Mon, 18 Jan 2016 08:34:42 +0000 Despite improvements in cancer therapy and treatments, tumor recurrence is a common event in cancer patients. One explanation of recurrence is that cancer therapy focuses on treatment of tumor cells and does not eradicate cancer stem cells (CSCs). CSCs are postulated to behave similar to normal stem cells in that their role is to maintain homeostasis. That is, when the population of tumor cells is reduced or depleted by treatment, CSCs will repopulate the tumor, causing recurrence. In this paper, we study the application of the CSC Hypothesis to the treatment of glioblastoma multiforme by immunotherapy. We extend the work of Kogan et al. (2008) to incorporate the dynamics of CSCs, prove the existence of a recurrence state, and provide an analysis of possible cancerous states and their dependence on treatment levels. Kristen Abernathy and Jeremy Burke Copyright © 2016 Kristen Abernathy and Jeremy Burke. All rights reserved. Prediction Accuracy in Multivariate Repeated-Measures Bayesian Forecasting Models with Examples Drawn from Research on Sleep and Circadian Rhythms Thu, 14 Jan 2016 09:19:14 +0000 In study designs with repeated measures for multiple subjects, population models capturing within- and between-subjects variances enable efficient individualized prediction of outcome measures (response variables) by incorporating individuals response data through Bayesian forecasting. When measurement constraints preclude reasonable levels of prediction accuracy, additional (secondary) response variables measured alongside the primary response may help to increase prediction accuracy. We investigate this for the case of substantial between-subjects correlation between primary and secondary response variables, assuming negligible within-subjects correlation. We show how to determine the accuracy of primary response predictions as a function of secondary response observations. Given measurement costs for primary and secondary variables, we determine the number of observations that produces, with minimal cost, a fixed average prediction accuracy for a model of subject means. We illustrate this with estimation of subject-specific sleep parameters using polysomnography and wrist actigraphy. We also consider prediction accuracy in an example time-dependent, linear model and derive equations for the optimal timing of measurements to achieve, on average, the best prediction accuracy. Finally, we examine an example involving a circadian rhythm model and show numerically that secondary variables can improve individualized predictions in this time-dependent nonlinear model as well. Clark Kogan, Leonid Kalachev, and Hans P. A. Van Dongen Copyright © 2016 Clark Kogan et al. All rights reserved. Rescaled Local Interaction Simulation Approach for Shear Wave Propagation Modelling in Magnetic Resonance Elastography Wed, 13 Jan 2016 07:23:35 +0000 Properties of soft biological tissues are increasingly used in medical diagnosis to detect various abnormalities, for example, in liver fibrosis or breast tumors. It is well known that mechanical stiffness of human organs can be obtained from organ responses to shear stress waves through Magnetic Resonance Elastography. The Local Interaction Simulation Approach is proposed for effective modelling of shear wave propagation in soft tissues. The results are validated using experimental data from Magnetic Resonance Elastography. These results show the potential of the method for shear wave propagation modelling in soft tissues. The major advantage of the proposed approach is a significant reduction of computational effort. Z. Hashemiyan, P. Packo, W. J. Staszewski, and T. Uhl Copyright © 2016 Z. Hashemiyan et al. All rights reserved. Systems Medicine of Cancer: Bringing Together Clinical Data and Nonlinear Dynamics of Genetic Networks Mon, 11 Jan 2016 12:10:21 +0000 Konstantin B. Blyuss, Ranjit Manchanda, Jürgen Kurths, Ahmed Alsaedi, and Alexey Zaikin Copyright © 2016 Konstantin B. Blyuss et al. All rights reserved. Diagnosing Parkinson’s Diseases Using Fuzzy Neural System Sun, 10 Jan 2016 11:30:32 +0000 This study presents the design of the recognition system that will discriminate between healthy people and people with Parkinson’s disease. A diagnosing of Parkinson’s diseases is performed using fusion of the fuzzy system and neural networks. The structure and learning algorithms of the proposed fuzzy neural system (FNS) are presented. The approach described in this paper allows enhancing the capability of the designed system and efficiently distinguishing healthy individuals. It was proved through simulation of the system that has been performed using data obtained from UCI machine learning repository. A comparative study was carried out and the simulation results demonstrated that the proposed fuzzy neural system improves the recognition rate of the designed system. Rahib H. Abiyev and Sanan Abizade Copyright © 2016 Rahib H. Abiyev and Sanan Abizade. All rights reserved. P300 Detection Based on EEG Shape Features Sun, 10 Jan 2016 06:51:43 +0000 We present a novel approach to describe a P300 by a shape-feature vector, which offers several advantages over the feature vector used by the BCI2000 system. Additionally, we present a calibration algorithm that reduces the dimensionality of the shape-feature vector, the number of trials, and the electrodes needed by a Brain Computer Interface to accurately detect P300s; we also define a method to find a template that best represents, for a given electrode, the subject’s P300 based on his/her own acquired signals. Our experiments with 21 subjects showed that the SWLDA’s performance using our shape-feature vector was , that is, higher than the one obtained with BCI2000-feature’s vector. The shape-feature vector is 34-dimensional for every electrode; however, it is possible to significantly reduce its dimensionality while keeping a high sensitivity. The validation of the calibration algorithm showed an averaged area under the ROC (AUROC) curve of . Also, most of the subjects needed less than trials to have an AUROC superior to . Finally, we found that the electrode C4 also leads to better classification. Montserrat Alvarado-González, Edgar Garduño, Ernesto Bribiesca, Oscar Yáñez-Suárez, and Verónica Medina-Bañuelos Copyright © 2016 Montserrat Alvarado-González et al. All rights reserved. A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials Thu, 31 Dec 2015 14:33:05 +0000 The movement-related cortical potential (MRCP) is a low-frequency negative shift in the electroencephalography (EEG) recording that takes place about 2 seconds prior to voluntary movement production. MRCP replicates the cortical processes employed in planning and preparation of movement. In this study, we recapitulate the features such as signal’s acquisition, processing, and enhancement and different electrode montages used for EEG data recoding from different studies that used MRCPs to predict the upcoming real or imaginary movement. An authentic identification of human movement intention, accompanying the knowledge of the limb engaged in the performance and its direction of movement, has a potential implication in the control of external devices. This information could be helpful in development of a proficient patient-driven rehabilitation tool based on brain-computer interfaces (BCIs). Such a BCI paradigm with shorter response time appears more natural to the amputees and can also induce plasticity in brain. Along with different training schedules, this can lead to restoration of motor control in stroke patients. Aqsa Shakeel, Muhammad Samran Navid, Muhammad Nabeel Anwar, Suleman Mazhar, Mads Jochumsen, and Imran Khan Niazi Copyright © 2015 Aqsa Shakeel et al. All rights reserved. Donor/Recipient Delta Age: A Possible Risk for Arterial Stenosis in Renal Transplantation Thu, 31 Dec 2015 07:54:57 +0000 Different arterial wall properties can significantly increase the risk of blood turbulent fluxes leading to complications such as atherosclerosis. Since the mechanical properties of arterial vessels are influenced by age, we investigated, in a retrospective study, the effects on renal artery stenosis of an age difference >15 years between donor and recipient in a cohort of 164 patients undergoing renal transplantation between 1981 and 1991. The age difference between donor and recipient was ≤15 years in 87 patients (53.0%) (Group A) and >15 years in 77 patients (47.0%) (Group B, ). None of the Group A patients developed an anastomotic arterial stenosis, whereas 8/77 Group B patients (10.4%) had an anastomotic arterial stenosis (). This study shows that an age difference >15 years is significantly linked to the risk of developing arterial stenosis after renal transplantation. Indeed, different wall properties can significantly increase the risk of generation of blood turbulent fluxes and involve, in the arterial vessels, the development of complications such as atherosclerosis. Giovanni Pallotti, Gabriele Donati, Irene Capelli, Olga Baraldi, Giorgia Comai, Patrizia Agati, Michele Nichelatti, Giuseppe Cianciolo, and Gaetano La Manna Copyright © 2015 Giovanni Pallotti et al. All rights reserved. Modeling Illicit Drug Use Dynamics and Its Optimal Control Analysis Sun, 27 Dec 2015 11:52:11 +0000 The global burden of death and disability attributable to illicit drug use, remains a significant threat to public health for both developed and developing nations. This paper presents a new mathematical modeling framework to investigate the effects of illicit drug use in the community. In our model the transmission process is captured as a social “contact” process between the susceptible individuals and illicit drug users. We conduct both epidemic and endemic analysis, with a focus on the threshold dynamics characterized by the basic reproduction number. Using our model, we present illustrative numerical results with a case study in Cape Town, Gauteng, Mpumalanga and Durban communities of South Africa. In addition, the basic model is extended to incorporate time dependent intervention strategies. Steady Mushayabasa and Gift Tapedzesa Copyright © 2015 Steady Mushayabasa and Gift Tapedzesa. All rights reserved. FEM Analysis of Mandibular Prosthetic Overdenture Supported by Dental Implants: Evaluation of Different Retention Methods Tue, 22 Dec 2015 05:43:53 +0000 Prosthetic rehabilitation of total edentulous jaws patients is today a common technique that clinicians approach in their daily practice. The use of dental implants for replacing missing teeth is going to be a safe technique and the implant-prosthetic materials give the possibility of having long-term clinical success. Aim of this work is to evaluate the mechanical features of three different prosthetic retention systems. By applying engineering systems of investigations like FEM and von Mises analyses, how the dental implant material holds out against the masticatory strength during the chewing cycles has been investigated. Three common dental implant overdenture retention systems have been investigated. The ball attachment system, the locator system, and the common dental abutment have been processed by Ansys Workbench 15.0 and underwent FEM and von Mises investigations. The elastic features of the materials used in the study have been taken from recent literature data. Results revealed different response for both types of device, although locator system showed better results for all conditions of loading. The data of this virtual model show all the features of different prosthetic retention systems under the masticatory load. Clinicians should find the better prosthetic solution related to the patients clinical condition in order to obtain long-term results. M. Cicciù, G. Cervino, E. Bramanti, F. Lauritano, G. Lo Gudice, L. Scappaticci, A. Rapparini, E. Guglielmino, and G. Risitano Copyright © 2015 M. Cicciù et al. All rights reserved. Application of Random Forest Survival Models to Increase Generalizability of Decision Trees: A Case Study in Acute Myocardial Infarction Mon, 21 Dec 2015 05:54:09 +0000 Background. Tree models provide easily interpretable prognostic tool, but instable results. Two approaches to enhance the generalizability of the results are pruning and random survival forest (RSF). The aim of this study is to assess the generalizability of saturated tree (ST), pruned tree (PT), and RSF. Methods. Data of 607 patients was randomly divided into training and test set applying 10-fold cross-validation. Using training sets, all three models were applied. Using Log-Rank test, ST was constructed by searching for optimal cutoffs. PT was selected plotting error rate versus minimum sample size in terminal nodes. In construction of RSF, 1000 bootstrap samples were drawn from the training set. C-index and integrated Brier score (IBS) statistic were used to compare models. Results. ST provides the most overoptimized statistics. Mean difference between C-index in training and test set was 0.237. Corresponding figure in PT and RSF was 0.054 and 0.007. In terms of IBS, the difference was 0.136 in ST, 0.021 in PT, and 0.0003 in RSF. Conclusion. Pruning of tree and assessment of its performance of a test set partially improve the generalizability of decision trees. RSF provides results that are highly generalizable. Iman Yosefian, Ehsan Mosa Farkhani, and Mohammad Reza Baneshi Copyright © 2015 Iman Yosefian et al. All rights reserved. Segmentation of Brain Tissues from Magnetic Resonance Images Using Adaptively Regularized Kernel-Based Fuzzy -Means Clustering Thu, 17 Dec 2015 13:44:10 +0000 An adaptively regularized kernel-based fuzzy -means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity. Ahmed Elazab, Changmiao Wang, Fucang Jia, Jianhuang Wu, Guanglin Li, and Qingmao Hu Copyright © 2015 Ahmed Elazab et al. All rights reserved. Prediction of Protein Structural Classes for Low-Similarity Sequences Based on Consensus Sequence and Segmented PSSM Tue, 15 Dec 2015 13:11:58 +0000 Prediction of protein structural classes for low-similarity sequences is useful for understanding fold patterns, regulation, functions, and interactions of proteins. It is well known that feature extraction is significant to prediction of protein structural class and it mainly uses protein primary sequence, predicted secondary structure sequence, and position-specific scoring matrix (PSSM). Currently, prediction solely based on the PSSM has played a key role in improving the prediction accuracy. In this paper, we propose a novel method called CSP-SegPseP-SegACP by fusing consensus sequence (CS), segmented PsePSSM, and segmented autocovariance transformation (ACT) based on PSSM. Three widely used low-similarity datasets (1189, 25PDB, and 640) are adopted in this paper. Then a 700-dimensional (700D) feature vector is constructed and the dimension is decreased to 224D by using principal component analysis (PCA). To verify the performance of our method, rigorous jackknife cross-validation tests are performed on 1189, 25PDB, and 640 datasets. Comparison of our results with the existing PSSM-based methods demonstrates that our method achieves the favorable and competitive performance. This will offer an important complementary to other PSSM-based methods for prediction of protein structural classes for low-similarity sequences. Yunyun Liang, Sanyang Liu, and Shengli Zhang Copyright © 2015 Yunyun Liang et al. All rights reserved. Increasing the Time Interval between PCV Chemotherapy Cycles as a Strategy to Improve Duration of Response in Low-Grade Gliomas: Results from a Model-Based Clinical Trial Simulation Tue, 15 Dec 2015 09:30:06 +0000 Background. We previously developed a mathematical model capturing tumor size dynamics of adult low-grade gliomas (LGGs) before and after treatment either with PCV (Procarbazine, CCNU, and Vincristine) chemotherapy alone or with radiotherapy (RT) alone. Objective. The aim of the present study was to present how the model could be used as a simulation tool to suggest more effective therapeutic strategies in LGGs. Simulations were performed to identify schedule modifications that might improve PCV chemotherapy efficacy. Methods. Virtual populations of LGG patients were generated on the basis of previously evaluated parameter distributions. Monte Carlo simulations were performed to compare treatment efficacy across in silico clinical trials. Results. Simulations predicted that RT plus PCV would be more effective in terms of duration of response than RT alone. Additional simulations suggested that, in patients treated with PCV chemotherapy, increasing the interval between treatment cycles up to 6 months from the standard 6 weeks can increase treatment efficacy. The predicted median duration of response was 4.3 years in LGGs treated with PCV cycles given every 6 months versus 3.1 years in patients treated with the classical regimen. Conclusion. The present study suggests that, in LGGs, mathematical modeling could facilitate clinical research by helping to identify, in silico, potentially more effective therapeutic strategies. Pauline Mazzocco, Jérôme Honnorat, François Ducray, and Benjamin Ribba Copyright © 2015 Pauline Mazzocco et al. All rights reserved. Modeling Gene Networks in Saccharomyces cerevisiae Based on Gene Expression Profiles Mon, 14 Dec 2015 07:14:31 +0000 Detailed and innovative analysis of gene regulatory network structures may reveal novel insights to biological mechanisms. Here we study how gene regulatory network in Saccharomyces cerevisiae can differ under aerobic and anaerobic conditions. To achieve this, we discretized the gene expression profiles and calculated the self-entropy of down- and upregulation of gene expression as well as joint entropy. Based on these quantities the uncertainty coefficient was calculated for each gene triplet, following which, separate gene logic networks were constructed for the aerobic and anaerobic conditions. Four structural parameters such as average degree, average clustering coefficient, average shortest path, and average betweenness were used to compare the structure of the corresponding aerobic and anaerobic logic networks. Five genes were identified to be putative key components of the two energy metabolisms. Furthermore, community analysis using the Newman fast algorithm revealed two significant communities for the aerobic but only one for the anaerobic network. David Gene Functional Classification suggests that, under aerobic conditions, one such community reflects the cell cycle and cell replication, while the other one is linked to the mitochondrial respiratory chain function. Yulin Zhang, Kebo Lv, Shudong Wang, Jionglong Su, and Dazhi Meng Copyright © 2015 Yulin Zhang et al. All rights reserved. A Dual Hesitant Fuzzy Multigranulation Rough Set over Two-Universe Model for Medical Diagnoses Wed, 09 Dec 2015 11:41:40 +0000 In medical science, disease diagnosis is one of the difficult tasks for medical experts who are confronted with challenges in dealing with a lot of uncertain medical information. And different medical experts might express their own thought about the medical knowledge base which slightly differs from other medical experts. Thus, to solve the problems of uncertain data analysis and group decision making in disease diagnoses, we propose a new rough set model called dual hesitant fuzzy multigranulation rough set over two universes by combining the dual hesitant fuzzy set and multigranulation rough set theories. In the framework of our study, both the definition and some basic properties of the proposed model are presented. Finally, we give a general approach which is applied to a decision making problem in disease diagnoses, and the effectiveness of the approach is demonstrated by a numerical example. Chao Zhang, Deyu Li, and Yan Yan Copyright © 2015 Chao Zhang et al. All rights reserved. Evaluation of the Acceleration and Deceleration Phase-Rectified Slope to Detect and Improve IUGR Clinical Management Tue, 08 Dec 2015 13:28:38 +0000 Objective. This study used a new method called Acceleration (or Deceleration) Phase-Rectified Slope, APRS (or DPRS) to analyze computerized Cardiotocographic (cCTG) traces in intrauterine growth restriction (IUGR), in order to calculate acceleration- and deceleration-related fluctuations of the fetal heart rate, and to enhance the prediction of neonatal outcome. Method. Cardiotocograms from a population of 59 healthy and 61 IUGR fetuses from the 30th gestation week matched for gestational age were included. APRS and DPRS analysis was compared to the standard linear and nonlinear cCTG parameters. Statistical analysis was performed through the -test, ANOVA test, Pearson correlation test and receiver operator characteristic (ROC) curves (). Results. APRS and DPRS showed high performance to discriminate between Healthy and IUGR fetuses, according to gestational week. A linear correlation with the fetal pH at birth was found in IUGR. The area under the ROC curve was 0.865 for APRS and 0.900 for DPRS before the 34th gestation week. Conclusions. APRS and DPRS could be useful in the identification and management of IUGR fetuses and in the prediction of the neonatal outcome, especially before the 34th week of gestation. Salvatore Tagliaferri, Andrea Fanelli, Giuseppina Esposito, Francesca Giovanna Esposito, Giovanni Magenes, Maria Gabriella Signorini, Marta Campanile, and Pasquale Martinelli Copyright © 2015 Salvatore Tagliaferri et al. All rights reserved. Parameter Optimization for Selected Correlation Analysis of Intracranial Pathophysiology Sun, 29 Nov 2015 12:06:09 +0000 Recently we proposed a mathematical tool set, called selected correlation analysis, that reliably detects positive and negative correlations between arterial blood pressure (ABP) and intracranial pressure (ICP). Such correlations are associated with severe impairment of the cerebral autoregulation and intracranial compliance, as predicted by a mathematical model. The time resolved selected correlation analysis is based on a windowing technique combined with Fourier-based coherence calculations and therefore depends on several parameters. For real time application of this method at an ICU it is inevitable to adjust this mathematical tool for high sensitivity and distinct reliability. In this study, we will introduce a method to optimize the parameters of the selected correlation analysis by correlating an index, called selected correlation positive (SCP), with the outcome of the patients represented by the Glasgow Outcome Scale (GOS). For that purpose, the data of twenty-five patients were used to calculate the SCP value for each patient and multitude of feasible parameter sets of the selected correlation analysis. It could be shown that an optimized set of parameters is able to improve the sensitivity of the method by a factor greater than four in comparison to our first analyses. Rupert Faltermeier, Martin A. Proescholdt, Sylvia Bele, and Alexander Brawanski Copyright © 2015 Rupert Faltermeier et al. All rights reserved. Machine Learning and Network Methods for Biology and Medicine Sun, 29 Nov 2015 11:16:48 +0000 Lei Chen, Tao Huang, Chuan Lu, Lin Lu, and Dandan Li Copyright © 2015 Lei Chen et al. All rights reserved. A VidEo-Based Intelligent Recognition and Decision System for the Phacoemulsification Cataract Surgery Thu, 26 Nov 2015 12:13:27 +0000 The phacoemulsification surgery is one of the most advanced surgeries to treat cataract. However, the conventional surgeries are always with low automatic level of operation and over reliance on the ability of surgeons. Alternatively, one imaginative scene is to use video processing and pattern recognition technologies to automatically detect the cataract grade and intelligently control the release of the ultrasonic energy while operating. Unlike cataract grading in the diagnosis system with static images, complicated background, unexpected noise, and varied information are always introduced in dynamic videos of the surgery. Here we develop a VidEo-Based Intelligent Recognitionand Decision (VEBIRD) system, which breaks new ground by providing a generic framework for automatically tracking the operation process and classifying the cataract grade in microscope videos of the phacoemulsification cataract surgery. VEBIRD comprises a robust eye (iris) detector with randomized Hough transform to precisely locate the eye in the noise background, an effective probe tracker with Tracking-Learning-Detection to thereafter track the operation probe in the dynamic process, and an intelligent decider with discriminative learning to finally recognize the cataract grade in the complicated video. Experiments with a variety of real microscope videos of phacoemulsification verify VEBIRD’s effectiveness. Shu Tian, Xu-Cheng Yin, Zhi-Bin Wang, Fang Zhou, and Hong-Wei Hao Copyright © 2015 Shu Tian et al. All rights reserved.