Computational and Mathematical Methods in Medicine The latest articles from Hindawi Publishing Corporation © 2015 , Hindawi Publishing Corporation . 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. Fatigue Modeling via Mammalian Auditory System for Prediction of Noise Induced Hearing Loss Tue, 24 Nov 2015 07:29:23 +0000 Noise induced hearing loss (NIHL) remains as a severe health problem worldwide. Existing noise metrics and modeling for evaluation of NIHL are limited on prediction of gradually developing NIHL (GDHL) caused by high-level occupational noise. In this study, we proposed two auditory fatigue based models, including equal velocity level (EVL) and complex velocity level (CVL), which combine the high-cycle fatigue theory with the mammalian auditory model, to predict GDHL. The mammalian auditory model is introduced by combining the transfer function of the external-middle ear and the triple-path nonlinear (TRNL) filter to obtain velocities of basilar membrane (BM) in cochlea. The high-cycle fatigue theory is based on the assumption that GDHL can be considered as a process of long-cycle mechanical fatigue failure of organ of Corti. Furthermore, a series of chinchilla experimental data are used to validate the effectiveness of the proposed fatigue models. The regression analysis results show that both proposed fatigue models have high corrections with four hearing loss indices. It indicates that the proposed models can accurately predict hearing loss in chinchilla. Results suggest that the CVL model is more accurate compared to the EVL model on prediction of the auditory risk of exposure to hazardous occupational noise. Pengfei Sun, Jun Qin, and Kathleen Campbell Copyright © 2015 Pengfei Sun et al. All rights reserved. Detection of Dendritic Spines Using Wavelet-Based Conditional Symmetric Analysis and Regularized Morphological Shared-Weight Neural Networks Tue, 24 Nov 2015 07:21:20 +0000 Identification and detection of dendritic spines in neuron images are of high interest in diagnosis and treatment of neurological and psychiatric disorders (e.g., Alzheimer’s disease, Parkinson’s diseases, and autism). In this paper, we have proposed a novel automatic approach using wavelet-based conditional symmetric analysis and regularized morphological shared-weight neural networks (RMSNN) for dendritic spine identification involving the following steps: backbone extraction, localization of dendritic spines, and classification. First, a new algorithm based on wavelet transform and conditional symmetric analysis has been developed to extract backbone and locate the dendrite boundary. Then, the RMSNN has been proposed to classify the spines into three predefined categories (mushroom, thin, and stubby). We have compared our proposed approach against the existing methods. The experimental result demonstrates that the proposed approach can accurately locate the dendrite and accurately classify the spines into three categories with the accuracy of 99.1% for “mushroom” spines, 97.6% for “stubby” spines, and 98.6% for “thin” spines. Shuihua Wang, Mengmeng Chen, Yang Li, Yudong Zhang, Liangxiu Han, Jane Wu, and Sidan Du Copyright © 2015 Shuihua Wang et al. All rights reserved. Voice Disorder Classification Based on Multitaper Mel Frequency Cepstral Coefficients Features Sun, 22 Nov 2015 12:48:12 +0000 The Mel Frequency Cepstral Coefficients (MFCCs) are widely used in order to extract essential information from a voice signal and became a popular feature extractor used in audio processing. However, MFCC features are usually calculated from a single window (taper) characterized by large variance. This study shows investigations on reducing variance for the classification of two different voice qualities (normal voice and disordered voice) using multitaper MFCC features. We also compare their performance by newly proposed windowing techniques and conventional single-taper technique. The results demonstrate that adapted weighted Thomson multitaper method could distinguish between normal voice and disordered voice better than the results done by the conventional single-taper (Hamming window) technique and two newly proposed windowing methods. The multitaper MFCC features may be helpful in identifying voices at risk for a real pathology that has to be proven later. Ömer Eskidere and Ahmet Gürhanlı Copyright © 2015 Ömer Eskidere and Ahmet Gürhanlı. All rights reserved. Multimodal Medical Image Fusion by Adaptive Manifold Filter Wed, 18 Nov 2015 13:34:23 +0000 Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. The modified local contrast information is proposed to fuse multimodal medical images. Firstly, the adaptive manifold filter is introduced into filtering source images as the low-frequency part in the modified local contrast. Secondly, the modified spatial frequency of the source images is adopted as the high-frequency part in the modified local contrast. Finally, the pixel with larger modified local contrast is selected into the fused image. The presented scheme outperforms the guided filter method in spatial domain, the dual-tree complex wavelet transform-based method, nonsubsampled contourlet transform-based method, and four classic fusion methods in terms of visual quality. Furthermore, the mutual information values by the presented method are averagely 55%, 41%, and 62% higher than the three methods and those values of edge based similarity measure by the presented method are averagely 13%, 33%, and 14% higher than the three methods for the six pairs of source images. Peng Geng, Shuaiqi Liu, and Shanna Zhuang Copyright © 2015 Peng Geng et al. All rights reserved. Modeling Seasonal Influenza Transmission and Its Association with Climate Factors in Thailand Using Time-Series and ARIMAX Analyses Wed, 18 Nov 2015 06:46:35 +0000 Influenza is a worldwide respiratory infectious disease that easily spreads from one person to another. Previous research has found that the influenza transmission process is often associated with climate variables. In this study, we used autocorrelation and partial autocorrelation plots to determine the appropriate autoregressive integrated moving average (ARIMA) model for influenza transmission in the central and southern regions of Thailand. The relationships between reported influenza cases and the climate data, such as the amount of rainfall, average temperature, average maximum relative humidity, average minimum relative humidity, and average relative humidity, were evaluated using cross-correlation function. Based on the available data of suspected influenza cases and climate variables, the most appropriate ARIMA(X) model for each region was obtained. We found that the average temperature correlated with influenza cases in both central and southern regions, but average minimum relative humidity played an important role only in the southern region. The ARIMAX model that includes the average temperature with a 4-month lag and the minimum relative humidity with a 2-month lag is the appropriate model for the central region, whereas including the minimum relative humidity with a 4-month lag results in the best model for the southern region. Sudarat Chadsuthi, Sopon Iamsirithaworn, Wannapong Triampo, and Charin Modchang Copyright © 2015 Sudarat Chadsuthi et al. All rights reserved. A Boolean Consistent Fuzzy Inference System for Diagnosing Diseases and Its Application for Determining Peritonitis Likelihood Tue, 17 Nov 2015 12:41:05 +0000 Fuzzy inference systems (FIS) enable automated assessment and reasoning in a logically consistent manner akin to the way in which humans reason. However, since no conventional fuzzy set theory is in the Boolean frame, it is proposed that Boolean consistent fuzzy logic should be used in the evaluation of rules. The main distinction of this approach is that it requires the execution of a set of structural transformations before the actual values can be introduced, which can, in certain cases, lead to different results. While a Boolean consistent FIS could be used for establishing the diagnostic criteria for any given disease, in this paper it is applied for determining the likelihood of peritonitis, as the leading complication of peritoneal dialysis (PD). Given that patients could be located far away from healthcare institutions (as peritoneal dialysis is a form of home dialysis) the proposed Boolean consistent FIS would enable patients to easily estimate the likelihood of them having peritonitis (where a high likelihood would suggest that prompt treatment is indicated), when medical experts are not close at hand. Ivana Dragović, Nina Turajlić, Dejan Pilčević, Bratislav Petrović, and Dragan Radojević Copyright © 2015 Ivana Dragović et al. All rights reserved. Speech Signal and Facial Image Processing for Obstructive Sleep Apnea Assessment Tue, 17 Nov 2015 11:10:10 +0000 Obstructive sleep apnea (OSA) is a common sleep disorder characterized by recurring breathing pauses during sleep caused by a blockage of the upper airway (UA). OSA is generally diagnosed through a costly procedure requiring an overnight stay of the patient at the hospital. This has led to proposing less costly procedures based on the analysis of patients’ facial images and voice recordings to help in OSA detection and severity assessment. In this paper we investigate the use of both image and speech processing to estimate the apnea-hypopnea index, AHI (which describes the severity of the condition), over a population of 285 male Spanish subjects suspected to suffer from OSA and referred to a Sleep Disorders Unit. Photographs and voice recordings were collected in a supervised but not highly controlled way trying to test a scenario close to an OSA assessment application running on a mobile device (i.e., smartphones or tablets). Spectral information in speech utterances is modeled by a state-of-the-art low-dimensional acoustic representation, called i-vector. A set of local craniofacial features related to OSA are extracted from images after detecting facial landmarks using Active Appearance Models (AAMs). Support vector regression (SVR) is applied on facial features and i-vectors to estimate the AHI. Fernando Espinoza-Cuadros, Rubén Fernández-Pozo, Doroteo T. Toledano, José D. Alcázar-Ramírez, Eduardo López-Gonzalo, and Luis A. Hernández-Gómez Copyright © 2015 Fernando Espinoza-Cuadros et al. All rights reserved. Two 27 MHz Simple Inductive Loops, as Hyperthermia Treatment Applicators: Theoretical Analysis and Development Mon, 16 Nov 2015 16:25:42 +0000 Background. Deep heating is still the main subject for research in hyperthermia treatment. Aim. The purpose of this study was to develop and analyze a simple loop as a heating applicator. Methods. The performance of two 27 MHz inductive loop antennas as potential applicators in hyperthermia treatment was studied theoretically as well as experimentally in phantoms. Two inductive loop antennas with radii 7 cm and 9 cm were designed, simulated, and constructed. The theoretical analysis was performed by using Green’s function and Bessel’s function technique. Experiments were performed with phantoms radiated by the aforementioned loop antennas. Results. The specific absorption rate (SAR) distributions were estimated from the respective local phantom temperature measurements. Comparisons of the theoretical, simulation, and experimental studies showed satisfying agreement. The penetration depth was measured theoretically and experimentally in the range of 2–3.5 cm. Conclusion. The theoretical and experimental analysis showed that current loops are efficient in the case where the peripheral heating of spherical tumor formation located at 2–3.5 cm depth is required. Vassilis Kouloulias, Irene Karanasiou, Maria Koutsoupidou, George Matsopoulos, John Kouvaris, and Nikolaos Uzunoglu Copyright © 2015 Vassilis Kouloulias et al. All rights reserved. The Importance of Stochastic Effects for Explaining Entrainment in the Zebrafish Circadian Clock Mon, 16 Nov 2015 16:13:43 +0000 The circadian clock plays a pivotal role in modulating physiological processes and has been implicated, either directly or indirectly, in a range of pathological states including cancer. Here we investigate how the circadian clock is entrained by external cues such as light. Working with zebrafish cell lines and combining light pulse experiments with simulation efforts focused on the role of synchronization effects, we find that even very modest doses of light exposure are sufficient to trigger some entrainment, whereby a higher light intensity or duration correlates with strength of the circadian signal. Moreover, we observe in the simulations that stochastic effects may be considered an essential feature of the circadian clock in order to explain the circadian signal decay in prolonged darkness, as well as light initiated resynchronization as a strong component of entrainment. Raphaela Heussen and David Whitmore Copyright © 2015 Raphaela Heussen and David Whitmore. All rights reserved. A Hybrid Method for Image Segmentation Based on Artificial Fish Swarm Algorithm and Fuzzy -Means Clustering Mon, 16 Nov 2015 12:52:07 +0000 Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artificial fish swarm algorithm (HAFSA). The proposed algorithm combines artificial fish swarm algorithm (AFSA) with FCM whose advantages of global optimization searching and parallel computing ability of AFSA are utilized to find a superior result. Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability. The artificial grid graph and Magnetic Resonance Imaging (MRI) are used in the experiments, and the experimental results show that the proposed algorithm has stronger antinoise ability and higher precision. A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM). Li Ma, Yang Li, Suohai Fan, and Runzhu Fan Copyright © 2015 Li Ma et al. All rights reserved. Image-Processing Scheme to Detect Superficial Fungal Infections of the Skin Mon, 16 Nov 2015 08:35:20 +0000 The incidence of superficial fungal infections is assumed to be 20 to 25% of the global human population. Fluorescence microscopy of extracted skin samples is frequently used for a swift assessment of infections. To support the dermatologist, an image-analysis scheme has been developed that evaluates digital microscopic images to detect fungal hyphae. The aim of the study was to increase diagnostic quality and to shorten the time-to-diagnosis. The analysis, consisting of preprocessing, segmentation, parameterization, and classification of identified structures, was performed on digital microscopic images. A test dataset of hyphae and false-positive objects was created to evaluate the algorithm. Additionally, the performance for real clinical images was investigated using 415 images. The results show that the sensitivity for hyphae is 94% and 89% for singular and clustered hyphae, respectively. The mean exclusion rate is 91% for the false-positive objects. The sensitivity for clinical images was 83% and the specificity was 79%. Although the performance is lower for the clinical images than for the test dataset, a reliable and fast diagnosis can be achieved since it is not crucial to detect every hypha to conclude that a sample consisting of several images is infected. The proposed analysis therefore enables a high diagnostic quality and a fast sample assessment to be achieved. Ulf Mäder, Niko Quiskamp, Sören Wildenhain, Thomas Schmidts, Peter Mayser, Frank Runkel, and Martin Fiebich Copyright © 2015 Ulf Mäder et al. All rights reserved. Dimensionality Reduction in Complex Medical Data: Improved Self-Adaptive Niche Genetic Algorithm Mon, 16 Nov 2015 06:34:36 +0000 With the development of medical technology, more and more parameters are produced to describe the human physiological condition, forming high-dimensional clinical datasets. In clinical analysis, data are commonly utilized to establish mathematical models and carry out classification. High-dimensional clinical data will increase the complexity of classification, which is often utilized in the models, and thus reduce efficiency. The Niche Genetic Algorithm (NGA) is an excellent algorithm for dimensionality reduction. However, in the conventional NGA, the niche distance parameter is set in advance, which prevents it from adjusting to the environment. In this paper, an Improved Niche Genetic Algorithm (INGA) is introduced. It employs a self-adaptive niche-culling operation in the construction of the niche environment to improve the population diversity and prevent local optimal solutions. The INGA was verified in a stratification model for sepsis patients. The results show that, by applying INGA, the feature dimensionality of datasets was reduced from 77 to 10 and that the model achieved an accuracy of 92% in predicting 28-day death in sepsis patients, which is significantly higher than other methods. Min Zhu, Jing Xia, Molei Yan, Guolong Cai, Jing Yan, and Gangmin Ning Copyright © 2015 Min Zhu et al. All rights reserved. Wireless Sensor-Based Smart-Clothing Platform for ECG Monitoring Thu, 12 Nov 2015 09:03:43 +0000 The goal of this study is to use wireless sensor technologies to develop a smart clothes service platform for health monitoring. Our platform consists of smart clothes, a sensor node, a gateway server, and a health cloud. The smart clothes have fabric electrodes to detect electrocardiography (ECG) signals. The sensor node improves the accuracy of QRS complexes detection by morphology analysis and reduces power consumption by the power-saving transmission functionality. The gateway server provides a reconfigurable finite state machine (RFSM) software architecture for abnormal ECG detection to support online updating. Most normal ECG can be filtered out, and the abnormal ECG is further analyzed in the health cloud. Three experiments are conducted to evaluate the platform’s performance. The results demonstrate that the signal-to-noise ratio (SNR) of the smart clothes exceeds 37 dB, which is within the “very good signal” interval. The average of the QRS sensitivity and positive prediction is above 99.5%. Power-saving transmission is reduced by nearly 1980 times the power consumption in the best-case analysis. Jie Wang, Chung-Chih Lin, Yan-Shuo Yu, and Tsang-Chu Yu Copyright © 2015 Jie Wang et al. All rights reserved. The Vertex Version of Weighted Wiener Number for Bicyclic Molecular Structures Tue, 10 Nov 2015 12:57:31 +0000 Graphs are used to model chemical compounds and drugs. In the graphs, each vertex represents an atom of molecule and edges between the corresponding vertices are used to represent covalent bounds between atoms. We call such a graph, which is derived from a chemical compound, a molecular graph. Evidence shows that the vertex-weighted Wiener number, which is defined over this molecular graph, is strongly correlated to both the melting point and boiling point of the compounds. In this paper, we report the extremal vertex-weighted Wiener number of bicyclic molecular graph in terms of molecular structural analysis and graph transformations. The promising prospects of the application for the chemical and pharmacy engineering are illustrated by theoretical results achieved in this paper. Wei Gao and Weifan Wang Copyright © 2015 Wei Gao and Weifan Wang. All rights reserved. Phase Response Synchronization in Neuronal Population with Time-Varying Coupling Strength Tue, 10 Nov 2015 09:02:45 +0000 We present the dynamic model of global coupled neuronal population subject to external stimulus by the use of phase sensitivity function. We investigate the effect of time-varying coupling strength on the synchronized phase response of neural population subjected to external harmonic stimulus. For a time-periodic coupling strength, we found that the stimulus with increasing intensity or frequency can reinforce the phase response synchronization in neuronal population of the weakly coupled neural oscillators, and the neuronal population with stronger coupling strength has good adaptability to stimulus. When we consider the dynamics of coupling strength, we found that a strong stimulus can quickly cause the synchronization in the neuronal population, the degree of synchronization grows with the increasing stimulus intensity, and the period of synchronized oscillation induced by external stimulation is related to stimulus frequency. Xianfa Jiao, Wanyu Zhao, and Jinde Cao Copyright © 2015 Xianfa Jiao et al. All rights reserved. Analyzing the Correlation between the Level of Serum Markers and Ischemic Cerebral Vascular Disease by Multiple Parameters Sun, 01 Nov 2015 09:12:40 +0000 Objective. To explore the serum markers associated with ischemic cerebral vascular disease (ICVD) and discuss their diagnostic value. Methods. Two hundred and eighty-eight patients with ICVD and one hundred and eighty healthy persons were enrolled as the case group and the control group, respectively. This paper then carried out the univariate and multivariate logistic regression analyses of their respective levels of serum markers, made combined analysis of related factors, and detected the diagnostic value. Results. Meta-analysis results showed that for ICVD patients the levels of CRP, S-100, TNF-α, HCY, NSE, and IL-6 were higher than those of the healthy persons, while the level of HDL was obviously lower than that of the healthy persons. The multivariate regression analysis indicated that the association between the level of HDL and TNF-α and the occurrence of ICVD was statistically significant (). The area under the curves (AUC) of receiver operating characteristic (ROC) curve of HDL and TNF-α was 0.916, with sensitivity of 90.91% and specificity of 76.47%. Conclusion. HDL has negative correlation with the occurrence of ICVD, while TNF-α was positively correlated with it. The combination test of HDL and TNF-α could raise the accuracy of ICVD diagnosis. Laibin Dong, Rongzhi Hou, Yuxia Xu, Jiaying Yuan, Lijun Li, Chao Zheng, and Hehua Zhao Copyright © 2015 Laibin Dong et al. All rights reserved. Novel Method for Border Irregularity Assessment in Dermoscopic Color Images Thu, 29 Oct 2015 09:20:09 +0000 Background. One of the most important lesion features predicting malignancy is border irregularity. Accurate assessment of irregular borders is clinically important due to significantly different occurrence in benign and malignant skin lesions. Method. In this research, we present a new approach for the detection of border irregularities, as one of the major parameters in a widely used diagnostic algorithm the ABCD rule of dermoscopy. The proposed work is focused on designing an efficient automatic algorithm containing the following steps: image enhancement, lesion segmentation, borderline calculation, and irregularities detection. The challenge lies in determining the exact borderline. For solving this problem we have implemented a new method based on lesion rotation and borderline division. Results. The algorithm has been tested on 350 dermoscopic images and achieved accuracy of 92% indicating that the proposed computational approach captured most of the irregularities and provides reliable information for effective skin mole examination. Compared to the state of the art, we obtained improved classification results. Conclusions. The current study suggests that computer-aided system is a practical tool for dermoscopic image assessment and could be recommended for both research and clinical applications. The proposed algorithm can be applied in different fields of medical image analysis including, for example, CT and MRI images. Joanna Jaworek-Korjakowska Copyright © 2015 Joanna Jaworek-Korjakowska. All rights reserved. Simulation on the Comparison of Steady-State Responses Synthesized by Transient Templates Based on Superposition Hypothesis Tue, 27 Oct 2015 09:48:23 +0000 The generation of auditory-evoked steady-state responses (SSRs) is associated with the linear superposition of transient auditory-evoked potentials (AEPs) that cannot be directly observed. A straightforward way to justify the superposition hypothesis is the use of synthesized SSRs by a transient AEP under a predefined condition based on the forward process of this hypothesis. However, little is known about the inverse relation between the transient AEP and its synthetic SSR, which makes the interpretation of the latter less convincible because it may not necessarily underlie the true solution. In this study, we chose two pairs of AEPs from the conventional and deconvolution paradigms, which represent the homo-AEPs from a homogenous group and the hetero-AEPs from two heterogeneous groups. Both pairs of AEPs were used as templates to synthesize SSRs at rates of 20–120 Hz. The peak-peak amplitudes and the differences between the paired waves were measured. Although amplitude enhancement occurred at ~40 Hz, comparisons between the available waves demonstrated that the relative differences of the synthetic SSRs could be dramatically larger at other rates. Moreover, two virtually identical SSRs may come from clearly different AEPs. These results suggested inconsistent relationships between the AEPs and their corresponding SSRs over the tested rates. Xiao-dan Tan, Xue-fei Yu, Lin Lin, and Tao Wang Copyright © 2015 Xiao-dan Tan et al. All rights reserved. An Overview of Biomolecular Event Extraction from Scientific Documents Mon, 26 Oct 2015 09:43:55 +0000 This paper presents a review of state-of-the-art approaches to automatic extraction of biomolecular events from scientific texts. Events involving biomolecules such as genes, transcription factors, or enzymes, for example, have a central role in biological processes and functions and provide valuable information for describing physiological and pathogenesis mechanisms. Event extraction from biomedical literature has a broad range of applications, including support for information retrieval, knowledge summarization, and information extraction and discovery. However, automatic event extraction is a challenging task due to the ambiguity and diversity of natural language and higher-level linguistic phenomena, such as speculations and negations, which occur in biological texts and can lead to misunderstanding or incorrect interpretation. Many strategies have been proposed in the last decade, originating from different research areas such as natural language processing, machine learning, and statistics. This review summarizes the most representative approaches in biomolecular event extraction and presents an analysis of the current state of the art and of commonly used methods, features, and tools. Finally, current research trends and future perspectives are also discussed. Jorge A. Vanegas, Sérgio Matos, Fabio González, and José L. Oliveira Copyright © 2015 Jorge A. Vanegas et al. All rights reserved. Parallel Optimization of 3D Cardiac Electrophysiological Model Using GPU Sun, 25 Oct 2015 11:35:39 +0000 Large-scale 3D virtual heart model simulations are highly demanding in computational resources. This imposes a big challenge to the traditional computation resources based on CPU environment, which already cannot meet the requirement of the whole computation demands or are not easily available due to expensive costs. GPU as a parallel computing environment therefore provides an alternative to solve the large-scale computational problems of whole heart modeling. In this study, using a 3D sheep atrial model as a test bed, we developed a GPU-based simulation algorithm to simulate the conduction of electrical excitation waves in the 3D atria. In the GPU algorithm, a multicellular tissue model was split into two components: one is the single cell model (ordinary differential equation) and the other is the diffusion term of the monodomain model (partial differential equation). Such a decoupling enabled realization of the GPU parallel algorithm. Furthermore, several optimization strategies were proposed based on the features of the virtual heart model, which enabled a 200-fold speedup as compared to a CPU implementation. In conclusion, an optimized GPU algorithm has been developed that provides an economic and powerful platform for 3D whole heart simulations. Yong Xia, Kuanquan Wang, and Henggui Zhang Copyright © 2015 Yong Xia et al. All rights reserved. High-Resolution and Quantitative X-Ray Phase-Contrast Tomography for Mouse Brain Research Tue, 20 Oct 2015 11:57:24 +0000 Imaging techniques for visualizing cerebral vasculature and distinguishing functional areas are essential and critical to the study of various brain diseases. In this paper, with the X-ray phase-contrast imaging technique, we proposed an experiment scheme for the ex vivo mouse brain study, achieving both high spatial resolution and improved soft-tissue contrast. This scheme includes two steps: sample preparation and volume reconstruction. In the first step, we use heparinized saline to displace the blood inside cerebral vessels and then replace it with air making air-filled mouse brain. After sample preparation, X-ray phase-contrast tomography is performed to collect the data for volume reconstruction. Here, we adopt a phase-retrieval combined filtered backprojection method to reconstruct its three-dimensional structure and redesigned the reconstruction kernel. To evaluate its performance, we carried out experiments at Shanghai Synchrotron Radiation Facility. The results show that the air-tissue structured cerebral vasculatures are highly visible with propagation-based phase-contrast imaging and can be clearly resolved in reconstructed cross-images. Besides, functional areas, such as the corpus callosum, corpus striatum, and nuclei, are also clearly resolved. The proposed method is comparable with hematoxylin and eosin staining method but represents the studied mouse brain in three dimensions, offering a potential powerful tool for the research of brain disorders. Yan Xi, Xiaojie Lin, Falei Yuan, Guo-Yuan Yang, and Jun Zhao Copyright © 2015 Yan Xi et al. All rights reserved. Ensemble Merit Merge Feature Selection for Enhanced Multinomial Classification in Alzheimer’s Dementia Tue, 20 Oct 2015 10:31:06 +0000 The objective of this study is to develop an ensemble classifier with Merit Merge feature selection that will enhance efficiency of classification in a multivariate multiclass medical data for effective disease diagnostics. The large volumes of features extracted from brain Magnetic Resonance Images and neuropsychological tests for diagnosis lead to more complexity in classification procedures. A higher level of objectivity than what readers have is needed to produce reliable dementia diagnostic techniques. Ensemble approach which is trained with features selected from multiple biomarkers facilitated accurate classification when compared with conventional classification techniques. Ensemble approach for feature selection is experimented with classifiers like Naïve Bayes, Random forest, Support Vector Machine, and C4.5. Feature search is done with Particle Swarm Optimisation to retrieve the subset of features for further selection with the ensemble classifier. Features selected by the proposed C4.5 ensemble classifier with Particle Swarm Optimisation search, coupled with Merit Merge technique (CPEMM), outperformed bagging feature selection of SVM, NB, and Random forest classifiers. The proposed CPEMM feature selection found the best subset of features that efficiently discriminated normal individuals and patients affected with Mild Cognitive Impairment and Alzheimer’s Dementia with 98.7% accuracy. T. R. Sivapriya, A. R. Nadira Banu Kamal, and P. Ranjit Jeba Thangaiah Copyright © 2015 T. R. Sivapriya et al. All rights reserved. Time-Delayed Models of Gene Regulatory Networks Tue, 20 Oct 2015 07:56:31 +0000 We discuss different mathematical models of gene regulatory networks as relevant to the onset and development of cancer. After discussion of alternative modelling approaches, we use a paradigmatic two-gene network to focus on the role played by time delays in the dynamics of gene regulatory networks. We contrast the dynamics of the reduced model arising in the limit of fast mRNA dynamics with that of the full model. The review concludes with the discussion of some open problems. K. Parmar, K. B. Blyuss, Y. N. Kyrychko, and S. J. Hogan Copyright © 2015 K. Parmar et al. All rights reserved. Mortality Prediction Model of Septic Shock Patients Based on Routinely Recorded Data Sun, 18 Oct 2015 14:39:49 +0000 We studied the problem of mortality prediction in two datasets, the first composed of 23 septic shock patients and the second composed of 73 septic subjects selected from the public database MIMIC-II. For each patient we derived hemodynamic variables, laboratory results, and clinical information of the first 48 hours after shock onset and we performed univariate and multivariate analyses to predict mortality in the following 7 days. The results show interesting features that individually identify significant differences between survivors and nonsurvivors and features which gain importance only when considered together with the others in a multivariate regression model. This preliminary study on two small septic shock populations represents a novel contribution towards new personalized models for an integration of multiparameter patient information to improve critical care management of shock patients. Marta Carrara, Giuseppe Baselli, and Manuela Ferrario Copyright © 2015 Marta Carrara et al. All rights reserved. Enhanced Z-LDA for Small Sample Size Training in Brain-Computer Interface Systems Tue, 13 Oct 2015 13:47:40 +0000 Background. Usually the training set of online brain-computer interface (BCI) experiment is small. For the small training set, it lacks enough information to deeply train the classifier, resulting in the poor classification performance during online testing. Methods. In this paper, on the basis of Z-LDA, we further calculate the classification probability of Z-LDA and then use it to select the reliable samples from the testing set to enlarge the training set, aiming to mine the additional information from testing set to adjust the biased classification boundary obtained from the small training set. The proposed approach is an extension of previous Z-LDA and is named enhanced Z-LDA (EZ-LDA). Results. We evaluated the classification performance of LDA, Z-LDA, and EZ-LDA on simulation and real BCI datasets with different sizes of training samples, and classification results showed EZ-LDA achieved the best classification performance. Conclusions. EZ-LDA is promising to deal with the small sample size training problem usually existing in online BCI system. Dongrui Gao, Rui Zhang, Tiejun Liu, Fali Li, Teng Ma, Xulin Lv, Peiyang Li, Dezhong Yao, and Peng Xu Copyright © 2015 Dongrui Gao et al. All rights reserved. Exponentially Fitted Two-Derivative Runge-Kutta Methods for Simulation of Oscillatory Genetic Regulatory Systems Tue, 13 Oct 2015 12:56:24 +0000 Oscillation is one of the most important phenomena in the chemical reaction systems in living cells. The general purpose simulation algorithms fail to take into account this special character and produce unsatisfying results. In order to enhance the accuracy of the integrator, the second-order derivative is incorporated in the scheme. The oscillatory feature of the solution is captured by the integrators with an exponential fitting property. Three practical exponentially fitted TDRK (EFTDRK) methods are derived. To test the effectiveness of the new EFTDRK methods, the two-gene system with cross-regulation and the circadian oscillation of the period protein in Drosophila are simulated. Each EFTDRK method has the best fitting frequency which minimizes the global error. The numerical results show that the new EFTDRK methods are more accurate and more efficient than their prototype TDRK methods or RK methods of the same order and the traditional exponentially fitted RK method in the literature. Zhaoxia Chen, Juan Li, Ruqiang Zhang, and Xiong You Copyright © 2015 Zhaoxia Chen et al. All rights reserved. Temporal Identification of Dysregulated Genes and Pathways in Clear Cell Renal Cell Carcinoma Based on Systematic Tracking of Disrupted Modules Mon, 12 Oct 2015 13:42:19 +0000 Objective. The objective of this work is to identify dysregulated genes and pathways of ccRCC temporally according to systematic tracking of the dysregulated modules of reweighted Protein-Protein Interaction (PPI) networks. Methods. Firstly, normal and ccRCC PPI network were inferred and reweighted based on Pearson correlation coefficient (PCC). Then, we identified altered modules using maximum weight bipartite matching and ranked them in nonincreasing order. Finally, gene compositions of altered modules were analyzed, and pathways enrichment analyses of genes in altered modules were carried out based on Expression Analysis Systematic Explored (EASE) test. Results. We obtained 136, 576, 693, and 531 disrupted modules of ccRCC stages I, II, III, and IV, respectively. Gene composition analyses of altered modules revealed that there were 56 common genes (such as MAPK1, CCNA2, and GSTM3) existing in the four stages. Besides pathway enrichment analysis identified 5 common pathways (glutathione metabolism, cell cycle, alanine, aspartate, and glutamate metabolism, arginine and proline metabolism, and metabolism of xenobiotics by cytochrome P450) across stages I, II, III, and IV. Conclusions. We successfully identified dysregulated genes and pathways of ccRCC in different stages, and these might be potential biological markers and processes for treatment and etiology mechanism in ccRCC. Shao-Mei Wang, Ze-Qiang Sun, Hong-Yun Li, Jin Wang, and Qing-Yong Liu Copyright © 2015 Shao-Mei Wang et al. All rights reserved. Optimal Placement of Irradiation Sources in the Planning of Radiotherapy: Mathematical Models and Methods of Solving Mon, 12 Oct 2015 09:54:20 +0000 This paper proposes and analyses a mathematical model for the problem of distribution of a finite number of irradiation sources during radiotherapy in continuous environments to maximize the minimal cumulative effects. A new algorithm based on nondifferentiable optimization techniques has been developed to solve this problem. Oleg Blyuss, Larysa Koriashkina, Elena Kiseleva, and Robert Molchanov Copyright © 2015 Oleg Blyuss et al. All rights reserved. NMFBFS: A NMF-Based Feature Selection Method in Identifying Pivotal Clinical Symptoms of Hepatocellular Carcinoma Mon, 12 Oct 2015 09:24:04 +0000 Background. Hepatocellular carcinoma (HCC) is a highly aggressive malignancy. Traditional Chinese Medicine (TCM), with the characteristics of syndrome differentiation, plays an important role in the comprehensive treatment of HCC. This study aims to develop a nonnegative matrix factorization- (NMF-) based feature selection approach (NMFBFS) to identify potential clinical symptoms for HCC patient stratification. Methods. The NMFBFS approach consisted of three major steps. Firstly, statistics-based preliminary feature screening was designed to detect and remove irrelevant symptoms. Secondly, NMF was employed to infer redundant symptoms. Based on NMF-derived basis matrix, we defined a novel similarity measurement of intersymptoms. Finally, we converted each group of redundant symptoms to a new single feature so that the dimension was further reduced. Results. Based on a clinical dataset consisting of 407 patient samples of HCC with 57 symptoms, NMFBFS approach detected 8 irrelevant symptoms and then identified 16 redundant symptoms within 6 groups. Finally, an optimal feature subset with 39 clinical features was generated after compressing the redundant symptoms by groups. The validation of classification performance shows that these 39 features obviously improve the prediction accuracy of HCC patients. Conclusions. Compared with other methods, NMFBFS has obvious advantages in identifying important clinical features of HCC. Zhiwei Ji, Guanmin Meng, Deshuang Huang, Xiaoqiang Yue, and Bing Wang Copyright © 2015 Zhiwei Ji et al. All rights reserved.