Computational and Mathematical Methods in Medicine The latest articles from Hindawi Publishing Corporation © 2016 , Hindawi Publishing Corporation . All rights reserved. Modeling Radiotherapy Induced Normal Tissue Complications: An Overview beyond Phenomenological Models Thu, 01 Dec 2016 12:38:03 +0000 An overview of radiotherapy (RT) induced normal tissue complication probability (NTCP) models is presented. NTCP models based on empirical and mechanistic approaches that describe a specific radiation induced late effect proposed over time for conventional RT are reviewed with particular emphasis on their basic assumptions and related mathematical translation and their weak and strong points. Marco D’Andrea, Marcello Benassi, and Lidia Strigari Copyright © 2016 Marco D’Andrea et al. All rights reserved. A Model for Spheroid versus Monolayer Response of SK-N-SH Neuroblastoma Cells to Treatment with 15-Deoxy-PGJ2 Thu, 01 Dec 2016 09:10:13 +0000 Researchers have observed that response of tumor cells to treatment varies depending on whether the cells are grown in monolayer, as in vitro spheroids or in vivo. This study uses data from the literature on monolayer treatment of SK-N-SH neuroblastoma cells with 15-deoxy- and couples it with data on growth rates for untreated SK-N-SH neuroblastoma cells grown as multicellular spheroids. A linear model is constructed for untreated and treated monolayer data sets, which is tuned to growth, death, and cell cycle data for the monolayer case for both control and treatment with 15-deoxy-. The monolayer model is extended to a five-dimensional nonlinear model of in vitro tumor spheroid growth and treatment that includes compartments of the cell cycle () as well as quiescent () and necrotic () cells. Monolayer treatment data for 15-deoxy- is used to derive a prediction of spheroid response under similar treatments. For short periods of treatment, spheroid response is less pronounced than monolayer response. The simulations suggest that the difference in response to treatment of monolayer versus spheroid cultures observed in laboratory studies is a natural consequence of tumor spheroid physiology rather than any special resistance to treatment. Dorothy I. Wallace, Ann Dunham, Paula X. Chen, Michelle Chen, Milan Huynh, Evan Rheingold, and Olivia Prosper Copyright © 2016 Dorothy I. Wallace et al. All rights reserved. Statistical Analyses of Femur Parameters for Designing Anatomical Plates Thu, 01 Dec 2016 09:08:37 +0000 Femur parameters are key prerequisites for scientifically designing anatomical plates. Meanwhile, individual differences in femurs present a challenge to design well-fitting anatomical plates. Therefore, to design anatomical plates more scientifically, analyses of femur parameters with statistical methods were performed in this study. The specific steps were as follows. First, taking eight anatomical femur parameters as variables, 100 femur samples were classified into three classes with factor analysis and Q-type cluster analysis. Second, based on the mean parameter values of the three classes of femurs, three sizes of average anatomical plates corresponding to the three classes of femurs were designed. Finally, based on Bayes discriminant analysis, a new femur could be assigned to the proper class. Thereafter, the average anatomical plate suitable for that new femur was selected from the three available sizes of plates. Experimental results showed that the classification of femurs was quite reasonable based on the anatomical aspects of the femurs. For instance, three sizes of condylar buttress plates were designed. Meanwhile, 20 new femurs are judged to which classes the femurs belong. Thereafter, suitable condylar buttress plates were determined and selected. Lin Wang, Kunjin He, and Zhengming Chen Copyright © 2016 Lin Wang et al. All rights reserved. Lung Cancer Classification Employing Proposed Real Coded Genetic Algorithm Based Radial Basis Function Neural Network Classifier Wed, 30 Nov 2016 09:48:10 +0000 A proposed real coded genetic algorithm based radial basis function neural network classifier is employed to perform effective classification of healthy and cancer affected lung images. Real Coded Genetic Algorithm (RCGA) is proposed to overcome the Hamming Cliff problem encountered with the Binary Coded Genetic Algorithm (BCGA). Radial Basis Function Neural Network (RBFNN) classifier is chosen as a classifier model because of its Gaussian Kernel function and its effective learning process to avoid local and global minima problem and enable faster convergence. This paper specifically focused on tuning the weights and bias of RBFNN classifier employing the proposed RCGA. The operators used in RCGA enable the algorithm flow to compute weights and bias value so that minimum Mean Square Error (MSE) is obtained. With both the lung healthy and cancer images from Lung Image Database Consortium (LIDC) database and Real time database, it is noted that the proposed RCGA based RBFNN classifier has performed effective classification of the healthy lung tissues and that of the cancer affected lung nodules. The classification accuracy computed using the proposed approach is noted to be higher in comparison with that of the classifiers proposed earlier in the literatures. I. Jasmine Selvakumari Jeya and S. N. Deepa Copyright © 2016 I. Jasmine Selvakumari Jeya and S. N. Deepa. All rights reserved. Computational Methods and Models in Circulatory and Reproductive Systems Thu, 24 Nov 2016 12:13:46 +0000 Fang-Bao Tian, Yi Sui, Luoding Zhu, Chang Shu, and Hyung J. Sung Copyright © 2016 Fang-Bao Tian et al. All rights reserved. Poisson Mixture Regression Models for Heart Disease Prediction Wed, 23 Nov 2016 14:20:43 +0000 Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. Chipo Mufudza and Hamza Erol Copyright © 2016 Chipo Mufudza and Hamza Erol. All rights reserved. Effects of Na+ Current and Mechanogated Channels in Myofibroblasts on Myocyte Excitability and Repolarization Thu, 17 Nov 2016 14:22:53 +0000 Fibrotic remodeling, characterized by fibroblast phenotype switching, is often associated with atrial fibrillation and heart failure. This study aimed to investigate the effects on electrotonic myofibroblast-myocyte (Mfb-M) coupling on cardiac myocytes excitability and repolarization of the voltage-gated sodium channels (VGSCs) and single mechanogated channels (MGCs) in human atrial Mfbs. Mathematical modeling was developed from a combination of (1) models of the human atrial myocyte (including the stretch activated ion channel current, ) and Mfb and (2) our formulation of currents through VGSCs () and MGCs () based upon experimental findings. The effects of changes in the intercellular coupling conductance, the number of coupled Mfbs, and the basic cycle length on the myocyte action potential were simulated. The results demonstrated that the integration of , , and reduced the amplitude of the myocyte membrane potential and the action potential duration (APD), increased the depolarization of the resting myocyte membrane potential , and made it easy to trigger spontaneous excitement in myocytes. For Mfbs, significant electrotonic depolarizations were exhibited with the addition of and . Our results indicated that , , and significantly influenced myocytes and Mfbs properties and should be considered in future cardiac pathological mathematical modeling. Heqing Zhan, Jingtao Zhang, Jialun Lin, and Guilai Han Copyright © 2016 Heqing Zhan et al. All rights reserved. Automatic Approach for Lung Segmentation with Juxta-Pleural Nodules from Thoracic CT Based on Contour Tracing and Correction Wed, 16 Nov 2016 06:19:26 +0000 This paper presents a fully automatic framework for lung segmentation, in which juxta-pleural nodule problem is brought into strong focus. The proposed scheme consists of three phases: skin boundary detection, rough segmentation of lung contour, and pulmonary parenchyma refinement. Firstly, chest skin boundary is extracted through image aligning, morphology operation, and connective region analysis. Secondly, diagonal-based border tracing is implemented for lung contour segmentation, with maximum cost path algorithm used for separating the left and right lungs. Finally, by arc-based border smoothing and concave-based border correction, the refined pulmonary parenchyma is obtained. The proposed scheme is evaluated on 45 volumes of chest scans, with volume difference (VD)  cm3, volume overlap error (VOE) %, average surface distance (ASD)  mm, root mean square distance (RMSD)  mm, maximum symmetric absolute surface distance (MSD)  mm, and average time-cost 2 seconds per image. The preliminary results on accuracy and complexity prove that our scheme is a promising tool for lung segmentation with juxta-pleural nodules. Jinke Wang and Haoyan Guo Copyright © 2016 Jinke Wang and Haoyan Guo. All rights reserved. An Analytical Study of Prostate-Specific Antigen Dynamics Sun, 13 Nov 2016 09:14:20 +0000 The purpose of this research is to carry out a quantitative study of prostate-specific antigen dynamics for patients with prostatic diseases, such as benign prostatic hyperplasia (BPH) and localized prostate cancer (LPC). The proposed PSA mathematical model was implemented using clinical data of 218 Japanese patients with histological proven BPH and 147 Japanese patients with LPC (stages T2a and T2b). For prostatic diseases (BPH and LPC) a nonlinear equation was obtained and solved in a close form to predict PSA progression with patients’ age. The general solution describes PSA dynamics for patients with both diseases LPC and BPH. Particular solutions allow studying PSA dynamics for patients with BPH or LPC. Analytical solutions have been obtained and solved in a close form to develop nomograms for a better understanding of PSA dynamics in patients with BPH and LPC. This study may be useful to improve the diagnostic and prognosis of prostatic diseases. Ernesto P. Esteban, Giovanni Deliz, Jaileen Rivera-Rodriguez, and Stephanie M. Laureano Copyright © 2016 Ernesto P. Esteban et al. All rights reserved. A Computational Model for Investigating Tumor Apoptosis Induced by Mesenchymal Stem Cell-Derived Secretome Wed, 09 Nov 2016 08:44:01 +0000 Apoptosis is a programmed cell death that occurs naturally in physiological and pathological conditions. Defective apoptosis can trigger the development and progression of cancer. Experiments suggest the ability of secretome derived from mesenchymal stem cells (MSC) to induce apoptosis in cancer cells. We develop a hybrid discrete-continuous multiscale model to further investigate the effect of MSC-derived secretome in tumor growth. The model encompasses three biological scales. At the molecular scale, a system of ordinary differential equations regulate the expression of proteins involved in apoptosis signaling pathways. At the cellular scale, discrete equations control cellular migration, phenotypic switching, and proliferation. At the extracellular scale, a system of partial differential equations are employed to describe the dynamics of microenvironmental chemicals concentrations. The simulation is able to produce both avascular tumor growth rate and phenotypic patterns as observed in the experiments. In addition, we obtain good quantitative agreements with the experimental data on the apoptosis of HeLa cancer cells treated with MSC-derived secretome. We use this model to predict the growth of avascular tumor under various secretome concentrations over time. Melisa Hendrata and Janti Sudiono Copyright © 2016 Melisa Hendrata and Janti Sudiono. All rights reserved. Vortex Analysis of Intra-Aneurismal Flow in Cerebral Aneurysms Mon, 07 Nov 2016 09:06:02 +0000 This study aims to develop an alternative vortex analysis method by measuring structure ofIntracranial aneurysm (IA) flow vortexes across the cardiac cycle, to quantify temporal stability of aneurismal flow. Hemodynamics were modeled in “patient-specific” geometries, using computational fluid dynamics (CFD) simulations. Modified versions of known and -criterion methods identified vortex regions; then regions were segmented out using the classical marching cube algorithm. Temporal stability was measured by the degree of vortex overlap (DVO) at each step of a cardiac cycle against a cycle-averaged vortex and by the change in number of cores over the cycle. No statistical differences exist in DVO or number of vortex cores between 5 terminal IAs and 5 sidewall IAs. No strong correlation exists between vortex core characteristics and geometric or hemodynamic characteristics of IAs. Statistical independence suggests this proposed method may provide novel IA information. However, threshold values used to determine the vortex core regions and resolution of velocity data influenced analysis outcomes and have to be addressed in future studies. In conclusions, preliminary results show that the proposed methodology may help give novel insight toward aneurismal flow characteristic and help in future risk assessment given more developments. Kevin Sunderland, Christopher Haferman, Gouthami Chintalapani, and Jingfeng Jiang Copyright © 2016 Kevin Sunderland et al. All rights reserved. In Silico Evaluation of the Potential Antiarrhythmic Effect of Epigallocatechin-3-Gallate on Cardiac Channelopathies Wed, 02 Nov 2016 13:02:58 +0000 Ion channels are transmembrane proteins that allow the passage of ions according to the direction of their electrochemical gradients. Mutations in more than 30 genes encoding ion channels have been associated with an increasingly wide range of inherited cardiac arrhythmias. In this line, ion channels become one of the most important molecular targets for several classes of drugs, including antiarrhythmics. Nevertheless, antiarrhythmic drugs are usually accompanied by some serious side effects. Thus, developing new approaches could offer added values to prevent and treat the episodes of arrhythmia. In this sense, green tea catechins seem to be a promising alternative because of the significant effect of Epigallocatechin-3-Gallate (E3G) on the electrocardiographic wave forms of guinea pig hearts. Thus, the aim of this study was to evaluate the benefits-risks balance of E3G consumption in the setting of ion channel mutations linked with aberrant cardiac excitability phenotypes. Two gain-of-function mutations, -p.R222Q and -p.I141V, which are linked with cardiac hyperexcitability phenotypes were studied. Computer simulations of action potentials (APs) show that 30 μM E3G reduces and suppresses AP abnormalities characteristics of these phenotypes. These results suggest that E3G may have a beneficial effect in the setting of cardiac sodium channelopathies displaying a hyperexcitability phenotype. Maroua Boukhabza, Jaouad El Hilaly, Nourdine Attiya, Ahmed El-Haidani, Younes Filali-Zegzouti, Driss Mazouzi, and Mohamed-Yassine Amarouch Copyright © 2016 Maroua Boukhabza et al. All rights reserved. Survival Prediction and Feature Selection in Patients with Breast Cancer Using Support Vector Regression Tue, 01 Nov 2016 13:47:25 +0000 The Support Vector Regression (SVR) model has been broadly used for response prediction. However, few researchers have used SVR for survival analysis. In this study, a new SVR model is proposed and SVR with different kernels and the traditional Cox model are trained. The models are compared based on different performance measures. We also select the best subset of features using three feature selection methods: combination of SVR and statistical tests, univariate feature selection based on concordance index, and recursive feature elimination. The evaluations are performed using available medical datasets and also a Breast Cancer (BC) dataset consisting of 573 patients who visited the Oncology Clinic of Hamadan province in Iran. Results show that, for the BC dataset, survival time can be predicted more accurately by linear SVR than nonlinear SVR. Based on the three feature selection methods, metastasis status, progesterone receptor status, and human epidermal growth factor receptor 2 status are the best features associated to survival. Also, according to the obtained results, performance of linear and nonlinear kernels is comparable. The proposed SVR model performs similar to or slightly better than other models. Also, SVR performs similar to or better than Cox when all features are included in model. Shahrbanoo Goli, Hossein Mahjub, Javad Faradmal, Hoda Mashayekhi, and Ali-Reza Soltanian Copyright © 2016 Shahrbanoo Goli et al. All rights reserved. Novel Burst Suppression Segmentation in the Joint Time-Frequency Domain for EEG in Treatment of Status Epilepticus Sun, 30 Oct 2016 13:35:50 +0000 We developed a method to distinguish bursts and suppressions for EEG burst suppression from the treatments of status epilepticus, employing the joint time-frequency domain. We obtained the feature used in the proposed method from the joint use of the time and frequency domains, and we estimated the decision as to whether the measured EEG was a burst segment or suppression segment by the maximum likelihood estimation. We evaluated the performance of the proposed method in terms of its accordance with the visual scores and estimation of the burst suppression ratio. The accuracy was higher than the sole use of the time or frequency domains, as well as conventional methods conducted in the time domain. In addition, probabilistic modeling provided a more simplified optimization than conventional methods. Burst suppression quantification necessitated precise burst suppression segmentation with an easy optimization; therefore, the excellent discrimination and the easy optimization of burst suppression by the proposed method appear to be beneficial. Jaeyun Lee, Woo-Jin Song, Hyang Woon Lee, and Hyun-Chool Shin Copyright © 2016 Jaeyun Lee et al. All rights reserved. Combined Application of Ultrasound and CT Increased Diagnostic Value in Female Patients with Pelvic Masses Thu, 27 Oct 2016 14:49:26 +0000 Purpose. The current study aimed to evaluate whether combined application of ultrasound and CT had increased Diagnostic Value in Female Patients with Pelvic Masses over either method alone. Patients and Methods. 240 female patients with pelvic masses were detected preoperatively with ultrasound and CT prior to surgery. The sensitivity, specificity, and accuracy of ultrasound, CT, and combined ultrasound/CT application were evaluated, respectively. Results. The sensitivity, specificity, and accuracy of ultrasound were 52.8%, 86.7%, and 68.75%, respectively. The sensitivity, specificity, and accuracy of CT were 80.3%, 90.3%, and 85%, respectively. The sensitivity, specificity, and accuracy of combined application of ultrasound and CT were 89%, 94.7%, and 91.7%. The sensitivity, specificity, and accuracy of combined application of ultrasound and CT were higher than those of either ultrasound or CT. Conclusions. The combined application of ultrasound and CT had higher Diagnostic Value in Female Patients with Pelvic Masses than either method alone. Yan Liu, Hui Zhang, Xiaoqian Li, and Guiqin Qi Copyright © 2016 Yan Liu et al. All rights reserved. CRF-Based Model for Instrument Detection and Pose Estimation in Retinal Microsurgery Thu, 27 Oct 2016 10:09:53 +0000 Detection of instrument tip in retinal microsurgery videos is extremely challenging due to rapid motion, illumination changes, the cluttered background, and the deformable shape of the instrument. For the same reason, frequent failures in tracking add the overhead of reinitialization of the tracking. In this work, a new method is proposed to localize not only the instrument center point but also its tips and orientation without the need of manual reinitialization. Our approach models the instrument as a Conditional Random Field (CRF) where each part of the instrument is detected separately. The relations between these parts are modeled to capture the translation, rotation, and the scale changes of the instrument. The tracking is done via separate detection of instrument parts and evaluation of confidence via the modeled dependence functions. In case of low confidence feedback an automatic recovery process is performed. The algorithm is evaluated on in vivo ophthalmic surgery datasets and its performance is comparable to the state-of-the-art methods with the advantage that no manual reinitialization is needed. Mohamed Alsheakhali, Abouzar Eslami, Hessam Roodaki, and Nassir Navab Copyright © 2016 Mohamed Alsheakhali et al. All rights reserved. Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification Wed, 26 Oct 2016 12:19:28 +0000 Recently, Deep Learning, especially through Convolutional Neural Networks (CNNs) has been widely used to enable the extraction of highly representative features. This is done among the network layers by filtering, selecting, and using these features in the last fully connected layers for pattern classification. However, CNN training for automated endoscopic image classification still provides a challenge due to the lack of large and publicly available annotated databases. In this work we explore Deep Learning for the automated classification of colonic polyps using different configurations for training CNNs from scratch (or full training) and distinct architectures of pretrained CNNs tested on 8-HD-endoscopic image databases acquired using different modalities. We compare our results with some commonly used features for colonic polyp classification and the good results suggest that features learned by CNNs trained from scratch and the “off-the-shelf” CNNs features can be highly relevant for automated classification of colonic polyps. Moreover, we also show that the combination of classical features and “off-the-shelf” CNNs features can be a good approach to further improve the results. Eduardo Ribeiro, Andreas Uhl, Georg Wimmer, and Michael Häfner Copyright © 2016 Eduardo Ribeiro et al. All rights reserved. Effects of Reynolds and Womersley Numbers on the Hemodynamics of Intracranial Aneurysms Wed, 26 Oct 2016 07:35:19 +0000 The effects of Reynolds and Womersley numbers on the hemodynamics of two simplified intracranial aneurysms (IAs), that is, sidewall and bifurcation IAs, and a patient-specific IA are investigated using computational fluid dynamics. For this purpose, we carried out three numerical experiments for each IA with various Reynolds ( to ) and Womersley ( to ) numbers. Although the dominant flow feature, which is the vortex ring formation, is similar for all test cases here, the propagation of the vortex ring is controlled by both and in both simplified IAs (bifurcation and sidewall) and the patient-specific IA. The location of the vortex ring in all tested IAs is shown to be proportional to which is in agreement with empirical formulations for the location of a vortex ring in a tank. In sidewall IAs, the oscillatory shear index is shown to increase with and because the vortex reached the distal wall later in the cycle (higher resident time). However, this trend was not observed in the bifurcation IA because the stresses were dominated by particle trapping structures, which were absent at low in contrast to higher . Hafez Asgharzadeh and Iman Borazjani Copyright © 2016 Hafez Asgharzadeh and Iman Borazjani. All rights reserved. Efficient Regularized Regression with Penalty for Variable Selection and Network Construction Mon, 24 Oct 2016 08:09:34 +0000 Variable selections for regression with high-dimensional big data have found many applications in bioinformatics and computational biology. One appealing approach is the regularized regression which penalizes the number of nonzero features in the model directly. However, it is well known that optimization is NP-hard and computationally challenging. In this paper, we propose efficient EM (EM) and dual EM (DEM) algorithms that directly approximate the optimization problem. While EM is efficient with large sample size, DEM is efficient with high-dimensional () data. They also provide a natural solution to all    problems, including lasso with and elastic net with . The regularized parameter can be determined through cross validation or AIC and BIC. We demonstrate our methods through simulation and high-dimensional genomic data. The results indicate that has better performance than lasso, SCAD, and MC+, and with AIC or BIC has similar performance as computationally intensive cross validation. The proposed algorithms are efficient in identifying the nonzero variables with less bias and constructing biologically important networks with high-dimensional big data. Zhenqiu Liu and Gang Li Copyright © 2016 Zhenqiu Liu and Gang Li. All rights reserved. Learning Latent Variable Gaussian Graphical Model for Biomolecular Network with Low Sample Complexity Sun, 23 Oct 2016 11:31:10 +0000 Learning a Gaussian graphical model with latent variables is ill posed when there is insufficient sample complexity, thus having to be appropriately regularized. A common choice is convex plus nuclear norm to regularize the searching process. However, the best estimator performance is not always achieved with these additive convex regularizations, especially when the sample complexity is low. In this paper, we consider a concave additive regularization which does not require the strong irrepresentable condition. We use concave regularization to correct the intrinsic estimation biases from Lasso and nuclear penalty as well. We establish the proximity operators for our concave regularizations, respectively, which induces sparsity and low rankness. In addition, we extend our method to also allow the decomposition of fused structure-sparsity plus low rankness, providing a powerful tool for models with temporal information. Specifically, we develop a nontrivial modified alternating direction method of multipliers with at least local convergence. Finally, we use both synthetic and real data to validate the excellence of our method. In the application of reconstructing two-stage cancer networks, “the Warburg effect” can be revealed directly. Yanbo Wang, Quan Liu, and Bo Yuan Copyright © 2016 Yanbo Wang et al. All rights reserved. The Role of Parvalbumin, Sarcoplasmatic Reticulum Calcium Pump Rate, Rates of Cross-Bridge Dynamics, and Ryanodine Receptor Calcium Current on Peripheral Muscle Fatigue: A Simulation Study Thu, 20 Oct 2016 10:36:41 +0000 A biophysical model of the excitation-contraction pathway, which has previously been validated for slow-twitch and fast-twitch skeletal muscles, is employed to investigate key biophysical processes leading to peripheral muscle fatigue. Special emphasis hereby is on investigating how the model’s original parameter sets can be interpolated such that realistic behaviour with respect to contraction time and fatigue progression can be obtained for a continuous distribution of the model’s parameters across the muscle units, as found for the functional properties of muscles. The parameters are divided into 5 groups describing (i) the sarcoplasmatic reticulum calcium pump rate, (ii) the cross-bridge dynamics rates, (iii) the ryanodine receptor calcium current, (iv) the rates of binding of magnesium and calcium ions to parvalbumin and corresponding dissociations, and (v) the remaining processes. The simulations reveal that the first two parameter groups are sensitive to contraction time but not fatigue, the third parameter group affects both considered properties, and the fourth parameter group is only sensitive to fatigue progression. Hence, within the scope of the underlying model, further experimental studies should investigate parvalbumin dynamics and the ryanodine receptor calcium current to enhance the understanding of peripheral muscle fatigue. Oliver Röhrle, Verena Neumann, and Thomas Heidlauf Copyright © 2016 Oliver Röhrle et al. All rights reserved. ADMM-EM Method for -Norm Regularized Weighted Least Squares PET Reconstruction Wed, 19 Oct 2016 12:36:34 +0000 The -norm regularization is usually used in positron emission tomography (PET) reconstruction to suppress noise artifacts while preserving edges. The alternating direction method of multipliers (ADMM) is proven to be effective for solving this problem. It sequentially updates the additional variables, image pixels, and Lagrangian multipliers. Difficulties lie in obtaining a nonnegative update of the image. And classic ADMM requires updating the image by greedy iteration to minimize the cost function, which is computationally expensive. In this paper, we consider a specific application of ADMM to the -norm regularized weighted least squares PET reconstruction problem. Main contribution is derivation of a new approach to iteratively and monotonically update the image while self-constraining in the nonnegativity region and the absence of a predetermined step size. We give a rigorous convergence proof on the quadratic subproblem of the ADMM algorithm considered in the paper. A simplified version is also developed by replacing the minima of the image-related cost function by one iteration that only decreases it. The experimental results show that the proposed algorithm with greedy iterations provides a faster convergence than other commonly used methods. Furthermore, the simplified version gives a comparable reconstructed result with far lower computational costs. Yueyang Teng, Hang Sun, Chen Guo, and Yan Kang Copyright © 2016 Yueyang Teng et al. All rights reserved. Deformation of a Capsule in a Power-Law Shear Flow Wed, 19 Oct 2016 12:19:36 +0000 An immersed boundary-lattice Boltzmann method is developed for fluid-structure interactions involving non-Newtonian fluids (e.g., power-law fluid). In this method, the flexible structure (e.g., capsule) dynamics and the fluid dynamics are coupled by using the immersed boundary method. The incompressible viscous power-law fluid motion is obtained by solving the lattice Boltzmann equation. The non-Newtonian rheology is achieved by using a shear rate-dependant relaxation time in the lattice Boltzmann method. The non-Newtonian flow solver is then validated by considering a power-law flow in a straight channel which is one of the benchmark problems to validate an in-house solver. The numerical results present a good agreement with the analytical solutions for various values of power-law index. Finally, we apply this method to study the deformation of a capsule in a power-law shear flow by varying the Reynolds number from 0.025 to 0.1, dimensionless shear rate from 0.004 to 0.1, and power-law index from 0.2 to 1.8. It is found that the deformation of the capsule increases with the power-law index for different Reynolds numbers and nondimensional shear rates. In addition, the Reynolds number does not have significant effect on the capsule deformation in the flow regime considered. Moreover, the power-law index effect is stronger for larger dimensionless shear rate compared to smaller values. Fang-Bao Tian Copyright © 2016 Fang-Bao Tian. All rights reserved. Interaction between Thalamus and Hippocampus in Termination of Amygdala-Kindled Seizures in Mice Mon, 17 Oct 2016 09:52:35 +0000 The thalamus and hippocampus have been found both involved in the initiation, propagation, and termination of temporal lobe epilepsy. However, the interaction of these regions during seizures is not clear. The present study is to explore whether some regular patterns exist in their interaction during the termination of seizures. Multichannel in vivo recording techniques were used to record the neural activities from the cornu ammonis 1 (CA1) of hippocampus and mediodorsal thalamus (MDT) in mice. The mice were kindled by electrically stimulating basolateral amygdala neurons, and Racine’s rank standard was employed to classify the stage of behavioral responses (stage 1~5). The coupling index and directionality index were used to investigate the synchronization and information flow direction between CA1 and MDT. Two main results were found in this study. High levels of synchronization between the thalamus and hippocampus were observed before the termination of seizures at stage 4~5 but after the termination of seizures at stage 1~2. In the end of seizures at stage 4~5, the information tended to flow from MDT to CA1. Those results indicate that the synchronization and information flow direction between the thalamus and the hippocampus may participate in the termination of seizures. Zhen Zhang, Jia-Jia Li, Qin-Chi Lu, Hai-Qing Gong, Pei-Ji Liang, and Pu-Ming Zhang Copyright © 2016 Zhen Zhang et al. All rights reserved. Corrigendum to “Solution of Radiative Transfer Equation with a Continuous and Stochastic Varying Refractive Index by Legendre Transform Method” Mon, 17 Oct 2016 08:02:57 +0000 R. Baazaoui and M. Gantri Copyright © 2016 R. Baazaoui and M. Gantri. All rights reserved. Random Survival Forests for Predicting the Bed Occupancy in the Intensive Care Unit Thu, 13 Oct 2016 14:46:01 +0000 Predicting the bed occupancy of an intensive care unit (ICU) is a daunting task. The uncertainty associated with the prognosis of critically ill patients and the random arrival of new patients can lead to capacity problems and the need for reactive measures. In this paper, we work towards a predictive model based on Random Survival Forests which can assist physicians in estimating the bed occupancy. As input data, we make use of the Sequential Organ Failure Assessment (SOFA) score collected and calculated from 4098 patients at two ICU units of Ghent University Hospital over a time period of four years. We compare the performance of our system with a baseline performance and a standard Random Forest regression approach. Our results indicate that Random Survival Forests can effectively be used to assist in the occupancy prediction problem. Furthermore, we show that a group based approach, such as Random Survival Forests, performs better compared to a setting in which the length of stay of a patient is individually assessed. Joeri Ruyssinck, Joachim van der Herten, Rein Houthooft, Femke Ongenae, Ivo Couckuyt, Bram Gadeyne, Kirsten Colpaert, Johan Decruyenaere, Filip De Turck, and Tom Dhaene Copyright © 2016 Joeri Ruyssinck et al. All rights reserved. Changes in Obesity Odds Ratio among Iranian Adults, since 2000: Quadratic Inference Functions Method Mon, 10 Oct 2016 08:05:33 +0000 Background. Monitoring changes in obesity prevalence by risk factors is relevant to public health programs that focus on reducing or preventing obesity. The purpose of this paper was to study trends in obesity odds ratios (ORs) for individuals aged 20 years and older in Iran by using a new statistical methodology. Methods. Data collected by the National Surveys in Iran, from 2000 through 2011. Since responses of the member of each cluster are correlated, the quadratic inference functions (QIF) method was used to model the relationship between the odds of obesity and risk factors. Results. During the study period, the prevalence rate of obesity increased from 12% to 22%. By using QIF method and a model selection criterion for performing stepwise regression analysis, we found that while obesity prevalence generally increased in both sexes, all ages, all employment, residence, and smoking levels, it seems to have changes in obesity ORs since 2000. Conclusions. Because obesity is one of the main risk factors for many diseases, awareness of the differences by factors allows development of targets for prevention and early intervention. Enayatollah Bakhshi, Koorosh Etemad, Behjat Seifi, Kazem Mohammad, Akbar Biglarian, and Jalil Koohpayehzadeh Copyright © 2016 Enayatollah Bakhshi et al. All rights reserved. Active Contours Using Additive Local and Global Intensity Fitting Models for Intensity Inhomogeneous Image Segmentation Sun, 09 Oct 2016 14:17:19 +0000 This paper introduces an improved region based active contour method with a level set formulation. The proposed energy functional integrates both local and global intensity fitting terms in an additive formulation. Local intensity fitting term influences local force to pull the contour and confine it to object boundaries. In turn, the global intensity fitting term drives the movement of contour at a distance from the object boundaries. The global intensity term is based on the global division algorithm, which can better capture intensity information of an image than Chan-Vese (CV) model. Both local and global terms are mutually assimilated to construct an energy function based on a level set formulation to segment images with intensity inhomogeneity. Experimental results show that the proposed method performs better both qualitatively and quantitatively compared to other state-of-the-art-methods. Shafiullah Soomro, Farhan Akram, Jeong Heon Kim, Toufique Ahmed Soomro, and Kwang Nam Choi Copyright © 2016 Shafiullah Soomro et al. All rights reserved. Compatibility and Conjugacy on Partial Arrays Wed, 28 Sep 2016 10:49:00 +0000 Research in combinatorics on words goes back a century. Berstel and Boasson introduced the partial words in the context of gene comparison. Alignment of two genes can be viewed as a construction of two partial words that are said to be compatible. In this paper, we examine to which extent the fundamental properties of partial words such as compatbility and conjugacy remain true for partial arrays. This paper studies a relaxation of the compatibility relation called -compability. It also studies -conjugacy of partial arrays. S. Vijayachitra and K. Sasikala Copyright © 2016 S. Vijayachitra and K. Sasikala. All rights reserved. Age-Related Evolution Patterns in Online Handwriting Mon, 26 Sep 2016 12:51:44 +0000 Characterizing age from handwriting (HW) has important applications, as it is key to distinguishing normal HW evolution with age from abnormal HW change, potentially triggered by neurodegenerative decline. We propose, in this work, an original approach for online HW style characterization based on a two-level clustering scheme. The first level generates writer-independent word clusters from raw spatial-dynamic HW information. At the second level, each writer’s words are converted into a Bag of Prototype Words that is augmented by an interword stability measure. This two-level HW style representation is input to an unsupervised learning technique, aiming at uncovering HW style categories and their correlation with age. To assess the effectiveness of our approach, we propose information theoretic measures to quantify the gain on age information from each clustering layer. We have carried out extensive experiments on a large public online HW database, augmented by HW samples acquired at Broca Hospital in Paris from people mostly between 60 and 85 years old. Unlike previous works claiming that there is only one pattern of HW change with age, our study reveals three major aging HW styles, one specific to aged people and the two others shared by other age groups. Gabriel Marzinotto, José C. Rosales, Mounîm A. EL-Yacoubi, Sonia Garcia-Salicetti, Christian Kahindo, Hélène Kerhervé, Victoria Cristancho-Lacroix, and Anne-Sophie Rigaud Copyright © 2016 Gabriel Marzinotto et al. All rights reserved.