The Scientific World Journal: Computational Biology The latest articles from Hindawi © 2017 , Hindawi Limited . All rights reserved. Numerical Investigation of the Effect of Stenosis Geometry on the Coronary Diagnostic Parameters Mon, 01 Sep 2014 12:05:12 +0000 The present study deals with the functional severity of a coronary artery stenosis assessed by the fractional flow reserve (FFR). The effects of different geometrical shapes of lesion on the diagnostic parameters are unknown. In this study, 3D computational simulation of blood flow in three different geometrical shapes of stenosis (triangular, elliptical, and trapezium) is considered in steady and transient conditions for 70% (moderate), 80% (intermediate), and 90% (severe) area stenosis (AS). For a given percentage AS, the variation of diagnostic parameters which are derived from pressure drop across the stenosis was found in three different geometrical shapes of stenosis and it was observed that FFR is higher in triangular shape and lower in trapezium shape. The pressure drop coefficient (CDP) was higher in trapezium shape and lower in triangular model whereas the LFC shows opposite trend. From the clinical perspective, the relationship between percentage AS and FFR is linear and inversely related in all the three models. A cut-off value of 0.75 for FFR was observed at 76.5% AS in trapezium model, 79.5% in elliptical model, and 82.7% AS for the triangular shaped model. The misinterpretation of the functional severity of the stenosis is in the region of 76.5%-82.7 % AS from different shapes of stenosis models. Sarfaraz Kamangar, Govindaraju Kalimuthu, Irfan Anjum Badruddin, A. Badarudin, N. J. Salman Ahmed, and T. M. Yunus Khan Copyright © 2014 Sarfaraz Kamangar et al. All rights reserved. A New Graph-Based Molecular Descriptor Using the Canonical Representation of the Molecule Tue, 22 Jul 2014 00:00:00 +0000 Molecular similarity is a pervasive concept in drug design. The basic idea underlying molecular similarity is the similar property principle, which states that structurally similar molecules will exhibit similar physicochemical and biological properties. In this paper, a new graph-based molecular descriptor (GBMD) is introduced. The GBMD is a new method of obtaining a rough description of 2D molecular structure in textual form based on the canonical representations of the molecule outline shape and it allows rigorous structure specification using small and natural grammars. Simulated virtual screening experiments with the MDDR database show clearly the superiority of the graph-based descriptor compared to many standard descriptors (ALOGP, MACCS, EPFP4, CDKFP, PCFP, and SMILE) using the Tanimoto coefficient (TAN) and the basic local alignment search tool (BLAST) when searches were carried. Hamza Hentabli, Faisal Saeed, Ammar Abdo, and Naomie Salim Copyright © 2014 Hamza Hentabli et al. All rights reserved. Mutation and Chaos in Nonlinear Models of Heredity Mon, 21 Jul 2014 08:29:07 +0000 We shall explore a nonlinear discrete dynamical system that naturally occurs in population systems to describe a transmission of a trait from parents to their offspring. We consider a Mendelian inheritance for a single gene with three alleles and assume that to form a new generation, each gene has a possibility to mutate, that is, to change into a gene of the other kind. We investigate the derived models and observe chaotic behaviors of such models. Nasir Ganikhodjaev, Mansoor Saburov, and Ashraf Mohamed Nawi Copyright © 2014 Nasir Ganikhodjaev et al. All rights reserved. Modeling of Scale-Dependent Bacterial Growth by Chemical Kinetics Approach Thu, 03 Jul 2014 00:00:00 +0000 We applied the so-called chemical kinetics approach to complex bacterial growth patterns that were dependent on the liquid-surface-area-to-volume ratio (SA/V) of the bacterial cultures. The kinetic modeling was based on current experimental knowledge in terms of autocatalytic bacterial growth, its inhibition by the metabolite CO2, and the relief of inhibition through the physical escape of the inhibitor. The model quantitatively reproduces kinetic data of SA/V-dependent bacterial growth and can discriminate between differences in the growth dynamics of enteropathogenic E. coli, E. coli  JM83, and Salmonella typhimurium on one hand and Vibrio cholerae on the other hand. Furthermore, the data fitting procedures allowed predictions about the velocities of the involved key processes and the potential behavior in an open-flow bacterial chemostat, revealing an oscillatory approach to the stationary states. Haydee Martínez, Joaquín Sánchez, José-Manuel Cruz, Guadalupe Ayala, Marco Rivera, and Thomas Buhse Copyright © 2014 Haydee Martínez et al. All rights reserved. Prediction of Protein-Protein Interaction Strength Using Domain Features with Supervised Regression Tue, 24 Jun 2014 08:20:08 +0000 Proteins in living organisms express various important functions by interacting with other proteins and molecules. Therefore, many efforts have been made to investigate and predict protein-protein interactions (PPIs). Analysis of strengths of PPIs is also important because such strengths are involved in functionality of proteins. In this paper, we propose several feature space mappings from protein pairs using protein domain information to predict strengths of PPIs. Moreover, we perform computational experiments employing two machine learning methods, support vector regression (SVR) and relevance vector machine (RVM), for dataset obtained from biological experiments. The prediction results showed that both SVR and RVM with our proposed features outperformed the best existing method. Mayumi Kamada, Yusuke Sakuma, Morihiro Hayashida, and Tatsuya Akutsu Copyright © 2014 Mayumi Kamada et al. All rights reserved. Many Local Pattern Texture Features: Which Is Better for Image-Based Multilabel Human Protein Subcellular Localization Classification? Tue, 24 Jun 2014 00:00:00 +0000 Human protein subcellular location prediction can provide critical knowledge for understanding a protein’s function. Since significant progress has been made on digital microscopy, automated image-based protein subcellular location classification is urgently needed. In this paper, we aim to investigate more representative image features that can be effectively used for dealing with the multilabel subcellular image samples. We prepared a large multilabel immunohistochemistry (IHC) image benchmark from the Human Protein Atlas database and tested the performance of different local texture features, including completed local binary pattern, local tetra pattern, and the standard local binary pattern feature. According to our experimental results from binary relevance multilabel machine learning models, the completed local binary pattern, and local tetra pattern are more discriminative for describing IHC images when compared to the traditional local binary pattern descriptor. The combination of these two novel local pattern features and the conventional global texture features is also studied. The enhanced performance of final binary relevance classification model trained on the combined feature space demonstrates that different features are complementary to each other and thus capable of improving the accuracy of classification. Fan Yang, Ying-Ying Xu, and Hong-Bin Shen Copyright © 2014 Fan Yang et al. All rights reserved. Efficient Feature Selection and Classification of Protein Sequence Data in Bioinformatics Thu, 19 Jun 2014 12:48:53 +0000 Bioinformatics has been an emerging area of research for the last three decades. The ultimate aims of bioinformatics were to store and manage the biological data, and develop and analyze computational tools to enhance their understanding. The size of data accumulated under various sequencing projects is increasing exponentially, which presents difficulties for the experimental methods. To reduce the gap between newly sequenced protein and proteins with known functions, many computational techniques involving classification and clustering algorithms were proposed in the past. The classification of protein sequences into existing superfamilies is helpful in predicting the structure and function of large amount of newly discovered proteins. The existing classification results are unsatisfactory due to a huge size of features obtained through various feature encoding methods. In this work, a statistical metric-based feature selection technique has been proposed in order to reduce the size of the extracted feature vector. The proposed method of protein classification shows significant improvement in terms of performance measure metrics: accuracy, sensitivity, specificity, recall, F-measure, and so forth. Muhammad Javed Iqbal, Ibrahima Faye, Brahim Belhaouari Samir, and Abas Md Said Copyright © 2014 Muhammad Javed Iqbal et al. All rights reserved. An Incus-Body Driving Type Piezoelectric Middle Ear Implant Design and Evaluation in 3D Computational Model and Temporal Bone Wed, 18 Jun 2014 00:00:00 +0000 A new incus-body driving type transducer relying on piezoelectric stack, with broad frequency bandwidth, is proposed for use in a middle ear implant. To aid the design process of this transducer, a coupling biomechanical model of the human middle ear and the piezoelectric transducer was established by reverse engineering technology. The validity of this model was confirmed by comparing model predicted motions with experimental measurements. Based on this verified biomechanical model, the main parameters of the transducer were determined. And its power consumption was calculated. Finally, to verify the capability of the designed piezoelectric transducer, a human temporal bone experimental platform was built. And the dynamic characteristics and the stimulated performance of the piezoelectric transducer were tested. The result showed that stapes displacement stimulated by the transducer excitation at 10.5 V RMS was equivalent to that from acoustic stimulation at 100 dB SPL, which is an adequate stimulation to the ossicular chain. The corresponding power consumption is 0.31 mW per volt of excitation at 1 kHz, which is low enough for the transducer to be used in a middle ear implant. Besides, this transducer demonstrates high performance at high frequencies. Houguang Liu, Zhushi Rao, Xinsheng Huang, Gang Cheng, Jiabin Tian, and Na Ta Copyright © 2014 Houguang Liu et al. All rights reserved. Defining Biological Networks for Noise Buffering and Signaling Sensitivity Using Approximate Bayesian Computation Thu, 05 Jun 2014 08:51:26 +0000 Reliable information processing in cells requires high sensitivity to changes in the input signal but low sensitivity to random fluctuations in the transmitted signal. There are often many alternative biological circuits qualifying for this biological function. Distinguishing theses biological models and finding the most suitable one are essential, as such model ranking, by experimental evidence, will help to judge the support of the working hypotheses forming each model. Here, we employ the approximate Bayesian computation (ABC) method based on sequential Monte Carlo (SMC) to search for biological circuits that can maintain signaling sensitivity while minimizing noise propagation, focusing on cases where the noise is characterized by rapid fluctuations. By systematically analyzing three-component circuits, we rank these biological circuits and identify three-basic-biological-motif buffering noise while maintaining sensitivity to long-term changes in input signals. We discuss in detail a particular implementation in control of nutrient homeostasis in yeast. The principal component analysis of the posterior provides insight into the nature of the reaction between nodes. Shuqiang Wang, Yanyan Shen, Changhong Shi, Tao Wang, Zhiming Wei, and Hanxiong Li Copyright © 2014 Shuqiang Wang et al. All rights reserved. Computational Simulations in the Cardiovascular System Mon, 07 Apr 2014 06:28:00 +0000 Aike Qiao, Hai-Chao Han, Makoto Ohta, and Yi Qian Copyright © 2014 Aike Qiao et al. All rights reserved. How Does Calcification Influence Plaque Vulnerability? Insights from Fatigue Analysis Sun, 06 Apr 2014 07:23:13 +0000 Background. Calcification is commonly believed to be associated with cardiovascular disease burden. But whether or not the calcifications have a negative effect on plaque vulnerability is still under debate. Methods and Results. Fatigue rupture analysis and the fatigue life were used to evaluate the rupture risk. An idealized baseline model containing no calcification was first built. Based on the baseline model, we investigated the influence of calcification on rupture path and fatigue life by adding a circular calcification and changing its location within the fibrous cap area. Results show that 84.0% of calcified cases increase the fatigue life up to 11.4%. For rupture paths 10 far from the calcification, the life change is negligible. Calcifications close to lumen increase more fatigue life than those close to the lipid pool. Also, calcifications in the middle area of fibrous cap increase more fatigue life than those in the shoulder area. Conclusion. Calcifications may play a positive role in the plaque stability. The influence of the calcification only exists in a local area. Calcifications close to lumen may be influenced more than those close to lipid pool. And calcifications in the middle area of fibrous cap are seemly influenced more than those in the shoulder area. Baijian Wu, Xuan Pei, and Zhi-Yong Li Copyright © 2014 Baijian Wu et al. All rights reserved. The Comparison of Tree-Sibling Time Consistent Phylogenetic Networks Is Graph Isomorphism-Complete Wed, 02 Apr 2014 15:26:50 +0000 Several polynomial time computable metrics on the class of semibinary tree-sibling time consistent phylogenetic networks are available in the literature; in particular, the problem of deciding if two networks of this kind are isomorphic is in P. In this paper, we show that if we remove the semibinarity condition, then the problem becomes much harder. More precisely, we prove that the isomorphism problem for generic tree-sibling time consistent phylogenetic networks is polynomially equivalent to the graph isomorphism problem. Since the latter is believed not to belong to P, the chances are that it is impossible to define a metric on the class of all tree-sibling time consistent phylogenetic networks that can be computed in polynomial time. Gabriel Cardona, Mercè Llabrés, Francesc Rosselló, and Gabriel Valiente Copyright © 2014 Gabriel Cardona et al. All rights reserved. A Perspective Review on Numerical Simulations of Hemodynamics in Aortic Dissection Mon, 03 Feb 2014 08:40:18 +0000 Aortic dissection, characterized by separation of the layers of the aortic wall, poses a significant challenge for clinicians. While type A aortic dissection patients are normally managed using surgical treatment, optimal treatment strategy for type B aortic dissection remains controversial and requires further evaluation. Although aortic diameter measured by CT angiography has been clinically used as a guideline to predict dilation in aortic dissection, hemodynamic parameters (e.g., pressure and wall shear stress), geometrical factors, and composition of the aorta wall are known to substantially affect disease progression. Due to the limitations of cardiac imaging modalities, numerical simulations have been widely used for the prediction of disease progression and therapeutic outcomes, by providing detailed insights into the hemodynamics. This paper presents a comprehensive review of the existing numerical models developed to investigate reasons behind tear initiation and progression, as well as the effectiveness of various treatment strategies, particularly the stent graft treatment. Wan Naimah Wan Ab Naim, Poo Balan Ganesan, Zhonghua Sun, Kok Han Chee, Shahrul Amry Hashim, and Einly Lim Copyright © 2014 Wan Naimah Wan Ab Naim et al. All rights reserved. Rule-Based Knowledge Acquisition Method for Promoter Prediction in Human and Drosophila Species Wed, 29 Jan 2014 00:00:00 +0000 The rapid and reliable identification of promoter regions is important when the number of genomes to be sequenced is increasing very speedily. Various methods have been developed but few methods investigate the effectiveness of sequence-based features in promoter prediction. This study proposes a knowledge acquisition method (named PromHD) based on if-then rules for promoter prediction in human and Drosophila species. PromHD utilizes an effective feature-mining algorithm and a reference feature set of 167 DNA sequence descriptors (DNASDs), comprising three descriptors of physicochemical properties (absorption maxima, molecular weight, and molar absorption coefficient), 128 top-ranked descriptors of 4-mer motifs, and 36 global sequence descriptors. PromHD identifies two feature subsets with 99 and 74 DNASDs and yields test accuracies of 96.4% and 97.5% in human and Drosophila species, respectively. Based on the 99- and 74-dimensional feature vectors, PromHD generates several if-then rules by using the decision tree mechanism for promoter prediction. The top-ranked informative rules with high certainty grades reveal that the global sequence descriptor, the length of nucleotide A at the first position of the sequence, and two physicochemical properties, absorption maxima and molecular weight, are effective in distinguishing promoters from non-promoters in human and Drosophila species, respectively. Wen-Lin Huang, Chun-Wei Tung, Chyn Liaw, Hui-Ling Huang, and Shinn-Ying Ho Copyright © 2014 Wen-Lin Huang et al. All rights reserved. Drug Release Analysis and Optimization for Drug-Eluting Stents Sun, 29 Dec 2013 11:28:53 +0000 The drug release analysis and optimization for drug-eluting stents in the arterial wall are studied, which involves mechanics, fluid dynamics, and mass transfer processes and design optimization. The Finite Element Method (FEM) is used to analyze the process of drug release in the vessels for drug-eluting stents (DES). Kriging surrogate model is used to build an approximate function relationship between the drug distribution and the coating parameters, replacing the expensive FEM reanalysis of drug release for DES in the optimization process. The diffusion coefficients and the coating thickness are selected as design variables. An adaptive optimization approach based on kriging surrogate model is proposed to optimize the lifetime of the drug in artery wall. The adaptive process is implemented by an infilling sampling criterion named Expected Improvement (EI), which is used to balance local and global search and tends to find the global optimal design. The effect of coating diffusivity and thickness on the drug release process for a typical DES is analyzed by means of FEM. An implementation of the optimization method for the drug release is then discussed. The results demonstrate that the optimized design can efficiently improve the efficacy of drug deposition and penetration into the arterial walls. Hongxia Li, Yihao Zhang, Bao Zhu, Jinying Wu, and Xicheng Wang Copyright © 2013 Hongxia Li et al. All rights reserved. Blood Clot Simulation Model by Using the Bond-Graph Technique Sun, 22 Dec 2013 18:09:53 +0000 The World Health Organization estimates that 17 million people die of cardiovascular disease, particularly heart attacks and strokes, every year. Most strokes are caused by a blood clot that occludes an artery in the cerebral circulation and the process concerning the removal of this obstruction involves catheterisation. The fundamental object of the presented study consists in determining and optimizing the necessary simulation model corresponding with the blood clot zone to be implemented jointly with other Mechanical Thrombectomy Device simulation models, which have become more widely used during the last decade. To do so, a multidomain technique is used to better explain the different aspects of the attachment to the artery wall and between the existing platelets, it being possible to obtain the mathematical equations that define the full model. For a better understanding, a consecutive approximation to the definitive model will be presented, analyzing the different problems found during the study. The final presented model considers an elastic characterization of the blood clot composition and the possibility of obtaining a consecutive detachment process from the artery wall. In conclusion, the presented model contains the necessary behaviour laws to be implemented in future blood clot simulation models. Gregorio Romero, M. Luisa Martinez, Joaquin Maroto, and Jesus Felez Copyright © 2013 Gregorio Romero et al. All rights reserved. Use of Computational Fluid Dynamics to Estimate Hemodynamic Effects of Respiration on Hypoplastic Left Heart Syndrome Surgery: Total Cavopulmonary Connection Treatments Mon, 09 Dec 2013 11:45:03 +0000 Total cavopulmonary connection (TCPC), a typical kind of Fontan procedure, is commonly used in the treatment of a functional single ventricle. The palliative cardiothoracic procedure is performed by connecting the superior vena cava and the inferior vena cava to the pulmonary arteries. Due to the difficulty of direct study in vivo, in this paper, computational fluid dynamics (CFD) was introduced to estimate the outcomes of patient-specific TCPC configuration. We mainly focused on the influence of blood pulsation and respiration. Fast Fourier transforms method was employed to separate the measured flow conditions into the rate of breath and heart beat. Blood flow performance around the TCPC connection was investigated by analyzing the results of time-varying energy losses, blood flow distribution rate, local pressure, and wall shear stress distributions. It was found that the value of energy loss including the influence of respiration was 1.5 times higher than the value of energy loss disregarding respiratory influences. The results indicated that the hemodynamic outcomes of TCPC treatment are obviously influenced by respiration. The influence of respiration plays an important role in estimating the results of TCPC treatment and thus should be included as one of the important conditions of computational haemodynamic analysis. Jinlong Liu, Yi Qian, Qi Sun, Jinfen Liu, and Mitsuo Umezu Copyright © 2013 Jinlong Liu et al. All rights reserved. Feature-Based Classification of Amino Acid Substitutions outside Conserved Functional Protein Domains Sun, 17 Nov 2013 16:12:17 +0000 There are more than 500 amino acid substitutions in each human genome, and bioinformatics tools irreplaceably contribute to determination of their functional effects. We have developed feature-based algorithm for the detection of mutations outside conserved functional domains (CFDs) and compared its classification efficacy with the most commonly used phylogeny-based tools, PolyPhen-2 and SIFT. The new algorithm is based on the informational spectrum method (ISM), a feature-based technique, and statistical analysis. Our dataset contained neutral polymorphisms and mutations associated with myeloid malignancies from epigenetic regulators ASXL1, DNMT3A, EZH2, and TET2. PolyPhen-2 and SIFT had significantly lower accuracies in predicting the effects of amino acid substitutions outside CFDs than expected, with especially low sensitivity. On the other hand, only ISM algorithm showed statistically significant classification of these sequences. It outperformed PolyPhen-2 and SIFT by 15% and 13%, respectively. These results suggest that feature-based methods, like ISM, are more suitable for the classification of amino acid substitutions outside CFDs than phylogeny-based tools. Branislava Gemovic, Vladimir Perovic, Sanja Glisic, and Nevena Veljkovic Copyright © 2013 Branislava Gemovic et al. All rights reserved. Preliminary Computational Hemodynamics Study of Double Aortic Aneurysms under Multistage Surgical Procedures: An Idealised Model Study Sun, 17 Nov 2013 14:45:10 +0000 Double aortic aneurysm (DAA) falls under the category of multiple aortic aneurysms. Repair is generally done through staged surgery due to low invasiveness. In this approach, one aneurysm is cured per operation. Therefore, two operations are required for DAA. However, post-first-surgery rupture cases have been reported. Although the problems involved with managing staged surgery have been discussed for more than 30 years, investigation from a hemodynamic perspective has not been attempted. Hence, this is the first computational fluid dynamics approach to the DAA problem. Three idealized geometries were prepared: presurgery, thoracic aortic aneurysm (TAA) cured, and abdominal aortic aneurysm (AAA) cured. By applying identical boundary conditions for flow rate and pressure, the Navier-Stokes equation and continuity equations were solved under the Newtonian fluid assumption. Average pressure in TAA was increased by AAA repair. On the other hand, average pressure in AAA was decreased after TAA repair. Average wall shear stress was decreased at the peak in post-first-surgery models. However, the wave profile of TAA average wall shear stress was changed in the late systole phase after AAA repair. Since the average wall shear stress in the post-first-surgery models decreased and pressure at TAA after AAA repair increased, the TAA might be treated first to prevent rupture. Yosuke Otsuki, Nhat Bui Minh, Hiroshi Ohtake, Go Watanabe, and Teruo Matsuzawa Copyright © 2013 Yosuke Otsuki et al. All rights reserved. Studying the Relationship between Robustness against Mutations in Metabolic Networks and Lifestyle of Organisms Thu, 14 Nov 2013 09:48:44 +0000 Robustness is the key feature of biological networks that enables living organisms to keep their homeostatic state and to survive against external and internal perturbations. Variations in environmental conditions or nutrients and intracellular changes such as genetic mutations have the potential to change stability and efficiency of an organism. Structural robustness helps biological systems to choose alternative routes of adaptation to varying conditions. In this study, in order to estimate the structural robustness in metabolic networks we presented a novel flux balance-based approach inspired by bond percolation theory. Fourteen in silico metabolic models were studied in this work in order to examine the possible relationship between the lifestyle of organisms and their metabolic robustness. The results of this study confirm that in organisms which are highly adapted to their environment robustness to mutations may decrease compared to other organisms. Sayed-Amir Marashi, Hawa Kouhestani, and Majid Mahdavi Copyright © 2013 Sayed-Amir Marashi et al. All rights reserved. Transient Hemodynamic Changes upon Changing a BCPA into a TCPC in Staged Fontan Operation: A Computational Model Study Sun, 10 Nov 2013 09:20:02 +0000 The clinical benefits of the Fontan operation in treating single-ventricle defects have been well documented. However, perioperative mortality or morbidity remains a critical problem. The purpose of the present study was to identify the cardiovascular factors that dominate the transient hemodynamic changes upon the change of a bidirectional cavopulmonary (Glenn) anastomosis (BCPA) into a total cavopulmonary connection (TCPC). For this purpose, two computational models were constructed to represent, respectively, a single-ventricle circulation with a BCPA and that with a TCPC. A series of model-based simulations were carried out to quantify the perioperative hemodynamic changes under various cardiovascular conditions. Obtained results indicated that the presence of a low pulmonary vascular resistance and/or a low lower-body vascular resistance is beneficial to the increase in transpulmonary flow upon the BCPA to TCPC change. Moreover, it was found that ventricular diastolic dysfunction and mitral valve regurgitation, despite being well-known risk factors for poor postoperative outcomes, do not cause a considerable perioperative reduction in transpulmonary flow. The findings may help physicians to assess the perioperative risk of the TCPC surgery based on preoperative measurement of cardiovascular function. Fuyou Liang, Hideaki Senzaki, Zhaofang Yin, Yuqi Fan, Koichi Sughimoto, and Hao Liu Copyright © 2013 Fuyou Liang et al. All rights reserved. Theoretical Prediction of Ultrasound Elastography for Detection of Early Osteoarthritis Tue, 05 Nov 2013 15:59:33 +0000 Ultrasound elastography could be used as a new noninvasive technique for detecting early osteoarthritis. As the first critical step, this study theoretically predicted the excitation power and the measurement errors in detecting cartilage detect. A finite element model was used to simulate wave propagation of elastography in the cartilage. The wave was produced by a force , and the wave speed was calculated. The normal cartilage model was used to define the relationship between the wave speed and elastic modulus. Various stiffness values were simulated.  N with a duration of 0.5 ms was required for having measurable deformation (10 m) at the distal site. The deformation had a significant rise when the wave crossed the defect. The relationship between the wave speed and elastic parameters was found as , where was the elastic modulus, was Poisson’s ratio, and was the density. For the simulated defect with an elastic modulus of 7 MPa which was slightly stiffer than the normal cartilage, the measurement error was 0.1 MPa. The results suggested that, given the simulated conditions, this new technique could be used to detect the defect in early osteoarthritis. Lan Wang, Shigao Chen, Kai-Nan An, Hui-Lin Yang, and Zong-Ping Luo Copyright © 2013 Lan Wang et al. All rights reserved. Common Force Field Thermodynamics of Cholesterol Tue, 05 Nov 2013 14:26:41 +0000 Four different force fields are examined for dynamic characteristics using cholesterol as a case study. The extent to which various types of internal degrees of freedom become thermodynamically relevant is evaluated by means of principal component analysis. More complex degrees of freedom (angle bending, dihedral rotations) show a trend towards force field independence. Moreover, charge assignments for membrane-embedded compounds are revealed to be critical with significant impact on biological reasoning. Francesco Giangreco, Eiji Yamamoto, Yoshinori Hirano, Milan Hodoscek, Volker Knecht, Matteo di Giosia, Matteo Calvaresi, Francesco Zerbetto, Kenji Yasuoka, Tetsu Narumi, Masato Yasui, and Siegfried Höfinger Copyright © 2013 Francesco Giangreco et al. All rights reserved. Design Optimization of Coronary Stent Based on Finite Element Models Thu, 03 Oct 2013 15:24:35 +0000 This paper presents an effective optimization method using the Kriging surrogate model combing with modified rectangular grid sampling to reduce the stent dogboning effect in the expansion process. An infilling sampling criterion named expected improvement (EI) is used to balance local and global searches in the optimization iteration. Four commonly used finite element models of stent dilation were used to investigate stent dogboning rate. Thrombosis models of three typical shapes are built to test the effectiveness of optimization results. Numerical results show that two finite element models dilated by pressure applied inside the balloon are available, one of which with the artery and plaque can give an optimal stent with better expansion behavior, while the artery and plaque unincluded model is more efficient and takes a smaller amount of computation. Hongxia Li, Tianshuang Qiu, Bao Zhu, Jinying Wu, and Xicheng Wang Copyright © 2013 Hongxia Li et al. All rights reserved. Propose a Wall Shear Stress Divergence to Estimate the Risks of Intracranial Aneurysm Rupture Thu, 26 Sep 2013 15:27:43 +0000 Although wall shear stress (WSS) has long been considered a critical indicator of intracranial aneurysm rupture, there is still no definite conclusion as to whether a high or a low WSS results in aneurysm rupture. The reason may be that the effect of WSS direction has not been fully considered. The objectives of this study are to investigate the magnitude of WSS () and its divergence on the aneurysm surface and to test the significance of both in relation to the aneurysm rupture. Patient-specific computational fluid dynamics (CFD) was used to compute WSS and wall shear stress divergence (WSSD) on the aneurysm surface for nineteen patients. Our results revealed that if high is stretching aneurysm luminal surface, and the stretching region is concentrated, the aneurysm is under a high risk of rupture. It seems that, by considering both direction and magnitude of WSS, WSSD may be a better indicator for the risk estimation of aneurysm rupture (154). Y. Zhang, H. Takao, Y. Murayama, and Y. Qian Copyright © 2013 Y. Zhang et al. All rights reserved. Why Is ABI Effective in Detecting Vascular Stenosis? Investigation Based on Multibranch Hemodynamic Model Thu, 05 Sep 2013 19:10:16 +0000 The ankle-brachial index (ABI), defined as the ratio of systolic pressure in the ankle arteries and that in the brachial artery, was a useful noninvasive method to detect arterial stenoses. There had been a lot of researches about clinical regularities of ABI; however, mechanism studies were less addressed. For the purpose of a better understanding of the correlation between vascular stenoses and ABI, a computational model for simulating blood pressure and flow propagation in various arterial stenosis circumstances was developed with a detailed compartmental description of the heart and main arteries. Particular attention was paid to the analysis of effects of vascular stenoses in different large-sized arteries on ABI in theory. Moreover, the variation of ABI during the increase of the stenosis severity was also studied. Results showed that stenoses in lower limb arteries, as well as, brachial artery, caused different variations of blood pressure in ankle and brachial arteries, resulting in a significant change of ABI. Furthermore, the variation of ABI became faster when the severity of the stenosis increased, validating that ABI was more sensitive to severe stenoses than to mild/moderate ones. All these in findings revealed the reason why ABI was an effective index for detecting stenoses, especially in lower limb arteries. Xiaoyun Li, Ling Wang, Chi Zhang, Shuyu Li, Fang Pu, Yubo Fan, and Deyu Li Copyright © 2013 Xiaoyun Li et al. All rights reserved. Nonlinear Analysis of Electrocardiography Signals for Atrial Fibrillation Mon, 13 May 2013 08:49:25 +0000 This paper aims to analyze the electrocardiography (ECG) signals for patient with atrial fibrillation (AF) by using bispectrum and extreme learning machine (ELM). AF is the most common irregular heart beat disease which may cause many cardiac diseases as well. Bispectral analysis was used to extract the nonlinear information in the ECG signals. The bispectral features of each ECG episode were determined and fed to the ELM classifier. The classification accuracy of ELM to distinguish nonterminating, terminating AF, and terminating immediately AF was 96.25%. In this study, the normal ECG signal was also compared with AF ECG signal due to the nonlinearity which was determined by bispectrum. The classification result of ELM was 99.15% to distinguish AF ECGs from normal ECGs. Necmettin Sezgin Copyright © 2013 Necmettin Sezgin. All rights reserved. Mathematical Characterization of Protein Transmembrane Regions Mon, 15 Apr 2013 15:30:02 +0000 Graphical bioinformatics has paved a unique way of mathematical characterization of proteins and proteomic maps. The graphics representations and the corresponding mathematical descriptors have proved to be useful and have provided unique solutions to problems related to identification, comparisons, and analyses of protein sequences and proteomics maps. Based on sequence information alone, these descriptors are independent from physiochemical properties of amino acids and evolutionary information. In this work, we have presented invariants from amino acid adjacency matrix and decagonal isometries matrix as potential descriptors of protein sequences. Encoding protein sequences into amino acid adjacency matrix is already well established. We have shown its application in classification of transmembrane and nontransmembrane regions of membrane protein sequences. We have introduced the dodecagonal isometries matrix, which is a novel method of encoding protein sequences based on decagonal isometries group. Amrita Roy Choudhury, Nikolay Zhukov, and Marjana Novič Copyright © 2013 Amrita Roy Choudhury et al. All rights reserved. Computational Systems Biology Tue, 12 Mar 2013 09:46:12 +0000 Xing-Ming Zhao, Weidong Tian, Rui Jiang, and Jun Wan Copyright © 2013 Xing-Ming Zhao et al. All rights reserved. From Ontology to Semantic Similarity: Calculation of Ontology-Based Semantic Similarity Thu, 28 Feb 2013 17:05:58 +0000 Advances in high-throughput experimental techniques in the past decade have enabled the explosive increase of omics data, while effective organization, interpretation, and exchange of these data require standard and controlled vocabularies in the domain of biological and biomedical studies. Ontologies, as abstract description systems for domain-specific knowledge composition, hence receive more and more attention in computational biology and bioinformatics. Particularly, many applications relying on domain ontologies require quantitative measures of relationships between terms in the ontologies, making it indispensable to develop computational methods for the derivation of ontology-based semantic similarity between terms. Nevertheless, with a variety of methods available, how to choose a suitable method for a specific application becomes a problem. With this understanding, we review a majority of existing methods that rely on ontologies to calculate semantic similarity between terms. We classify existing methods into five categories: methods based on semantic distance, methods based on information content, methods based on properties of terms, methods based on ontology hierarchy, and hybrid methods. We summarize characteristics of each category, with emphasis on basic notions, advantages and disadvantages of these methods. Further, we extend our review to software tools implementing these methods and applications using these methods. Mingxin Gan, Xue Dou, and Rui Jiang Copyright © 2013 Mingxin Gan et al. All rights reserved. A Robust Hybrid Approach Based on Estimation of Distribution Algorithm and Support Vector Machine for Hunting Candidate Disease Genes Thu, 07 Feb 2013 16:31:26 +0000 Microarray data are high dimension with high noise ratio and relatively small sample size, which makes it a challenge to use microarray data to identify candidate disease genes. Here, we have presented a hybrid method that combines estimation of distribution algorithm with support vector machine for selection of key feature genes. We have benchmarked the method using the microarray data of both diffuse B cell lymphoma and colon cancer to demonstrate its performance for identifying key features from the profile data of high-dimension gene expression. The method was compared with a probabilistic model based on genetic algorithm and another hybrid method based on both genetics algorithm and support vector machine. The results showed that the proposed method provides new computational strategy for hunting candidate disease genes from the profile data of disease gene expression. The selected candidate disease genes may help to improve the diagnosis and treatment for diseases. Li Li, Hongmei Chen, Chang Liu, Fang Wang, Fangfang Zhang, Lihua Bai, Yihan Chen, and Luying Peng Copyright © 2013 Li Li et al. All rights reserved. Prediction of Deleterious Nonsynonymous Single-Nucleotide Polymorphism for Human Diseases Wed, 30 Jan 2013 14:28:01 +0000 The identification of genetic variants that are responsible for human inherited diseases is a fundamental problem in human and medical genetics. As a typical type of genetic variation, nonsynonymous single-nucleotide polymorphisms (nsSNPs) occurring in protein coding regions may alter the encoded amino acid, potentially affect protein structure and function, and further result in human inherited diseases. Therefore, it is of great importance to develop computational approaches to facilitate the discrimination of deleterious nsSNPs from neutral ones. In this paper, we review databases that collect nsSNPs and summarize computational methods for the identification of deleterious nsSNPs. We classify the existing methods for characterizing nsSNPs into three categories (sequence based, structure based, and annotation based), and we introduce machine learning models for the prediction of deleterious nsSNPs. We further discuss methods for identifying deleterious nsSNPs in noncoding variants and those for dealing with rare variants. Jiaxin Wu and Rui Jiang Copyright © 2013 Jiaxin Wu and Rui Jiang. All rights reserved. A Local Genetic Algorithm for the Identification of Condition-Specific MicroRNA-Gene Modules Mon, 21 Jan 2013 13:09:19 +0000 Transcription factor and microRNA are two types of key regulators of gene expression. Their regulatory mechanisms are highly complex. In this study, we propose a computational method to predict condition-specific regulatory modules that consist of microRNAs, transcription factors, and their commonly regulated genes. We used matched global expression profiles of mRNAs and microRNAs together with the predicted targets of transcription factors and microRNAs to construct an underlying regulatory network. Our method searches for highly scored modules from the network based on a two-step heuristic method that combines genetic and local search algorithms. Using two matched expression datasets, we demonstrate that our method can identify highly scored modules with statistical significance and biological relevance. The identified regulatory modules may provide useful insights on the mechanisms of transcription factors and microRNAs. Wenbo Mu, Damian Roqueiro, and Yang Dai Copyright © 2013 Wenbo Mu et al. All rights reserved. Computational and Bioinformatics Frameworks for Next-Generation Whole Exome and Genome Sequencing Sun, 13 Jan 2013 11:56:13 +0000 It has become increasingly apparent that one of the major hurdles in the genomic age will be the bioinformatics challenges of next-generation sequencing. We provide an overview of a general framework of bioinformatics analysis. For each of the three stages of (1) alignment, (2) variant calling, and (3) filtering and annotation, we describe the analysis required and survey the different software packages that are used. Furthermore, we discuss possible future developments as data sources grow and highlight opportunities for new bioinformatics tools to be developed. Marisa P. Dolled-Filhart, Michael Lee Jr., Chih-wen Ou-yang, Rajini Rani Haraksingh, and Jimmy Cheng-Ho Lin Copyright © 2013 Marisa P. Dolled-Filhart et al. All rights reserved. Hierarchical Modular Structure Identification with Its Applications in Gene Coexpression Networks Sun, 30 Dec 2012 14:00:14 +0000 Network module (community) structure has been a hot research topic in recent years. Many methods have been proposed for module detection and identification. Hierarchical structure of modules is shown to exist in many networks such as biological networks and social networks. Compared to the partitional module identification methods, less research is done on the inference of hierarchical modular structure. In this paper, we propose a method for constructing the hierarchical modular structure based on the stochastic block model. Statistical tests are applied to test the hierarchical relations between different modules. We give both artificial networks and real data examples to illustrate the performance of our approach. Application of the proposed method to yeast gene coexpression network shows that it does have a hierarchical modular structure with the modules on different levels corresponding to different gene functions. Shuqin Zhang Copyright © 2012 Shuqin Zhang. All rights reserved. Large Scale Association Analysis for Drug Addiction: Results from SNP to Gene Thu, 27 Dec 2012 14:03:37 +0000 Many genetic association studies used single nucleotide polymorphisms (SNPs) data to identify genetic variants for complex diseases. Although SNP-based associations are most common in genome-wide association studies (GWAS), gene-based association analysis has received increasing attention in understanding genetic etiologies for complex diseases. While both methods have been used to analyze the same data, few genome-wide association studies compare the results or observe the connection between them. We performed a comprehensive analysis of the data from the Study of Addiction: Genetics and Environment (SAGE) and compared the results from the SNP-based and gene-based analyses. Our results suggest that the gene-based method complements the individual SNP-based analysis, and conceptually they are closely related. In terms of gene findings, our results validate many genes that were either reported from the analysis of the same dataset or based on animal studies for substance dependence. Xiaobo Guo, Zhifa Liu, Xueqin Wang, and Heping Zhang Copyright © 2012 Xiaobo Guo et al. All rights reserved. A Review of Integration Strategies to Support Gene Regulatory Network Construction Thu, 27 Dec 2012 13:56:03 +0000 Gene regulatory network (GRN) construction is a central task of systems biology. Integration of different data sources to infer and construct GRNs is an important consideration for the success of this effort. In this paper, we will discuss distinctive strategies of data integration for GRN construction. Basically, the process of integration of different data sources is divided into two phases: the first phase is collection of the required data and the second phase is data processing with advanced algorithms to infer the GRNs. In this paper these two phases are called “structural integration” and “analytic integration,” respectively. Compared with the nonintegration strategies, the integration strategies perform quite well and have better agreement with the experimental evidence. Hailin Chen and Vincent VanBuren Copyright © 2012 Hailin Chen and Vincent VanBuren. All rights reserved. In Silico Evolution of Gene Cooption in Pattern-Forming Gene Networks Tue, 25 Dec 2012 08:09:33 +0000 Gene recruitment or cooption occurs when a gene, which may be part of an existing gene regulatory network (GRN), comes under the control of a new regulatory system. Such re-arrangement of pre-existing networks is likely more common for increasing genomic complexity than the creation of new genes. Using evolutionary computations (EC), we investigate how cooption affects the evolvability, outgrowth and robustness of GRNs. We use a data-driven model of insect segmentation, for the fruit fly Drosophila, and evaluate fitness by robustness to maternal variability—a major constraint in biological development. We compare two mechanisms of gene cooption: a simpler one with gene Introduction and Withdrawal operators; and one in which GRN elements can be altered by transposon infection. Starting from a minimal 2-gene network, insufficient for fitting the Drosophila gene expression patterns, we find a general trend of coopting available genes into the GRN, in order to better fit the data. With the transposon mechanism, we find co-evolutionary oscillations between genes and their transposons. These oscillations may offer a new technique in EC for overcoming premature convergence. Finally, we comment on how a differential equations (in contrast to Boolean) approach is necessary for addressing realistic continuous variation in biochemical parameters. Alexander V. Spirov, Marat A. Sabirov, and David M. Holloway Copyright © 2012 Alexander V. Spirov et al. All rights reserved. Gene Expression Network Reconstruction by LEP Method Using Microarray Data Sun, 23 Dec 2012 15:32:54 +0000 Gene expression network reconstruction using microarray data is widely studied aiming to investigate the behavior of a gene cluster simultaneously. Under the Gaussian assumption, the conditional dependence between genes in the network is fully described by the partial correlation coefficient matrix. Due to the high dimensionality and sparsity, we utilize the LEP method to estimate it in this paper. Compared to the existing methods, the LEP reaches the highest PPV with the sensitivity controlled at the satisfactory level. A set of gene expression data from the HapMap project is analyzed for illustration. Na You, Peng Mou, Ting Qiu, Qiang Kou, Huaijin Zhu, Yuexi Chen, and Xueqin Wang Copyright © 2012 Na You et al. All rights reserved. Molecular Mechanisms and Function Prediction of Long Noncoding RNA Sun, 23 Dec 2012 15:12:41 +0000 The central dogma of gene expression considers RNA as the carrier of genetic information from DNA to protein. However, it has become more and more clear that RNA plays more important roles than simply being the information carrier. Recently, whole genome transcriptomic analyses have identified large numbers of dynamically expressed long noncoding RNAs (lncRNAs), many of which are involved in a variety of biological functions. Even so, the functions and molecular mechanisms of most lncRNAs still remain elusive. Therefore, it is necessary to develop computational methods to predict the function of lncRNAs in order to accelerate the study of lncRNAs. Here, we review the recent progress in the identification of lncRNAs, the molecular functions and mechanisms of lncRNAs, and the computational methods for predicting the function of lncRNAs. Handong Ma, Yun Hao, Xinran Dong, Qingtian Gong, Jingqi Chen, Jifeng Zhang, and Weidong Tian Copyright © 2012 Handong Ma et al. All rights reserved. The Spinal Curvature of Three Different Sitting Positions Analysed in an Open MRI Scanner Sun, 25 Nov 2012 13:18:29 +0000 Sitting is the most frequently performed posture of everyday life. Biomechanical interactions with office chairs have therefore a long-term effect on our musculoskeletal system and ultimately on our health and wellbeing. This paper highlights the kinematic effect of office chairs on the spinal column and its single segments. Novel chair concepts with multiple degrees of freedom provide enhanced spinal mobility. The angular changes of the spinal column in the sagittal plane in three different sitting positions (forward inclined, reclined, and upright) for six healthy subjects (aged 23 to 45 years) were determined using an open magnetic resonance imaging (MRI) scanner. An MRI-compatible and commercially available office chair was adapted for use in the scanner. The midpoint coordinates of the vertebral bodies, the wedge angles of the intervertebral discs, and the lumbar lordotic angle were analysed. The mean lordotic angles were (mean ± standard deviation) in a forward inclined position, in an upright position, and in a reclined position. All segments from T10-T11 to L5-S1 were involved in movement during positional changes, whereas the range of motion in the lower lumbar segments was increased in comparison to the upper segments. Daniel Baumgartner, Roland Zemp, Renate List, Mirjam Stoop, Jaroslav Naxera, Jean Pierre Elsig, and Silvio Lorenzetti Copyright © 2012 Daniel Baumgartner et al. All rights reserved. Diffusion Tensor Imaging-Based Research on Human White Matter Anatomy Sun, 25 Nov 2012 07:43:22 +0000 The aim of this study is to investigate the white matter by the diffusion tensor imaging and the Chinese visible human dataset and to provide the 3D anatomical data of the corticospinal tract for the neurosurgical planning by studying the probabilistic maps and the reproducibility of the corticospinal tract. Diffusion tensor images and high-resolution T1-weighted images of 15 healthy volunteers were acquired; the DTI data were processed using DtiStudio and FSL software. The FA and color FA maps were compared with the sectional images of the Chinese visible human dataset. The probability maps of the corticospinal tract were generated as a quantitative measure of reproducibility for each voxel of the stereotaxic space. The fibers displayed by the diffusion tensor imaging were well consistent with the sectional images of the Chinese visible human dataset and the existing anatomical knowledge. The three-dimensional architecture of the white matter fibers could be clearly visualized on the diffusion tensor tractography. The diffusion tensor tractography can establish the 3D probability maps of the corticospinal tract, in which the degree of intersubject reproducibility of the corticospinal tract is consistent with the previous architectonic report. DTI is a reliable method of studying the fiber connectivity in human brain, but it is difficult to identify the tiny fibers. The probability maps are useful for evaluating and identifying the corticospinal tract in the DTI, providing anatomical information for the preoperative planning and improving the accuracy of surgical risk assessments preoperatively. Ming-guo Qiu, Jing-na Zhang, Ye Zhang, Qi-yu Li, Bing Xie, and Jian Wang Copyright © 2012 Ming-guo Qiu et al. All rights reserved. Network Completion Using Dynamic Programming and Least-Squares Fitting Thu, 01 Nov 2012 14:38:43 +0000 We consider the problem of network completion, which is to make the minimum amount of modifications to a given network so that the resulting network is most consistent with the observed data. We employ here a certain type of differential equations as gene regulation rules in a genetic network, gene expression time series data as observed data, and deletions and additions of edges as basic modification operations. In addition, we assume that the numbers of deleted and added edges are specified. For this problem, we present a novel method using dynamic programming and least-squares fitting and show that it outputs a network with the minimum sum squared error in polynomial time if the maximum indegree of the network is bounded by a constant. We also perform computational experiments using both artificially generated and real gene expression time series data. Natsu Nakajima, Takeyuki Tamura, Yoshihiro Yamanishi, Katsuhisa Horimoto, and Tatsuya Akutsu Copyright © 2012 Natsu Nakajima et al. All rights reserved. Numerical Characterization of DNA Sequence Based on Dinucleotides Tue, 24 Apr 2012 11:37:04 +0000 Sequence comparison is a primary technique for the analysis of DNA sequences. In order to make quantitative comparisons, one devises mathematical descriptors that capture the essence of the base composition and distribution of the sequence. Alignment methods and graphical techniques (where each sequence is represented by a curve in high-dimension Euclidean space) have been used popularly for a long time. In this contribution we will introduce a new nongraphical and nonalignment approach based on the frequencies of the dinucleotide XY in DNA sequences. The most important feature of this method is that it not only identifies adjacent XY pairs but also nonadjacent XY ones where X and Y are separated by some number of nucleotides. This methodology preserves information in DNA sequence that is ignored by other methods. We test our method on the coding regions of exon-1 of β–globin for 11 species, and the utility of this new method is demonstrated. Xingqin Qi, Edgar Fuller, Qin Wu, and Cun-Quan Zhang Copyright © 2012 Xingqin Qi et al. All rights reserved. Pathway Detection from Protein Interaction Networks and Gene Expression Data Using Color-Coding Methods and A* Search Algorithms Sun, 01 Apr 2012 08:34:59 +0000 With the large availability of protein interaction networks and microarray data supported, to identify the linear paths that have biological significance in search of a potential pathway is a challenge issue. We proposed a color-coding method based on the characteristics of biological network topology and applied heuristic search to speed up color-coding method. In the experiments, we tested our methods by applying to two datasets: yeast and human prostate cancer networks and gene expression data set. The comparisons of our method with other existing methods on known yeast MAPK pathways in terms of precision and recall show that we can find maximum number of the proteins and perform comparably well. On the other hand, our method is more efficient than previous ones and detects the paths of length 10 within 40 seconds using CPU Intel 1.73GHz and 1GB main memory running under windows operating system. Cheng-Yu Yeh, Hsiang-Yuan Yeh, Carlos Roberto Arias, and Von-Wun Soo Copyright © 2012 Cheng-Yu Yeh et al. All rights reserved. Cell-Oriented Modeling of Angiogenesis Tue, 18 Oct 2011 00:00:00 +0000 Due to its significant involvement in various physiological and pathological conditions, angiogenesis (the development of new blood vessels from an existing vasculature) represents an important area of the actual biological research and a field in which mathematical modeling proved particularly useful in supporting the experimental work. In this paper, we focus on a specific modeling strategy, known as “cell-centered” approach. This type of mathematical models work at a “mesoscopic scale,” assuming the cell as the natural level of abstraction for computational modeling of development. They treat cells phenomenologically, considering their essential behaviors to study how tissue structure and organization emerge from the collective dynamics of multiple cells. The main contributions of the cell-oriented approach to the study of the angiogenic process will be described. From one side, they have generated “basic science understanding” about the process of capillary assembly during development, growth, and pathology. On the other side, models were also developed supporting “applied biomedical research” for the purpose of identifying new therapeutic targets and clinically relevant approaches for either inhibiting or stimulating angiogenesis. Diego Guidolin, Piera Rebuffat, and Giovanna Albertin Copyright © 2011 Diego Guidolin et al. All rights reserved. Thermodynamic Molecular Switch in Sequence-Specific Hydrophobic Interaction: Two Computational Models Compared Mon, 01 Jan 1900 00:00:00 +0000 We have shown in our published work the existence of a thermodynamic switch in biological systems wherein a change of sign in ΔCp°(T)reaction leads to a true negative minimum in the Gibbs free energy change of reaction, and hence, a maximum in the related Keq. We have examined 35 pair-wise, sequence-specific hydrophobic interactions over the temperature range of 273–333 K, based on data reported by Nemethy and Scheraga in 1962. A closer look at a single example, the pair-wise hydrophobic interaction of leucine-isoleucine, will demonstrate the significant differences when the data are analyzed using the Nemethy-Scheraga model or treated by the Planck-Benzinger methodology which we have developed. The change in inherent chemical bond energy at 0 K, ΔH°(T0) is 7.53 kcal mol-1 compared with 2.4 kcal mol-1, while ‹ts› is 365 K as compared with 355 K, for the Nemethy-Scheraga and Planck-Benzinger model, respectively. At ‹tm›, the thermal agitation energy is about five times greater than ΔH°(T0) in the Planck-Benzinger model, that is 465 K compared to 497 K in the Nemethy-Scheraga model. The results imply that the negative Gibbs free energy minimum at a well-defined ‹ts›, where TΔS° = 0 at about 355 K, has its origin in the sequence-specific hydrophobic interactions, which are highly dependent on details of molecular structure. The Nemethy-Scheraga model shows no evidence of the thermodynamic molecular switch that we have found to be a universal feature of biological interactions. The Planck-Benzinger method is the best known for evaluating the innate temperature-invariant enthalpy, ΔH°(T0), and provides for better understanding of the heat of reaction for biological molecules. Paul Chun Copyright © 2003 Paul Chun. All rights reserved. CAMM Techniques for the Prediction of the Mechanical Properties of Tendons and Ligaments Nanostructures Mon, 01 Jan 1900 00:00:00 +0000 Theoretical prediction of the mechanical properties of soft tissues usually relies on a top-down approach; that is analysis is gradually refined to observe smaller structures and properties until technical limits are reached. Computer-Assisted Molecular Modeling (CAMM) allows for the reversal of this approach and the performance of bottom-up modeling instead. The wealth of available sequences and structures provides an enormous database for computational efforts to predict structures, simulate docking and folding processes, simulate molecular interactions, and understand them in quantitative energetic terms. Tendons and ligaments can be considered an ideal arena due to their well defined and highly organized architecture which involves not only the main structural constituent, the collagen molecule, but also other important molecular “actors” such as proteoglycans and glycosaminoglycans. In this ideal arena each structure is well organized and recognizable, and using the molecular modeling tool it is possible to evaluate their mutual interactions and to characterize their mechanical function. Knowledge of these relationships can be useful in understanding connective tissue performance as a result of the cooperation and mutual interaction between different biological structures at the nanoscale. Simone Vesentini, Franco M. Montevecchi, and Alberto Redaelli Copyright © 2005 Simone Vesentini et al. All rights reserved. Accumulation of Deficits as a Proxy Measure of Aging Mon, 01 Jan 1900 00:00:00 +0000 This paper develops a method for appraising health status in elderly people. A frailty index was defined as the proportion of accumulated deficits (symptoms, signs, functional impairments, and laboratory abnormalities). It serves as an individual state variable, reflecting severity of illness and proximity to death. In a representative database of elderly Canadians we found that deficits accumulated at 3% per year, and show a gamma distribution, typical for systems with redundant components that can be used in case of failure of a given subsystem. Of note, the slope of the index is insensitive to the individual nature of the deficits, and serves as an important prognostic factor for life expectancy. The formula for estimating an individual�s life span given the frailty index value is presented. For different patterns of cognitive impairments the average within-group index value increases with the severity of the cognitive impairment, and the relative variability of the index is significantly reduced. Finally, the statistical distribution of the frailty index sharply differs between well groups (gamma distribution) and morbid groups (normal distribution). This pattern reflects an increase in uncompensated deficits in impaired organisms, which would lead to illness of various etiologies, and ultimately to increased mortality. The accumulation of deficits is as an example of a macroscopic variable, i.e., one that reflects general properties of aging at the level of the whole organism rather than any given functional deficiency. In consequence, we propose that it may be used as a proxy measure of aging. Arnold B. Mitnitski, Alexander J. Mogilner, and Kenneth Rockwood Copyright © 2001 Arnold B. Mitnitski et al. All rights reserved. A Biased Median Filtering Algorithm for Segmentation of Intestinal Cell Gland Images Mon, 01 Jan 1900 00:00:00 +0000 In this paper, we introduce a biased median filtering image segmentation algorithm for intestinal cell glands consisting of goblet cells. While segmentation of individual cells are generally based on the dissimilarities in intensities, textures, and shapes between cell regions and background, the proposed segmentation algorithm of intestine cell glands is based on the differences in cell distributions. The intestine cell glands consist of goblet cells that are distributed in the chain-organized patterns in contrast to the more randomly distributed nongoblet cells scattered in the bright background. Four biased median filters with long rectangular windows of identical dimension, but different orientations, are designed based on the shapes and distributions of cells. Each biased median filter identifies a part of gland segments in a particular direction. The complete gland regions are the combined responses of the four biased median filters. A postprocessing procedure is designed to reduce the defects that may occur when glands are located very close together and to narrow the gapping areas because of the thin distribution of goblet cells. Segmentation results of real intestinal cell gland images are provided to show the effectiveness of the proposed algorithm. Hai-Shan Wu and Joan Gil Copyright © 2006 Hai-Shan Wu and Joan Gil. All rights reserved. Nitrogen Cycling in a Norway Spruce Plantation in Denmark — A SOILN Model Application Including Organic N Uptake Mon, 01 Jan 1900 00:00:00 +0000 A dynamic carbon (C) and nitrogen (N) circulation model, SOILN, was applied and tested on 7�years of control data and 3 years of manipulation data from an experiment involving monthly N addition in a Norway spruce (Picea abies, L. Karst) forest in Denmark. The model includes two pathways for N uptake: (1) as mineral N after mineralisation of organic N, or (2) directly from soil organic matter as amino acids proposed to mimic N uptake by mycorrhiza. The model was parameterised and applied to the data from the control plot both with and without the organic N uptake included. After calibration, the model�s performance was tested against data from the N-addition experiment by comparing model output with measurements. The model reproduced well the overall trends in C and N pools and the N concentrations in soil solutions in the top soil layers whereas discrepancies in soil-solution concentrations in the deeper soil layers are seen. In the control data, the needle-N concentration was well reproduced except for small underestimations in some years because of drought effects not included in the model. In the N-addition experiment, SOILN reproduces the observed changes; in particular, the changes in needle-N concentrations and the overall distribution within the ecosystem of the extra added 3.5 g N m�2 year�1 parallel the observations. When organic N uptake is included, the simulations indicate that in the control plot receiving c. 1.9 g N m�2 year�1, the organic N uptake in average supplies 35% of the total plant N uptake. By addition of an extra 35 kg N ha�1 year�1, the organic N uptake is reduced to 16% of the total N uptake. Generally, inclusion of the pathway for organic N uptake improves model performance compared with observations for both C and N. This is because mineral N uptake alone implies a larger mineralisation rate, leading to bigger concentrations of N in the soil and soil water, bigger N losses, and net loss of c. 100 kg C ha�1 year�1, thereby causing depletion of the organic soil layer. Claus Beier, Henrik Eckersten, and Per Gundersen Copyright © 2001 Claus Beier et al. All rights reserved. Visualizing Vertebrate Embryos with Episcopic 3D Imaging Techniques Mon, 01 Jan 1900 00:00:00 +0000 The creation of highly detailed, three-dimensional (3D) computer models is essential in order to understand the evolution and development of vertebrate embryos, and the pathogenesis of hereditary diseases. A still-increasing number of methods allow for generating digital volume data sets as the basis of virtual 3D computer models. This work aims to provide a brief overview about modern volume data–generation techniques, focusing on episcopic 3D imaging methods. The technical principles, advantages, and problems of episcopic 3D imaging are described. The strengths and weaknesses in its ability to visualize embryo anatomy and labeled gene product patterns, specifically, are discussed. Stefan H. Geyer, Timothy J. Mohun, and Wolfgang J. Weninger Copyright © 2009 Stefan H. Geyer et al. All rights reserved. Assessment of Heart Disease using Fuzzy Classification Techniques Mon, 01 Jan 1900 00:00:00 +0000 In this paper we discuss the classification results of cardiac patients of ischemical cardiopathy, valvular heart disease, and arterial hypertension, based on 19 characteristics (descriptors) including ECHO data, effort testings, and age and weight. In this order we have used different fuzzy clustering algorithms, namely hierarchical fuzzy clustering, hierarchical and horizontal fuzzy characteristics clustering, and a new clustering technique, fuzzy hierarchical cross-classification. The characteristics clustering techniques produce fuzzy partitions of the characteristics involved and, thus, are useful tools for studying the similarities between different characteristics and for essential characteristics selection. The cross-classification algorithm produces not only a fuzzy partition of the cardiac patients analyzed, but also a fuzzy partition of their considered characteristics. In this way it is possible to identify which characteristics are responsible for the similarities or dissimilarities observed between different groups of patients. Horia F. Pop, Tudor L. Pop, and Costel Sarbu Copyright © 2001 Horia F. Pop et al. All rights reserved. Using Protein Homology Models for Structure-Based Studies: Approaches to Model Refinement Mon, 01 Jan 1900 00:00:00 +0000 Homology modeling is a computational methodology to assign a 3-D structure to a target protein when experimental data are not available. The methodology uses another protein with a known structure that shares some sequence identity with the target as a template. The crudest approach is to thread the target protein backbone atoms over the backbone atoms of the template protein, but necessary refinement methods are needed to produce realistic models. In this mini-review anchored within the scope of drug design, we show the validity of using homology models of proteins in the discovery of binders for potential therapeutic targets. We also report several different approaches to homology model refinement, going from very simple to the most elaborate. Results show that refinement approaches are system dependent and that more elaborate methodologies do not always correlate with better performances from built homology models. V. Kairys, M.K. Gilson, and Miguel Xavier Fernandes Copyright © 2006 Miguel Xavier Fernandes et al. All rights reserved. Comparison of Artificial Neural Network (ANN) Model Development Methods for Prediction of Macroinvertebrate Communities in the Zwalm River Basin in Flanders, Belgium Mon, 01 Jan 1900 00:00:00 +0000 Modelling has become an interesting tool to support decision making in water management. River ecosystem modelling methods have improved substantially during recent years. New concepts, such as artificial neural networks, fuzzy logic, evolutionary algorithms, chaos and fractals, cellular automata, etc., are being more commonly used to analyse ecosystem databases and to make predictions for river management purposes. In this context, artificial neural networks were applied to predict macroinvertebrate communities in the Zwalm River basin (Flanders, Belgium). Structural characteristics (meandering, substrate type, flow velocity) and physical and chemical variables (dissolved oxygen, pH) were used as predictive variables to predict the presence or absence of macroinvertebrate taxa in the headwaters and brooks of the Zwalm River basin. Special interest was paid to the frequency of occurrence of the taxa as well as the selection of the predictors and variables to be predicted on the prediction reliability of the developed models. Sensitivity analyses allowed us to study the impact of the predictive variables on the prediction of presence or absence of macroinvertebrate taxa and to define which variables are the most influential in determining the neural network outputs. Andy P. Dedecker, Peter L.M. Goethals, and Niels De Pauw Copyright © 2002 Andy P. Dedecker et al. All rights reserved.