Bioautomation

Volume 19, Issue 2
New Books
Bioinformatics
Advanced Modelling and Functional Characterization of B2 Bradykinin Receptor	123-134
Muhammad Saad Khan, Sahar Fazal [ +/- abstract ][ full text ]
Hereditary angioedema (giant hives) is an autosomal dominant malady characterized by repetitive episodes of probably life-threatening angioedema due to a partial deficiency of C1 inhibitor. B2 Bradykinin Receptor's (BKRB2) amino acid sequence is deposited within UniProt under accession number P30411. The Physicochemical properties of BKRB2 sequence are determined by using ProtParam. BKRB2's secondary structure was predicted through PROTEUS. Pfam domain was used for functional characterization of BKRB2. PSI-BLAST was used to find homologs of known structure. Modelling by satisfaction of spatial restraints, either uses distance geometry or optimization techniques to satisfy spatial restraints performed by MODELLER. The quality of the generated model was evaluated with PROCHECK by Ramachandran plot analysis. Validation of the generated models was further performed by WHAT IF. ProSA was used for the analysis of Z-scores and energy plots. The 3D structures of the modeled proteins were analyzed using UCSF Chimera. Clustal Omega is used for multiple sequence alignment that uses seeded guide trees and HMM profile-profile techniques to generate alignments.
A Modified Method Combined with a Support Vector Machine and Bayesian Algorithms in Biological Information	135-146
Wen-Gang Zhou, Yong-Feng Cui, Ya Li [ +/- abstract ][ full text ]
With the deep research of genomics and proteomics, the number of new protein sequences has expanded rapidly. With the obvious shortcomings of high cost and low efficiency of the traditional experimental method, the calculation method for protein localization prediction has attracted a lot of attention due to its convenience and low cost. In the machine learning techniques, neural network and support vector machine (SVM) are often used as learning tools. Due to its complete theoretical framework, SVM has been widely applied. In this paper, we make an improvement on the existing machine learning algorithm of the support vector machine algorithm, and a new improved algorithm has been developed, combined with Bayesian algorithms. The proposed algorithm can improve calculation efficiency, and defects of the original algorithm are eliminated. According to the verification, the method has proved to be valid. At the same time, it can reduce calculation time and improve prediction efficiency.
Study of the Artificial Fish Swarm Algorithm for Hybrid Clustering	147-160
Hongwei Zhao, Liwei Tian [ +/- abstract ][ full text ]
The basic Artificial Fish Swarm (AFS) Algorithm is a new type of an heuristic swarm intelligence algorithm, but it is difficult to optimize to get high precision due to the randomness of the artificial fish behavior, which belongs to the intelligence algorithm. This paper presents an extended AFS algorithm, namely the Cooperative Artificial Fish Swarm (CAFS), which significantly improves the original AFS in solving complex optimization problems. K-medoids clustering algorithm is being used to classify data, but the approach is sensitive to the initial selection of the centers with low quality of the divided cluster. A novel hybrid clustering method based on the CAFS and K-medoids could be used for solving clustering problems. In this work, first, CAFS algorithm is used for optimizing six widely-used benchmark functions, coming up with comparative results produced by AFS and CAFS, then Particle Swarm Optimization (PSO) is studied. Second, the hybrid algorithm with K-medoids and CAFS algorithms is used for data clustering on several benchmark data sets. The performance of the hybrid algorithm based on K-medoids and CAFS is compared with AFS and CAFS algorithms on a clustering problem. The simulation results show that the proposed CAFS outperforms the other two algorithms in terms of accuracy and robustness.
Biclustering of the Gene Expression Data by Coevolution Cuckoo Search	161-176
Lu Yin, Yongguo Liu [ +/- abstract ][ full text ]
Biclustering has a potential to discover the local expression patterns analyzing the gene expression data which provide clues about biological processes. However, since it is proven that the biclustering problem is NP-hard, it is necessary to seek more effective algorithm. Cuckoo Search (CS) models the brood parasitism behavior of cuckoo to solve the optimization problem and outperforms the other existing algorithms. In this paper, we introduce a new algorithm for biclustering gene expression data, called cuckoo search biclustering (CSB), which adopts CS to solve the biclustering problem. For further improving the performance of CSB, three modifications to CSB are made with the aim of increasing the convergence rate and the coevolution cuckoo search biclustering (COCSB) is designed. CSB and COCSB are tested on the six gene expression data and their results are compared to those of CC, FLOC, ISA, SEBI, BIC-aiNet, PSOB, SAB and SSB. The comparison shows that CSB and COCSB have achieved great success in biological significance and time performance.
Bioprocess systems
Covalent Immobilization of Peroxidase onto Hybrid Membranes for the Construction of Optical Biosensor	177-186
Lyubov Yotova, Ahmed Hassaan, Spaska Yaneva [ +/- abstract ][ full text ]
The aim of this study is to covalently immobilize horse radish peroxidase (HRP) onto new hybrid membranes synthesized by the sol-gel method based on silica precursors, dendrimers and cellulose derivatives. This new system will be used for designing biosensor. For investigation of the properties of membranes, HRP was used as a modeling enzyme. Kinetic parameters, pH and temperature optimum were determined, and the structure of the membranes surface was examined. Results showed higher relative and residual activity of HRP immobilized onto membranes with cellulose acetate butyrate with high molecular weight CAB/H. This novel biosensor could offer a simple, cheap and rapid tool with enhanced sensing performance as well as having potentials to find application in medicine, pharmacy, food and process control and environmental monitoring.
Adsorption of Direct of Yellow ARLE Dye by Activated Carbon of Shell of Coconut Palm: Diffusional Effects on Kinetics and Equilibrium States	187-206
Aparecido Nivaldo Módenes, Fabiano Bisinella Scheufele, Fernando Rodolfo Espinoza-Quiñones, Patrícia Simões Carraro de Souza, Camila Raquel Betin Cripa, Joelmir dos Santos, Vilmar Steffen, Alexander Dimitrov Kroumov [ +/- abstract ][ full text ]
In this paper, the characteristics and potential removal of direct yellow ARLE (DYA) dye by using coconut palm shell-based activated carbon (CPS-AC) were assessed. Both kinetic and equilibrium experimental data were obtained from a series of DYA dye sorption experiments. All the sorption experiments were performed in closed batch system under constant temperature and stirring speed, at the predetermined pH of initial solution. The kinetic mathematical models of pseudo-first order, pseudo-second order, Elovich and intra-particle diffusion model were used in order to better interpret the adsorption kinetics phenomenon. Equilibrium data were described by applying the isotherm models of Langmuir, Freundlich, Tóth, Sips and Khan. The best description of DYA sorption equilibrium data was achieved for the Langmuir isotherm model, reaching a maximum adsorption capacity of 100 mg·g-1. Finally, the DYA dye adsorption functional groups characterizations were successfully accomplished and the results elucidated the most important groups linked with CPS-AC surface where molecular interactions could occur. Hence, the quantitative evaluation of equilibrium and kinetic experiments of adsorption process have demonstrated that the CPS-AC adsorbent was a promising high effective adsorbent and its potential can be successfully used for DYA dye removal.
Soft-sensing Modeling Based on MLS-SVM Inversion for L-lysine Fermentation Processes	207-222
Bo Wang, Xiaofu Ji [ +/- abstract ][ full text ]
A modeling approach 63 based on multiple output variables least squares support vector machine (MLS-SVM) inversion is presented by a combination of inverse system and support vector machine theory. Firstly, a dynamic system model is developed based on material balance relation of a fed-batch fermentation process, with which it is analyzed whether an inverse system exists or not, and into which characteristic information of a fermentation process is introduced to set up an extended inversion model. Secondly, an initial extended inversion model is developed off-line by the use of the fitting capacity of MLS-SVM; on-line correction is made by the use of a differential evolution (DE) algorithm on the basis of deviation information. Finally, a combined pseudo-linear system is formed by means of a serial connection of a corrected extended inversion model behind the L-lysine fermentation processes; thereby crucial biochemical parameters of a fermentation process could be predicted on-line. The simulation experiment shows that this soft-sensing modeling method features very high prediction precision and can predict crucial biochemical parameters of L-lysine fermentation process very well.
Biomedical systems
Tangential Volumetric Modulated Radiotherapy - A New Technique for Large Scalp Lesions with a Case Study in Lentigo Maligna	223-236
E. Daniel Santos, Julia A. Green, Nastik Bhandari, Angela Hong, Pascale Guitera, Gerald B. Fogarty [ +/- abstract ][ full text ]
Introduction: Dose homogeneity within and dose conformity to the target volume can be a challenge to achieve when treating large area scalp lesions. Traditionally High Dose Rate (HDR) brachytherapy (BT) scalp moulds have been considered the ultimate conformal therapy. We have developed a new technique, Tangential Volumetric Modulated Arc Therapy (TVMAT) that treats with the beam tangential to the surface of the scalp. In the TVMAT plan the collimating jaws protect dose-sensitive tissue in close proximity to the planning target volume (PTV). Not all the PTV is within the beam aperture as defined by the jaws during all the beam-on time. We report the successful treatment of one patient. Methods: A patient with biopsy proven extensive lentigo maligna on the scalp was simulated and three plans were created; one using a HDR brachytherapy surface mould, another using a conventional VMAT technique and a third using our new TVMAT technique. The patient was prescribed 55 Gy in 25 fractions. Plans were optimised so that PTV V100% = 100%. Plans were compared using Dose-Value Histogram (DVH) analysis, and homogeneity and conformity indices. Results: BT, VMAT and TVMAT PTV median coverage was 105.51%, 103.46% and 103.62%, with homogeneity index of 0.33, 0.07 and 0.07 and the conformity index of 0.30, 0.69 and 0.83 respectively. The median dose to the left hippocampus was 11.8 Gy, 9.0 Gy and 0.6 Gy and the median dose to the right hippocampus was 12.6 Gy, 9.4 Gy and 0.7 Gy for the BT, VMAT and TVMAT respectively. Overall TVMAT delivered the least doses to the surrounding organs, BT delivered the highest. Conclusions: TVMAT was superior to VMAT which was in turn superior to BT in PTV coverage, conformity and homogeneity and delivery of dose to the surrounding organs at risk. The patient was successfully treated to full dose with TVMAT. TVMAT was verified as being the best amongst the three techniques in a second patient.
Generalized Net Model of a Body Temperature Data Logger Embedded System	237-244
Lenko Erbakanov, Krassimir Atanassov, Sotir Sotirov [ +/- abstract ][ full text ]
This paper represents a prototype embedded system, intended for measuring, gathering and transmitting the obtained data of the temperature of the skin and the ambient air. The system is designed to collect data in relatively short intervals, for fairly long periods of time. The skin temperature measurements are performed using an infrared temperature sensor. Some experimental results are also presented.
Hand Vein Images Enhancement Based on Local Gray-level Information Histogram	245-258
Jun Wang, Guoqing Wang, Ming Li, Wenkai Du, Wenhui Yu [ +/- abstract ][ full text ]
Based on the Histogram equalization theory, this paper presents a novel concept of histogram to realize the contrast enhancement of hand vein images, avoiding the lost of topological vein structure or importing the fake vein information. Firstly, we propose the concept of gray-level information histogram, the fundamental characteristic of which is that the amplitudes of the components can objectively reflect the contribution of the gray levels and information to the representation of image information. Then, we propose the histogram equalization method that is composed of an automatic histogram separation module and an intensity transformation module, and the histogram separation module is a combination of the proposed prompt multiple threshold procedure and an optimum peak signal-to-noise (PSNR) calculation to separate the histogram into small-scale detail, the use of the intensity transformation module can enhance the vein images with vein topological structure and gray information preservation for each generated sub-histogram. Experimental results show that the proposed method can achieve extremely good contrast enhancement effect.
Biomedical physics
Image-guided Electro-assisted Drug Delivery: Comparison Between Two Types of Electrodes	259-266
Biliana Nikolova, Severina Atanasova, Tsvetan Mudrov, Iana Tsoneva, Zhivko Zhelev, Rumiana Bakalova, Ichio Aoki [ +/- abstract ][ full text ]
Electroporation-based cancer treatment techniques are currently after active investigations in the field of drug delivery, optimization of electrical parameters and elucidation of the exact mechanisms at a molecular level. The present study is designed to investigate the exact in vivo redistribution and persistence of nanoparticles in the tumor tissue of colon-cancer grafted mice after electroporation with two different kinds of electrodes. The aim of the study is to avoid artifacts during electroporation due to accumulation of nanoparticles in the surrounding non-cancer tissues. The isolated electrodes are appropriate for the treatment of 3-dimensional tumors and have a large potential in this field.

Sponsored by National Science Fund of Bulgaria, Grant No DNP 04-35