Search results for: protein structure prediction

Authors: Maryam Jamela Ismail, Rosdiazli Ibrahim, Idris Ismail

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In the oil and gas industry, energy prediction can help the distributor and customer to forecast the outgoing and incoming gas through the pipeline. It will also help to eliminate any uncertainties in gas metering for billing purposes. The objective of this paper is to develop Neural Network Model for energy consumption and analyze the performance model. This paper provides a comprehensive review on published research on the energy consumption prediction which focuses on structures and the parameters used in developing Neural Network models. This paper is then focused on the parameter selection of the neural network prediction model development for energy consumption and analysis on the result. The most reliable model that gives the most accurate result is proposed for the prediction. The result shows that the proposed neural network energy prediction model is able to demonstrate an adequate performance with least Root Mean Square Error.

Keywords: Energy Prediction, Multilayer Feedforward, Levenberg-Marquardt, Root Mean Square Error (RMSE)

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Authors: B. Tah, P. Pal, M. Mahato, R. Sarkar, G. B. Talapatra

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Herein, we report the different types of surface morphology due to the interaction between the pure protein Insulin (INS) and catanionic surfactant mixture of Sodium Dodecyl Sulfate (SDS) and Cetyl Trimethyl Ammonium Bromide (CTAB) at air/water interface obtained by the Langmuir-Blodgett (LB) technique. We characterized the aggregations by Scanning Electron Microscopy (SEM), Atomic Force Microscopy (AFM) and Fourier transform infrared spectroscopy (FTIR) in LB films. We found that the INS adsorption increased in presence of catanionic surfactant at air/water interface. The presence of small amount of surfactant induces two-stage growth kinetics due to the pure protein absorption and protein-catanionic surface micelle interaction. The protein remains in native state in presence of small amount of surfactant mixture. Smaller amount of surfactant mixture with INS is producing surface micelle type structure. This may be considered for drug delivery system. On the other hand, INS becomes unfolded and fibrillated in presence of higher amount of surfactant mixture. In both the cases, the protein was successfully immobilized on a glass substrate by the LB technique. These results may find applications in the fundamental science of the physical chemistry of surfactant systems, as well as in the preparation of drug-delivery system.

Keywords: Air/water interface, Catanionic micelle, Insulin, Langmuir-Blodgett film

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Authors: Rahib Abiyev, Vasif H. Abiyev, C. Ardil

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In this paper the development of neural network based fuzzy inference system for electricity consumption prediction is considered. The electricity consumption depends on number of factors, such as number of customers, seasons, type-s of customers, number of plants, etc. It is nonlinear process and can be described by chaotic time-series. The structure and algorithms of neuro-fuzzy system for predicting future values of electricity consumption is described. To determine the unknown coefficients of the system, the supervised learning algorithm is used. As a result of learning, the rules of neuro-fuzzy system are formed. The developed system is applied for predicting future values of electricity consumption of Northern Cyprus. The simulation of neuro-fuzzy system has been performed.

Keywords: Fuzzy logic, neural network, neuro-fuzzy system, neuro-fuzzy prediction.

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Authors: Norzita Ngadi, John Abrahamson, Conan Fee, Ken Morison

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Nonspecific protein adsorption generally occurs on any solid surfaces and usually has adverse consequences. Adsorption of proteins onto a solid surface is believed to be the initial and controlling step in biofouling. Surfaces modified with end-tethered poly(ethylene glycol) (PEG) have been shown to be protein-resistant to some degree. In this study, the adsorption of β-casein and lysozyme was performed on 6 different types of surfaces where PEG was tethered onto stainless steel by polyethylene imine (PEI) through either OH or NHS end groups. Protein adsorption was also performed on the bare stainless steel surface as a control. The adsorption was conducted at 23 °C and pH 7.2. In situ QCM-D was used to determine PEG adsorption kinetics, plateau PEG chain densities, protein adsorption kinetics and plateau protein adsorbed quantities. PEG grafting density was the highest for a NHS coupled chain, around 0.5 chains / nm2. Interestingly, lysozyme which has smaller size than β-casein, appeared to adsorb much less mass than that of β- casein. Overall, the surface with high PEG grafting density exhibited a good protein rejection.

Keywords: QCM-D, PEG, stainless steel, β-casein, lysozyme.

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Authors: Rania A. Abul Seoud, Nahed H. Solouma, Abou-Baker M. Youssef, Yasser M. Kadah

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Due to the ever growing amount of publications about protein-protein interactions, information extraction from text is increasingly recognized as one of crucial technologies in bioinformatics. This paper presents a Protein Interaction Extraction System using a Link Grammar Parser from biomedical abstracts (PIELG). PIELG uses linkage given by the Link Grammar Parser to start a case based analysis of contents of various syntactic roles as well as their linguistically significant and meaningful combinations. The system uses phrasal-prepositional verbs patterns to overcome preposition combinations problems. The recall and precision are 74.4% and 62.65%, respectively. Experimental evaluations with two other state-of-the-art extraction systems indicate that PIELG system achieves better performance. For further evaluation, the system is augmented with a graphical package (Cytoscape) for extracting protein interaction information from sequence databases. The result shows that the performance is remarkably promising.

Keywords: Link Grammar Parser, Interaction extraction, protein-protein interaction, Natural language processing.

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Authors: N. Z. A. Hamid, M. S. M. Noorani

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This study is focused on the development of prediction models of the Ozone concentration time series. Prediction model is built based on chaotic approach. Firstly, the chaotic nature of the time series is detected by means of phase space plot and the Cao method. Then, the prediction model is built and the local linear approximation method is used for the forecasting purposes. Traditional prediction of autoregressive linear model is also built. Moreover, an improvement in local linear approximation method is also performed. Prediction models are applied to the hourly Ozone time series observed at the benchmark station in Malaysia. Comparison of all models through the calculation of mean absolute error, root mean squared error and correlation coefficient shows that the one with improved prediction method is the best. Thus, chaotic approach is a good approach to be used to develop a prediction model for the Ozone concentration time series.

Keywords: Chaotic approach, phase space, Cao method, local linear approximation method.

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Authors: N. Namviriyachote, N. Bang, P. Aramwit

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Silk sericin (SS) is a glue-like protein from silkworm cocoon. With its outstanding moisturization and activation collagen synthesis properties, silk protein is applied for wound healing. Since wound dressing in film preparation can facilitate patients- convenience and reduce risk of wound contraction, SS and polyvinyl alcohol (PVA) films were prepared with various concentrations of SS. Their physical properties such as surface density, light transmission, protein dissolution and tensile modulus were investigated. The results presented that 3% SS with 2% PVA is the best ingredient for SS film forming.

Keywords: Sericin, silk protein, film, wound healing.

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Authors: T. K. Sriram, Joydip Dhar

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Models based on stage structure have found varied applications in population models. This paper proposes a stage structured model to study the trends in the computer and video game playing population of US. The game paying population is divided into three compartments based on their age group. After simulating the mathematical model, a forecast of the number of game players in each stage as well as an approximation of the average age of game players in future has been made.

Keywords: Age structure, Forecasting, Mathematical modeling, Stage structure.

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Authors: Zurinahni Zainol, Rosalina Abdul Salam, Rosni Abdullah, Nur'Aini, Wahidah Husain

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The size, complexity and number of databases used for protein information have caused bioinformatics to lag behind in adapting to the need to handle this distributed information. Integrating all the information from different databases into one database is a challenging problem. Our main research is to develop a tool which can be used to access and manipulate protein information from difference databases. In our approach, we have integrated difference databases such as Swiss-prot, PDB, Interpro, and EMBL and transformed these databases in flat file format into relational form using XML and Bioperl. As a result, we showed this tool can search different sizes of protein information stored in relational database and the result can be retrieved faster compared to flat file database. A web based user interface is provided to allow user to access or search for protein information in the local database.

Keywords: Protein sequence database, relational database, integrated database.

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Authors: Ibrahim A. Akasha, Lydia Campbell, Stephen R. Euston

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Date palm (Phoenix dactylifera L.) seeds are waste streams which are considered a major problem to the food industry. They contain potentially useful protein (10-15% of the whole date-s weight). Global production, industrialisation and utilisation of dates are increasing steadily. The worldwide production of date palm fruit has increased from 1.8 million tons in 1961 to 6.9 million tons in 2005, thus from the global production of dates are almost 800.000 tonnes of date palm seeds are not currently used [1]. The current study was carried out to convert the date palm seeds into useful protein powder. Compositional analysis showed that the seeds were rich in protein and fat 5.64 and 8.14% respectively. We used several laboratory scale methods to extract proteins from seed to produce a high protein powder. These methods included simple acid or alkali extraction, with or without ultrafiltration and phenol trichloroacetic acid with acetone precipitation (Ph/TCA method). The highest protein content powder (68%) was obtained by Ph/TCA method with yield of material (44%) whereas; the use of just alkali extraction gave the lowest protein content of 8%, and a yield of 32%.

Keywords: Date palm seed, Phoenix dactylifera L., extraction of date palm seed protein

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Authors: Insung Jung, lockjo Koo, Gi-Nam Wang

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The objective of this paper is to design a model of human vital sign prediction for decreasing prediction error by using two states mapping based time series neural network BP (back-propagation) model. Normally, lot of industries has been applying the neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has a residual error between real value and prediction output. Therefore, we designed two states of neural network model for compensation of residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We found that most of simulations cases were satisfied by the two states mapping based time series prediction model compared to normal BP. In particular, small sample size of times series were more accurate than the standard MLP model. We expect that this algorithm can be available to sudden death prevention and monitoring AGENT system in a ubiquitous homecare environment.

Keywords: Neural network, U-healthcare, prediction, timeseries, computer aided prediction.

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Authors: A. F. Ali, D. M. Shawky

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Discovering new biological knowledge from the highthroughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed a new approach for protein classification. Proteins that are evolutionarily- and thereby functionally- related are said to belong to the same classification. Identifying protein classification is of fundamental importance to document the diversity of the known protein universe. It also provides a means to determine the functional roles of newly discovered protein sequences. Our goal is to predict the functional classification of novel protein sequences based on a set of features extracted from each protein sequence. The proposed technique used datasets extracted from the Structural Classification of Proteins (SCOP) database. A set of spectral domain features based on Fast Fourier Transform (FFT) is used. The proposed classifier uses multilayer back propagation (MLBP) neural network for protein classification. The maximum classification accuracy is about 91% when applying the classifier to the full four levels of the SCOP database. However, it reaches a maximum of 96% when limiting the classification to the family level. The classification results reveal that spectral domain contains information that can be used for classification with high accuracy. In addition, the results emphasize that sequence similarity measures are of great importance especially at the family level.

Keywords: Bioinformatics, Artificial Neural Networks, Protein Sequence Analysis, Feature Extraction.

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Authors: Liene Strauta, Sandra Muizniece-Brasava, Ina Alsina

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Phaseolus coccineus L. is the third most important cultivated Phaseolus species in the world. It is widely grown in Latvia due to its earliness, good taste and uniform and qualitative yield. Experiments were carried out in the laboratories of Department of Food Technology and Agronomical Analysis Scientific Laboratory at Latvia Universityof Agriculture. Beans (Phaseolus coccineus L.) crude protein, crude ash content as well as colour measurements were analyzed. Results show, that brown coloured beans have less crude protein content than others, and ash content have significant differences.

Keywords: Phaseoluscoccineus, protein, ash, colour.

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Authors: Ö. Öztürk, O. Şaman

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Sesame is one of the oldest and most important oil crops as main crop and second crop agriculture. This study was carried out to determine the effects of different inter- and intra-row spacings on the yield and yield components on second crop sesame; was set up in Antalya West Mediterranean Agricultural Research Institue in 2009. Muganlı 57 sesame cultivar was used as plant material. The field experiment was set up in a split plot design and row spacings (30, 40, 50, 60 and 70 cm) were assigned to the main plots and and intra-row spacings (5, 10, 20 and 30 cm) were assigned to the subplots. Seed yield, oil ratio, oil yield, protein ratio and protein yield were investigated. In general, wided inter row spacings and intra-row spacings, resulted in decreased seed yield, oil yield and protein yield. The highest seed yield, oil yield and protein yield (respectively, 1115.0 kg ha-1, 551.3 kg ha-1, 224.7 kg ha-1) were obtained from 30x5 cm plant density while the lowest seed yield, oil yield and protein yield (respectively, 677.0 kg ha-1, 327.0 kg ha-1, 130.0 kg ha-1) were recorded from 70x30 cm plant density. As a result, in terms of oil yield for second crop sesame agriculture, 30 cm row spacing, and 5 cm intra row spacing are the most suitable plant densities.

Keywords: Sesamum indicum L., oil ratio, oil yield, protein ratio, protein yield

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Authors: Geng Xiangren, Liu Lei, Gui Ye-Wei, Tang Wei, Wang An-ling

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The malfunction of thermal protection system (TPS) caused by aerodynamic heating is a latent trouble to aircraft structure safety. Accurately predicting the structure temperature field is quite important for the TPS design of hypersonic vehicle. Since Thornton’s work in 1988, the coupled method of aerodynamic heating and heat transfer has developed rapidly. However, little attention has been paid to the influence of structural deformation on aerodynamic heating and structural temperature field. In the flight, especially the long-endurance flight, the structural deformation, caused by the aerodynamic heating and temperature rise, has a direct impact on the aerodynamic heating and structural temperature field. Thus, the coupled interaction cannot be neglected. In this paper, based on the method of static aero-thermo-elasticity, considering the influence of aero-thermo-elasticity deformation, the aerodynamic heating and heat transfer coupled results of hypersonic vehicle wing model were calculated. The results show that, for the low-curvature region, such as fuselage or center-section wing, structure deformation has little effect on temperature field. However, for the stagnation region with high curvature, the coupled effect is not negligible. Thus, it is quite important for the structure temperature prediction to take into account the effect of elastic deformation. This work has laid a solid foundation for improving the prediction accuracy of the temperature distribution of aircraft structures and the evaluation capacity of structural performance.

Keywords: Aero-thermo-elasticity, elastic deformation, structural temperature, multi-field coupling.

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Authors: Hy Dang, Bo Mei

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The development of machine learning methods and techniques has opened doors for investigation in many areas such as medicines, economics, finance, etc. One active research area involving machine learning is stock market prediction. This research paper tries to consider multiple techniques and methods for stock movement prediction using historical price or price factors. The paper explores the effectiveness of some deep learning frameworks for forecasting stock. Moreover, an architecture (TimeStock) is proposed which takes the representation of time into account apart from the price information itself. Our model achieves a promising result that shows a potential approach for the stock movement prediction problem.

Keywords: Classification, machine learning, time representation, stock prediction.

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Authors: Eszter Tóth, Ervin Welker

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Shadoo protein (Sho) was described in 2003 as the newest member of Prion protein superfamily [1]. Sho has similar structural motifs like prion protein (PrP) that is known for its central role in transmissible spongiform enchephalopathies. Although a great number of functions have been proposed, the exact physiological function of PrP is not known yet. Investigation of the function and localization of Sho may help us to understand the function of the Prion protein superfamily. Analyzing the subcellular localization of YFP-tagged forms of Sho, we detected the protein in the plasma membrane and in the nucleus of various cell lines. To reveal the localization of the endogenous protein we generated antibodies against Shadoo as well as employed commercially available anti-Shadoo antibodies: i) EG62 anti-mouse Shadoo antibody generated by Eurogentec Ltd.; ii) S-12 anti-human Shadoo antibody by Santa Cruz Biotechnology Inc.; iii) R-12 anti-mouse Shadoo antibody by Santa Cruz Biotechnology Inc.; iv) SPRN antibody against human Shadoo by Abgent Inc. We carried out immunocytochemistry on non-transfected HeLa, Zpl 2-1, Zw 3-5, GT1-1, GT1-7 and SHSY5Y cells as well as on YFP-Sho, Sho-YFP, and YFP-GPI transfected HeLa cells. Their specificity (in antibody-peptide competition assay) and co-localization (with the YFP signal) were assessed.

Keywords: Shadoo, prion protein, immunocytochemistry, antibody-peptide competition assay, antibody.

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Authors: F. M. Abu-Salem, M. H. Mahmoud, M. H. El-Kalyoubi, A. Y. Gibriel, A. A. Abou-Arab Arab

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Dried soy protein hydrolysate powder was added to the burger in order to enhance the oxidative stability as well as decreases the microbial spoilage. The soybean bioactive compounds (soy protein hydrolysate) as antioxidant and antimicrobial were added at level of 1, 2 and 3 %.Chemical analysis and physical properties were affected by protein hydrolysate addition. The TBA values were significantly affected (P < 0.05) by the storage period and the level of soy protein hydrolysate. All the tested soybean protein hydrolysate additives showed strong antioxidant properties. Samples of soybean protein hydrolysate showed the lowest (P < 0.05) TBA values at each time of storage. The counts of all determined microbiological indicators were significantly (P < 0.05) affected by the addition of the soybean protein hydrolysate. Decreasing trends of different extent were also observed in samples of the treatments for total viable counts, Coliform, Staphylococcus aureus, yeast and molds. Storage period was being significantly (P < 0.05) affected on microbial counts in all samples Staphylococcus aureus were the most sensitive microbe followed by Coliform group of the sample containing protein hydrolysate, while molds and yeast count showed a decreasing trend but not significant (P < 0.05) until the end of the storage period compared with control sample. Sensory attributes were also performed, added protein hydrolysate exhibits beany flavor which was clear about samples of 3% protein hydrolysate.

Keywords: Antioxidant, antimicrobial, isoflavones, bioactive peptide, antioxidant peptides, soybean protein hydrolysate.

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Authors: Kalyankumar M. Matti, Chandrashekhar J. Savanurmath, Shivayogeppa B. Hinchigeri

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A rare phenomenon of SDS-induced activation of a latent protease activity associated with the purified silkworm excretory red fluorescent protein (SE-RFP) was noticed. SE-RFP aliquots incubated with SDS for different time intervals indicated that the protein undergoes an obligatory breakdown into a number of subunits which exhibit autoproteolytic (acting upon themselves) and/or heteroproteolytic (acting on other proteins) activities. A strong serine protease activity of SE-RFP subunits on Bombyx mori nucleopolyhedrovirus (BmNPV) polyhedral protein was detected by zymography technique. A complete inhibition of BmNPV infection to silkworms was observed by the oral administration assay of the SE-RFP. Here, it is proposed that the SE-RFP prevents the initial infection of BmNPV to silkworms by obliterating the polyhedral protein. This is the first report on a silkworm red fluorescent protein that exhibits a protease activity on exposure to SDS. The present studies would help in understanding the antiviral mechanism of silkworm red fluorescent proteins.

Keywords: BmNPV, polyhedra, SE-RFP, SDS-induced protease activity, zymography.

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Authors: Irene Luque Ruiz, Manuel Urbano Cuadrado, Miguel Ángel Gómez-Nieto

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In this paper we study different similarity based approaches for the development of QSAR model devoted to the prediction of activity of antiobesity drugs. Classical similarity approaches are compared regarding to dissimilarity models based on the consideration of the calculation of Euclidean distances between the nonisomorphic fragments extracted in the matching process. Combining the classical similarity and dissimilarity approaches into a new similarity measure, the Approximate Similarity was also studied, and better results were obtained. The application of the proposed method to the development of quantitative structure-activity relationships (QSAR) has provided reliable tools for predicting of inhibitory activity of drugs. Acceptable results were obtained for the models presented here.

Keywords: Graph similarity, Nonisomorphic dissimilarity, Approximate similarity, Drugs activity prediction.

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Authors: ElShazali A. Mohamed, Isam A. Mohamed Ahmed, Elfadil E. Babiker

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This work describes refrigeration effects during storage on total protein and amino acids composition of raw and processed flour of two pearl millet cultivars (Ashana and Dembi). The protein content of the whole raw flour was found to be 14.46 and 13.38% for Ashana and Dembi cultivars, respectively. Dehulling of the grains reduced the protein content to 13.38 and 12.67% for the cultivars, respectively. For both cultivars, the protein content of the whole and dehulled raw flour before and after cooking was slightly decreased when the flour was stored for 60 days even after refrigeration. The effect of refrigeration process in combination with the storage period, cooking or dehulling was found to be vary between amino acids and even between cultivars. Regardless of the storage period and processing method, the amino acids content was remained unchanged after refrigeration for both cultivars.

Keywords: Amino acids, dehulling, Irradiation, Millet, protein content.

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Authors: Alexander Kaplunovsky, Vladimir Khailenko, Alexander Bolshoy, Shara Atambayeva, AnatoliyIvashchenko

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Eukaryotic protein-coding genes are interrupted by spliceosomal introns, which are removed from the RNA transcripts before translation into a protein. The exon-intron structures of different eukaryotic species are quite different from each other, and the evolution of such structures raises many questions. We try to address some of these questions using statistical analysis of whole genomes. We go through all the protein-coding genes in a genome and study correlations between the net length of all the exons in a gene, the number of the exons, and the average length of an exon. We also take average values of these features for each chromosome and study correlations between those averages on the chromosomal level. Our data show universal features of exon-intron structures common to animals, plants, and protists (specifically, Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Cryptococcus neoformans, Homo sapiens, Mus musculus, Oryza sativa, and Plasmodium falciparum). We have verified linear correlation between the number of exons in a gene and the length of a protein coded by the gene, while the protein length increases in proportion to the number of exons. On the other hand, the average length of an exon always decreases with the number of exons. Finally, chromosome clustering based on average chromosome properties and parameters of linear regression between the number of exons in a gene and the net length of those exons demonstrates that these average chromosome properties are genome-specific features.

Keywords: Comparative genomics, exon-intron structure, eukaryotic clustering, linear regression.

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Authors: R. Charoen, S. Tipkanon, W. Savedboworn, N. Phonsatta, A. Panya

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Rice bran protein hydrolysates (RBPH) were prepared from defatted rice bran of two different Thai rice cultivars (Plai-Ngahm-Prachinburi; PNP and Khao Dok Mali 105; KDM105) using an enzymatic method. This research aimed to optimize enzyme-assisted protein extraction. In addition, the functional properties of RBPH and their stabilities to environmental stresses including pH (3 to 8), ionic strength (0 mM to 500 mM) and the thermal treatment (30 °C to 90 °C) were investigated. Results showed that enzymatic process for protein extraction of defatted rice bran was as follows: enzyme concentration 0.075 g/ 5 g of protein, extraction temperature 50 °C and extraction time 4 h. The obtained protein hydrolysate powders had a degree of hydrolysis (%) of 21.05% in PNP and 19.92% in KDM105. The solubility of protein hydrolysates at pH 4-6 was ranged from 27.28-38.57% and 27.60-43.00% in PNP and KDM105, respectively. In general, antioxidant activities indicated by total phenolic content, FRAP, ferrous ion-chelating (FIC), and 2,2’-azino-bis-3-ethylbenzthiazoline-6-sulphonic acid (ABTS) of KDM105 had higher than PNP. In terms of functional properties, the emulsifying activity index (EAI) was was 8.78 m²/g protein in KDM105, whereas PNP was 5.05 m²/g protein. The foaming capacity at 5 minutes (%) was 47.33 and 52.98 in PNP and KDM105, respectively. Glutamine, Alanine, Valine, and Leucine are the major amino acid in protein hydrolysates where the total amino acid of KDM105 gave higher than PNP. Furthermore, we investigated environmental stresses on the stability of 5% oil in water emulsion (5% oil, 10 mM citrate buffer) stabilized by RBPH (3.5%). The droplet diameter of emulsion stabilized by KDM105 was smaller (d < 250 nm) than produced by PNP. For environmental stresses, RBPH stabilized emulsions were stable at pH around 3 and 5-6, at high salt (< 400 mM, pH 7) and at temperatures range between 30-50°C.

Keywords: Functional properties, oil in water emulsion, protein hydrolysates, rice bran protein.

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Authors: Ying Wang

Abstract:

From the perspective of industrial structure coordination and based on an explicit definition for the connotation of industrial structure coordination, the synergetic coefficients are used to measure the coordination degree between three industries' input structure and output structure, and then the efficacy function method is employed to comprehensively evaluate the level of China-s industrial structure optimization. It is showed that Chinese industrial structure presented a "v-shaped" variation tendency between 1996 and 2008, and its industrial structure adjustment got obvious achievements after 2003, with the industrial structure optimization level increasing continuously. However in 2009, the level of China-s industrial structure optimization declined sharply due to the decreasing contribution degree of value added structure and energy structure coordination and the lower coordination degree of value added structure and capital structure.

Keywords: China's industrial structure, Coordination degree, Efficacy function, Synergetic coefficients

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Authors: N. Tuncbag, T. Haliloglu, O. Keskin

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Understanding the cell's large-scale organization is an interesting task in computational biology. Thus, protein-protein interactions can reveal important organization and function of the cell. Here, we investigated the correspondence between protein interactions and function for the yeast. We obtained the correlations among the set of proteins. Then these correlations are clustered using both the hierarchical and biclustering methods. The detailed analyses of proteins in each cluster were carried out by making use of their functional annotations. As a result, we found that some functional classes appear together in almost all biclusters. On the other hand, in hierarchical clustering, the dominancy of one functional class is observed. In brief, from interaction data to function, some correlated results are noticed about the relationship between interaction and function which might give clues about the organization of the proteins.

Keywords: Pair-wise protein interactions, DIP database, functional correlations, biclustering.

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Authors: Vijay Pal Singh, Yogesh Chandra Kotiyal

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Structures are a combination of various load carrying members which transfer the loads to the foundation from the superstructure safely. At the design stage, the loading of the structure is defined and appropriate material choices are made based upon their properties, mainly related to strength. The strength of materials kept on reducing with time because of many factors like environmental exposure and deformation caused by unpredictable external loads. Hence, to predict the strength of materials used in structures, various techniques are used. Among these techniques, Non-destructive techniques (NDT) are the one that can be used to predict the strength without damaging the structure. In the present study, the compressive strength of concrete has been predicted using Artificial Neural Network (ANN). The predicted strength was compared with the experimentally obtained actual compressive strength of concrete and equations were developed for different models. A good co-relation has been obtained between the predicted strength by these models and experimental values. Further, the co-relation has been developed using two NDT techniques for prediction of strength by regression analysis. It was found that the percentage error has been reduced between the predicted strength by using combined techniques in place of single techniques.

Keywords: Rebound, ultra-sonic pulse, penetration, ANN, NDT, regression.

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Authors: Damith Senanayake, Lakmal Muthugama, Laksheen Mendis, Tiroshan Madushanka

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Customer churn prediction is one of the most useful areas of study in customer analytics. Due to the enormous amount of data available for such predictions, machine learning and data mining have been heavily used in this domain. There exist many machine learning algorithms directly applicable for the problem of customer churn prediction, and here, we attempt to experiment on a novel approach by using a cognitive learning based technique in an attempt to improve the results obtained by using a combination of supervised learning methods, with cognitive unsupervised learning methods.

Keywords: Growing Self Organizing Maps, Kernel Methods, Churn Prediction.

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Authors: J.Yojina, W. Ngamsaad, N. Nuttavut, D.Triampo, Y. Lenbury, W. Triampo, P. Kanthang, S.Sriyab

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The dynamics of Min proteins plays a center role in accurate cell division. Although the nucleoids may presumably play an important role in prokaryotic cell division, there is a lack of models to account for its participation. In this work, we apply the lattice Boltzmann method to investigate protein oscillation based on a mesoscopic model that takes into account the nucleoid-s role. We found that our numerical results are in reasonably good agreement with the previous experimental results On comparing with the other computational models without the presence of nucleoids, the highlight of our finding is that the local densities of MinD and MinE on the cytoplasmic membrane increases, especially along the cell width, when the size of the obstacle increases, leading to a more distinct cap-like structure at the poles. This feature indicated the realistic pattern and reflected the combination of Min protein dynamics and nucleoid-s role.

Keywords: lattice Boltzmann method, cell division, Minproteins oscillation, nucleoid

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Authors: Ines Hamdi, Mohamed Ben Ahmed

Abstract:

The understanding of the system level of biological behavior and phenomenon variously needs some elements such as gene sequence, protein structure, gene functions and metabolic pathways. Challenging problems are representing, learning and reasoning about these biochemical reactions, gene and protein structure, genotype and relation between the phenotype, and expression system on those interactions. The goal of our work is to understand the behaviors of the interactions networks and to model their evolution in time and in space. We propose in this study an ontological meta-model for the knowledge representation of the genetic regulatory networks. Ontology in artificial intelligence means the fundamental categories and relations that provide a framework for knowledge models. Domain ontology's are now commonly used to enable heterogeneous information resources, such as knowledge-based systems, to communicate with each other. The interest of our model is to represent the spatial, temporal and spatio-temporal knowledge. We validated our propositions in the genetic regulatory network of the Aarbidosis thaliana flower

Keywords: Ontological model, spatio-temporal modeling, Genetic Regulatory Networks (GRNs), knowledge representation.

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Authors: Mohammad Zavid Parvez, Manoranjan Paul

Abstract:

A seizure prediction method is proposed by extracting global features using phase correlation between adjacent epochs for detecting relative changes and local features using fluctuation/ deviation within an epoch for determining fine changes of different EEG signals. A classifier and a regularization technique are applied for the reduction of false alarms and improvement of the overall prediction accuracy. The experiments show that the proposed method outperforms the state-of-the-art methods and provides high prediction accuracy (i.e., 97.70%) with low false alarm using EEG signals in different brain locations from a benchmark data set.

Keywords: Epilepsy, Seizure, Phase Correlation, Fluctuation, Deviation.

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