Search results for: Sequenced Facial Expression Dataset.

Authors: Jienny Lee, In-Soo Cho, Sang-Ho Cha

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Adult mesenchymal stem cells (MSCs) have been investigated using preclinical approaches for tissue regeneration. Porcine MSCs (pMSCs) are capable of growing and attaching to plastic with a fibroblast-like morphology and then differentiating into bone, adipose, and cartilage tissues in vitro. This study was conducted to investigate the proliferating abilities, differentiation potentials, and multipotency of miniature pig adipose tissue-derived MSCs (mpAD-MSCs) with or without long-term cryopreservation, considering that cryostorage has the potential for use in clinical applications. After confirming the characteristics of the mpAD-MSCs, we examined the effect of long-term cryopreservation (> 2 years) on expression of cell surface markers (CD34, CD90 and CD105), proliferating abilities (cumulative population doubling level, doubling time, colony-forming unit, and MTT assay) and differentiation potentials into mesodermal cell lineages. As a result, the expression of cell surface markers is similar between thawed and fresh mpAD-MSCs. However, long-term cryopreservation significantly lowered the differentiation potentials (adipogenic, chondrogenic, and osteogenic) of mpAD-MSCs. When compared with fresh mpAD-MSCs, thawed mpAD-MSCs exhibited lower expression of mesodermal cell lineage-related genes such as peroxisome proliferator-activated receptor-g2, lipoprotein lipase, collagen Type II alpha 1, osteonectin, and osteocalcin. Interestingly, long-term cryostoraged mpAD-MSCs exhibited significantly higher cell viability than the fresh mpAD-MSCs. Long-term cryopreservation induced a 30% increase in the cell viability of mpAD-MSCs when compared with the fresh mpAD-MSCs at 5 days after thawing. However, long-term cryopreservation significantly lowered expression of stemness markers such as Oct3/4, Sox2, and Nanog. Furthermore, long-term cryopreservation negatively affected expression of senescence-associated genes such as telomerase reverse transcriptase and heat shock protein 90 of mpAD-MSCs when compared with the fresh mpAD-MSCs. The results from this study might be important for the successful application of MSCs in clinical trials after long-term cryopreservation.

Keywords: Mesenchymal stem cells, Cryopreservation, Stemness, Senescence.

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Authors: Martin Bader

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During the last years, the genomes of more and more species have been sequenced, providing data for phylogenetic recon- struction based on genome rearrangement measures. A main task in all phylogenetic reconstruction algorithms is to solve the median of three problem. Although this problem is NP-hard even for the sim- plest distance measures, there are exact algorithms for the breakpoint median and the reversal median that are fast enough for practical use. In this paper, this approach is extended to the transposition median as well as to the weighted reversal and transposition median. Although there is no exact polynomial algorithm known even for the pairwise distances, we will show that it is in most cases possible to solve these problems exactly within reasonable time by using a branch and bound algorithm.

Keywords: Comparative genomics, genome rearrangements, me-dian, reversals, transpositions.

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Authors: Mohamed Mounir Tellache, Hiroyuki Kambara, Yasuharu Koike, Makoto Miyakoshi, Natsue Yoshimura

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This study explores the practicality of using electroencephalographic (EEG) independent components to predict eight-direction finger movements in pseudo-real-time. Six healthy participants with individual-head MRI images performed finger movements in eight directions with two different arm configurations. The analysis was performed in two stages. The first stage consisted of using independent component analysis (ICA) to separate the signals representing brain activity from non-brain activity signals and to obtain the unmixing matrix. The resulting independent components (ICs) were checked, and those reflecting brain-activity were selected. Finally, the time series of the selected ICs were used to predict eight finger-movement directions using Sparse Logistic Regression (SLR). The second stage consisted of using the previously obtained unmixing matrix, the selected ICs, and the model obtained by applying SLR to classify a different EEG dataset. This method was applied to two different settings, namely the single-participant level and the group-level. For the single-participant level, the EEG dataset used in the first stage and the EEG dataset used in the second stage originated from the same participant. For the group-level, the EEG datasets used in the first stage were constructed by temporally concatenating each combination without repetition of the EEG datasets of five participants out of six, whereas the EEG dataset used in the second stage originated from the remaining participants. The average test classification results across datasets (mean ± S.D.) were 38.62 ± 8.36% for the single-participant, which was significantly higher than the chance level (12.50 ± 0.01%), and 27.26 ± 4.39% for the group-level which was also significantly higher than the chance level (12.49% ± 0.01%). The classification accuracy within [–45°, 45°] of the true direction is 70.03 ± 8.14% for single-participant and 62.63 ± 6.07% for group-level which may be promising for some real-life applications. Clustering and contribution analyses further revealed the brain regions involved in finger movement and the temporal aspect of their contribution to the classification. These results showed the possibility of using the ICA-based method in combination with other methods to build a real-time system to control prostheses.

Keywords: Brain-computer interface, BCI, electroencephalography, EEG, finger motion decoding, independent component analysis, pseudo-real-time motion decoding.

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Authors: M. Marjaneh, M. Y. Narazah, H. Shahrul

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Array-based gene expression analysis is a powerful tool to profile expression of genes and to generate information on therapeutic effects of new anti-cancer compounds. Anti-apoptotic effect of thymoquinone was studied in MCF7 breast cancer cell line using gene expression profiling with cDNA microarray. The purity and yield of RNA samples were determined using RNeasyPlus Mini kit. The Agilent RNA 6000 NanoLabChip kit evaluated the quantity of the RNA samples. AffinityScript RT oligo-dT promoter primer was used to generate cDNA strands. T7 RNA polymerase was used to convert cDNA to cRNA. The cRNA samples and human universal reference RNA were labelled with Cy-3-CTP and Cy-5-CTP, respectively. Feature Extraction and GeneSpring softwares analysed the data. The single experiment analysis revealed involvement of 64 pathways with up-regulated genes and 78 pathways with downregulated genes. The MAPK and p38-MAPK pathways were inhibited due to the up-regulation of PTPRR gene. The inhibition of p38-MAPK suggested up-regulation of TGF-ß pathway. Inhibition of p38-MAPK caused up-regulation of TP53 and down-regulation of Bcl2 genes indicating involvement of intrinsic apoptotic pathway. Down-regulation of CARD16 gene as an adaptor molecule regulated CASP1 and suggested necrosis-like programmed cell death and involvement of caspase in apoptosis. Furthermore, down-regulation of GPCR, EGF-EGFR signalling pathways suggested reduction of ER. Involvement of AhR pathway which control cytochrome P450 and glucuronidation pathways showed metabolism of Thymoquinone. The findings showed differential expression of several genes in apoptosis pathways with thymoquinone treatment in estrogen receptor-positive breast cancer cells.

Keywords: CARD16, CASP10, cDNA microarray, PTPRR, Thymoquinone.

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Authors: Nathan C. Lea, Tony Austin, Stephen Hailes, Dipak Kalra

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This paper introduces a tool that is being developed for the expression of information security policy controls that govern electronic healthcare records. By reference to published findings, the paper introduces the theory behind the use of knowledge management for automatic and consistent security policy assertion using the formalism called the Secutype; the development of the tool and functionality is discussed; some examples of Secutypes generated by the tool are provided; proposed integration with existing medical record systems is described. The paper is concluded with a section on further work and critique of the work achieved to date.

Keywords: Information Security Policy, Electronic Healthcare Records, Knowledge Management, Archetypes, Secutypes.

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Authors: E. Ramprasath, P. Manojkumar, P. Veena

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Proposed paper dealt with the modelling and analysis of induction motor based on the mathematical expression using the graphical programming environment of Laboratory Virtual Instrument Engineering Workbench (LabVIEW). Induction motor modelling with the mathematical expression enables the motor to be simulated with the various required parameters. Owing to the invention of variable speed drives study about the induction motor characteristics became complex. In this simulation motor internal parameter such as stator resistance and reactance, rotor resistance and reactance, phase voltage, frequency and losses will be given as input. By varying the speed of motor corresponding parameters can be obtained they are input power, output power, efficiency, torque induced, slip and current.

Keywords: Induction motor, LabVIEW software, modelling and analysis, electrical and mechanical characteristics of motor.

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Authors: Francesco Maiorana

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In this paper a one-dimension Self Organizing Map algorithm (SOM) to perform feature selection is presented. The algorithm is based on a first classification of the input dataset on a similarity space. From this classification for each class a set of positive and negative features is computed. This set of features is selected as result of the procedure. The procedure is evaluated on an in-house dataset from a Knowledge Discovery from Text (KDT) application and on a set of publicly available datasets used in international feature selection competitions. These datasets come from KDT applications, drug discovery as well as other applications. The knowledge of the correct classification available for the training and validation datasets is used to optimize the parameters for positive and negative feature extractions. The process becomes feasible for large and sparse datasets, as the ones obtained in KDT applications, by using both compression techniques to store the similarity matrix and speed up techniques of the Kohonen algorithm that take advantage of the sparsity of the input matrix. These improvements make it feasible, by using the grid, the application of the methodology to massive datasets.

Keywords: Clustering algorithm, Data mining, Feature selection, Grid, Kohonen Self Organizing Map.

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Authors: C. Gunavathi, K. Premalatha

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Tumor classification is a key area of research in the field of bioinformatics. Microarray technology is commonly used in the study of disease diagnosis using gene expression levels. The main drawback of gene expression data is that it contains thousands of genes and a very few samples. Feature selection methods are used to select the informative genes from the microarray. These methods considerably improve the classification accuracy. In the proposed method, Genetic Algorithm (GA) is used for effective feature selection. Informative genes are identified based on the T-Statistics, Signal-to-Noise Ratio (SNR) and F-Test values. The initial candidate solutions of GA are obtained from top-m informative genes. The classification accuracy of k-Nearest Neighbor (kNN) method is used as the fitness function for GA. In this work, kNN and Support Vector Machine (SVM) are used as the classifiers. The experimental results show that the proposed work is suitable for effective feature selection. With the help of the selected genes, GA-kNN method achieves 100% accuracy in 4 datasets and GA-SVM method achieves in 5 out of 10 datasets. The GA with kNN and SVM methods are demonstrated to be an accurate method for microarray based tumor classification.

Keywords: F-Test, Gene Expression, Genetic Algorithm, k- Nearest-Neighbor, Microarray, Signal-to-Noise Ratio, Support Vector Machine, T-statistics, Tumor Classification.

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Authors: Lae-Jeong Park, Jung-Ho Moon

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People detection from images has a variety of applications such as video surveillance and driver assistance system, but is still a challenging task and more difficult in crowded environments such as shopping malls in which occlusion of lower parts of human body often occurs. Lack of the full-body information requires more effective features than common features such as HOG. In this paper, new features are introduced that exploits global self-symmetry (GSS) characteristic in head-shoulder patterns. The features encode the similarity or difference of color histograms and oriented gradient histograms between two vertically symmetric blocks. The domain-specific features are rapid to compute from the integral images in Viola-Jones cascade-of-rejecters framework. The proposed features are evaluated with our own head-shoulder dataset that, in part, consists of a well-known INRIA pedestrian dataset. Experimental results show that the GSS features are effective in reduction of false alarmsmarginally and the gradient GSS features are preferred more often than the color GSS ones in the feature selection.

Keywords: Pedestrian detection, cascade of rejecters, feature extraction, self-symmetry, HOG.

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Authors: Seyeon Park, Mi Jang

Abstract:

Tumor cells have an invasive and metastatic phenotype that is the main cause of death for cancer patients. Tumor establishment and penetration consists of a series of complex processes involving multiple changes in gene expression. In this study, intraperitoneal administration of a high concentration of ascorbic acid inhibited tumor establishment and decreased tumor mass in BALB/C mice implanted with S-180 sarcoma cancer cells. To identify proteins involved in the ascorbic acid-mediated inhibition of tumor progression, changes in the tumor proteome associated with ascorbic acid treatment of BALB/C mice implanted with S-180 were investigated using two-dimensional gel electrophoresis and mass spectrometry. Twenty protein spots were identified whose expression was different between control and ascorbic acid treatment groups.

Keywords: Ascorbic acid, Proteomic analysis, S-180 implantedBALB/C mouse

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Authors: Simon Brown

Abstract:

Glomerular filtration rate (GFR) is a measure of kidney function. It is usually estimated from serum concentrations of cystatin C or creatinine although there has been considerable debate in the literature about (i) the best equation to use and (ii) the variability in the correlation between the concentrations of creatinine and cystatin C. The equations for GFR can be written in a general form and from these I calculate the error of the GFR estimates associated with analyte measurement error. These show that the error of the GFR estimates is such that it is not possible to distinguish between the equations over much of the concentration range of either analyte. The general forms of the equations are also used to derive an expression for the concentration of cystatin C as a function of the concentration of creatinine. This equation shows that these analyte concentrations are not linearly related. Clinical reports of cystatin C and creatinine concentration are consistent with the expression derived.

Keywords: creatinine, cystatin C, error analysis, glomerularfiltration rate, measurement error.

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Authors: Nnamdi I. Nwulu, Shola G. Oroja

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It has become crucial over the years for nations to improve their credit scoring methods and techniques in light of the increasing volatility of the global economy. Statistical methods or tools have been the favoured means for this; however artificial intelligence or soft computing based techniques are becoming increasingly preferred due to their proficient and precise nature and relative simplicity. This work presents a comparison between Support Vector Machines and Artificial Neural Networks two popular soft computing models when applied to credit scoring. Amidst the different criteria-s that can be used for comparisons; accuracy, computational complexity and processing times are the selected criteria used to evaluate both models. Furthermore the German credit scoring dataset which is a real world dataset is used to train and test both developed models. Experimental results obtained from our study suggest that although both soft computing models could be used with a high degree of accuracy, Artificial Neural Networks deliver better results than Support Vector Machines.

Keywords: Artificial Neural Networks, Credit Scoring, SoftComputing Models, Support Vector Machines.

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Authors: Kulaya Wimolwat, Panlop Chakravitthamrong, Neti Waranuch

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Background: Seborrheic dermatitis is a common chronic skin condition affecting the face, scalp, chest, and trunk. The cause of seborrheic dermatitis is still unknown. Sebum production, lipid composition, hormone levels, and Malassezia species have been suggested as important factors in the development of seborrheic dermatitis. Curcuma aeruginosa Roxb. extract-containing cream with anti-inflammatory and anti-androgenic properties may be beneficial for treating mild to moderate facial seborrheic dermatitis. Objectives: We evaluated the efficacy and safety of 2% C. aeruginosa Roxb. extract-containing cream in the treatment of mild to moderate seborrheic dermatitis. Methods: This was a prospective, open-label, and non-comparative study. Ten adult patients clinically diagnosed with mild to moderate seborrheic dermatitis were enrolled in a four-week study. The 2% C. aeruginosa Roxb. cream was applied twice daily to a lesional area on the face for four weeks. The Scoring Index (SI) ranking system on days 14 and 28 was compared with that at baseline to determine the efficacy of treatment. The adverse events (burning sensation and erythema) were evaluated on days 14 and 28 to determine the safety of the treatment. Results: Significant improvement was observed in the reduction of the mean SI at day 14 (2.9) and 28 (1.4) compared to that at baseline (4.9). An adverse reaction was observed on day 14 (mild erythema 20% and mild burning sensation 10%) and was resolved by the end of the study. Conclusion: This open-label pilot study has shown that there was a significant improvement in the severity in these seborrheic patients and most reported they were satisfied with it. Reported adverse events were all mild.

Keywords: Anti-androgenic, antifungals, anti-inflammatory, Curcuma aeruginosa, seborrheic dermatitis, efficacy, safety.

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Authors: Neelima Gupta, Seema Aggarwal

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Most of the biclustering/projected clustering algorithms are based either on the Euclidean distance or correlation coefficient which capture only linear relationships. However, in many applications, like gene expression data and word-document data, non linear relationships may exist between the objects. Mutual Information between two variables provides a more general criterion to investigate dependencies amongst variables. In this paper, we improve upon our previous algorithm that uses mutual information for biclustering in terms of computation time and also the type of clusters identified. The algorithm is able to find biclusters with mixed relationships and is faster than the previous one. To the best of our knowledge, none of the other existing algorithms for biclustering have used mutual information as a similarity measure. We present the experimental results on synthetic data as well as on the yeast expression data. Biclusters on the yeast data were found to be biologically and statistically significant using GO Tool Box and FuncAssociate.

Keywords: Biclustering, mutual information.

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Authors: A. M. Fahim, G. Saake, A. M. Salem, F. A. Torkey, M. A. Ramadan

Abstract:

Data clustering is an important data exploration technique with many applications in data mining. We present an enhanced version of the well known single link clustering algorithm. We will refer to this algorithm as DCBOR. The proposed algorithm alleviates the chain effect by removing the outliers from the given dataset. So this algorithm provides outlier detection and data clustering simultaneously. This algorithm does not need to update the distance matrix, since the algorithm depends on merging the most k-nearest objects in one step and the cluster continues grow as long as possible under specified condition. So the algorithm consists of two phases; at the first phase, it removes the outliers from the input dataset. At the second phase, it performs the clustering process. This algorithm discovers clusters of different shapes, sizes, densities and requires only one input parameter; this parameter represents a threshold for outlier points. The value of the input parameter is ranging from 0 to 1. The algorithm supports the user in determining an appropriate value for it. We have tested this algorithm on different datasets contain outlier and connecting clusters by chain of density points, and the algorithm discovers the correct clusters. The results of our experiments demonstrate the effectiveness and the efficiency of DCBOR.

Keywords: Data Clustering, Clustering Algorithms, Handling Noise, Arbitrary Shape of Clusters.

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Authors: G. Candel, D. Naccache

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t-SNE is an embedding method that the data science community has widely used. It helps two main tasks: to display results by coloring items according to the item class or feature value; and for forensic, giving a first overview of the dataset distribution. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric. The transformation from a high to low dimensional space is described but not learned. Two initializations of the algorithm would lead to two different embedding. In a forensic approach, analysts would like to compare two or more datasets using their embedding. A naive approach would be to embed all datasets together. However, this process is costly as the complexity of t-SNE is quadratic, and would be infeasible for too many datasets. Another approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding’ match. The embedding with the support process can be repeated more than once, with the newly obtained embedding. The successive embedding can be used to study the impact of one variable over the dataset distribution or monitor changes over time. This method has the same complexity as t-SNE per embedding, and memory requirements are only doubled. For a dataset of n elements sorted and split into k subsets, the total embedding complexity would be reduced from O(n2) to O(n2/k), and the memory requirement from n2 to 2(n/k)2 which enables computation on recent laptops. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets’ dynamics.

Keywords: Concept drift, data visualization, dimension reduction, embedding, monitoring, reusability, t-SNE, unsupervised learning.

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Authors: Elyse Sili, Jean Pascal Cambronne

Abstract:

In aircraft applications, according to the nature of electrical equipment its location may be in unpressurized area or very close to the engine; thus, the environmental conditions may change from atmospheric pressure to less than 100 mbar, and the temperature may be higher than the ambient one as in most real working conditions of electrical equipment. Then, the classical Paschen curve has to be replotted since these parameters may affect the discharge ignition voltage. In this paper, we firstly investigate the domain of validity of two corrective expressions on the Paschen-s law found in the literature, in case of changing the air environment and known as Peek and Dunbar corrections. Results show that these corrections are no longer valid for combined variation of temperature and pressure. After that, a new empirical expression for breakdown voltage is proposed and is validated in the case of combined variations of temperature and pressure.

Keywords: Gas breakdown, gas density, Paschen curve, temperature effects

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Authors: Jing Lei, Yoram Vodovotz, Timothy R. Billiar

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To identify an endothelial cell-specific promoter suitable for vascular-specific targeting, we tested five promoters in vitro--Tie2SE, Tie2LE, ICAM2, Flt-1 and vWF--for promoter activity and specificity in endothelial cells, smooth muscle cells and non-vascular resident cells as well as tissues. These promoters, except for vWF, exhibited good endothelial activity and specificity in vitro. In a syngenic heart transplantation model, the ICAM2 promoter was variably functional in coronary endothelial cells of donor hearts. Thus, the ICAM2, Flt-1, Tie2SE and Tie2LE promoters hold promise for endothelial-specific targeting, but in vitro expression may not predict in vivo expression.

Keywords: vascular-specific targeting, endothelial cell-specificpromoter, endothelial specificity.

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Authors: Emine Tirali

Abstract:

This article provides a conceptual framework of the freedom of media and its correlation with democracy. In a democracy, media should serve the publics’ right to know and reflect human rights violations and offer options for meaningful political choices and effective participation in civic affairs. On that point, the 2013 events at Gezi Park in Turkey are a good empirical example to be discussed. During the events, when self-censorship was broadly employed by mainstream Turkish media, social media filled the important role of providing information to the public. New technologies have made information into a fundamental tool for change and growth, and as a consequence, societies worldwide have merged into a single, interdependent, and autonomous organism. For this reason, violations of human rights can no longer be considered domestic issues, but rather global ones. Only global political action is an adequate response. Democracy depends on people shaping the society they live in, and in order to accomplish this, they need to express themselves. Freedom of expression is therefore necessary in order to understand diversity and differing perspectives, which in turn are necessary to resolve conflicts among people. Moreover, freedom of information is integral to freedom of expression. In this context, the international rules and laws regarding freedom of expression and freedom of information – indispensable for a free and independent media – are examined. These were put in place by international institutions such as the United Nations, UNESCO, the Council of Europe, and the European Union, which have aimed to build a free, democratic, and pluralist world committed to human rights and the rule of law. The methods of international human rights institutions depend on effective and frequent employment of mass media to relay human rights violations to the public. Therefore, in this study, the relationship between mass media and democracy, the process of how mass media forms public opinion, the problems of mass media, the neo-liberal theory of mass media, and the use of mass media by NGOs will be evaluated.

Keywords: Freedom of expression, democracy, public opinion, self-censorship.

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Authors: Jehad A. H. Hammad, Nur'Aini binti Abdul Rashid

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With the rapid development in the field of life sciences and the flooding of genomic information, the need for faster and scalable searching methods has become urgent. One of the approaches that were investigated is indexing. The indexing methods have been categorized into three categories which are the lengthbased index algorithms, transformation-based algorithms and mixed techniques-based algorithms. In this research, we focused on the transformation based methods. We embedded the N-gram method into the transformation-based method to build an inverted index table. We then applied the parallel methods to speed up the index building time and to reduce the overall retrieval time when querying the genomic database. Our experiments show that the use of N-Gram transformation algorithm is an economical solution; it saves time and space too. The result shows that the size of the index is smaller than the size of the dataset when the size of N-Gram is 5 and 6. The parallel N-Gram transformation algorithm-s results indicate that the uses of parallel programming with large dataset are promising which can be improved further.

Keywords: Biological sequence, Database index, N-gram indexing, Parallel computing, Sequence retrieval.

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Authors: Tiancheng Lan

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E10A is a kind of replication-defective adenovirus which carries the human endostatin gene to inhibit the growth of tumors. Kringle 5(K5) has almost the same function as angiostatin to also inhibit the growth of tumors since they are all the byproduct of the proteolytic cleavage of plasminogen. Tumor size increasing can be suppressed because both of the endostatin and K5 can restrain the angiogenesis process. Therefore, in order to improve the treatment effect on tumor, 2A peptide is used to construct a fusion gene carrying both E10A and K5. Using 2A peptide is an ideal strategy when a fusion gene is expressed because it can avoid many problems during the expression of more than one kind of protein. The overlap extension PCR is also used to connect 2A peptide with E10A and K5. The final construction of fusion gene E10A-2A-K5 can provide a possible new method of the anti-angiogenesis treatment with a better expression performance.

Keywords: E10A, Kringle 5, 2A peptide, overlap extension PCR.

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Authors: Rafael Tedrus Bento

Abstract:

The analysis of proportionality as a test is raised as a basic foundation for the achievement of Fundamental Rights. We used legal dogmatics and empirical analysis to seek the expected results, from the reading of the RV Oakes trial by the Supreme Court of Canada. In cases involving freedom of expression, two tests are used to resolve disputes. The first examines whether, in fact, the case can be characterized as a violation of freedom of expression; the second assesses whether this violation can be justified by the reasonable limit clause. This test was defined in the RV Oakes trial by the Supreme Court of Canada, concluding with the Oakes Test, used worldwide as a proportionality test. Resulting is a proportionality between the effects of the limiting measure and the objective - the more serious the harmful effects of a measure, the more important the objective must be.

Keywords: Oakes, proportionality. fundamental rights, Canadian Charter of Rights and Freedoms.

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Authors: Mathew Wakefield, Matthew Mitchell, Lisa Wise, Christopher McCarthy

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This paper aims to provide an interpretation of artificial neural networks (ANNs) and explore some of its implications. The interpretation views ANNs as a memory which encodes instances of experience. An experiment explores the behavior of encoding and retrieval of instances from memory. A localised representation ANN is created that allows control over encoding and retrieved memory sample size and is experimented with using the MNIST digits dataset. The relationship between input familiarity, conflict within retrieved samples, and error rates is described and demonstrated to be an effective driver for memory encoding. Results indicate that selective encoding and retrieval samples that allow detection of memory conflicts produce optimal performance, and that error rates are normally distributed with input familiarity and conflict. By using input familiarity and sample consistency to guide memory encoding, the number of encoding trials on the dataset were reduced to 18.33% of the training data while maintaining good recognition performance on the test data.

Keywords: Artificial Neural Networks, ANNs, representation, memory, conflict monitoring, confidence.

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Authors: Kobra Nalbandi, Bahram Baghban Kohnehrouz, Khalil Alami Saeed

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The low level of foreign genes expression in transgenic plants is a key factor that limits plant genetic engineering. Because of the critical regulatory activity of the promoters on gene transcription, they are studied extensively to improve the efficiency of the plant transgenic system. The strong constitutive promoters, such as CaMV 35S promoter and Ubiqutin 1 maize are usually used in plant biotechnology research. However the expression level of the foreign genes in all tissues is often undesirable. But using a strong seed-specific promoter to limit gene expression in the seed solves such problems. The purpose of this study is to isolate one of the seed specific promoters of Hordeum vulgare. So one of the common varieties of Hordeum vulgare in Iran was selected and their genomes extracted then the D-Hordein promoter amplified using the specific designed primers. Then the amplified fragment of the insert cloned in an appropriate vector and then transformed to E. coli. At last for the final admission of accuracy the cloned fragments sent for sequencing. Sequencing analysis showed that the cloned fragment DHPcontained motifs; like TATA box, CAAT-box, CCGTCC-box, AMYBOX1 and E-box etc., which constituted the seed-specific promoter activity. The results were compared with sequences existing in data banks. D-Hordein promoters of Alger has 99% similarity at 100 % coverage. The results also showed that D-Hordein promoter of barley and HMW promoter of wheat are too similar.

Keywords: Barley, Seed specific promoter, Hordein.

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Authors: Olga A. Berillo, Gaukhar K. Baidildinova, Аnatoliy Т. Ivashchenko

Abstract:

Tumour suppressors are key participants in the prevention of cancer. Regulation of their expression through miRNAs is important for comprehensive translation inhibition of tumour suppressors and elucidation of carcinogenesis mechanisms. We studies the possibility of 1521 miRNAs to bind with 873 mRNAs of human tumour suppressors using RNAHybrid 2.1 and ERNAhybrid programmes. Only 978 miRNAs were found to be translational regulators of 812 mRNAs, and 61 mRNAs did not have any miRNA binding sites. Additionally, 45.9% of all miRNA binding sites were located in coding sequences (CDSs), 33.8% were located in 3' untranslated region (UTR), and 20.3% were located in the 5'UTR. MiRNAs binding with more than 50 target mRNAs and mRNAs binding with several miRNAs were selected. Hsa-miR-5096 had 15 perfectly complementary binding sites with mRNAs of 14 tumour suppressors. These newly indentified miRNA binding sites can be used in the development of medicines (anti-sense therapies) for cancer treatment.

Keywords: Exonic miRNA, intergenic miRNA, intronic miRNA, tumor suppressor.

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Authors: Hadeer R. M. Tawfik, Rania A. K. Birry, Amani A. Saad

Abstract:

Eyes are considered to be the most sensitive and important organ for human being. Thus, any eye disorder will affect the patient in all aspects of life. Cataract is one of those eye disorders that lead to blindness if not treated correctly and quickly. This paper demonstrates a model for automatic detection, classification, and grading of cataracts based on image processing techniques and artificial intelligence. The proposed system is developed to ease the cataract diagnosis process for both ophthalmologists and patients. The wavelet transform combined with 2D Log Gabor Wavelet transform was used as feature extraction techniques for a dataset of 120 eye images followed by a classification process that classified the image set into three classes; normal, early, and advanced stage. A comparison between the two used classifiers, the support vector machine SVM and the artificial neural network ANN were done for the same dataset of 120 eye images. It was concluded that SVM gave better results than ANN. SVM success rate result was 96.8% accuracy where ANN success rate result was 92.3% accuracy.

Keywords: Cataract, classification, detection, feature extraction, grading, log-gabor, neural networks, support vector machines, wavelet.

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Authors: Ramaswamy Palaniappan

Abstract:

In this paper, several improvements are proposed to previous work of automated classification of alcoholics and nonalcoholics. In the previous paper, multiplayer-perceptron neural network classifying energy of gamma band Visual Evoked Potential (VEP) signals gave the best classification performance using 800 VEP signals from 10 alcoholics and 10 non-alcoholics. Here, the dataset is extended to include 3560 VEP signals from 102 subjects: 62 alcoholics and 40 non-alcoholics. Three modifications are introduced to improve the classification performance: i) increasing the gamma band spectral range by increasing the pass-band width of the used filter ii) the use of Multiple Signal Classification algorithm to obtain the power of the dominant frequency in gamma band VEP signals as features and iii) the use of the simple but effective knearest neighbour classifier. To validate that these two modifications do give improved performance, a 10-fold cross validation classification (CVC) scheme is used. Repeat experiments of the previously used methodology for the extended dataset are performed here and improvement from 94.49% to 98.71% in maximum averaged CVC accuracy is obtained using the modifications. This latest results show that VEP based classification of alcoholics is worth exploring further for system development.

Keywords: Alcoholic, Multilayer-perceptron, Nearest neighbour, Gamma band, MUSIC, Visual evoked potential.

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Authors: Fabio Fabris, Alex A. Freitas

Abstract:

Hierarchical classification is a special type of classification task where the class labels are organised into a hierarchy, with more generic class labels being ancestors of more specific ones. Meta-learning for classification-algorithm recommendation consists of recommending to the user a classification algorithm, from a pool of candidate algorithms, for a dataset, based on the past performance of the candidate algorithms in other datasets. Meta-learning is normally used in conventional, non-hierarchical classification. By contrast, this paper proposes a meta-learning approach for more challenging task of hierarchical classification, and evaluates it in a large number of bioinformatics datasets. Hierarchical classification is especially relevant for bioinformatics problems, as protein and gene functions tend to be organised into a hierarchy of class labels. This work proposes meta-learning approach for recommending the best hierarchical classification algorithm to a hierarchical classification dataset. This work’s contributions are: 1) proposing an algorithm for splitting hierarchical datasets into new datasets to increase the number of meta-instances, 2) proposing meta-features for hierarchical classification, and 3) interpreting decision-tree meta-models for hierarchical classification algorithm recommendation.

Keywords: Algorithm recommendation, meta-learning, bioinformatics, hierarchical classification.

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Authors: Khadija Raissi, Bechir Ben Gouissem

Abstract:

In this paper, we perform the investigation of some routing protocols in Vehicular Ad-Hoc Network (VANET) context. Indeed, we study the efficiency of protocols like Dynamic Source Routing (DSR), Ad hoc On-demand Distance Vector Routing (AODV), Destination Sequenced Distance Vector (DSDV), Optimized Link State Routing convention (OLSR) and Vehicular Multi-hop algorithm for Stable Clustering (VMASC) in terms of packet delivery ratio (PDR) and throughput. The performance evaluation and comparison between the studied protocols shows that the VMASC is the best protocols regarding fast data transmission and link stability in VANETs. The validation of all results is done by the NS3 simulator.

Keywords: VANET, smart city, AODV, OLSR, DSR, OLSR, VMASC, routing protocols, NS3.

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Authors: Lavanya Srinivasan

Abstract:

Biometric identification is to identify unique features in a person like fingerprints, iris, ear, and voice recognition that need the subject's permission and physical contact. Gait biometric is used to identify the unique gait of the person by extracting moving features. The main advantage of gait biometric to identify the gait of a person at a distance, without any physical contact. In this work, the gait biometric is used for person re-identification. The person walking naturally compared with the same person walking with bag, coat and case recorded using long wave infrared, short wave infrared, medium wave infrared and visible cameras. The videos are recorded in rural and in urban environments. The pre-processing technique includes human identified using You Only Look Once, background subtraction, silhouettes extraction and synthesis Gait Entropy Image by averaging the silhouettes. The moving features are extracted from the Gait Entropy Energy Image. The extracted features are dimensionality reduced by the Principal Component Analysis and recognized using different classifiers. The comparative results with the different classifier show that Linear Discriminant Analysis outperform other classifiers with 95.8% for visible in the rural dataset and 94.8% for longwave infrared in the urban dataset.

Keywords: biometric, gait, silhouettes, You Only Look Once

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