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Sample records for svm practical dna

  1. SVM2Motif--Reconstructing Overlapping DNA Sequence Motifs by Mimicking an SVM Predictor.

    Directory of Open Access Journals (Sweden)

    Marina M-C Vidovic

    Full Text Available Identifying discriminative motifs underlying the functionality and evolution of organisms is a major challenge in computational biology. Machine learning approaches such as support vector machines (SVMs achieve state-of-the-art performances in genomic discrimination tasks, but--due to its black-box character--motifs underlying its decision function are largely unknown. As a remedy, positional oligomer importance matrices (POIMs allow us to visualize the significance of position-specific subsequences. Although being a major step towards the explanation of trained SVM models, they suffer from the fact that their size grows exponentially in the length of the motif, which renders their manual inspection feasible only for comparably small motif sizes, typically k ≤ 5. In this work, we extend the work on positional oligomer importance matrices, by presenting a new machine-learning methodology, entitled motifPOIM, to extract the truly relevant motifs--regardless of their length and complexity--underlying the predictions of a trained SVM model. Our framework thereby considers the motifs as free parameters in a probabilistic model, a task which can be phrased as a non-convex optimization problem. The exponential dependence of the POIM size on the oligomer length poses a major numerical challenge, which we address by an efficient optimization framework that allows us to find possibly overlapping motifs consisting of up to hundreds of nucleotides. We demonstrate the efficacy of our approach on a synthetic data set as well as a real-world human splice site data set.

  2. SVM2Motif--Reconstructing Overlapping DNA Sequence Motifs by Mimicking an SVM Predictor.

    Science.gov (United States)

    Vidovic, Marina M-C; Görnitz, Nico; Müller, Klaus-Robert; Rätsch, Gunnar; Kloft, Marius

    2015-01-01

    Identifying discriminative motifs underlying the functionality and evolution of organisms is a major challenge in computational biology. Machine learning approaches such as support vector machines (SVMs) achieve state-of-the-art performances in genomic discrimination tasks, but--due to its black-box character--motifs underlying its decision function are largely unknown. As a remedy, positional oligomer importance matrices (POIMs) allow us to visualize the significance of position-specific subsequences. Although being a major step towards the explanation of trained SVM models, they suffer from the fact that their size grows exponentially in the length of the motif, which renders their manual inspection feasible only for comparably small motif sizes, typically k ≤ 5. In this work, we extend the work on positional oligomer importance matrices, by presenting a new machine-learning methodology, entitled motifPOIM, to extract the truly relevant motifs--regardless of their length and complexity--underlying the predictions of a trained SVM model. Our framework thereby considers the motifs as free parameters in a probabilistic model, a task which can be phrased as a non-convex optimization problem. The exponential dependence of the POIM size on the oligomer length poses a major numerical challenge, which we address by an efficient optimization framework that allows us to find possibly overlapping motifs consisting of up to hundreds of nucleotides. We demonstrate the efficacy of our approach on a synthetic data set as well as a real-world human splice site data set.

  3. svmPRAT: SVM-based Protein Residue Annotation Toolkit

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    Kauffman Christopher

    2009-12-01

    Full Text Available Abstract Background Over the last decade several prediction methods have been developed for determining the structural and functional properties of individual protein residues using sequence and sequence-derived information. Most of these methods are based on support vector machines as they provide accurate and generalizable prediction models. Results We present a general purpose protein residue annotation toolkit (svmPRAT to allow biologists to formulate residue-wise prediction problems. svmPRAT formulates the annotation problem as a classification or regression problem using support vector machines. One of the key features of svmPRAT is its ease of use in incorporating any user-provided information in the form of feature matrices. For every residue svmPRAT captures local information around the reside to create fixed length feature vectors. svmPRAT implements accurate and fast kernel functions, and also introduces a flexible window-based encoding scheme that accurately captures signals and pattern for training effective predictive models. Conclusions In this work we evaluate svmPRAT on several classification and regression problems including disorder prediction, residue-wise contact order estimation, DNA-binding site prediction, and local structure alphabet prediction. svmPRAT has also been used for the development of state-of-the-art transmembrane helix prediction method called TOPTMH, and secondary structure prediction method called YASSPP. This toolkit developed provides practitioners an efficient and easy-to-use tool for a wide variety of annotation problems. Availability: http://www.cs.gmu.edu/~mlbio/svmprat

  4. A Novel and Practical Chromatographic "Fingerprint-ROC-SVM" Strategy Applied to Quality Analysis of Traditional Chinese Medicine Injections: Using KuDieZi Injection as a Case Study.

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    Yang, Bin; Wang, Yuan; Shan, Lanlan; Zou, Jingtao; Wu, Yuanyuan; Yang, Feifan; Zhang, Yani; Li, Yubo; Zhang, Yanjun

    2017-07-23

    Fingerprinting is widely and commonly used in the quality control of traditional Chinese medicine (TCM) injections. However, current studies informed that the fingerprint similarity evaluation was less sensitive and easily generated false positive results. For this reason, a novel and practical chromatographic "Fingerprint-ROC-SVM" strategy was established by using KuDieZi (KDZ) injection as a case study in the present article. Firstly, the chromatographic fingerprints of KDZ injection were obtained by UPLC and the common characteristic peaks were identified with UPLC/Q-TOF-MS under the same chromatographic conditions. Then, the receiver operating characteristic (ROC) curve was used to optimize common characteristic peaks by the AUCs value greater than 0.7. Finally, a support vector machine (SVM) model, with the accuracy of 97.06%, was established by the optimized characteristic peaks and applied to monitor the quality of KDZ injection. As a result, the established model could sensitively and accurately distinguish the qualified products (QPs) with the unqualified products (UPs), high-temperature processed samples (HTPs) and high-illumination processed samples (HIPs) of KDZ injection, and the prediction accuracy was 100.00%, 93.75% and 100.00%, respectively. Furthermore, through the comparison with other chemometrics methods, the superiority of the novel analytical strategy was more prominent. It indicated that the novel and practical chromatographic "Fingerprint-ROC-SVM" strategy could be further applied to facilitate the development of the quality analysis of TCM injections.

  5. Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations

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    Yi Zhang

    2015-01-01

    Full Text Available Maximum likelihood classifier (MLC and support vector machines (SVM are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

  6. Research on Classification of Chinese Text Data Based on SVM

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    Lin, Yuan; Yu, Hongzhi; Wan, Fucheng; Xu, Tao

    2017-09-01

    Data Mining has important application value in today’s industry and academia. Text classification is a very important technology in data mining. At present, there are many mature algorithms for text classification. KNN, NB, AB, SVM, decision tree and other classification methods all show good classification performance. Support Vector Machine’ (SVM) classification method is a good classifier in machine learning research. This paper will study the classification effect based on the SVM method in the Chinese text data, and use the support vector machine method in the chinese text to achieve the classify chinese text, and to able to combination of academia and practical application.

  7. A structural SVM approach for reference parsing.

    Science.gov (United States)

    Zhang, Xiaoli; Zou, Jie; Le, Daniel X; Thoma, George R

    2011-06-09

    Automated extraction of bibliographic data, such as article titles, author names, abstracts, and references is essential to the affordable creation of large citation databases. References, typically appearing at the end of journal articles, can also provide valuable information for extracting other bibliographic data. Therefore, parsing individual reference to extract author, title, journal, year, etc. is sometimes a necessary preprocessing step in building citation-indexing systems. The regular structure in references enables us to consider reference parsing a sequence learning problem and to study structural Support Vector Machine (structural SVM), a newly developed structured learning algorithm on parsing references. In this study, we implemented structural SVM and used two types of contextual features to compare structural SVM with conventional SVM. Both methods achieve above 98% token classification accuracy and above 95% overall chunk-level accuracy for reference parsing. We also compared SVM and structural SVM to Conditional Random Field (CRF). The experimental results show that structural SVM and CRF achieve similar accuracies at token- and chunk-levels. When only basic observation features are used for each token, structural SVM achieves higher performance compared to SVM since it utilizes the contextual label features. However, when the contextual observation features from neighboring tokens are combined, SVM performance improves greatly, and is close to that of structural SVM after adding the second order contextual observation features. The comparison of these two methods with CRF using the same set of binary features show that both structural SVM and CRF perform better than SVM, indicating their stronger sequence learning ability in reference parsing.

  8. Adaptive SVM for Data Stream Classification

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    Isah A. Lawal

    2017-07-01

    Full Text Available In this paper, we address the problem of learning an adaptive classifier for the classification of continuous streams of data. We present a solution based on incremental extensions of the Support Vector Machine (SVM learning paradigm that updates an existing SVM whenever new training data are acquired. To ensure that the SVM effectiveness is guaranteed while exploiting the newly gathered data, we introduce an on-line model selection approach in the incremental learning process. We evaluated the proposed method on real world applications including on-line spam email filtering and human action classification from videos. Experimental results show the effectiveness and the potential of the proposed approach.

  9. Designing easy DNA extraction: Teaching creativity through laboratory practice.

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    Susantini, Endang; Lisdiana, Lisa; Isnawati; Tanzih Al Haq, Aushia; Trimulyono, Guntur

    2017-05-01

    Subject material concerning Deoxyribose Nucleic Acid (DNA) structure in the format of creativity-driven laboratory practice offers meaningful learning experience to the students. Therefore, a laboratory practice in which utilizes simple procedures and easy-safe-affordable household materials should be promoted to students to develop their creativity. This study aimed to examine whether designing and conducting DNA extraction with household materials could foster students' creative thinking. We also described how this laboratory practice affected students' knowledge and views. A total of 47 students participated in this study. These students were grouped and asked to utilize available household materials and modify procedures using hands-on worksheet. Result showed that this approach encouraged creative thinking as well as improved subject-related knowledge. Students also demonstrated positive views about content knowledge, social skills, and creative thinking skills. This study implies that extracting DNA with household materials is able to develop content knowledge, social skills, and creative thinking of the students. © 2016 by The International Union of Biochemistry and Molecular Biology, 45(3):216-225, 2017. © 2016 The International Union of Biochemistry and Molecular Biology.

  10. DNA methylation based biomarkers: Practical considerations and applications

    DEFF Research Database (Denmark)

    Nielsen, Helene Myrtue; How Kit, Alexandre; Tost, Jorg

    2012-01-01

    of biomarker, DNA methylation, which is of great potential for many applications. This stable and heritable covalent modification mostly affects cytosines in the context of a CpG dinucleotide in humans. It can be detected and quantified by a number of technologies including genome-wide screening methods...... as a biomarker, the practical considerations for their development, and their use in disease detection, prediction of outcome or treatment response, through multiple examples mainly focusing on cancer, but also to evoke their potential for complex diseases and prenatal diagnostics....

  11. Towards understanding the influence of SVM hyperparameters

    CSIR Research Space (South Africa)

    Van Heerden, CJ

    2010-11-01

    Full Text Available -consuming and resource-intensive. On large datasets, 10-fold cross-validation grid searches can become intractable without supercomputers or high performance computing clusters. They present theoretical and empirical arguments as to how SVM hyperparameters scale with N...

  12. DNA Fingerprinting Using PCR: A Practical Forensic Science Activity

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    Choi, Hyun-Jung; Ahn, Jung Hoon; Ko, Minsu

    2008-01-01

    This paper describes a forensic science simulation programme applicable for use in colleges. Students were asked to find a putative suspect by DNA fingerprinting using a simple protocol developed in this study. DNA samples were obtained from a hair root and a drop of blood, common sources of DNA in forensic science. The DNA fingerprinting protocol…

  13. Data Driven Constraints for the SVM

    DEFF Research Database (Denmark)

    Darkner, Sune; Clemmensen, Line Katrine Harder

    2012-01-01

    . Assuming that two observations of the same subject in different states span a vector, we hypothesise that such structure of the data contains implicit information which can aid the classification, thus the name data driven constraints. We derive a constraint based on the data which allow for the use...... classifier solution, compared to the SVM i.e. reduces variance and improves classification rates. We present a quantitative measure of the information level contained in the pairing and test the method on simulated as well as a high-dimensional paired data set of ear-canal surfaces....

  14. Generalized SMO algorithm for SVM-based multitask learning.

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    Cai, Feng; Cherkassky, Vladimir

    2012-06-01

    Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed a general approach to formalizing such problems, known as "learning with structured data" and its support vector machine (SVM) based optimization formulation called SVM+. Liang and Cherkassky showed the connection between SVM+ and multitask learning (MTL) approaches in machine learning, and proposed an SVM-based formulation for MTL called SVM+MTL for classification. Training the SVM+MTL classifier requires the solution of a large quadratic programming optimization problem which scales as O(n(3)) with sample size n. So there is a need to develop computationally efficient algorithms for implementing SVM+MTL. This brief generalizes Platt's sequential minimal optimization (SMO) algorithm to the SVM+MTL setting. Empirical results show that, for typical SVM+MTL problems, the proposed generalized SMO achieves over 100 times speed-up, in comparison with general-purpose optimization routines.

  15. A Practical Workshop for Generating Simple DNA Fingerprints of Plants

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    Rouziere, A.-S.; Redman, J. E.

    2011-01-01

    Gel electrophoresis DNA fingerprints offer a graphical and visually appealing illumination of the similarities and differences between DNA sequences of different species and individuals. A polymerase chain reaction (PCR) and restriction digest protocol was designed to give high-school students the opportunity to generate simple fingerprints of…

  16. A novel stepwise support vector machine (SVM) method based on ...

    African Journals Online (AJOL)

    ajl yemi

    2011-11-23

    Nov 23, 2011 ... began to use computational approaches, particularly machine learning methods to identify pre-miRNAs (Xue et al., 2005; Huang et al., 2007; Jiang et al., 2007). Xue et al. (2005) presented a support vector machine (SVM)- based classifier called triplet-SVM, which classifies human pre-miRNAs from pseudo ...

  17. Estimating grassland biomass using SVM band shaving of hyperspectral data

    NARCIS (Netherlands)

    Clevers, J.G.P.W.; Heijden, van der G.W.A.M.; Verzakov, S.; Schaepman, M.E.

    2007-01-01

    In this paper, the potential of a band shaving algorithm based on support vector machines (SVM) applied to hyperspectral data for estimating biomass within grasslands is studied. Field spectrometer data and biomass measurements were collected from a homogeneously managed grassland field. The SVM

  18. Parameter optimization using GA in SVM to predict damage level of non-reshaped berm breakwater.

    Digital Repository Service at National Institute of Oceanography (India)

    Harish, N.; Lokesha.; Mandal, S.; Rao, S.; Patil, S.G.

    In the present study, Support Vector Machines (SVM) and hybrid of Genetic Algorithm (GA) with SVM models are developed to predict the damage level of non-reshaped berm breakwaters. Optimal kernel parameters of SVM are determined by using GA...

  19. Classifying smoke in laparoscopic videos using SVM

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    Alshirbaji Tamer Abdulbaki

    2017-09-01

    Full Text Available Smoke in laparoscopic videos usually appears due to the use of electrocautery when cutting or coagulating tissues. Therefore, detecting smoke can be used for event-based annotation in laparoscopic surgeries by retrieving the events associated with the electrocauterization. Furthermore, smoke detection can also be used for automatic smoke removal. However, detecting smoke in laparoscopic video is a challenge because of the changeability of smoke patterns, the moving camera and the different lighting conditions. In this paper, we present a video-based smoke detection algorithm to detect smoke of different densities such as fog, low and high density in laparoscopic videos. The proposed method depends on extracting various visual features from the laparoscopic images and providing them to support vector machine (SVM classifier. Features are based on motion, colour and texture patterns of the smoke. We validated our algorithm using experimental evaluation on four laparoscopic cholecystectomy videos. These four videos were manually annotated by defining every frame as smoke or non-smoke frame. The algorithm was applied to the videos by using different feature combinations for classification. Experimental results show that the combination of all proposed features gives the best classification performance. The overall accuracy (i.e. correctly classified frames is around 84%, with the sensitivity (i.e. correctly detected smoke frames and the specificity (i.e. correctly detected non-smoke frames are 89% and 80%, respectively.

  20. Customer and performance rating in QFD using SVM classification

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    Dzulkifli, Syarizul Amri; Salleh, Mohd Najib Mohd; Leman, A. M.

    2017-09-01

    In a classification problem, where each input is associated to one output. Training data is used to create a model which predicts values to the true function. SVM is a popular method for binary classification due to their theoretical foundation and good generalization performance. However, when trained with noisy data, the decision hyperplane might deviate from optimal position because of the sum of misclassification errors in the objective function. In this paper, we introduce fuzzy in weighted learning approach for improving the accuracy of Support Vector Machine (SVM) classification. The main aim of this work is to determine appropriate weighted for SVM to adjust the parameters of learning method from a given set of noisy input to output data. The performance and customer rating in Quality Function Deployment (QFD) is used as our case study to determine implementing fuzzy SVM is highly scalable for very large data sets and generating high classification accuracy.

  1. A Multi-Classification Method of Improved SVM-based Information Fusion for Traffic Parameters Forecasting

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    Hongzhuan Zhao

    2016-04-01

    Full Text Available With the enrichment of perception methods, modern transportation system has many physical objects whose states are influenced by many information factors so that it is a typical Cyber-Physical System (CPS. Thus, the traffic information is generally multi-sourced, heterogeneous and hierarchical. Existing research results show that the multisourced traffic information through accurate classification in the process of information fusion can achieve better parameters forecasting performance. For solving the problem of traffic information accurate classification, via analysing the characteristics of the multi-sourced traffic information and using redefined binary tree to overcome the shortcomings of the original Support Vector Machine (SVM classification in information fusion, a multi-classification method using improved SVM in information fusion for traffic parameters forecasting is proposed. The experiment was conducted to examine the performance of the proposed scheme, and the results reveal that the method can get more accurate and practical outcomes.

  2. Estimating grassland biomass using SVM band shaving of hyperspectral data

    OpenAIRE

    Clevers, J G P W; van Der Heijden, G.W.A.M.; Verzakov, S; Schaepman, M. E.

    2007-01-01

    In this paper, the potential of a band shaving algorithm based on support vector machines (SVM) applied to hyperspectral data for estimating biomass within grasslands is studied. Field spectrometer data and biomass measurements were collected from a homogeneously managed grassland field. The SVM band shaving technique was compared with a partial least squares (PLS) and a stepwise forward selection analysis. Using their results, a range of vegetation indices was used as predictors for grasslan...

  3. SVM Method used to Study Gender Differences Based on Microelement

    Science.gov (United States)

    Chun, Yang; Yuan, Liu; Jun, Du; Bin, Tang

    [objective] Intelligent Algorithm of SVM is used for studying gender differences based on microelement data, which provide reference For the application of Microelement in healthy people, such as providing technical support for the investigation of cases.[Method] Our Long-term test results on hair microelement of health people were consolidated. Support vector machine (SVM) is used to classified model of male and female based on microelement data. The radical basis function (RBF) is adopted as a kernel function of SVM, and the model adjusts C and σ to build the optimization classifier, [Result] Healthy population of men and women of manganese, cadmium and nickel are quite different, The classified model of Microelement based on SVM can classifies the male and female, the correct classification ratio set to be 81.71% and 66.47% by SVM based on 7 test date and 3 test data selection. [conclusion] The classified model of microelement data based on SVM can classifies male and female.

  4. SVM-Based Control System for a Robot Manipulator

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    Foudil Abdessemed

    2012-12-01

    Full Text Available Real systems are usually non-linear, ill-defined, have variable parameters and are subject to external disturbances. Modelling these systems is often an approximation of the physical phenomena involved. However, it is from this approximate system of representation that we propose - in this paper - to build a robust control, in the sense that it must ensure low sensitivity towards parameters, uncertainties, variations and external disturbances. The computed torque method is a well-established robot control technique which takes account of the dynamic coupling between the robot links. However, its main disadvantage lies on the assumption of an exactly known dynamic model which is not realizable in practice. To overcome this issue, we propose the estimation of the dynamics model of the nonlinear system with a machine learning regression method. The output of this regressor is used in conjunction with a PD controller to achieve the tracking trajectory task of a robot manipulator. In cases where some of the parameters of the plant undergo a change in their values, poor performance may result. To cope with this drawback, a fuzzy precompensator is inserted to reinforce the SVM computed torque-based controller and avoid any deterioration. The theory is developed and the simulation results are carried out on a two-degree of freedom robot manipulator to demonstrate the validity of the proposed approach.

  5. DNA barcoding works in practice but not in (neutral theory.

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    Mark Y Stoeckle

    Full Text Available BACKGROUND: DNA barcode differences within animal species are usually much less than differences among species, making it generally straightforward to match unknowns to a reference library. Here we aim to better understand the evolutionary mechanisms underlying this usual "barcode gap" pattern. We employ avian barcode libraries to test a central prediction of neutral theory, namely, intraspecific variation equals 2 Nµ, where N is population size and µ is mutations per site per generation. Birds are uniquely suited for this task: they have the best-known species limits, are well represented in barcode libraries, and, most critically, are the only large group with documented census population sizes. In addition, we ask if mitochondrial molecular clock measurements conform to neutral theory prediction of clock rate equals µ. RESULTS: Intraspecific COI barcode variation was uniformly low regardless of census population size (n = 142 species in 15 families. Apparent outliers reflected lumping of reproductively isolated populations or hybrid lineages. Re-analysis of a published survey of cytochrome b variation in diverse birds (n = 93 species in 39 families further confirmed uniformly low intraspecific variation. Hybridization/gene flow among species/populations was the main limitation to DNA barcode identification. CONCLUSIONS/SIGNIFICANCE: To our knowledge, this is the first large study of animal mitochondrial diversity using actual census population sizes and the first to test outliers for population structure. Our finding of universally low intraspecific variation contradicts a central prediction of neutral theory and is not readily accounted for by commonly proposed ad hoc modifications. We argue that the weight of evidence-low intraspecific variation and the molecular clock-indicates neutral evolution plays a minor role in mitochondrial sequence evolution. As an alternate paradigm consistent with empirical data, we propose extreme

  6. A method of neighbor classes based SVM classification for optical printed Chinese character recognition.

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    Jie Zhang

    Full Text Available In optical printed Chinese character recognition (OPCCR, many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR.

  7. Moving environmental DNA methods from concept to practice for monitoring aquatic macroorganisms

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    Goldberg, Caren S.; Strickler, Katherine M.; Pilliod, David S.

    2015-01-01

    The discovery that macroorganisms can be detected from their environmental DNA (eDNA) in aquatic systems has immense potential for the conservation of biological diversity. This special issue contains 11 papers that review and advance the field of eDNA detection of vertebrates and other macroorganisms, including studies of eDNA production, transport, and degradation; sample collection and processing to maximize detection rates; and applications of eDNA for conservation using citizen scientists. This body of work is an important contribution to the ongoing efforts to take eDNA detection of macroorganisms from technical breakthrough to established, reliable method that can be used in survey, monitoring, and research applications worldwide. While the rapid advances in this field are remarkable, important challenges remain, including consensus on best practices for collection and analysis, understanding of eDNA diffusion and transport, and avoidance of inhibition in sample collection and processing. Nonetheless, as demonstrated in this special issue, eDNA techniques for research and monitoring are beginning to realize their potential for contributing to the conservation of biodiversity globally.

  8. A Fault Diagnosis Approach for Gears Based on IMF AR Model and SVM

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    Yu Yang

    2008-05-01

    Full Text Available An accurate autoregressive (AR model can reflect the characteristics of a dynamic system based on which the fault feature of gear vibration signal can be extracted without constructing mathematical model and studying the fault mechanism of gear vibration system, which are experienced by the time-frequency analysis methods. However, AR model can only be applied to stationary signals, while the gear fault vibration signals usually present nonstationary characteristics. Therefore, empirical mode decomposition (EMD, which can decompose the vibration signal into a finite number of intrinsic mode functions (IMFs, is introduced into feature extraction of gear vibration signals as a preprocessor before AR models are generated. On the other hand, by targeting the difficulties of obtaining sufficient fault samples in practice, support vector machine (SVM is introduced into gear fault pattern recognition. In the proposed method in this paper, firstly, vibration signals are decomposed into a finite number of intrinsic mode functions, then the AR model of each IMF component is established; finally, the corresponding autoregressive parameters and the variance of remnant are regarded as the fault characteristic vectors and used as input parameters of SVM classifier to classify the working condition of gears. The experimental analysis results show that the proposed approach, in which IMF AR model and SVM are combined, can identify working condition of gears with a success rate of 100% even in the case of smaller number of samples.

  9. Physicians' current practices and opportunities for DNA banking of dying patients with cancer.

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    Quillin, John M; Bodurtha, Joann N; Siminoff, Laura A; Smith, Thomas J

    2011-05-01

    The availability of genetic tests for cancer susceptibility is increasing. Current tests, however, have limited clinical sensitivity. Even when clinically valid tests are available, the genetic counseling and informed consent process might not be feasible for dying patients with cancer. DNA banking preserves the opportunity for future research or clinical testing and may provide critical opportunities for surviving relatives. This study explored the current practices and potential for DNA banking for cancer susceptibility among oncologists specializing in palliative care. Palliative care oncologists actively providing clinical care for dying patients with cancer were recruited for an online survey. Descriptive statistics for DNA banking practices, perceived qualification to recommend banking, and potential predictors were assessed. Data were collected from 49 physicians (37% recruitment rate). Eighty percent reported assessing at least some patients for genetic cancer susceptibility in the past 12 months. No participants reported banking DNA for patients in the past 12 months. Only 5% reported feeling at least somewhat qualified to order DNA banking. A Web-based risk assessment tool and genetic counselor on staff were perceived as the most helpful potential resources. Despite its potential, DNA banking is not being used by palliative care oncologists.

  10. Physicians' Current Practices and Opportunities for DNA Banking of Dying Patients With Cancer

    Science.gov (United States)

    Quillin, John M.; Bodurtha, Joann N.; Siminoff, Laura A.; Smith, Thomas J.

    2011-01-01

    Purpose: The availability of genetic tests for cancer susceptibility is increasing. Current tests, however, have limited clinical sensitivity. Even when clinically valid tests are available, the genetic counseling and informed consent process might not be feasible for dying patients with cancer. DNA banking preserves the opportunity for future research or clinical testing and may provide critical opportunities for surviving relatives. This study explored the current practices and potential for DNA banking for cancer susceptibility among oncologists specializing in palliative care. Methods: Palliative care oncologists actively providing clinical care for dying patients with cancer were recruited for an online survey. Descriptive statistics for DNA banking practices, perceived qualification to recommend banking, and potential predictors were assessed. Results: Data were collected from 49 physicians (37% recruitment rate). Eighty percent reported assessing at least some patients for genetic cancer susceptibility in the past 12 months. No participants reported banking DNA for patients in the past 12 months. Only 5% reported feeling at least somewhat qualified to order DNA banking. A Web-based risk assessment tool and genetic counselor on staff were perceived as the most helpful potential resources. Conclusion: Despite its potential, DNA banking is not being used by palliative care oncologists. PMID:21886501

  11. Multiclass Posterior Probability Twin SVM for Motor Imagery EEG Classification.

    Science.gov (United States)

    She, Qingshan; Ma, Yuliang; Meng, Ming; Luo, Zhizeng

    2015-01-01

    Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this paper. First, two-class posterior probability model is constructed to approximate the posterior probability by the ranking continuous output techniques and Platt's estimating method. Secondly, a solution of multiclass probabilistic outputs for twin SVM is provided by combining every pair of class probabilities according to the method of pairwise coupling. Finally, the proposed method is compared with multiclass SVM and twin SVM via voting, and multiclass posterior probability SVM using different coupling approaches. The efficacy on the classification accuracy and time complexity of the proposed method has been demonstrated by both the UCI benchmark datasets and real world EEG data from BCI Competition IV Dataset 2a, respectively.

  12. Multiclass Posterior Probability Twin SVM for Motor Imagery EEG Classification

    Directory of Open Access Journals (Sweden)

    Qingshan She

    2015-01-01

    Full Text Available Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this paper. First, two-class posterior probability model is constructed to approximate the posterior probability by the ranking continuous output techniques and Platt’s estimating method. Secondly, a solution of multiclass probabilistic outputs for twin SVM is provided by combining every pair of class probabilities according to the method of pairwise coupling. Finally, the proposed method is compared with multiclass SVM and twin SVM via voting, and multiclass posterior probability SVM using different coupling approaches. The efficacy on the classification accuracy and time complexity of the proposed method has been demonstrated by both the UCI benchmark datasets and real world EEG data from BCI Competition IV Dataset 2a, respectively.

  13. DNA as Genetic Material: Revisiting Classic Experiments through a Simple, Practical Class

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    Malago, Wilson, Jr.; Soares-Costa, Andrea; Henrique-Silva, Flavio

    2009-01-01

    In 1928, Frederick Griffith demonstrated a transmission process of genetic information by transforming "Pneumococcus". In 1944, Avery et al. demonstrated that Griffith's transforming principle was DNA. We revisited these classic experiments in a practical class for undergraduate students. Both experiments were reproduced in simple, adapted forms.…

  14. Guided Practice Software for Teaching DNA Replication to Senior High School Students

    Science.gov (United States)

    Woods, Eric C.; McKinnon, Alan E.; Hickford, Jonathan G. H.; Abell, Walt A.

    2008-01-01

    The prototype of a guided practice application was developed to instruct year 13 biology students in the process of DNA replication. The application uses a high degree of interaction to engage the student in a guided exploration and problem solving exercise. An evaluation revealed that the students showed considerable enthusiasm and significant…

  15. COMPARISON OF SVM AND FUZZY CLASSIFIER FOR AN INDIAN SCRIPT

    Directory of Open Access Journals (Sweden)

    M. J. Baheti

    2012-01-01

    Full Text Available With the advent of technological era, conversion of scanned document (handwritten or printed into machine editable format has attracted many researchers. This paper deals with the problem of recognition of Gujarati handwritten numerals. Gujarati numeral recognition requires performing some specific steps as a part of preprocessing. For preprocessing digitization, segmentation, normalization and thinning are done with considering that the image have almost no noise. Further affine invariant moments based model is used for feature extraction and finally Support Vector Machine (SVM and Fuzzy classifiers are used for numeral classification. . The comparison of SVM and Fuzzy classifier is made and it can be seen that SVM procured better results as compared to Fuzzy Classifier.

  16. SA-SVM based automated diagnostic system for skin cancer

    Science.gov (United States)

    Masood, Ammara; Al-Jumaily, Adel

    2015-03-01

    Early diagnosis of skin cancer is one of the greatest challenges due to lack of experience of general practitioners (GPs). This paper presents a clinical decision support system aimed to save time and resources in the diagnostic process. Segmentation, feature extraction, pattern recognition, and lesion classification are the important steps in the proposed decision support system. The system analyses the images to extract the affected area using a novel proposed segmentation method H-FCM-LS. The underlying features which indicate the difference between melanoma and benign lesions are obtained through intensity, spatial/frequency and texture based methods. For classification purpose, self-advising SVM is adapted which showed improved classification rate as compared to standard SVM. The presented work also considers analyzed performance of linear and kernel based SVM on the specific skin lesion diagnostic problem and discussed corresponding findings. The best diagnostic rates obtained through the proposed method are around 90.5 %.

  17. Differential dependence on DNA ligase of type II restriction enzymes: a practical way toward ligase-free DNA automaton.

    Science.gov (United States)

    Chen, Peng; Li, Jing; Zhao, Jian; He, Lin; Zhang, Zhizhou

    2007-02-16

    DNA computing study is a new paradigm in computer science and biological computing fields. As one of DNA computing approaches, DNA automaton is composed of the hardware, input DNA molecule and state transition molecules. By now restriction enzymes are key hardware for DNA computing automaton. It has been found that DNA computing efficiency may be independent on DNA ligases when type IIS restriction enzymes like FokI are used as hardware. In this study, we compared FokI with four other distinct enzymes HgaI, BsmFI, BbsI, and BseMII, and found their differential independence on T4 DNA ligase when performing automaton reactions. Since DNA automaton is a potential powerful tool to tackle gene relationship in genomic network scale, the feasible ligase-free DNA automaton may set an initial base to develop functional DNA automata for various DNA technology development and implications in genetics study in the near future.

  18. Arrhythmia classification using SVM with selected features | Kohli ...

    African Journals Online (AJOL)

    The various types of arrhythmias in the cardiac arrhythmias ECG database chosen from University of California at Irvine (UCI) to train SVM include ischemic changes (coronary artery disease), old inferior myocardial infarction, sinus bradycardy, right bundle branch block, and others. ECG arrhythmia datasets are of generally ...

  19. Accurate Multisteps Traffic Flow Prediction Based on SVM

    Directory of Open Access Journals (Sweden)

    Zhang Mingheng

    2013-01-01

    Full Text Available Accurate traffic flow prediction is prerequisite and important for realizing intelligent traffic control and guidance, and it is also the objective requirement for intelligent traffic management. Due to the strong nonlinear, stochastic, time-varying characteristics of urban transport system, artificial intelligence methods such as support vector machine (SVM are now receiving more and more attentions in this research field. Compared with the traditional single-step prediction method, the multisteps prediction has the ability that can predict the traffic state trends over a certain period in the future. From the perspective of dynamic decision, it is far important than the current traffic condition obtained. Thus, in this paper, an accurate multi-steps traffic flow prediction model based on SVM was proposed. In which, the input vectors were comprised of actual traffic volume and four different types of input vectors were compared to verify their prediction performance with each other. Finally, the model was verified with actual data in the empirical analysis phase and the test results showed that the proposed SVM model had a good ability for traffic flow prediction and the SVM-HPT model outperformed the other three models for prediction.

  20. Sales Growth Rate Forecasting Using Improved PSO and SVM

    Directory of Open Access Journals (Sweden)

    Xibin Wang

    2014-01-01

    Full Text Available Accurate forecast of the sales growth rate plays a decisive role in determining the amount of advertising investment. In this study, we present a preclassification and later regression based method optimized by improved particle swarm optimization (IPSO for sales growth rate forecasting. We use support vector machine (SVM as a classification model. The nonlinear relationship in sales growth rate forecasting is efficiently represented by SVM, while IPSO is optimizing the training parameters of SVM. IPSO addresses issues of traditional PSO, such as relapsing into local optimum, slow convergence speed, and low convergence precision in the later evolution. We performed two experiments; firstly, three classic benchmark functions are used to verify the validity of the IPSO algorithm against PSO. Having shown IPSO outperform PSO in convergence speed, precision, and escaping local optima, in our second experiment, we apply IPSO to the proposed model. The sales growth rate forecasting cases are used to testify the forecasting performance of proposed model. According to the requirements and industry knowledge, the sample data was first classified to obtain types of the test samples. Next, the values of the test samples were forecast using the SVM regression algorithm. The experimental results demonstrate that the proposed model has good forecasting performance.

  1. Linear SVM-Based Android Malware Detection for Reliable IoT Services

    National Research Council Canada - National Science Library

    Hyo-Sik Ham; Hwan-Hee Kim; Myung-Sup Kim; Mi-Jung Choi

    2014-01-01

    .... In this paper, we apply a linear support vector machine (SVM) to detect Android malware and compare the malware detection performance of SVM with that of other machine learning classifiers. Through experimental validation, we show that the SVM outperforms other machine learning classifiers.

  2. Quality-Oriented Classification of Aircraft Material Based on SVM

    Directory of Open Access Journals (Sweden)

    Hongxia Cai

    2014-01-01

    Full Text Available The existing material classification is proposed to improve the inventory management. However, different materials have the different quality-related attributes, especially in the aircraft industry. In order to reduce the cost without sacrificing the quality, we propose a quality-oriented material classification system considering the material quality character, Quality cost, and Quality influence. Analytic Hierarchy Process helps to make feature selection and classification decision. We use the improved Kraljic Portfolio Matrix to establish the three-dimensional classification model. The aircraft materials can be divided into eight types, including general type, key type, risk type, and leveraged type. Aiming to improve the classification accuracy of various materials, the algorithm of Support Vector Machine is introduced. Finally, we compare the SVM and BP neural network in the application. The results prove that the SVM algorithm is more efficient and accurate and the quality-oriented material classification is valuable.

  3. Research on Bearing Fault Diagnosis Using APSO-SVM Method

    Directory of Open Access Journals (Sweden)

    Guangchun Yang

    2014-07-01

    Full Text Available According to the statistics, over 30 % of rotating equipment faults occurred in bearings. Therefore, the fault diagnosis of bearing has a great significance. To achieve effective bearing faults diagnosis, a diagnosis model based on support vector machine (SVM and accelerated particle swarm optimization (APSO for bearing fault diagnosis is proposed. Firstly, empirical mode decomposition (EMD is adopted to decompose the fault signal into sum of several intrinsic mode function (IMF. Then, the feature vectors for bearing fault diagnosis are obtained from the IMF energy. Finally, the fault mode is identified by SVM model which is optimized by APSO. The experiment results show that the proposed diagnosis method can identify the bearing fault type effectively.

  4. SVM Intrusion Detection Model Based on Compressed Sampling

    Directory of Open Access Journals (Sweden)

    Shanxiong Chen

    2016-01-01

    Full Text Available Intrusion detection needs to deal with a large amount of data; particularly, the technology of network intrusion detection has to detect all of network data. Massive data processing is the bottleneck of network software and hardware equipment in intrusion detection. If we can reduce the data dimension in the stage of data sampling and directly obtain the feature information of network data, efficiency of detection can be improved greatly. In the paper, we present a SVM intrusion detection model based on compressive sampling. We use compressed sampling method in the compressed sensing theory to implement feature compression for network data flow so that we can gain refined sparse representation. After that SVM is used to classify the compression results. This method can realize detection of network anomaly behavior quickly without reducing the classification accuracy.

  5. Fault diagnosis of monoblock centrifugal pump using SVM

    Directory of Open Access Journals (Sweden)

    V. Muralidharan

    2014-09-01

    Full Text Available Monoblock centrifugal pumps are employed in variety of critical engineering applications. Continuous monitoring of such machine component becomes essential in order to reduce the unnecessary break downs. At the outset, vibration based approaches are widely used to carry out the condition monitoring tasks. Particularly fuzzy logic, support vector machine (SVM and artificial neural networks were employed for continuous monitoring and fault diagnosis. In the present study, the application of SVM algorithm in the field of fault diagnosis and condition monitoring is discussed. The continuous wavelet transforms were calculated for different families and at different levels. The computed transformation coefficients form the feature set for the classification of good and faulty conditions of the components of centrifugal pump. The classification accuracies of different continuous wavelet families at different levels were calculated and compared to find the best wavelet for the fault diagnosis of the monoblock centrifugal pump.

  6. A novel transmission line protection using DOST and SVM

    Directory of Open Access Journals (Sweden)

    M. Jaya Bharata Reddy

    2016-06-01

    Full Text Available This paper proposes a smart fault detection, classification and location (SFDCL methodology for transmission systems with multi-generators using discrete orthogonal Stockwell transform (DOST. The methodology is based on synchronized current measurements from remote telemetry units (RTUs installed at both ends of the transmission line. The energy coefficients extracted from the transient current signals due to occurrence of different types of faults using DOST are being utilized for real-time fault detection and classification. Support vector machine (SVM has been deployed for locating the fault distance using the extracted coefficients. A comparative study is performed for establishing the superiority of SVM over other popular computational intelligence methods, such as adaptive neuro-fuzzy inference system (ANFIS and artificial neural network (ANN, for more precise and reliable estimation of fault distance. The results corroborate the effectiveness of the suggested SFDCL algorithm for real-time transmission line fault detection, classification and localization.

  7. The efficacy of support vector machines (SVM) in robust ...

    Indian Academy of Sciences (India)

    (2006) by applying an SVM statistical learning machine on the time-scale wavelet decomposition methods. We used the data of 108 events in central Japan with magnitude ranging from 3 to 7.4 recorded at KiK-net network stations, for a source–receiver distance of up to 150 km during the period 1998–2011. We applied a ...

  8. Power quality events recognition using a SVM-based method

    Energy Technology Data Exchange (ETDEWEB)

    Cerqueira, Augusto Santiago; Ferreira, Danton Diego; Ribeiro, Moises Vidal; Duque, Carlos Augusto [Department of Electrical Circuits, Federal University of Juiz de Fora, Campus Universitario, 36036 900, Juiz de Fora MG (Brazil)

    2008-09-15

    In this paper, a novel SVM-based method for power quality event classification is proposed. A simple approach for feature extraction is introduced, based on the subtraction of the fundamental component from the acquired voltage signal. The resulting signal is presented to a support vector machine for event classification. Results from simulation are presented and compared with two other methods, the OTFR and the LCEC. The proposed method shown an improved performance followed by a reasonable computational cost. (author)

  9. Comparison of CIV, SIV and AIV using Decision Tree and SVM

    Directory of Open Access Journals (Sweden)

    Park Hyorin

    2016-01-01

    Full Text Available The H3N2, the canine influenza virus has numerous types of animal hosts that can live and reproduce on. They mostly settle on pigs and birds. However, some concerned voices are rising that there is high possibility that humans could be an additional victim for the canine flu. Consequently, our project group expect that the information about the H3N2’s DNA are valuable, since the information could attribute to development of vaccine and medicine. In the experiments of analysing the properties of CIV, Canine Influenza Virus with the comparison of SIV, Swine Influenza Virus and AIV, Avian Influenza Virus with the decision tree and SVM, Support Vector Machine. The result came out that CIV, SIV and AIV are alike but also different in some aspects.

  10. Ancient DNA and Forensics Mutual Benefits a Practical Sampling and Laboratory Guide Through a Virtual Ancient DNA Study

    Directory of Open Access Journals (Sweden)

    Jan Cemper-Kiesslich

    2014-09-01

    In this review the authors give a general overview on the field of ancient DNA analysis focussing of the potentials and limits, fields of application, requirements for samples, laboratory setup, reaction design and equipment as well as a brief outlook on current developments, future perspectives and potential cross links with associated scientific disciplines. Key words: Human DNA, Ancient DNA, Forensic DNA typing, Molecular archaeology, Application.

  11. Oil spill detection from SAR image using SVM based classification

    Directory of Open Access Journals (Sweden)

    A. A. Matkan

    2013-09-01

    Full Text Available In this paper, the potential of fully polarimetric L-band SAR data for detecting sea oil spills is investigated using polarimetric decompositions and texture analysis based on SVM classifier. First, power and magnitude measurements of HH and VV polarization modes and, Pauli, Freeman and Krogager decompositions are computed and applied in SVM classifier. Texture analysis is used for identification using SVM method. The texture features i.e. Mean, Variance, Contrast and Dissimilarity from them are then extracted. Experiments are conducted on full polarimetric SAR data acquired from PALSAR sensor of ALOS satellite on August 25, 2006. An accuracy assessment indicated overall accuracy of 78.92% and 96.46% for the power measurement of the VV polarization and the Krogager decomposition respectively in first step. But by use of texture analysis the results are improved to 96.44% and 96.65% quality for mean of power and magnitude measurements of HH and VV polarizations and the Krogager decomposition. Results show that the Krogager polarimetric decomposition method has the satisfying result for detection of sea oil spill on the sea surface and the texture analysis presents the good results.

  12. Automatic Language Identification with Discriminative Language Characterization Based on SVM

    Science.gov (United States)

    Suo, Hongbin; Li, Ming; Lu, Ping; Yan, Yonghong

    Robust automatic language identification (LID) is the task of identifying the language from a short utterance spoken by an unknown speaker. The mainstream approaches include parallel phone recognition language modeling (PPRLM), support vector machine (SVM) and the general Gaussian mixture models (GMMs). These systems map the cepstral features of spoken utterances into high level scores by classifiers. In this paper, in order to increase the dimension of the score vector and alleviate the inter-speaker variability within the same language, multiple data groups based on supervised speaker clustering are employed to generate the discriminative language characterization score vectors (DLCSV). The back-end SVM classifiers are used to model the probability distribution of each target language in the DLCSV space. Finally, the output scores of back-end classifiers are calibrated by a pair-wise posterior probability estimation (PPPE) algorithm. The proposed language identification frameworks are evaluated on 2003 NIST Language Recognition Evaluation (LRE) databases and the experiments show that the system described in this paper produces comparable results to the existing systems. Especially, the SVM framework achieves an equal error rate (EER) of 4.0% in the 30-second task and outperforms the state-of-art systems by more than 30% relative error reduction. Besides, the performances of proposed PPRLM and GMMs algorithms achieve an EER of 5.1% and 5.0% respectively.

  13. Hardware realization of an SVM algorithm implemented in FPGAs

    Science.gov (United States)

    Wiśniewski, Remigiusz; Bazydło, Grzegorz; Szcześniak, Paweł

    2017-08-01

    The paper proposes a technique of hardware realization of a space vector modulation (SVM) of state function switching in matrix converter (MC), oriented on the implementation in a single field programmable gate array (FPGA). In MC the SVM method is based on the instantaneous space-vector representation of input currents and output voltages. The traditional computation algorithms usually involve digital signal processors (DSPs) which consumes the large number of power transistors (18 transistors and 18 independent PWM outputs) and "non-standard positions of control pulses" during the switching sequence. Recently, hardware implementations become popular since computed operations may be executed much faster and efficient due to nature of the digital devices (especially concurrency). In the paper, we propose a hardware algorithm of SVM computation. In opposite to the existing techniques, the presented solution applies COordinate Rotation DIgital Computer (CORDIC) method to solve the trigonometric operations. Furthermore, adequate arithmetic modules (that is, sub-devices) used for intermediate calculations, such as code converters or proper sectors selectors (for output voltages and input current) are presented in detail. The proposed technique has been implemented as a design described with the use of Verilog hardware description language. The preliminary results of logic implementation oriented on the Xilinx FPGA (particularly, low-cost device from Artix-7 family from Xilinx was used) are also presented.

  14. Static Voltage Stability Analysis by Using SVM and Neural Network

    Directory of Open Access Journals (Sweden)

    Mehdi Hajian

    2013-01-01

    Full Text Available Voltage stability is an important problem in power system networks. In this paper, in terms of static voltage stability, and application of Neural Networks (NN and Supported Vector Machine (SVM for estimating of voltage stability margin (VSM and predicting of voltage collapse has been investigated. This paper considers voltage stability in power system in two parts. The first part calculates static voltage stability margin by Radial Basis Function Neural Network (RBFNN. The advantage of the used method is high accuracy in online detecting the VSM. Whereas the second one, voltage collapse analysis of power system is performed by Probabilistic Neural Network (PNN and SVM. The obtained results in this paper indicate, that time and number of training samples of SVM, are less than NN. In this paper, a new model of training samples for detection system, using the normal distribution load curve at each load feeder, has been used. Voltage stability analysis is estimated by well-know L and VSM indexes. To demonstrate the validity of the proposed methods, IEEE 14 bus grid and the actual network of Yazd Province are used.

  15. Multi-Sectional Views Textural Based SVM for MS Lesion Segmentation in Multi-Channels MRIs.

    Science.gov (United States)

    Abdullah, Bassem A; Younis, Akmal A; John, Nigel M

    2012-01-01

    In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data. The technique uses a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The classification is done on each of the axial, sagittal and coronal sectional brain view independently and the resultant segmentations are aggregated to provide more accurate output segmentation. The main contribution of the proposed technique described in this paper is the use of textural features to detect MS lesions in a fully automated approach that does not rely on manually delineating the MS lesions. In addition, the technique introduces the concept of the multi-sectional view segmentation to produce verified segmentation. The proposed textural-based SVM technique was evaluated using three simulated datasets and more than fifty real MRI datasets. The results were compared with state of the art methods. The obtained results indicate that the proposed method would be viable for use in clinical practice for the detection of MS lesions in MRI.

  16. An Efficient Normalized Rank Based SVM for Room Level Indoor WiFi Localization with Diverse Devices

    Directory of Open Access Journals (Sweden)

    Yasmine Rezgui

    2017-01-01

    Full Text Available This paper proposes an efficient and effective WiFi fingerprinting-based indoor localization algorithm, which uses the Received Signal Strength Indicator (RSSI of WiFi signals. In practical harsh indoor environments, RSSI variation and hardware variance can significantly degrade the performance of fingerprinting-based localization methods. To address the problem of hardware variance and signal fluctuation in WiFi fingerprinting-based localization, we propose a novel normalized rank based Support Vector Machine classifier (NR-SVM. Moving from RSSI value based analysis to the normalized rank transformation based analysis, the principal features are prioritized and the dimensionalities of signature vectors are taken into account. The proposed method has been tested using sixteen different devices in a shopping mall with 88 shops. The experimental results demonstrate its robustness with no less than 98.75% correct estimation in 93.75% of the tested cases and 100% correct rate in 56.25% of cases. In the experiments, the new method shows better performance over the KNN, Naïve Bayes, Random Forest, and Neural Network algorithms. Furthermore, we have compared the proposed approach with three popular calibration-free transformation based methods, including difference method (DIFF, Signal Strength Difference (SSD, and the Hyperbolic Location Fingerprinting (HLF based SVM. The results show that the NR-SVM outperforms these popular methods.

  17. A Hybrid ICA-SVM Approach for Determining the Quality Variables at Fault in a Multivariate Process

    Directory of Open Access Journals (Sweden)

    Yuehjen E. Shao

    2012-01-01

    Full Text Available The monitoring of a multivariate process with the use of multivariate statistical process control (MSPC charts has received considerable attention. However, in practice, the use of MSPC chart typically encounters a difficulty. This difficult involves which quality variable or which set of the quality variables is responsible for the generation of the signal. This study proposes a hybrid scheme which is composed of independent component analysis (ICA and support vector machine (SVM to determine the fault quality variables when a step-change disturbance existed in a multivariate process. The proposed hybrid ICA-SVM scheme initially applies ICA to the Hotelling T2 MSPC chart to generate independent components (ICs. The hidden information of the fault quality variables can be identified in these ICs. The ICs are then served as the input variables of the classifier SVM for performing the classification process. The performance of various process designs is investigated and compared with the typical classification method. Using the proposed approach, the fault quality variables for a multivariate process can be accurately and reliably determined.

  18. Melancholia EEG classification based on CSSD and SVM

    Science.gov (United States)

    Shi, Jian-Jun; Yuan, Qing-Wu; Zhou, La-Wu

    2011-10-01

    It takes an important role to get the disease information from melancholia electroencephalograph (EEG). Firstly, A common spatial subspace decomposition (CSSD) method was used to extract features from 16-channel EEG of melancholia and normal healthy persons. Then based on support vector machines (SVM), a classifier was designed to train and test its classification capability between Melancholia and healthy persons. The results indicated that the proposed method can reach a higher accuracy as 95% in EEG classification, while the accuracy of the method based on wavelet is only 88%.That is, the proposed method is feasible for the melancholia diagnosis and research.

  19. Prediction of nuclear proteins using SVM and HMM models

    Directory of Open Access Journals (Sweden)

    Raghava Gajendra PS

    2009-01-01

    Full Text Available Abstract Background The nucleus, a highly organized organelle, plays important role in cellular homeostasis. The nuclear proteins are crucial for chromosomal maintenance/segregation, gene expression, RNA processing/export, and many other processes. Several methods have been developed for predicting the nuclear proteins in the past. The aim of the present study is to develop a new method for predicting nuclear proteins with higher accuracy. Results All modules were trained and tested on a non-redundant dataset and evaluated using five-fold cross-validation technique. Firstly, Support Vector Machines (SVM based modules have been developed using amino acid and dipeptide compositions and achieved a Mathews correlation coefficient (MCC of 0.59 and 0.61 respectively. Secondly, we have developed SVM modules using split amino acid compositions (SAAC and achieved the maximum MCC of 0.66. Thirdly, a hidden Markov model (HMM based module/profile was developed for searching exclusively nuclear and non-nuclear domains in a protein. Finally, a hybrid module was developed by combining SVM module and HMM profile and achieved a MCC of 0.87 with an accuracy of 94.61%. This method performs better than the existing methods when evaluated on blind/independent datasets. Our method estimated 31.51%, 21.89%, 26.31%, 25.72% and 24.95% of the proteins as nuclear proteins in Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, mouse and human proteomes respectively. Based on the above modules, we have developed a web server NpPred for predicting nuclear proteins http://www.imtech.res.in/raghava/nppred/. Conclusion This study describes a highly accurate method for predicting nuclear proteins. SVM module has been developed for the first time using SAAC for predicting nuclear proteins, where amino acid composition of N-terminus and the remaining protein were computed separately. In addition, our study is a first documentation where exclusively nuclear

  20. A Linear-RBF Multikernel SVM to Classify Big Text Corpora

    Directory of Open Access Journals (Sweden)

    R. Romero

    2015-01-01

    Full Text Available Support vector machine (SVM is a powerful technique for classification. However, SVM is not suitable for classification of large datasets or text corpora, because the training complexity of SVMs is highly dependent on the input size. Recent developments in the literature on the SVM and other kernel methods emphasize the need to consider multiple kernels or parameterizations of kernels because they provide greater flexibility. This paper shows a multikernel SVM to manage highly dimensional data, providing an automatic parameterization with low computational cost and improving results against SVMs parameterized under a brute-force search. The model consists in spreading the dataset into cohesive term slices (clusters to construct a defined structure (multikernel. The new approach is tested on different text corpora. Experimental results show that the new classifier has good accuracy compared with the classic SVM, while the training is significantly faster than several other SVM classifiers.

  1. Penerapan Support Vector Machine (SVM untuk Pengkategorian Penelitian

    Directory of Open Access Journals (Sweden)

    Fithri Selva Jumeilah

    2017-07-01

    Full Text Available Research every college will continue to grow. Research will be stored in softcopy and hardcopy. The preparation of the research should be categorized in order to facilitate the search for people who need reference. To categorize the research, we need a method for text mining, one of them is with the implementation of Support Vector Machines (SVM. The data used to recognize the characteristics of each category then it takes secondary data which is a collection of abstracts of research. The data will be pre-processed with several stages: case folding converts all the letters into lowercase, stop words removal removal of very common words, tokenizing discard punctuation, and stemming searching for root words by removing the prefix and suffix. Further data that has undergone preprocessing will be converted into a numerical form with for the term weighting stage that is the weighting contribution of each word. From the results of term weighting then obtained data that can be used for data training and test data. The training process is done by providing input in the form of text data that is known to the class or category. Then by using the Support Vector Machines algorithm, the input data is transformed into a rule, function, or knowledge model that can be used in the prediction process. From the results of this study obtained that the categorization of research produced by SVM has been very good. This is proven by the results of the test which resulted in an accuracy of 90%.

  2. Forecasting Dry Bulk Freight Index with Improved SVM

    Directory of Open Access Journals (Sweden)

    Qianqian Han

    2014-01-01

    Full Text Available An improved SVM model is presented to forecast dry bulk freight index (BDI in this paper, which is a powerful tool for operators and investors to manage the market trend and avoid price risking shipping industry. The BDI is influenced by many factors, especially the random incidents in dry bulk market, inducing the difficulty in forecasting of BDI. Therefore, to eliminate the impact of random incidents in dry bulk market, wavelet transform is adopted to denoise the BDI data series. Hence, the combined model of wavelet transform and support vector machine is developed to forecast BDI in this paper. Lastly, the BDI data in 2005 to 2012 are presented to test the proposed model. The 84 prior consecutive monthly BDI data are the inputs of the model, and the last 12 monthly BDI data are the outputs of model. The parameters of the model are optimized by genetic algorithm and the final model is conformed through SVM training. This paper compares the forecasting result of proposed method and three other forecasting methods. The result shows that the proposed method has higher accuracy and could be used to forecast the short-term trend of the BDI.

  3. SVM-based glioma grading. Optimization by feature reduction analysis

    Energy Technology Data Exchange (ETDEWEB)

    Zoellner, Frank G.; Schad, Lothar R. [University Medical Center Mannheim, Heidelberg Univ., Mannheim (Germany). Computer Assisted Clinical Medicine; Emblem, Kyrre E. [Massachusetts General Hospital, Charlestown, A.A. Martinos Center for Biomedical Imaging, Boston MA (United States). Dept. of Radiology; Harvard Medical School, Boston, MA (United States); Oslo Univ. Hospital (Norway). The Intervention Center

    2012-11-01

    We investigated the predictive power of feature reduction analysis approaches in support vector machine (SVM)-based classification of glioma grade. In 101 untreated glioma patients, three analytic approaches were evaluated to derive an optimal reduction in features; (i) Pearson's correlation coefficients (PCC), (ii) principal component analysis (PCA) and (iii) independent component analysis (ICA). Tumor grading was performed using a previously reported SVM approach including whole-tumor cerebral blood volume (CBV) histograms and patient age. Best classification accuracy was found using PCA at 85% (sensitivity = 89%, specificity = 84%) when reducing the feature vector from 101 (100-bins rCBV histogram + age) to 3 principal components. In comparison, classification accuracy by PCC was 82% (89%, 77%, 2 dimensions) and 79% by ICA (87%, 75%, 9 dimensions). For improved speed (up to 30%) and simplicity, feature reduction by all three methods provided similar classification accuracy to literature values ({proportional_to}87%) while reducing the number of features by up to 98%. (orig.)

  4. Good Manufacturing Practices production and analysis of a DNA vaccine against dental caries.

    Science.gov (United States)

    Yang, Ya-ping; Li, Yu-hong; Zhang, Ai-hua; Bi, Lan; Fan, Ming-wen

    2009-11-01

    To prepare a clinical-grade anti-caries DNA vaccine pGJA-P/VAX and explore its immune effect and protective efficacy against a cariogenic bacterial challenge. A large-scale industrial production process was developed under Good Manufacturing Practices (GMP) by combining and optimizing common unit operations such as alkaline lysis, precipitation, endotoxin removal and column chromatography. Quality controls of the purified bulk and final lyophilized vaccine were conducted according to authoritative guidelines. Mice and gnotobiotic rats were intranasally immunized with clinical-grade pGJA-P/VAX with chitosan. Antibody levels of serum IgG and salivary SIgA were assessed by an enzyme-linked immunosorbent assay (ELISA), and caries activity was evaluated by the Keyes method. pGJA-P/VAX and pVAX1 prepared by a laboratory-scale commercial kit were used as controls. The production process proved to be scalable and reproducible. Impurities including host protein, residual RNA, genomic DNA and endotoxin in the purified plasmid were all under the limits of set specifications. Intranasal vaccination with clinical-grade pGJA-P/VAX induced higher serum IgG and salivary SIgA in both mice and gnotobiotic rats. While in the experimental caries model, the enamel (E), dentinal slight (Ds), and dentinal moderate (Dm) caries lesions were reduced by 21.1%, 33.0%, and 40.9%, respectively. The production process under GMP was efficient in preparing clinical-grade pGJA-P/VAX with high purity and intended effectiveness, thus facilitating future clinical trials for the anti-caries DNA vaccine.

  5. Good Manufacturing Practices production and analysis of a DNA vaccine against dental caries

    Science.gov (United States)

    Yang, Ya-ping; Li, Yu-hong; Zhang, Ai-hua; Bi, Lan; Fan, Ming-wen

    2009-01-01

    Aim: To prepare a clinical-grade anti-caries DNA vaccine pGJA-P/VAX and explore its immune effect and protective efficacy against a cariogenic bacterial challenge. Methods: A large-scale industrial production process was developed under Good Manufacturing Practices (GMP) by combining and optimizing common unit operations such as alkaline lysis, precipitation, endotoxin removal and column chromatography. Quality controls of the purified bulk and final lyophilized vaccine were conducted according to authoritative guidelines. Mice and gnotobiotic rats were intranasally immunized with clinical-grade pGJA-P/VAX with chitosan. Antibody levels of serum IgG and salivary SIgA were assessed by an enzyme-linked immunosorbent assay (ELISA), and caries activity was evaluated by the Keyes method. pGJA-P/VAX and pVAX1 prepared by a laboratory-scale commercial kit were used as controls. Results: The production process proved to be scalable and reproducible. Impurities including host protein, residual RNA, genomic DNA and endotoxin in the purified plasmid were all under the limits of set specifications. Intranasal vaccination with clinical-grade pGJA-P/VAX induced higher serum IgG and salivary SIgA in both mice and gnotobiotic rats. While in the experimental caries model, the enamel (E), dentinal slight (Ds), and dentinal moderate (Dm) caries lesions were reduced by 21.1%, 33.0%, and 40.9%, respectively. Conclusion: The production process under GMP was efficient in preparing clinical-grade pGJA-P/VAX with high purity and intended effectiveness, thus facilitating future clinical trials for the anti-caries DNA vaccine. PMID:19890359

  6. The Society for Translational Medicine: clinical practice guidelines for sperm DNA fragmentation testing in male infertility

    Science.gov (United States)

    Cho, Chak-Lam; Majzoub, Ahmad; Esteves, Sandro C.

    2017-01-01

    Sperm DNA fragmentation (SDF) testing has been emerging as a valuable tool for male fertility evaluation. While the essential role of sperm DNA integrity in human reproduction was extensively studied, the clinical indication of SDF testing is less clear. This clinical practice guideline provides recommendations of clinical utility of the test supported by evidence. It is intended to serve as a reference for fertility specialists in identifying the circumstances in which SDF testing should be of greatest clinical value. SDF testing is recommended in patients with clinical varicocele and borderline to normal semen parameters as it can better select varicocelectomy candidates. Outcomes of natural pregnancy and assisted reproductive techniques (ART) can be predicted by result of SDF tests. High SDF is also linked with recurrent pregnancy loss (RPL) and failure of ART. Result of SDF testing may change the management decision by selecting the most appropriate ART with the highest success rate for infertile couples. Several studies have demonstrated the benefit in using testicular instead of ejaculated sperm in men with high SDF, oligozoospermia or recurrent in vitro fertilization (IVF) failure. Infertile men with modifiable lifestyle factor may benefit from SDF testing by reinforcing risk factor modification and monitoring patient’s progress to intervention. PMID:29082206

  7. Prevention of DNA contamination during forensic medical examinations in a clinical forensic medical service: A best practice implementation project.

    Science.gov (United States)

    Lutz, Tasha

    2015-01-01

    Contamination of forensic specimens can have significant and detrimental effects on cases presented in court. In 2010 a wrongful conviction in Australia resulted in an inquiry with 25 recommendations to minimize the risk of DNA contamination of forensic specimens. DNA decontamination practices in a clinical forensic medical service currently attempt to comply with these recommendations. Evaluation of these practices has not been undertaken. The aim of this project was to audit the current DNA decontamination practices of forensic medical and nursing examiners in the forensic medical examination process and implement changes based on the audit findings. A re-audit following implementation would be undertaken to identify change and inform further research. The Joanna Briggs Institute's Practical Application of Clinical Evidence System and Getting Research into Practice were used as the audit tool in this project. A baseline audit was conducted; analysis of this audit process was then undertaken. Following education and awareness training targeted at clinicians, a re-audit was completed. There were a total of 24 audit criteria; the baseline audit reflected 20 of these criteria had 100% compliance. The remaining 4 audit criteria demonstrated compliance between 65% and 90%. Education and awareness training resulted in improved compliance in 2 of the 4 audit criteria, with the remaining 2 having unchanged compliance. The findings demonstrated that education and raising awareness can improve clinical practice; however there are also external factors outside the control of the clinicians that influence compliance with best practice.

  8. "Active Flux" DTFC-SVM Sensorless Control of IPMSM

    DEFF Research Database (Denmark)

    Boldea, Ion; Codruta Paicu, Mihaela; Gheorghe-Daniel, Andreescu,

    2009-01-01

    This paper proposes an implementation of a motionsensorless control system in wide speed range based on "active flux" observer, and direct torque and flux control with space vector modulation (DTFC-SVM) for the interior permanent magnet synchronous motor (IPMSM), without signal injection...... is obtained, because the active flux position is identical with the rotor position. Extensive experimental results are presented to verify the principles and to demonstrate the effectiveness of the proposed sensorless control system. With the active flux observer, the IPMSM drive system operates from very low....... The concept of "active flux" (or "torque producing flux") turns all the rotor salient-pole ac machines into fully nonsalient-pole ones. A new function for Lq inductance depending on torque is introduced to model the magnetic saturation. Notable simplification in the rotor position and speed estimation...

  9. [Application of SVM and wavelet analysis in EEG classification].

    Science.gov (United States)

    Zhao, Jianlin; Zhou, Weidong; Liu, Kai; Cai, Dongmei

    2011-04-01

    We employed two methods of support vector machines (SVM) combined with two kinds of wavelet analysis to classify these EEG signals, on the basis of the different profiles, energy, and frequency characteristics of the EEG during the seizures. One method was to classify these signals using waveform characteristics of the EEG signal. The other was to classify these signals based on fluctuation index and variation coefficient of the EEG signal. We compared the classification accuracies of these two methods with the intermittent EEG and epileptic EEG. The results of the experiments showed that both the two methods for distinguishing epileptic EEG and interictal EEG can achieve an effective performance. It was also confirmed that the latter, the method based on the fluctuation index and variation coefficient, possesses a better effect of classification.

  10. Optimal parameters of the SVM for temperature prediction

    Directory of Open Access Journals (Sweden)

    X. Shi

    2015-05-01

    Full Text Available This paper established three different optimization models in order to predict the Foping station temperature value. The dimension was reduced to change multivariate climate factors into a few variables by principal component analysis (PCA. And the parameters of support vector machine (SVM were optimized with genetic algorithm (GA, particle swarm optimization (PSO and developed genetic algorithm. The most suitable method was applied for parameter optimization by comparing the results of three different models. The results are as follows: The developed genetic algorithm optimization parameters of the predicted values were closest to the measured value after the analog trend, and it is the most fitting measured value trends, and its homing speed is relatively fast.

  11. Feature selection based on SVM significance maps for classification of dementia

    NARCIS (Netherlands)

    E.E. Bron (Esther); M. Smits (Marion); J.C. van Swieten (John); W.J. Niessen (Wiro); S. Klein (Stefan)

    2014-01-01

    textabstractSupport vector machine significance maps (SVM p-maps) previously showed clusters of significantly different voxels in dementiarelated brain regions. We propose a novel feature selection method for classification of dementia based on these p-maps. In our approach, the SVM p-maps are

  12. Spectral Reconstruction Based on Svm for Cross Calibration

    Science.gov (United States)

    Gao, H.; Ma, Y.; Liu, W.; He, H.

    2017-05-01

    Chinese HY-1C/1D satellites will use a 5nm/10nm-resolutional visible-near infrared(VNIR) hyperspectral sensor with the solar calibrator to cross-calibrate with other sensors. The hyperspectral radiance data are composed of average radiance in the sensor's passbands and bear a spectral smoothing effect, a transform from the hyperspectral radiance data to the 1-nm-resolution apparent spectral radiance by spectral reconstruction need to be implemented. In order to solve the problem of noise cumulation and deterioration after several times of iteration by the iterative algorithm, a novel regression method based on SVM is proposed, which can approach arbitrary complex non-linear relationship closely and provide with better generalization capability by learning. In the opinion of system, the relationship between the apparent radiance and equivalent radiance is nonlinear mapping introduced by spectral response function(SRF), SVM transform the low-dimensional non-linear question into high-dimensional linear question though kernel function, obtaining global optimal solution by virtue of quadratic form. The experiment is performed using 6S-simulated spectrums considering the SRF and SNR of the hyperspectral sensor, measured reflectance spectrums of water body and different atmosphere conditions. The contrastive result shows: firstly, the proposed method is with more reconstructed accuracy especially to the high-frequency signal; secondly, while the spectral resolution of the hyperspectral sensor reduces, the proposed method performs better than the iterative method; finally, the root mean square relative error(RMSRE) which is used to evaluate the difference of the reconstructed spectrum and the real spectrum over the whole spectral range is calculated, it decreses by one time at least by proposed method.

  13. SPECTRAL RECONSTRUCTION BASED ON SVM FOR CROSS CALIBRATION

    Directory of Open Access Journals (Sweden)

    H. Gao

    2017-05-01

    Full Text Available Chinese HY-1C/1D satellites will use a 5nm/10nm-resolutional visible-near infrared(VNIR hyperspectral sensor with the solar calibrator to cross-calibrate with other sensors. The hyperspectral radiance data are composed of average radiance in the sensor’s passbands and bear a spectral smoothing effect, a transform from the hyperspectral radiance data to the 1-nm-resolution apparent spectral radiance by spectral reconstruction need to be implemented. In order to solve the problem of noise cumulation and deterioration after several times of iteration by the iterative algorithm, a novel regression method based on SVM is proposed, which can approach arbitrary complex non-linear relationship closely and provide with better generalization capability by learning. In the opinion of system, the relationship between the apparent radiance and equivalent radiance is nonlinear mapping introduced by spectral response function(SRF, SVM transform the low-dimensional non-linear question into high-dimensional linear question though kernel function, obtaining global optimal solution by virtue of quadratic form. The experiment is performed using 6S-simulated spectrums considering the SRF and SNR of the hyperspectral sensor, measured reflectance spectrums of water body and different atmosphere conditions. The contrastive result shows: firstly, the proposed method is with more reconstructed accuracy especially to the high-frequency signal; secondly, while the spectral resolution of the hyperspectral sensor reduces, the proposed method performs better than the iterative method; finally, the root mean square relative error(RMSRE which is used to evaluate the difference of the reconstructed spectrum and the real spectrum over the whole spectral range is calculated, it decreses by one time at least by proposed method.

  14. Optimized testing for C. trachomatis DNA in synovial fluid samples in clinical practice.

    Science.gov (United States)

    Freise, J; Bernau, I; Meier, S; Zeidler, H; Kuipers, J G

    2015-11-01

    No standardized polymerase chain reaction (PCR) assay is available for detection of Chlamydia trachomatis (C. tr.) in synovial fluid (SF) for diagnostic use in clinical practice. This study tested the performance of two optimized molecular biology methods, to determine which is best suited for detecting C. tr. in SF clinical samples from patients with various rheumatologic diseases. Two DNA extraction methods, i.e., (1) alkaline lysis and (2) QIAEX II Gel Extraction Kit® + cetyltrimethylammonium bromide (CTAB; Qiagen, Hilden, Germany), and C. tr.-omp1-152 bp PCR were tested in SF samples from a total of 329 patients with the following diagnoses: reactive arthritis (ReA; n = 10, 4 patients had posturethritic ReA), undifferentiated arthritis (UA; n = 66), rheumatoid arthritis (RA; n = 169), psoriatic arthritis (PSA; n = 12), and osteoarthritis (OA) n = 72. In SF samples, C. tr.-omp1-152 bp PCR in combination with alkaline lysis DNA extraction allowed detection of more C. tr.-positive samples: 3/10 (30%) ReA patients (all with posturethritic ReA) and 20/66 (38%) UA patients were positive, compared to the 0/10 (0%) patients with ReA and 1/66 (2%) with UA detected using the QIAEX II Gel Extraction Kit® + CTAB. Moreover, 2/12 (17%) SF samples from PSA patients tested positive with alkaline lysis. All samples from patients with OA and RA tested negative. Alkaline lysis in combination with C. tr.-omp1-152 bp PCR emerged as the most sensitive method for identification of C. tr. in clinical SF samples.

  15. Settlement Prediction of Road Soft Foundation Using a Support Vector Machine (SVM Based on Measured Data

    Directory of Open Access Journals (Sweden)

    Yu Huiling

    2016-01-01

    Full Text Available The suppor1t vector machine (SVM is a relatively new artificial intelligence technique which is increasingly being applied to geotechnical problems and is yielding encouraging results. SVM is a new machine learning method based on the statistical learning theory. A case study based on road foundation engineering project shows that the forecast results are in good agreement with the measured data. The SVM model is also compared with BP artificial neural network model and traditional hyperbola method. The prediction results indicate that the SVM model has a better prediction ability than BP neural network model and hyperbola method. Therefore, settlement prediction based on SVM model can reflect actual settlement process more correctly. The results indicate that it is effective and feasible to use this method and the nonlinear mapping relation between foundation settlement and its influence factor can be expressed well. It will provide a new method to predict foundation settlement.

  16. DSP Based Direct Torque Control of Permanent Magnet Synchronous Motor (PMSM) using Space Vector Modulation (DTC-SVM)

    DEFF Research Database (Denmark)

    Swierczynski, Dariusz; Kazmierkowski, Marian P.; Blaabjerg, Frede

    2002-01-01

    DSP Based Direct Torque Control of Permanent Magnet Synchronous Motor (PMSM) using Space Vector Modulation (DTC-SVM)......DSP Based Direct Torque Control of Permanent Magnet Synchronous Motor (PMSM) using Space Vector Modulation (DTC-SVM)...

  17. Combination model of empirical mode decomposition and SVM for river flow forecasting

    Science.gov (United States)

    Ismail, Shuhaida; Shabri, Ani

    2017-04-01

    A reliable prediction of river flow is always important for sound planning and smooth operation of the water resource system. In this study, a combination models based on Empirical Mode Decomposition (EMD) and Support Vector Machine (SVM) model referred as EMD-SVM is proposed for estimating future value of monthly river flow data. The proposed EMD-SVM has three important stages. The first stage, the data were decomposed into several numbers of Intrinsic Mode Functions (IMF) and a residual using EMD technique. In the second stage, the meaningful signals are identified using a statistical measure and the new dataset are obtained in this stage. The final stage applied SVM as forecasting tool to perform the river flow forecasting. To assess the effectiveness of EMD-SVM model, Selangor and Bernam Rivers were used as case studies. The experiment results stated that the proposed EMD-SVM have outperformed other model based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Correlation Coefficient (r). This indicating that EMD-SVM is a useful tool to predict complex time series with non-stationary and nonlinearity issues as well as a promising new method for river flow forecasting.

  18. An Improved Grey Wolf Optimization Strategy Enhanced SVM and Its Application in Predicting the Second Major

    Directory of Open Access Journals (Sweden)

    Yan Wei

    2017-01-01

    Full Text Available In order to develop a new and effective prediction system, the full potential of support vector machine (SVM was explored by using an improved grey wolf optimization (GWO strategy in this study. An improved GWO, IGWO, was first proposed to identify the most discriminative features for major prediction. In the proposed approach, particle swarm optimization (PSO was firstly adopted to generate the diversified initial positions, and then GWO was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on SVM. The resultant methodology, IGWO-SVM, is rigorously examined based on the real-life data which includes a series of factors that influence the students’ final decision to choose the specific major. To validate the proposed method, other metaheuristic based SVM methods including GWO based SVM, genetic algorithm based SVM, and particle swarm optimization-based SVM were used for comparison in terms of classification accuracy, AUC (the area under the receiver operating characteristic (ROC curve, sensitivity, and specificity. The experimental results demonstrate that the proposed approach can be regarded as a promising success with the excellent classification accuracy, AUC, sensitivity, and specificity of 87.36%, 0.8735, 85.37%, and 89.33%, respectively. Promisingly, the proposed methodology might serve as a new candidate of powerful tools for second major selection.

  19. sw-SVM: sensor weighting support vector machines for EEG-based brain-computer interfaces

    Science.gov (United States)

    Jrad, N.; Congedo, M.; Phlypo, R.; Rousseau, S.; Flamary, R.; Yger, F.; Rakotomamonjy, A.

    2011-10-01

    In many machine learning applications, like brain-computer interfaces (BCI), high-dimensional sensor array data are available. Sensor measurements are often highly correlated and signal-to-noise ratio is not homogeneously spread across sensors. Thus, collected data are highly variable and discrimination tasks are challenging. In this work, we focus on sensor weighting as an efficient tool to improve the classification procedure. We present an approach integrating sensor weighting in the classification framework. Sensor weights are considered as hyper-parameters to be learned by a support vector machine (SVM). The resulting sensor weighting SVM (sw-SVM) is designed to satisfy a margin criterion, that is, the generalization error. Experimental studies on two data sets are presented, a P300 data set and an error-related potential (ErrP) data set. For the P300 data set (BCI competition III), for which a large number of trials is available, the sw-SVM proves to perform equivalently with respect to the ensemble SVM strategy that won the competition. For the ErrP data set, for which a small number of trials are available, the sw-SVM shows superior performances as compared to three state-of-the art approaches. Results suggest that the sw-SVM promises to be useful in event-related potentials classification, even with a small number of training trials.

  20. Online Fault Diagnosis for Biochemical Process Based on FCM and SVM.

    Science.gov (United States)

    Wang, Xianfang; Du, Haoze; Tan, Jinglu

    2016-12-01

    Fault diagnosis is becoming an important issue in biochemical process, and a novel online fault detection and diagnosis approach is designed by combining fuzzy c-means (FCM) and support vector machine (SVM). The samples are preprocessed via FCM algorithm to enhance the ability of classification firstly. Then, those samples are input to the SVM classifier to realize the biochemical process fault diagnosis. In this study, a glutamic acid fermentation process is chosen as an example to diagnose the fault by this method, the result shows that the diagnosis time is largely shortened, and the accuracy is extremely improved by comparing to a single SVM method.

  1. An improved conjugate gradient scheme to the solution of least squares SVM.

    Science.gov (United States)

    Chu, Wei; Ong, Chong Jin; Keerthi, S Sathiya

    2005-03-01

    The least square support vector machines (LS-SVM) formulation corresponds to the solution of a linear system of equations. Several approaches to its numerical solutions have been proposed in the literature. In this letter, we propose an improved method to the numerical solution of LS-SVM and show that the problem can be solved using one reduced system of linear equations. Compared with the existing algorithm for LS-SVM, the approach used in this letter is about twice as efficient. Numerical results using the proposed method are provided for comparisons with other existing algorithms.

  2. Environmental pollution and DNA methylation: carcinogenesis, clinical significance, and practical applications.

    Science.gov (United States)

    Cao, Yi

    2015-09-01

    Environmental pollution is one of the main causes of human cancer. Exposures to environmental carcinogens result in genetic and epigenetic alterations which induce cell transformation. Epigenetic changes caused by environmental pollution play important roles in the development and progression of environmental pollution-related cancers. Studies on DNA methylation are among the earliest and most conducted epigenetic research linked to cancer. In this review, the roles of DNA methylation in carcinogenesis and their significance in clinical medicine were summarized, and the effects of environmental pollutants, particularly air pollutants, on DNA methylation were introduced. Furthermore, prospective applications of DNA methylation to environmental pollution detection and cancer prevention were discussed.

  3. SVM-based automatic diagnosis method for keratoconus

    Science.gov (United States)

    Gao, Yuhong; Wu, Qiang; Li, Jing; Sun, Jiande; Wan, Wenbo

    2017-06-01

    Keratoconus is a progressive cornea disease that can lead to serious myopia and astigmatism, or even to corneal transplantation, if it becomes worse. The early detection of keratoconus is extremely important to know and control its condition. In this paper, we propose an automatic diagnosis algorithm for keratoconus to discriminate the normal eyes and keratoconus ones. We select the parameters obtained by Oculyzer as the feature of cornea, which characterize the cornea both directly and indirectly. In our experiment, 289 normal cases and 128 keratoconus cases are divided into training and test sets respectively. Far better than other kernels, the linear kernel of SVM has sensitivity of 94.94% and specificity of 97.87% with all the parameters training in the model. In single parameter experiment of linear kernel, elevation with 92.03% sensitivity and 98.61% specificity and thickness with 97.28% sensitivity and 97.82% specificity showed their good classification abilities. Combining elevation and thickness of the cornea, the proposed method can reach 97.43% sensitivity and 99.19% specificity. The experiments demonstrate that the proposed automatic diagnosis method is feasible and reliable.

  4. Multitask SVM learning for remote sensing data classification

    Science.gov (United States)

    Leiva-Murillo, Jose M.; Gómez-Chova, Luis; Camps-Valls, Gustavo

    2010-10-01

    Many remote sensing data processing problems are inherently constituted by several tasks that can be solved either individually or jointly. For instance, each image in a multitemporal classification setting could be taken as an individual task but relation to previous acquisitions should be properly considered. In such problems, different modalities of the data (temporal, spatial, angular) gives rise to changes between the training and test distributions, which constitutes a difficult learning problem known as covariate shift. Multitask learning methods aim at jointly solving a set of prediction problems in an efficient way by sharing information across tasks. This paper presents a novel kernel method for multitask learning in remote sensing data classification. The proposed method alleviates the dataset shift problem by imposing cross-information in the classifiers through matrix regularization. We consider the support vector machine (SVM) as core learner and two regularization schemes are introduced: 1) the Euclidean distance of the predictors in the Hilbert space; and 2) the inclusion of relational operators between tasks. Experiments are conducted in the challenging remote sensing problems of cloud screening from multispectral MERIS images and for landmine detection.

  5. A Realistic Seizure Prediction Study Based on Multiclass SVM.

    Science.gov (United States)

    Direito, Bruno; Teixeira, César A; Sales, Francisco; Castelo-Branco, Miguel; Dourado, António

    2017-05-01

    A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensional feature sets, is presented. The feature sets, combined with multiclass classification and post-processing schemes aim at the generation of alarms and reduced influence of false positives. This study considers 216 patients from the European Epilepsy Database, and includes 185 patients with scalp EEG recordings and 31 with intracranial data. The strategy was tested over a total of 16,729.80[Formula: see text]h of inter-ictal data, including 1206 seizures. We found an overall sensitivity of 38.47% and a false positive rate per hour of 0.20. The performance of the method achieved statistical significance in 24 patients (11% of the patients). Despite the encouraging results previously reported in specific datasets, the prospective demonstration on long-term EEG recording has been limited. Our study presents a prospective analysis of a large heterogeneous, multicentric dataset. The statistical framework based on conservative assumptions, reflects a realistic approach compared to constrained datasets, and/or in-sample evaluations. The improvement of these results, with the definition of an appropriate set of features able to improve the distinction between the pre-ictal and nonpre-ictal states, hence minimizing the effect of confounding variables, remains a key aspect.

  6. A modular spectrum sensing system based on PSO-SVM.

    Science.gov (United States)

    Cai, Zhuoran; Zhao, Honglin; Yang, Zhutian; Mo, Yun

    2012-11-08

    In the cognitive radio system, spectrum sensing for detecting the presence of primary users in a licensed spectrum is a fundamental problem. Energy detection is the most popular spectrum sensing scheme used to differentiate the case where the primary user’s signal is present from the case where there is only noise. In fact, the nature of spectrum sensing can be taken as a binary classification problem, and energy detection is a linear classifier. If the signal-to-noise ratio (SNR) of the received signal is low, and the number of received signal samples for sensing is small, the binary classification problem is linearly inseparable. In this situation the performance of energy detection will decrease seriously. In this paper, a novel approach for obtaining a nonlinear threshold based on support vector machine with particle swarm optimization (PSO-SVM) to replace the linear threshold used in traditional energy detection is proposed. Simulations demonstrate that the performance of the proposed algorithm is much better than that of traditional energy detection.

  7. DNA as Genetic Material: Revisiting Classical Experiments through an Easy, Practical Class

    Directory of Open Access Journals (Sweden)

    W. J. Malagó

    2009-05-01

    Full Text Available In 1928, Frederick Griffith demonstrated a process of transmission of genetic information by transforming  Pneumococcus. In 1944, Oswald Avery, Colin Munro MacLeod and Maclyn McCarty showed that Griffith´stransformation principle  is DNA. Here, we intend to revisit these classicalexperiments by reproducing them in easier adapted forms, for a practical class given to undergraduate students. The Griffith experiment was reproduced by mixing heat-killed, ampicillin - resistant  E. coliwith live ampicillin -susceptible  E. coli, followed by plating samples in the presence or absence of the antibiotic. Cells were also plated separately as control. Avery’s work was reproduced by pre-treating a purified plasmid harboring the ampicillin resistan ce gene with Dnase I. Treated and untreated plasmids were then used to transform  E. colicells, which were plated in culture media containing ampicillin. The students received a class guide with brief theoretical explanations and protocols to perform the experiments. The original papers by Griffith and Avery  et al. were also provided, along with a list of questions to encourage a discussion on the experimental approach and results obtained. The adapted experiments were successful completed and all expected results were obtained in class. Thus the students effectively revisited the classical experiments which revealed that DNA is the genetic material. Also, the class was very well accepted, as indicated by students’ evaluations. Thus, we presented an inexpens ive, quick class involving important concepts, which can be easily reproduced in any laboratory withminor resources.

  8. Equal before the law: on the machinery of sameness in forensic DNA practice

    NARCIS (Netherlands)

    M'charek, A.; Hagendijk, R.; de Vries, W.

    2013-01-01

    The social and legal implications of forensic DNA are paramount. For this reason, forensic DNA enjoys ample attention from legal, bioethics, and science and technology studies scholars. This article contributes to the scholarship by focusing on the neglected issue of sameness. We investigate a

  9. Spanish- and English-Speaking Pregnant Women's Views on cfDNA and Other Prenatal Screening: Practical and Ethical Reflections.

    Science.gov (United States)

    Floyd, Erin; Allyse, Megan A; Michie, Marsha

    2016-10-01

    The rapid clinical implementation of cell-free DNA (cfDNA) screening, a non-invasive method of prenatal genetic screening, has outpaced research on its social and ethical implications. This study is the first to compare the ethical and practical views of Spanish- and English-speaking pregnant women in the United States about cfDNA screening. Semi-structured interviews were conducted with diverse Spanish- and English-speaking women who had received prenatal care at a large academic medical center. Of the 24 interviewees, ten were Latinas who were interviewed in Spanish; English-language interviews were conducted with seven non-Hispanic Asian and seven non-Hispanic White women. Participants held positive opinions concerning the accuracy of cfDNA screening and often noted that it would enhance preparedness. Participants also expressed concerns about the possibility of inaccurate results and the potentially negative effects of cfDNA screening on the experience of pregnancy. Differences emerged between Spanish and English speakers in their portrayals of their relationships with prenatal health care providers, the extent to which they questioned providers' advice, their ethical concerns, and their informational needs. We emphasize the importance of customizing prenatal test counseling to the needs of the individual patient, providing educationally appropriate counseling and literature, and mitigating potential language barriers.

  10. Detecting microcalcifications in mammograms by using SVM method for the diagnostics of breast cancer

    Science.gov (United States)

    Wan, Baikun; Wang, Ruiping; Qi, Hongzhi; Cao, Xuchen

    2005-01-01

    Support vector machine (SVM) is a new statistical learning method. Compared with the classical machine learning methods, SVM learning discipline is to minimize the structural risk instead of the empirical risk of the classical methods, and it gives better generative performance. Because SVM algorithm is a convex quadratic optimization problem, the local optimal solution is certainly the global optimal one. In this paper a SVM algorithm is applied to detect the micro-calcifications (MCCs) in mammograms for the diagnostics of breast cancer that has not been reported yet. It had been tested with 10 mammograms and the results show that the algorithm can achieve a higher true positive in comparison with artificial neural network (ANN) based on the empirical risk minimization, and is valuable for further study and application in the clinical engineering.

  11. Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; SubbaRao; Harish, N.; Lokesha

    Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference system (ANFIS) models...

  12. SVM ensemble based transfer learning for large-scale membrane proteins discrimination.

    Science.gov (United States)

    Mei, Suyu

    2014-01-07

    Membrane proteins play important roles in molecular trans-membrane transport, ligand-receptor recognition, cell-cell interaction, enzyme catalysis, host immune defense response and infectious disease pathways. Up to present, discriminating membrane proteins remains a challenging problem from the viewpoints of biological experimental determination and computational modeling. This work presents SVM ensemble based transfer learning model for membrane proteins discrimination (SVM-TLM). To reduce the data constraints on computational modeling, this method investigates the effectiveness of transferring the homolog knowledge to the target membrane proteins under the framework of probability weighted ensemble learning. As compared to multiple kernel learning based transfer learning model, the method takes the advantages of sparseness based SVM optimization on large data, thus more computationally efficient for large protein data analysis. The experiments on large membrane protein benchmark dataset show that SVM-TLM achieves significantly better cross validation performance than the baseline model. © 2013 Elsevier Ltd. All rights reserved.

  13. Human Walking Pattern Recognition Based on KPCA and SVM with Ground Reflex Pressure Signal

    Directory of Open Access Journals (Sweden)

    Zhaoqin Peng

    2013-01-01

    Full Text Available Algorithms based on the ground reflex pressure (GRF signal obtained from a pair of sensing shoes for human walking pattern recognition were investigated. The dimensionality reduction algorithms based on principal component analysis (PCA and kernel principal component analysis (KPCA for walking pattern data compression were studied in order to obtain higher recognition speed. Classifiers based on support vector machine (SVM, SVM-PCA, and SVM-KPCA were designed, and the classification performances of these three kinds of algorithms were compared using data collected from a person who was wearing the sensing shoes. Experimental results showed that the algorithm fusing SVM and KPCA had better recognition performance than the other two methods. Experimental outcomes also confirmed that the sensing shoes developed in this paper can be employed for automatically recognizing human walking pattern in unlimited environments which demonstrated the potential application in the control of exoskeleton robots.

  14. Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

    Directory of Open Access Journals (Sweden)

    C. Fernandez-Lozano

    2013-01-01

    Full Text Available Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM. Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA, the most representative variables for a specific classification problem can be selected.

  15. Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

    Science.gov (United States)

    Fernandez-Lozano, C.; Canto, C.; Gestal, M.; Andrade-Garda, J. M.; Rabuñal, J. R.; Dorado, J.; Pazos, A.

    2013-01-01

    Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected. PMID:24453933

  16. Comparison of sensorless FOC and SVM-DTFC of PMSM for low-speed applications

    DEFF Research Database (Denmark)

    Basar, Mehmet Sertug

    2013-01-01

    This article presents the performance analysis of Field Oriented Control (FOC) and Space Vector Modulation (SVM) Direct Torque and Flux Control (DTFC) of a Non-Salient Permanent Magnet Synchronous Machine (PMSM) under sensorless control within low speed region. The high-frequency alternating...... with a commercially available PMSM machine. Both controllers show satisfactory sensorless performance. FOC provides smoother and more accurate response while SVM-DTFC has the advantage of faster control....

  17. Accurate Fluid Level Measurement in Dynamic Environment Using Ultrasonic Sensor and ν-SVM

    Directory of Open Access Journals (Sweden)

    Jenny TERZIC

    2009-10-01

    Full Text Available A fluid level measurement system based on a single Ultrasonic Sensor and Support Vector Machines (SVM based signal processing and classification system has been developed to determine the fluid level in automotive fuel tanks. The novel approach based on the ν-SVM classification method uses the Radial Basis Function (RBF to compensate for the measurement error induced by the sloshing effects in the tank caused by vehicle motion. A broad investigation on selected pre-processing filters, namely, Moving Mean, Moving Median, and Wavelet filter, has also been presented. Field drive trials were performed under normal driving conditions at various fuel volumes ranging from 5 L to 50 L to acquire sample data from the ultrasonic sensor for the training of SVM model. Further drive trials were conducted to obtain data to verify the SVM results. A comparison of the accuracy of the predicted fluid level obtained using SVM and the pre-processing filters is provided. It is demonstrated that the ν-SVM model using the RBF kernel function and the Moving Median filter has produced the most accurate outcome compared with the other signal filtration methods in terms of fluid level measurement.

  18. A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images.

    Science.gov (United States)

    Xu, Yongzheng; Yu, Guizhen; Wang, Yunpeng; Wu, Xinkai; Ma, Yalong

    2016-08-19

    A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles' in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians.

  19. Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics

    Directory of Open Access Journals (Sweden)

    Xiaohui Lin

    2017-12-01

    Full Text Available Feature selection is an important topic in bioinformatics. Defining informative features from complex high dimensional biological data is critical in disease study, drug development, etc. Support vector machine-recursive feature elimination (SVM-RFE is an efficient feature selection technique that has shown its power in many applications. It ranks the features according to the recursive feature deletion sequence based on SVM. In this study, we propose a method, SVM-RFE-OA, which combines the classification accuracy rate and the average overlapping ratio of the samples to determine the number of features to be selected from the feature rank of SVM-RFE. Meanwhile, to measure the feature weights more accurately, we propose a modified SVM-RFE-OA (M-SVM-RFE-OA algorithm that temporally screens out the samples lying in a heavy overlapping area in each iteration. The experiments on the eight public biological datasets show that the discriminative ability of the feature subset could be measured more accurately by combining the classification accuracy rate with the average overlapping degree of the samples compared with using the classification accuracy rate alone, and shielding the samples in the overlapping area made the calculation of the feature weights more stable and accurate. The methods proposed in this study can also be used with other RFE techniques to define potential biomarkers from big biological data.

  20. A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM.

    Science.gov (United States)

    Wang, Qi; Luo, ZhiHao; Huang, JinCai; Feng, YangHe; Liu, Zhong

    2017-01-01

    Class imbalance ubiquitously exists in real life, which has attracted much interest from various domains. Direct learning from imbalanced dataset may pose unsatisfying results overfocusing on the accuracy of identification and deriving a suboptimal model. Various methodologies have been developed in tackling this problem including sampling, cost-sensitive, and other hybrid ones. However, the samples near the decision boundary which contain more discriminative information should be valued and the skew of the boundary would be corrected by constructing synthetic samples. Inspired by the truth and sense of geometry, we designed a new synthetic minority oversampling technique to incorporate the borderline information. What is more, ensemble model always tends to capture more complicated and robust decision boundary in practice. Taking these factors into considerations, a novel ensemble method, called Bagging of Extrapolation Borderline-SMOTE SVM (BEBS), has been proposed in dealing with imbalanced data learning (IDL) problems. Experiments on open access datasets showed significant superior performance using our model and a persuasive and intuitive explanation behind the method was illustrated. As far as we know, this is the first model combining ensemble of SVMs with borderline information for solving such condition.

  1. Parallel SVM for the analysis of hyperspectral data

    Science.gov (United States)

    Cavallaro, Gabriele; Atli Benediktsson, Jón; Riedel, Morris

    2014-05-01

    .e., borders, edges, discontinuities, surfaces, shapes) by performing a detailed physical analysis of the structures. Mathematical morphology provides very useful tools which allow enriching the image analysis when dealing with very high resolution (VHR) images. One of the most promising of the recent developments in the field of pattern recognition are Support Vector Machines (SVMs). These are supervised learning methods which are widely used for classification and regression. In such a context, our work aims to explore some issues regarding the SVMs. In particular, SVMs require a significant computational and storage capacity due to the large number of training vectors used for the analysis of very high spatial and spectral resolution remote sensing data. Specifically, we will adopt a parallel SVM based on the iterative MapReduce in order to analyze large scale classification problems by improving the computation speed and preserving the classification accuracies.

  2. Comparative Analysis of ANN and SVM Models Combined with Wavelet Preprocess for Groundwater Depth Prediction

    Directory of Open Access Journals (Sweden)

    Ting Zhou

    2017-10-01

    Full Text Available Reliable prediction of groundwater depth fluctuations has been an important component in sustainable water resources management. In this study, a data-driven prediction model combining discrete wavelet transform (DWT preprocess and support vector machine (SVM was proposed for groundwater depth forecasting. Regular artificial neural networks (ANN, regular SVM, and wavelet preprocessed artificial neural networks (WANN models were also developed for comparison. These methods were applied to the monthly groundwater depth records over a period of 37 years from ten wells in the Mengcheng County, China. Relative absolute error (RAE, Pearson correlation coefficient (r, root mean square error (RMSE, and Nash-Sutcliffe efficiency (NSE were adopted for model evaluation. The results indicate that wavelet preprocess extremely improved the training and test performance of ANN and SVM models. The WSVM model provided the most precise and reliable groundwater depth prediction compared with ANN, SVM, and WSVM models. The criterion of RAE, r, RMSE, and NSE values for proposed WSVM model are 0.20, 0.97, 0.18 and 0.94, respectively. Comprehensive comparisons and discussion revealed that wavelet preprocess extremely improves the prediction precision and reliability for both SVM and ANN models. The prediction result of SVM model is superior to ANN model in generalization ability and precision. Nevertheless, the performance of WANN is superior to SVM model, which further validates the power of data preprocess in data-driven prediction models. Finally, the optimal model, WSVM, is discussed by comparing its subseries performances as well as model performance stability, revealing the efficiency and universality of WSVM model in data driven prediction field.

  3. Support vector machine regression (SVR/LS-SVM)--an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data.

    Science.gov (United States)

    Balabin, Roman M; Lomakina, Ekaterina I

    2011-04-21

    In this study, we make a general comparison of the accuracy and robustness of five multivariate calibration models: partial least squares (PLS) regression or projection to latent structures, polynomial partial least squares (Poly-PLS) regression, artificial neural networks (ANNs), and two novel techniques based on support vector machines (SVMs) for multivariate data analysis: support vector regression (SVR) and least-squares support vector machines (LS-SVMs). The comparison is based on fourteen (14) different datasets: seven sets of gasoline data (density, benzene content, and fractional composition/boiling points), two sets of ethanol gasoline fuel data (density and ethanol content), one set of diesel fuel data (total sulfur content), three sets of petroleum (crude oil) macromolecules data (weight percentages of asphaltenes, resins, and paraffins), and one set of petroleum resins data (resins content). Vibrational (near-infrared, NIR) spectroscopic data are used to predict the properties and quality coefficients of gasoline, biofuel/biodiesel, diesel fuel, and other samples of interest. The four systems presented here range greatly in composition, properties, strength of intermolecular interactions (e.g., van der Waals forces, H-bonds), colloid structure, and phase behavior. Due to the high diversity of chemical systems studied, general conclusions about SVM regression methods can be made. We try to answer the following question: to what extent can SVM-based techniques replace ANN-based approaches in real-world (industrial/scientific) applications? The results show that both SVR and LS-SVM methods are comparable to ANNs in accuracy. Due to the much higher robustness of the former, the SVM-based approaches are recommended for practical (industrial) application. This has been shown to be especially true for complicated, highly nonlinear objects.

  4. Principles, Practice, and Evolution of Capillary Electrophoresis as a Tool for Forensic DNA Analysis.

    Science.gov (United States)

    Shewale, J G; Qi, L; Calandro, L M

    2012-07-01

    Capillary electrophoresis (CE) is a versatile and widely used analysis platform with application in diverse areas such as analytical chemistry, chiral separations, clinical, forensics, molecular biology, natural products, organic chemistry, and the pharmaceutical industry. Forensic applications of CE include fragment analysis, DNA sequencing, SNP typing, and analysis of gunshot residues, explosive residues, and drugs. Fragment analysis is a widely used method for short tandem repeat (STR) profiling for human identification (HID) due to the single-base resolution capability of CE. This approach circumvents the tedious and expensive approach of DNA sequencing for STR typing. The high sizing precision, ability to detect fluorescence emitted from multiple dyes, automated electrophoretic runs, and data collection software are key factors in the worldwide adoption of CE as the preferred platform for forensic DNA analysis. The most common CE systems used in forensic DNA analysis include the ABI PRISM® 310, 3100, 3100 Avant, 3130, 3130xl, 3500, and 3500xL Genetic Analyzers (GAs). The 3500 series GAs are developed with features useful for forensic scientists, including a normalization feature for analysis of the data designed to reduce the variation in peak height from instrument to instrument and injection to injection. Other hardware and software features include improved temperature control, radio frequency identification (RFID) tags for monitoring instrument consumables, HID-focused software features, and security and maintenance. Copyright © 2012 Central Police University.

  5. Linear regression-based efficient SVM learning for large-scale classification.

    Science.gov (United States)

    Wu, Jianxin; Yang, Hao

    2015-10-01

    For large-scale classification tasks, especially in the classification of images, additive kernels have shown a state-of-the-art accuracy. However, even with the recent development of fast algorithms, learning speed and the ability to handle large-scale tasks are still open problems. This paper proposes algorithms for large-scale support vector machines (SVM) classification and other tasks using additive kernels. First, a linear regression SVM framework for general nonlinear kernel is proposed using linear regression to approximate gradient computations in the learning process. Second, we propose a power mean SVM (PmSVM) algorithm for all additive kernels using nonsymmetric explanatory variable functions. This nonsymmetric kernel approximation has advantages over the existing methods: 1) it does not require closed-form Fourier transforms and 2) it does not require extra training for the approximation either. Compared on benchmark large-scale classification data sets with millions of examples or millions of dense feature dimensions, PmSVM has achieved the highest learning speed and highest accuracy among recent algorithms in most cases.

  6. A Method for Aileron Actuator Fault Diagnosis Based on PCA and PGC-SVM

    Directory of Open Access Journals (Sweden)

    Wei-Li Qin

    2016-01-01

    Full Text Available Aileron actuators are pivotal components for aircraft flight control system. Thus, the fault diagnosis of aileron actuators is vital in the enhancement of the reliability and fault tolerant capability. This paper presents an aileron actuator fault diagnosis approach combining principal component analysis (PCA, grid search (GS, 10-fold cross validation (CV, and one-versus-one support vector machine (SVM. This method is referred to as PGC-SVM and utilizes the direct drive valve input, force motor current, and displacement feedback signal to realize fault detection and location. First, several common faults of aileron actuators, which include force motor coil break, sensor coil break, cylinder leakage, and amplifier gain reduction, are extracted from the fault quadrantal diagram; the corresponding fault mechanisms are analyzed. Second, the data feature extraction is performed with dimension reduction using PCA. Finally, the GS and CV algorithms are employed to train a one-versus-one SVM for fault classification, thus obtaining the optimal model parameters and assuring the generalization of the trained SVM, respectively. To verify the effectiveness of the proposed approach, four types of faults are introduced into the simulation model established by AMESim and Simulink. The results demonstrate its desirable diagnostic performance which outperforms that of the traditional SVM by comparison.

  7. Parallelization of multicategory support vector machines (PMC-SVM for classifying microarray data

    Directory of Open Access Journals (Sweden)

    Deng Youping

    2006-12-01

    Full Text Available Abstract Background Multicategory Support Vector Machines (MC-SVM are powerful classification systems with excellent performance in a variety of data classification problems. Since the process of generating models in traditional multicategory support vector machines for large datasets is very computationally intensive, there is a need to improve the performance using high performance computing techniques. Results In this paper, Parallel Multicategory Support Vector Machines (PMC-SVM have been developed based on the sequential minimum optimization-type decomposition method for support vector machines (SMO-SVM. It was implemented in parallel using MPI and C++ libraries and executed on both shared memory supercomputer and Linux cluster for multicategory classification of microarray data. PMC-SVM has been analyzed and evaluated using four microarray datasets with multiple diagnostic categories, such as different cancer types and normal tissue types. Conclusion The experiments show that the PMC-SVM can significantly improve the performance of classification of microarray data without loss of accuracy, compared with previous work.

  8. Intelligent gearbox diagnosis methods based on SVM, wavelet lifting and RBR.

    Science.gov (United States)

    Gao, Lixin; Ren, Zhiqiang; Tang, Wenliang; Wang, Huaqing; Chen, Peng

    2010-01-01

    Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis.

  9. Modeling of SVM Diode Clamping Three-Level Inverter Connected to Grid

    DEFF Research Database (Denmark)

    Guo, Yougui; Zeng, Ping; Zhu, Jieqiong

    2011-01-01

    PLECS is used to model the diode clamping three-level inverter connected to grid and good results are obtained. First the output voltage SVM is described for diode clamping three-level inverter with loads connected to Y. Then the output voltage SVM of diode clamping three-level inverter is simply...... analyzed with loads connected to △. But it will be further researched in the future. Third, PLECS is briefly introduced. Fourth, the modeling of diode clamping three-level inverter is briefly presented with PLECS. Finally, a series of simulations are carried out. The simulation results tell us PLECS...... is very powerful tool to real power circuits and it is very easy to simulate them. They have also verified that SVM control strategy is feasible to control the diode clamping three-level inverter....

  10. A RLS-SVM Aided Fusion Methodology for INS during GPS Outages

    Science.gov (United States)

    Yao, Yiqing; Xu, Xiaosu

    2017-01-01

    In order to maintain a relatively high accuracy of navigation performance during global positioning system (GPS) outages, a novel robust least squares support vector machine (LS-SVM)-aided fusion methodology is explored to provide the pseudo-GPS position information for the inertial navigation system (INS). The relationship between the yaw, specific force, velocity, and the position increment is modeled. Rather than share the same weight in the traditional LS-SVM, the proposed algorithm allocates various weights for different data, which makes the system immune to the outliers. Field test data was collected to evaluate the proposed algorithm. The comparison results indicate that the proposed algorithm can effectively provide position corrections for standalone INS during the 300 s GPS outage, which outperforms the traditional LS-SVM method. Historical information is also involved to better represent the vehicle dynamics. PMID:28245549

  11. Linear SVM-Based Android Malware Detection for Reliable IoT Services

    Directory of Open Access Journals (Sweden)

    Hyo-Sik Ham

    2014-01-01

    Full Text Available Current many Internet of Things (IoT services are monitored and controlled through smartphone applications. By combining IoT with smartphones, many convenient IoT services have been provided to users. However, there are adverse underlying effects in such services including invasion of privacy and information leakage. In most cases, mobile devices have become cluttered with important personal user information as various services and contents are provided through them. Accordingly, attackers are expanding the scope of their attacks beyond the existing PC and Internet environment into mobile devices. In this paper, we apply a linear support vector machine (SVM to detect Android malware and compare the malware detection performance of SVM with that of other machine learning classifiers. Through experimental validation, we show that the SVM outperforms other machine learning classifiers.

  12. A RLS-SVM Aided Fusion Methodology for INS during GPS Outages

    Directory of Open Access Journals (Sweden)

    Yiqing Yao

    2017-02-01

    Full Text Available In order to maintain a relatively high accuracy of navigation performance during global positioning system (GPS outages, a novel robust least squares support vector machine (LS-SVM-aided fusion methodology is explored to provide the pseudo-GPS position information for the inertial navigation system (INS. The relationship between the yaw, specific force, velocity, and the position increment is modeled. Rather than share the same weight in the traditional LS-SVM, the proposed algorithm allocates various weights for different data, which makes the system immune to the outliers. Field test data was collected to evaluate the proposed algorithm. The comparison results indicate that the proposed algorithm can effectively provide position corrections for standalone INS during the 300 s GPS outage, which outperforms the traditional LS-SVM method. Historical information is also involved to better represent the vehicle dynamics.

  13. Laos Organization Name Using Cascaded Model Based on SVM and CRF

    Directory of Open Access Journals (Sweden)

    Duan Shaopeng

    2017-01-01

    Full Text Available According to the characteristics of Laos organization name, this paper proposes a two layer model based on conditional random field (CRF and support vector machine (SVM for Laos organization name recognition. A layer of model uses CRF to recognition simple organization name, and the result is used to support the decision of the second level. Based on the driving method, the second layer uses SVM and CRF to recognition the complicated organization name. Finally, the results of the two levels are combined, And by a subsequent treatment to correct results of low confidence recognition. The results show that this approach based on SVM and CRF is efficient in recognizing organization name through open test for real linguistics, and the recalling rate achieve 80. 83%and the precision rate achieves 82. 75%.

  14. Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models

    Directory of Open Access Journals (Sweden)

    Sukomal Mandal

    2012-06-01

    Full Text Available The damage analysis of coastal structure is very important as it involves many design parameters to be considered for the better and safe design of structure. In the present study experimental data for non-reshaped berm breakwater are collected from Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN, Support Vector Machine (SVM and Adaptive Neuro Fuzzy Inference system (ANFIS models are constructed using experimental data sets to predict the damage level of non-reshaped berm breakwater. The experimental data are used to train ANN, SVM and ANFIS models and results are determined in terms of statistical measures like mean square error, root mean square error, correlation coefficient and scatter index. The result shows that soft computing techniques i.e., ANN, SVM and ANFIS can be efficient tools in predicting damage levels of non reshaped berm breakwater.

  15. Extraction of prostatic lumina and automated recognition for prostatic calculus image using PCA-SVM.

    Science.gov (United States)

    Wang, Zhuocai; Xu, Xiangmin; Ding, Xiaojun; Xiao, Hui; Huang, Yusheng; Liu, Jian; Xing, Xiaofen; Wang, Hua; Liao, D Joshua

    2011-01-01

    Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi.

  16. SVM-based spectrum mobility prediction scheme in mobile cognitive radio networks.

    Science.gov (United States)

    Wang, Yao; Zhang, Zhongzhao; Ma, Lin; Chen, Jiamei

    2014-01-01

    Spectrum mobility as an essential issue has not been fully investigated in mobile cognitive radio networks (CRNs). In this paper, a novel support vector machine based spectrum mobility prediction (SVM-SMP) scheme is presented considering time-varying and space-varying characteristics simultaneously in mobile CRNs. The mobility of cognitive users (CUs) and the working activities of primary users (PUs) are analyzed in theory. And a joint feature vector extraction (JFVE) method is proposed based on the theoretical analysis. Then spectrum mobility prediction is executed through the classification of SVM with a fast convergence speed. Numerical results validate that SVM-SMP gains better short-time prediction accuracy rate and miss prediction rate performance than the two algorithms just depending on the location and speed information. Additionally, a rational parameter design can remedy the prediction performance degradation caused by high speed SUs with strong randomness movements.

  17. Energy-efficient SVM learning control system for biped walking robots.

    Science.gov (United States)

    Wang, Liyang; Liu, Zhi; Chen, Chun Lung Philip; Zhang, Yun; Lee, Sukhan; Chen, Xin

    2013-05-01

    An energy-efficient support vector machine (EE-SVM) learning control system considering the energy cost of each training sample of biped dynamic is proposed to realize energy-efficient biped walking. Energy costs of the biped walking samples are calculated. Then the samples are weighed with the inverses of the energy costs. An EE-SVM objective function with energy-related slack variables is proposed, which follows the principle that the sample with the lowest energy consumption is treated as the most important one in the training. That means the samples with lower energy consumption contribute more to the EE-SVM regression function learning, which highly increases the energy efficiency of the biped walking. Simulation results demonstrate the effectiveness of the proposed method.

  18. Combined SVM-CRFs for biological named entity recognition with maximal bidirectional squeezing.

    Science.gov (United States)

    Zhu, Fei; Shen, Bairong

    2012-01-01

    Biological named entity recognition, the identification of biological terms in text, is essential for biomedical information extraction. Machine learning-based approaches have been widely applied in this area. However, the recognition performance of current approaches could still be improved. Our novel approach is to combine support vector machines (SVMs) and conditional random fields (CRFs), which can complement and facilitate each other. During the hybrid process, we use SVM to separate biological terms from non-biological terms, before we use CRFs to determine the types of biological terms, which makes full use of the power of SVM as a binary-class classifier and the data-labeling capacity of CRFs. We then merge the results of SVM and CRFs. To remove any inconsistencies that might result from the merging, we develop a useful algorithm and apply two rules. To ensure biological terms with a maximum length are identified, we propose a maximal bidirectional squeezing approach that finds the longest term. We also add a positive gain to rare events to reinforce their probability and avoid bias. Our approach will also gradually extend the context so more contextual information can be included. We examined the performance of four approaches with GENIA corpus and JNLPBA04 data. The combination of SVM and CRFs improved performance. The macro-precision, macro-recall, and macro-F(1) of the SVM-CRFs hybrid approach surpassed conventional SVM and CRFs. After applying the new algorithms, the macro-F1 reached 91.67% with the GENIA corpus and 84.04% with the JNLPBA04 data.

  19. PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons

    Directory of Open Access Journals (Sweden)

    Yi Long

    2016-09-01

    Full Text Available Locomotion mode identification is essential for the control of a robotic rehabilitation exoskeletons. This paper proposes an online support vector machine (SVM optimized by particle swarm optimization (PSO to identify different locomotion modes to realize a smooth and automatic locomotion transition. A PSO algorithm is used to obtain the optimal parameters of SVM for a better overall performance. Signals measured by the foot pressure sensors integrated in the insoles of wearable shoes and the MEMS-based attitude and heading reference systems (AHRS attached on the shoes and shanks of leg segments are fused together as the input information of SVM. Based on the chosen window whose size is 200 ms (with sampling frequency of 40 Hz, a three-layer wavelet packet analysis (WPA is used for feature extraction, after which, the kernel principal component analysis (kPCA is utilized to reduce the dimension of the feature set to reduce computation cost of the SVM. Since the signals are from two types of different sensors, the normalization is conducted to scale the input into the interval of [0, 1]. Five-fold cross validation is adapted to train the classifier, which prevents the classifier over-fitting. Based on the SVM model obtained offline in MATLAB, an online SVM algorithm is constructed for locomotion mode identification. Experiments are performed for different locomotion modes and experimental results show the effectiveness of the proposed algorithm with an accuracy of 96.00% ± 2.45%. To improve its accuracy, majority vote algorithm (MVA is used for post-processing, with which the identification accuracy is better than 98.35% ± 1.65%. The proposed algorithm can be extended and employed in the field of robotic rehabilitation and assistance.

  20. A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images

    Directory of Open Access Journals (Sweden)

    Yongzheng Xu

    2016-08-01

    Full Text Available A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J and linear SVM classifier with HOG feature (HOG + SVM methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV images. As both V-J and HOG + SVM are sensitive to on-road vehicles’ in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians.

  1. Novel Hybrid of LS-SVM and Kalman Filter for GPS/INS Integration

    Science.gov (United States)

    Xu, Zhenkai; Li, Yong; Rizos, Chris; Xu, Xiaosu

    Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) technologies can overcome the drawbacks of the individual systems. One of the advantages is that the integrated solution can provide continuous navigation capability even during GPS outages. However, bridging the GPS outages is still a challenge when Micro-Electro-Mechanical System (MEMS) inertial sensors are used. Methods being currently explored by the research community include applying vehicle motion constraints, optimal smoother, and artificial intelligence (AI) techniques. In the research area of AI, the neural network (NN) approach has been extensively utilised up to the present. In an NN-based integrated system, a Kalman filter (KF) estimates position, velocity and attitude errors, as well as the inertial sensor errors, to output navigation solutions while GPS signals are available. At the same time, an NN is trained to map the vehicle dynamics with corresponding KF states, and to correct INS measurements when GPS measurements are unavailable. To achieve good performance it is critical to select suitable quality and an optimal number of samples for the NN. This is sometimes too rigorous a requirement which limits real world application of NN-based methods.The support vector machine (SVM) approach is based on the structural risk minimisation principle, instead of the minimised empirical error principle that is commonly implemented in an NN. The SVM can avoid local minimisation and over-fitting problems in an NN, and therefore potentially can achieve a higher level of global performance. This paper focuses on the least squares support vector machine (LS-SVM), which can solve highly nonlinear and noisy black-box modelling problems. This paper explores the application of the LS-SVM to aid the GPS/INS integrated system, especially during GPS outages. The paper describes the principles of the LS-SVM and of the KF hybrid method, and introduces the LS-SVM regression algorithm. Field

  2. Combined SVM-CRFs for biological named entity recognition with maximal bidirectional squeezing.

    Directory of Open Access Journals (Sweden)

    Fei Zhu

    Full Text Available Biological named entity recognition, the identification of biological terms in text, is essential for biomedical information extraction. Machine learning-based approaches have been widely applied in this area. However, the recognition performance of current approaches could still be improved. Our novel approach is to combine support vector machines (SVMs and conditional random fields (CRFs, which can complement and facilitate each other. During the hybrid process, we use SVM to separate biological terms from non-biological terms, before we use CRFs to determine the types of biological terms, which makes full use of the power of SVM as a binary-class classifier and the data-labeling capacity of CRFs. We then merge the results of SVM and CRFs. To remove any inconsistencies that might result from the merging, we develop a useful algorithm and apply two rules. To ensure biological terms with a maximum length are identified, we propose a maximal bidirectional squeezing approach that finds the longest term. We also add a positive gain to rare events to reinforce their probability and avoid bias. Our approach will also gradually extend the context so more contextual information can be included. We examined the performance of four approaches with GENIA corpus and JNLPBA04 data. The combination of SVM and CRFs improved performance. The macro-precision, macro-recall, and macro-F(1 of the SVM-CRFs hybrid approach surpassed conventional SVM and CRFs. After applying the new algorithms, the macro-F1 reached 91.67% with the GENIA corpus and 84.04% with the JNLPBA04 data.

  3. SVM-Based CAC System for B-Mode Kidney Ultrasound Images.

    Science.gov (United States)

    Subramanya, M B; Kumar, Vinod; Mukherjee, Shaktidev; Saini, Manju

    2015-08-01

    The present study proposes a computer-aided classification (CAC) system for three kidney classes, viz. normal, medical renal disease (MRD) and cyst using B-mode ultrasound images. Thirty-five B-mode kidney ultrasound images consisting of 11 normal images, 8 MRD images and 16 cyst images have been used. Regions of interest (ROIs) have been marked by the radiologist from the parenchyma region of the kidney in case of normal and MRD cases and from regions inside lesions for cyst cases. To evaluate the contribution of texture features extracted from de-speckled images for the classification task, original images have been pre-processed by eight de-speckling methods. Six categories of texture features are extracted. One-against-one multi-class support vector machine (SVM) classifier has been used for the present work. Based on overall classification accuracy (OCA), features from ROIs of original images are concatenated with the features from ROIs of pre-processed images. On the basis of OCA, few feature sets are considered for feature selection. Differential evolution feature selection (DEFS) has been used to select optimal features for the classification task. DEFS process is repeated 30 times to obtain 30 subsets. Run-length matrix features from ROIs of images pre-processed by Lee's sigma concatenated with that of enhanced Lee method have resulted in an average accuracy (in %) and standard deviation of 86.3 ± 1.6. The results obtained in the study indicate that the performance of the proposed CAC system is promising, and it can be used by the radiologists in routine clinical practice for the classification of renal diseases.

  4. A comparative QSAR study on the estrogenic activities of persistent organic pollutants by PLS and SVM

    Directory of Open Access Journals (Sweden)

    Fei Li

    2015-11-01

    Full Text Available Quantitative structure-activity relationships (QSARs were determined using partial least square (PLS and support vector machine (SVM. The predicted values by the final QSAR models were in good agreement with the corresponding experimental values. Chemical estrogenic activities are related to atomic properties (atomic Sanderson electronegativities, van der Waals volumes and polarizabilities. Comparison of the results obtained from two models, the SVM method exhibited better overall performances. Besides, three PLS models were constructed for some specific families based on their chemical structures. These predictive models should be useful to rapidly identify potential estrogenic endocrine disrupting chemicals.

  5. OPTIMALISASI SUPPORT VEKTOR MACHINE (SVM UNTUK KLASIFIKASI TEMA TUGAS AKHIR BERBASIS K-MEANS

    Directory of Open Access Journals (Sweden)

    Oman Somantri

    2017-01-01

    Full Text Available The difficulty in determining the classification of students final project theme often experienced by each college. The purpose of this study is to provide a decision support for policy makers in the study program so that each student can be achieved in accordance with their own competence. From the research that has been done text mining algorithms using Support Vector Machine ( SVM and K -Means as the technology used was produced a better accuracy rate with an accuracy rate of 86.21 % when compared to the SVM without K -Means is 85 , 38 %

  6. SVM and ANFIS Models for precipitaton Modeling (Case Study: GonbadKavouse

    Directory of Open Access Journals (Sweden)

    N. Zabet Pishkhani

    2016-10-01

    Full Text Available Introduction: In recent years, according to the intelligent models increased as new techniques and tools in hydrological processes such as precipitation forecasting. ANFIS model has good ability in train, construction and classification, and also has the advantage that allows the extraction of fuzzy rules from numerical information or knowledge. Another intelligent technique in recent years has been used in various areas is support vector machine (SVM. In this paper the ability of artificial intelligence methods including support vector machine (SVM and adaptive neuro fuzzy inference system (ANFIS were analyzed in monthly precipitation prediction. Materials and Methods: The study area was the city of Gonbad in Golestan Province. The city has a temperate climate in the southern highlands and southern plains, mountains and temperate humid, semi-arid and semi-arid in the north of Gorganroud river. In total, the city's climate is temperate and humid. In the present study, monthly precipitation was modeled in Gonbad using ANFIS and SVM and two different database structures were designed. The first structure: input layer consisted of mean temperature, relative humidity, pressure and wind speed at Gonbad station. The second structure: According to Pearson coefficient, the monthly precipitation data were used from four stations: Arazkoose, Bahalke, Tamar and Aqqala which had a higher correlation with Gonbad station precipitation. In this study precipitation data was used from 1995 to 2012. 80% data were used for model training and the remaining 20% of data for validation. SVM was developed from support vector machines in the 1990s by Vapnik. SVM has been widely recognized as a powerful tool to deal with function fitting problems. An Adaptive Neuro-Fuzzy Inference System (ANFIS refers, in general, to an adaptive network which performs the function of a fuzzy inference system. The most commonly used fuzzy system in ANFIS architectures is the Sugeno model

  7. Single-cell gel (comet) assay as a promising tool for the detection of DNA damage induced by compounds used in dental practice: the oral cancer risk assessment.

    Science.gov (United States)

    Ribeiro, Daniel A

    2008-01-01

    Taking into consideration genetic damage plays an important role in oral carcinogenesis, the purpose of this article is to provide an overview on the application of single-cell gel (comet) assay as a promising tool for the detection of DNA damage induced by some compounds used in dental practice as far as to assess the oral cancer risk. The single-cell gel (comet) assay is a microgel electrophoresis technique able to detect DNA damage at the level of single cells. In the alkaline version, which is mainly used, DNA single-strand breaks, DNA double-strand breaks, alkali-labile sites, and single-strand breaks containing incomplete excision repair sites cause increased DNA migration. Therefore, this interesting methodology estimates the risk of these substances with respect to genotoxicity, and will be added to those used for regulatory purposes in improving oral health and preventing oral carcinogenesis.

  8. Estimation of hydraulic jump characteristics of channels with sudden diverging side walls via SVM.

    Science.gov (United States)

    Roushangar, Kiyoumars; Valizadeh, Reyhaneh; Ghasempour, Roghayeh

    2017-10-01

    Sudden diverging channels are one of the energy dissipaters which can dissipate most of the kinetic energy of the flow through a hydraulic jump. An accurate prediction of hydraulic jump characteristics is an important step in designing hydraulic structures. This paper focuses on the capability of the support vector machine (SVM) as a meta-model approach for predicting hydraulic jump characteristics in different sudden diverging stilling basins (i.e. basins with and without appurtenances). In this regard, different models were developed and tested using 1,018 experimental data. The obtained results proved the capability of the SVM technique in predicting hydraulic jump characteristics and it was found that the developed models for a channel with a central block performed more successfully than models for channels without appurtenances or with a negative step. The superior performance for the length of hydraulic jump was obtained for the model with parameters F 1 (Froude number) and (h 2- h 1 )/h 1 (h 1 and h 2 are sequent depth of upstream and downstream respectively). Concerning the relative energy dissipation and sequent depth ratio, the model with parameters F 1 and h 1 /B (B is expansion ratio) led to the best results. According to the outcome of sensitivity analysis, Froude number had the most significant effect on the modeling. Also comparison between SVM and empirical equations indicated the great performance of the SVM.

  9. Positioning Errors Predicting Method of Strapdown Inertial Navigation Systems Based on PSO-SVM

    Directory of Open Access Journals (Sweden)

    Xunyuan Yin

    2013-01-01

    Full Text Available The strapdown inertial navigation systems (SINS have been widely used for many vehicles, such as commercial airplanes, Unmanned Aerial Vehicles (UAVs, and other types of aircrafts. In order to evaluate the navigation errors precisely and efficiently, a prediction method based on support vector machine (SVM is proposed for positioning error assessment. Firstly, SINS error models that are used for error calculation are established considering several error resources with respect to inertial units. Secondly, flight paths for simulation are designed. Thirdly, the -SVR based prediction method is proposed to predict the positioning errors of navigation systems, and particle swarm optimization (PSO is used for the SVM parameters optimization. Finally, 600 sets of error parameters of SINS are utilized to train the SVM model, which is used for the performance prediction of new navigation systems. By comparing the predicting results with the real errors, the latitudinal predicting accuracy is 92.73%, while the longitudinal predicting accuracy is 91.64%, and PSO is effective to increase the prediction accuracy compared with traditional SVM with fixed parameters. This method is also demonstrated to be effective for error prediction for an entire flight process. Moreover, the prediction method can save 75% of calculation time compared with analyses based on error models.

  10. Pressure Model of Control Valve Based on LS-SVM with the Fruit Fly Algorithm

    Directory of Open Access Journals (Sweden)

    Huang Aiqin

    2014-07-01

    Full Text Available Control valve is a kind of essential terminal control component which is hard to model by traditional methodologies because of its complexity and nonlinearity. This paper proposes a new modeling method for the upstream pressure of control valve using the least squares support vector machine (LS-SVM, which has been successfully used to identify nonlinear system. In order to improve the modeling performance, the fruit fly optimization algorithm (FOA is used to optimize two critical parameters of LS-SVM. As an example, a set of actual production data from a controlling system of chlorine in a salt chemistry industry is applied. The validity of LS-SVM modeling method using FOA is verified by comparing the predicted results with the actual data with a value of MSE 2.474 × 10−3. Moreover, it is demonstrated that the initial position of FOA does not affect its optimal ability. By comparison, simulation experiments based on PSO algorithm and the grid search method are also carried out. The results show that LS-SVM based on FOA has equal performance in prediction accuracy. However, from the respect of calculation time, FOA has a significant advantage and is more suitable for the online prediction.

  11. Using evolutionary computation to optimize an SVM used in detecting buried objects in FLIR imagery

    Science.gov (United States)

    Paino, Alex; Popescu, Mihail; Keller, James M.; Stone, Kevin

    2013-06-01

    In this paper we describe an approach for optimizing the parameters of a Support Vector Machine (SVM) as part of an algorithm used to detect buried objects in forward looking infrared (FLIR) imagery captured by a camera installed on a moving vehicle. The overall algorithm consists of a spot-finding procedure (to look for potential targets) followed by the extraction of several features from the neighborhood of each spot. The features include local binary pattern (LBP) and histogram of oriented gradients (HOG) as these are good at detecting texture classes. Finally, we project and sum each hit into UTM space along with its confidence value (obtained from the SVM), producing a confidence map for ROC analysis. In this work, we use an Evolutionary Computation Algorithm (ECA) to optimize various parameters involved in the system, such as the combination of features used, parameters on the Canny edge detector, the SVM kernel, and various HOG and LBP parameters. To validate our approach, we compare results obtained from an SVM using parameters obtained through our ECA technique with those previously selected by hand through several iterations of "guess and check".

  12. SVM versus MAP on accelerometer data to distinguish among locomotor activities executed at different speeds.

    Science.gov (United States)

    Schmid, Maurizio; Riganti-Fulginei, Francesco; Bernabucci, Ivan; Laudani, Antonino; Bibbo, Daniele; Muscillo, Rossana; Salvini, Alessandro; Conforto, Silvia

    2013-01-01

    Two approaches to the classification of different locomotor activities performed at various speeds are here presented and evaluated: a maximum a posteriori (MAP) Bayes' classification scheme and a Support Vector Machine (SVM) are applied on a 2D projection of 16 features extracted from accelerometer data. The locomotor activities (level walking, stair climbing, and stair descending) were recorded by an inertial sensor placed on the shank (preferred leg), performed in a natural indoor-outdoor scenario by 10 healthy young adults (age 25-35 yrs.). From each segmented activity epoch, sixteen features were chosen in the frequency and time domain. Dimension reduction was then performed through 2D Sammon's mapping. An Artificial Neural Network (ANN) was trained to mimic Sammon's mapping on the whole dataset. In the Bayes' approach, the two features were then fed to a Bayes' classifier that incorporates an update rule, while, in the SVM scheme, the ANN was considered as the kernel function of the classifier. Bayes' approach performed slightly better than SVM on both the training set (91.4% versus 90.7%) and the testing set (84.2% versus 76.0%), favoring the proposed Bayes' scheme as more suitable than the proposed SVM in distinguishing among the different monitored activities.

  13. A hybrid particle swarm optimization-SVM classification for automatic cardiac auscultation

    Directory of Open Access Journals (Sweden)

    Prasertsak Charoen

    2017-04-01

    Full Text Available Cardiac auscultation is a method for a doctor to listen to heart sounds, using a stethoscope, for examining the condition of the heart. Automatic cardiac auscultation with machine learning is a promising technique to classify heart conditions without need of doctors or expertise. In this paper, we develop a classification model based on support vector machine (SVM and particle swarm optimization (PSO for an automatic cardiac auscultation system. The model consists of two parts: heart sound signal processing part and a proposed PSO for weighted SVM (WSVM classifier part. In this method, the PSO takes into account the degree of importance for each feature extracted from wavelet packet (WP decomposition. Then, by using principle component analysis (PCA, the features can be selected. The PSO technique is used to assign diverse weights to different features for the WSVM classifier. Experimental results show that both continuous and binary PSO-WSVM models achieve better classification accuracy on the heart sound samples, by reducing system false negatives (FNs, compared to traditional SVM and genetic algorithm (GA based SVM.

  14. Research on gesture recognition of augmented reality maintenance guiding system based on improved SVM

    Science.gov (United States)

    Zhao, Shouwei; Zhang, Yong; Zhou, Bin; Ma, Dongxi

    2014-09-01

    Interaction is one of the key techniques of augmented reality (AR) maintenance guiding system. Because of the complexity of the maintenance guiding system's image background and the high dimensionality of gesture characteristics, the whole process of gesture recognition can be divided into three stages which are gesture segmentation, gesture characteristic feature modeling and trick recognition. In segmentation stage, for solving the misrecognition of skin-like region, a segmentation algorithm combing background mode and skin color to preclude some skin-like regions is adopted. In gesture characteristic feature modeling of image attributes stage, plenty of characteristic features are analyzed and acquired, such as structure characteristics, Hu invariant moments features and Fourier descriptor. In trick recognition stage, a classifier based on Support Vector Machine (SVM) is introduced into the augmented reality maintenance guiding process. SVM is a novel learning method based on statistical learning theory, processing academic foundation and excellent learning ability, having a lot of issues in machine learning area and special advantages in dealing with small samples, non-linear pattern recognition at high dimension. The gesture recognition of augmented reality maintenance guiding system is realized by SVM after the granulation of all the characteristic features. The experimental results of the simulation of number gesture recognition and its application in augmented reality maintenance guiding system show that the real-time performance and robustness of gesture recognition of AR maintenance guiding system can be greatly enhanced by improved SVM.

  15. Hyperspectral recognition of processing tomato early blight based on GA and SVM

    Science.gov (United States)

    Yin, Xiaojun; Zhao, SiFeng

    2013-03-01

    Processing tomato early blight seriously affect the yield and quality of its.Determine the leaves spectrum of different disease severity level of processing tomato early blight.We take the sensitive bands of processing tomato early blight as support vector machine input vector.Through the genetic algorithm(GA) to optimize the parameters of SVM, We could recognize different disease severity level of processing tomato early blight.The result show:the sensitive bands of different disease severity levels of processing tomato early blight is 628-643nm and 689-692nm.The sensitive bands are as the GA and SVM input vector.We get the best penalty parameters is 0.129 and kernel function parameters is 3.479.We make classification training and testing by polynomial nuclear,radial basis function nuclear,Sigmoid nuclear.The best classification model is the radial basis function nuclear of SVM. Training accuracy is 84.615%,Testing accuracy is 80.681%.It is combined GA and SVM to achieve multi-classification of processing tomato early blight.It is provided the technical support of prediction processing tomato early blight occurrence, development and diffusion rule in large areas.

  16. Coding of DNA samples and data in the pharmaceutical industry: current practices and future directions--perspective of the I-PWG.

    Science.gov (United States)

    Franc, M A; Cohen, N; Warner, A W; Shaw, P M; Groenen, P; Snapir, A

    2011-04-01

    DNA samples collected in clinical trials and stored for future research are valuable to pharmaceutical drug development. Given the perceived higher risk associated with genetic research, industry has implemented complex coding methods for DNA. Following years of experience with these methods and with addressing questions from institutional review boards (IRBs), ethics committees (ECs) and health authorities, the industry has started reexamining the extent of the added value offered by these methods. With the goal of harmonization, the Industry Pharmacogenomics Working Group (I-PWG) conducted a survey to gain an understanding of company practices for DNA coding and to solicit opinions on their effectiveness at protecting privacy. The results of the survey and the limitations of the coding methods are described. The I-PWG recommends dialogue with key stakeholders regarding coding practices such that equal standards are applied to DNA and non-DNA samples. The I-PWG believes that industry standards for privacy protection should provide adequate safeguards for DNA and non-DNA samples/data and suggests a need for more universal standards for samples stored for future research.

  17. Prenatal cfDNA screening results indicative of maternal neoplasm: survey of current practice and management needs.

    Science.gov (United States)

    Giles, Meagan E; Murphy, Lauren; Krstić, Nevena; Sullivan, Cathy; Hashmi, Syed S; Stevens, Blair

    2017-02-01

    To determine genetic counselors' current practices and management needs for patients with prenatal cfDNA screening results indicative of maternal neoplasm. A survey was completed by genetic counselors recruited via the National Society of Genetic Counselors (NSGC). Over 300 genetic counselors were surveyed. Almost all participants (95%) were aware that Noninvasive Prenatal Testing (NIPT) results may suggest maternal neoplasm, and 77% reported they would disclose such results. However, only 29% routinely communicate this possibility to patients in a pre-test setting. Management recommendations made by counselors were highly variable, and over half (51.8%) stated they would feel uncomfortable or very uncomfortable counseling a patient with these results. While less than half (44.3%) believed the current benefits of NIPT's ability to suggest maternal neoplasm outweigh its potential harms, 80.2% recognized it would be beneficial in the future. A vast majority of counselors (91.3%) felt institutional or national guidelines were needed for patient management. A majority of counselors neither felt properly equipped nor comfortable counseling patients with prenatal cfDNA results suggestive of maternal neoplasm. This study demonstrates a need for collaboration amongst clinicians, researchers, and laboratories to publish data regarding NIPT results indicative of maternal neoplasm, and for the creation of management guidelines. © 2016 John Wiley & Sons, Ltd. © 2016 John Wiley & Sons, Ltd.

  18. Automatic epileptic seizure detection in EEGs using MF-DFA, SVM based on cloud computing.

    Science.gov (United States)

    Zhang, Zhongnan; Wen, Tingxi; Huang, Wei; Wang, Meihong; Li, Chunfeng

    2017-01-01

    Epilepsy is a chronic disease with transient brain dysfunction that results from the sudden abnormal discharge of neurons in the brain. Since electroencephalogram (EEG) is a harmless and noninvasive detection method, it plays an important role in the detection of neurological diseases. However, the process of analyzing EEG to detect neurological diseases is often difficult because the brain electrical signals are random, non-stationary and nonlinear. In order to overcome such difficulty, this study aims to develop a new computer-aided scheme for automatic epileptic seizure detection in EEGs based on multi-fractal detrended fluctuation analysis (MF-DFA) and support vector machine (SVM). New scheme first extracts features from EEG by MF-DFA during the first stage. Then, the scheme applies a genetic algorithm (GA) to calculate parameters used in SVM and classify the training data according to the selected features using SVM. Finally, the trained SVM classifier is exploited to detect neurological diseases. The algorithm utilizes MLlib from library of SPARK and runs on cloud platform. Applying to a public dataset for experiment, the study results show that the new feature extraction method and scheme can detect signals with less features and the accuracy of the classification reached up to 99%. MF-DFA is a promising approach to extract features for analyzing EEG, because of its simple algorithm procedure and less parameters. The features obtained by MF-DFA can represent samples as well as traditional wavelet transform and Lyapunov exponents. GA can always find useful parameters for SVM with enough execution time. The results illustrate that the classification model can achieve comparable accuracy, which means that it is effective in epileptic seizure detection.

  19. A DWT and SVM based method for rolling element bearing fault diagnosis and its comparison with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Sunil Tyagi

    2017-04-01

    Full Text Available A classification technique using Support Vector Machine (SVM classifier for detection of rolling element bearing fault is presented here.  The SVM was fed from features that were extracted from of vibration signals obtained from experimental setup consisting of rotating driveline that was mounted on rolling element bearings which were run in normal and with artificially faults induced conditions. The time-domain vibration signals were divided into 40 segments and simple features such as peaks in time domain and spectrum along with statistical features such as standard deviation, skewness, kurtosis etc. were extracted. Effectiveness of SVM classifier was compared with the performance of Artificial Neural Network (ANN classifier and it was found that the performance of SVM classifier is superior to that of ANN. The effect of pre-processing of the vibration signal by Discreet Wavelet Transform (DWT prior to feature extraction is also studied and it is shown that pre-processing of vibration signal with DWT enhances the effectiveness of both ANN and SVM classifiers. It has been demonstrated from experiment results that performance of SVM classifier is better than ANN in detection of bearing condition and pre-processing the vibration signal with DWT improves the performance of SVM classifier.

  20. alpha,beta-unsaturated 2-acyl imidazoles as a practical class of dienophiles for the DNA-Based catalytic asymmetric diels-alder reaction in water

    NARCIS (Netherlands)

    Boersma, A.J.; Feringa, B.L.; Roelfes, G.

    2007-01-01

    alpha,beta-Unsaturated 2-acyl imidazoles are a novel and practical class of dienophiles for the DNA-based catalytic asymmetric Diels-Alder reaction in water. The Diels-Alder products are obtained with very high diastereoselectivities and enantioselectivities in the range of 83-98%. The catalytic

  1. LS-SVM: uma nova ferramenta quimiométrica para regressão multivariada. Comparação de modelos de regressão LS-SVM e PLS na quantificação de adulterantes em leite em pó empregando NIR LS-SVM: a new chemometric tool for multivariate regression. Comparison of LS-SVM and pls regression for determination of common adulterants in powdered milk by nir spectroscopy

    Directory of Open Access Journals (Sweden)

    Marco F. Ferrão

    2007-08-01

    Full Text Available Least-squares support vector machines (LS-SVM were used as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants found in powdered milk samples, using near-infrared spectroscopy. Excellent models were built using LS-SVM for determining R², RMSECV and RMSEP values. LS-SVMs show superior performance for quantifying starch, whey and sucrose in powdered milk samples in relation to PLSR. This study shows that it is possible to determine precisely the amount of one and two common adulterants simultaneously in powdered milk samples using LS-SVM and NIR spectra.

  2. RBPPred: predicting RNA-binding proteins from sequence using SVM.

    Science.gov (United States)

    Zhang, Xiaoli; Liu, Shiyong

    2017-03-15

    Detection of RNA-binding proteins (RBPs) is essential since the RNA-binding proteins play critical roles in post-transcriptional regulation and have diverse roles in various biological processes. Moreover, identifying RBPs by computational prediction is much more efficient than experimental methods and may have guiding significance on the experiment design. In this study, we present the RBPPred (an RNA-binding protein predictor), a new method based on the support vector machine, to predict whether a protein binds RNAs, based on a comprehensive feature representation. By integrating the physicochemical properties with the evolutionary information of protein sequences, the new approach RBPPred performed much better than state-of-the-art methods. The results show that RBPPred correctly predicted 83% of 2780 RBPs and 96% out of 7093 non-RBPs with MCC of 0.808 using the 10-fold cross validation. Furthermore, we achieved a sensitivity of 84%, specificity of 97% and MCC of 0.788 on the testing set of human proteome. In addition we tested the capability of RBPPred to identify new RBPs, which further confirmed the practicability and predictability of the method. RBPPred program can be accessed at: http://rnabinding.com/RBPPred.html . liushiyong@gmail.com. Supplementary data are available at Bioinformatics online.

  3. Comparison of Alexa Fluor and CyDye for practical DNA microarray use.

    Science.gov (United States)

    Ballard, Joanne L; Peeva, Violet K; deSilva, Christopher J S; Lynch, Jessica L; Swanson, Nigel R

    2007-07-01

    Microarrays are a powerful tool for comparison and understanding of gene expression levels in healthy and diseased states. The method relies upon the assumption that signals from microarray features are a reflection of relative gene expression levels of the cell types under investigation. It has previously been reported that the classical fluorescent dyes used for microarray technology, Cy3 and Cy5, are not ideal due to the decreased stability and fluorescence intensity of the Cy5 dye relative to the Cy3, such that dye bias is an accepted phenomena necessitating dye swap experimental protocols and analysis of differential dye affects. The incentive to find new fluorophores is based on alleviating the problem of dye bias through synonymous performance between counterpart dyes. Alexa Fluor 555 and Alexa Fluor 647 are increasingly promoted as replacements for CyDye in microarray experiments. Performance relates to the molecular and steric similarities, which will vary for each new pair of dyes as well as the spectral integrity for the specific application required. Comparative analysis of the performance of these two competitive dye pairs in practical microarray applications is warranted towards this end. The findings of our study showed that both dye pairs were comparable but that conventional CyDye resulted in significantly higher signal intensities (P 0.05). This translated to greater levels of differential gene expression with CyDye than with the Alexa Fluor counterparts. However, CyDye fluorophores and in particular Cy5, were found to be less photostable over time and following repeated scans in microarray experiments. These results suggest that precautions against potential dye affects will continue to be necessary and that no one dye pair negates this need.

  4. "I think we've got too many tests!": Prenatal providers' reflections on ethical and clinical challenges in the practice integration of cell-free DNA screening.

    Science.gov (United States)

    Gammon, B L; Kraft, S A; Michie, M; Allyse, M

    2016-01-01

    The recent introduction of cell-free DNA-based non-invasive prenatal screening (cfDNA screening) into clinical practice was expected to revolutionize prenatal testing. cfDNA screening for fetal aneuploidy has demonstrated higher test sensitivity and specificity for some conditions than conventional serum screening and can be conducted early in the pregnancy. However, it is not clear whether and how clinical practices are assimilating this new type of testing into their informed consent and counselling processes. Since the introduction of cfDNA screening into practice in 2011, the uptake and scope have increased dramatically. Prenatal care providers are under pressure to stay up to date with rapidly changing cfDNA screening panels, manage increasing patient demands, and keep up with changing test costs, all while attempting to use the technology responsibly and ethically. While clinical literature on cfDNA screening has shown benefits for specific patient populations, it has also identified significant misunderstandings among providers and patients alike about the power of the technology. The unique features of cfDNA screening, in comparison to established prenatal testing technologies, have implications for informed decision-making and genetic counselling that must be addressed to ensure ethical practice. This study explored the experiences of prenatal care providers at the forefront of non-invasive genetic screening in the United States to understand how this testing changes the practice of prenatal medicine. We aimed to learn how the experience of providing and offering this testing differs from established prenatal testing methodologies. These differences may necessitate changes to patient education and consent procedures to maintain ethical practice. We used the online American Congress of Obstetricians and Gynecologists Physician Directory to identify a systematic sample of five prenatal care providers in each U.S. state and the District of Columbia. Beginning

  5. A PSO-SVM-based 24 Hours Power Load Forecasting Model

    Directory of Open Access Journals (Sweden)

    Yu Xiaoxu

    2015-01-01

    Full Text Available In order to improve the drawbacks of over-fitting and easily get stuck into local extremes of BACK propagation Neural Network, a new method of combination of wavelet transform and PSO-SVM (Particle Swarm Optimization- Support Vector Machine power load forecasting model is proposed. By employing wave-let transform, the authors decompose the time sequences of power load into high-frequency and low-frequency parts, namely the low-frequency part forecast with this model and the high-frequency part forecast with weighted average method. With PSO, which is a heuristic bionic optimization algorithm, the authors figure out the prefer-able parameters of SVM, and the model proposed in this paper is tested to be more accurately to forecast the 24h power load than BP model.

  6. Fault Diagnosis of Complex Industrial Process Using KICA and Sparse SVM

    Directory of Open Access Journals (Sweden)

    Jie Xu

    2013-01-01

    Full Text Available New approaches are proposed for complex industrial process monitoring and fault diagnosis based on kernel independent component analysis (KICA and sparse support vector machine (SVM. The KICA method is a two-phase algorithm: whitened kernel principal component analysis (KPCA. The data are firstly mapped into high-dimensional feature subspace. Then, the ICA algorithm seeks the projection directions in the KPCA whitened space. Performance monitoring is implemented through constructing the statistical index and control limit in the feature space. If the statistical indexes exceed the predefined control limit, a fault may have occurred. Then, the nonlinear score vectors are calculated and fed into the sparse SVM to identify the faults. The proposed method is applied to the simulation of Tennessee Eastman (TE chemical process. The simulation results show that the proposed method can identify various types of faults accurately and rapidly.

  7. Classification of surface defects on bridge cable based on PSO-SVM

    Science.gov (United States)

    Li, Xinke; Gao, Chao; Guo, Yongcai; Shao, Yanhua; He, Fuliang

    2014-07-01

    Distributed machine vision system was applied for the detection on the cable surface defect of the cable-stayed bridge, and access to surface defects including longitudinal cracking, transverse cracking, surface erosion and scarring pit holes and other scars. In order to achieve the automatic classification of surface defects, firstly, part of the texture features, gray features and shape features on the defect image were selected as the target classification feature quantities; then the particle swarm optimization (PSO) was introduced to optimize the punitive coefficient and kernel function parameter of the support vector machine (SVM) model; and finally the objective of defects was identified with the help of the PSOSVM classifier. Recognition experiments were performed on cable surface defects, presenting a recognition rate of 96.25 percent. The results showed that PSO-SVM has high recognition rate for classification of surface defects on bridge cable.

  8. SVM-based learning control of space robots in capturing operation.

    Science.gov (United States)

    Huang, Panfeng; Xu, Yangsheng

    2007-12-01

    In this paper, we presents a novel approach for tracking and catching operation of space robots using learning and transferring human control strategies (HCS). We firstly use an efficient support vector machine (SVM) to parametrize the model of HCS. Then we develop a new SVM-based learning structure to better implement human control strategy learning in tracking and capturing control. The approach is fundamentally valuable in dealing with some problems such as small sample data and local minima, and so on. Therefore this approach is efficient in modeling, understanding and transferring its learning process. The simulation results attest that this approach is useful and feasible in generating tracking trajectory and catching objects autonomously.

  9. Activity Recognition in Egocentric video using SVM, kNN and Combined SVMkNN Classifiers

    Science.gov (United States)

    Sanal Kumar, K. P.; Bhavani, R., Dr.

    2017-08-01

    Egocentric vision is a unique perspective in computer vision which is human centric. The recognition of egocentric actions is a challenging task which helps in assisting elderly people, disabled patients and so on. In this work, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. Here, the recognition is done using the features like Histogram of Oriented Gradients (HOG), Motion Boundary Histogram (MBH) and Trajectory. The features are fused together and it acts as a single feature. The extracted features are reduced using Principal Component Analysis (PCA). The features that are reduced are provided as input to the classifiers like Support Vector Machine (SVM), k nearest neighbor (kNN) and combined Support Vector Machine (SVM) and k Nearest Neighbor (kNN) (combined SVMkNN). These classifiers are evaluated and the combined SVMkNN provided better results than other classifiers in the literature.

  10. An IPSO-SVM algorithm for security state prediction of mine production logistics system

    Science.gov (United States)

    Zhang, Yanliang; Lei, Junhui; Ma, Qiuli; Chen, Xin; Bi, Runfang

    2017-06-01

    A theoretical basis for the regulation of corporate security warning and resources was provided in order to reveal the laws behind the security state in mine production logistics. Considering complex mine production logistics system and the variable is difficult to acquire, a superior security status predicting model of mine production logistics system based on the improved particle swarm optimization and support vector machine (IPSO-SVM) is proposed in this paper. Firstly, through the linear adjustments of inertia weight and learning weights, the convergence speed and search accuracy are enhanced with the aim to deal with situations associated with the changeable complexity and the data acquisition difficulty. The improved particle swarm optimization (IPSO) is then introduced to resolve the problem of parameter settings in traditional support vector machines (SVM). At the same time, security status index system is built to determine the classification standards of safety status. The feasibility and effectiveness of this method is finally verified using the experimental results.

  11. Power line identification of millimeter wave radar based on PCA-GS-SVM

    Science.gov (United States)

    Fang, Fang; Zhang, Guifeng; Cheng, Yansheng

    2017-12-01

    Aiming at the problem that the existing detection method can not effectively solve the security of UAV's ultra low altitude flight caused by power line, a power line recognition method based on grid search (GS) and the principal component analysis and support vector machine (PCA-SVM) is proposed. Firstly, the candidate line of Hough transform is reduced by PCA, and the main feature of candidate line is extracted. Then, upport vector machine (SVM is) optimized by grid search method (GS). Finally, using support vector machine classifier optimized parameters to classify the candidate line. MATLAB simulation results show that this method can effectively identify the power line and noise, and has high recognition accuracy and algorithm efficiency.

  12. Comparison of sensorless FOC and SVM-DTFC of PMSM for low-speed applications

    DEFF Research Database (Denmark)

    Basar, M. Sertug; Bech, Michael Møller; Andersen, Torben Ole

    2013-01-01

    This article presents the performance analysis of Field Oriented Control (FOC) and Space Vector Modulation (SVM) Direct Torque and Flux Control (DTFC) of a Non-Salient Permanent Magnet Synchronous Machine (PMSM) under sensorless control within low speed region. The high-frequency alternating...... voltage signal injection method has been chosen for sensorless control design. PMSM is modelled at high frequencies, and a rotor speed and position estimation algorithm is proposed. The proposed estimator is designed and implemented using MATLAB/Simulink® and is tested under several operating conditions...... with a commercially available PMSM machine. Both controllers show satisfactory sensorless performance. FOC provides smoother and more accurate response while SVM-DTFC has the advantage of faster control....

  13. A SVM-based method for sentiment analysis in Persian language

    Science.gov (United States)

    Hajmohammadi, Mohammad Sadegh; Ibrahim, Roliana

    2013-03-01

    Persian language is the official language of Iran, Tajikistan and Afghanistan. Local online users often represent their opinions and experiences on the web with written Persian. Although the information in those reviews is valuable to potential consumers and sellers, the huge amount of web reviews make it difficult to give an unbiased evaluation to a product. In this paper, standard machine learning techniques SVM and naive Bayes are incorporated into the domain of online Persian Movie reviews to automatically classify user reviews as positive or negative and performance of these two classifiers is compared with each other in this language. The effects of feature presentations on classification performance are discussed. We find that accuracy is influenced by interaction between the classification models and the feature options. The SVM classifier achieves as well as or better accuracy than naive Bayes in Persian movie. Unigrams are proved better features than bigrams and trigrams in capturing Persian sentiment orientation.

  14. SVM classification model in depression recognition based on mutation PSO parameter optimization

    Directory of Open Access Journals (Sweden)

    Zhang Ming

    2017-01-01

    Full Text Available At present, the clinical diagnosis of depression is mainly through structured interviews by psychiatrists, which is lack of objective diagnostic methods, so it causes the higher rate of misdiagnosis. In this paper, a method of depression recognition based on SVM and particle swarm optimization algorithm mutation is proposed. To address on the problem that particle swarm optimization (PSO algorithm easily trap in local optima, we propose a feedback mutation PSO algorithm (FBPSO to balance the local search and global exploration ability, so that the parameters of the classification model is optimal. We compared different PSO mutation algorithms about classification accuracy for depression, and found the classification accuracy of support vector machine (SVM classifier based on feedback mutation PSO algorithm is the highest. Our study promotes important reference value for establishing auxiliary diagnostic used in depression recognition of clinical diagnosis.

  15. Detection of Alzheimer's disease using group lasso SVM-based region selection

    Science.gov (United States)

    Sun, Zhuo; Fan, Yong; Lelieveldt, Boudewijn P. F.; van de Giessen, Martijn

    2015-03-01

    Alzheimer's disease (AD) is one of the most frequent forms of dementia and an increasing challenging public health problem. In the last two decades, structural magnetic resonance imaging (MRI) has shown potential in distinguishing patients with Alzheimer's disease and elderly controls (CN). To obtain AD-specific biomarkers, previous research used either statistical testing to find statistically significant different regions between the two clinical groups, or l1 sparse learning to select isolated features in the image domain. In this paper, we propose a new framework that uses structural MRI to simultaneously distinguish the two clinical groups and find the bio-markers of AD, using a group lasso support vector machine (SVM). The group lasso term (mixed l1- l2 norm) introduces anatomical information from the image domain into the feature domain, such that the resulting set of selected voxels are more meaningful than the l1 sparse SVM. Because of large inter-structure size variation, we introduce a group specific normalization factor to deal with the structure size bias. Experiments have been performed on a well-designed AD vs. CN dataset1 to validate our method. Comparing to the l1 sparse SVM approach, our method achieved better classification performance and a more meaningful biomarker selection. When we vary the training set, the selected regions by our method were more stable than the l1 sparse SVM. Classification experiments showed that our group normalization lead to higher classification accuracy with fewer selected regions than the non-normalized method. Comparing to the state-of-art AD vs. CN classification methods, our approach not only obtains a high accuracy with the same dataset, but more importantly, we simultaneously find the brain anatomies that are closely related to the disease.

  16. Prediction of protein-protein interactions between viruses and human by an SVM model

    Directory of Open Access Journals (Sweden)

    Cui Guangyu

    2012-05-01

    Full Text Available Abstract Background Several computational methods have been developed to predict protein-protein interactions from amino acid sequences, but most of those methods are intended for the interactions within a species rather than for interactions across different species. Methods for predicting interactions between homogeneous proteins are not appropriate for finding those between heterogeneous proteins since they do not distinguish the interactions between proteins of the same species from those of different species. Results We developed a new method for representing a protein sequence of variable length in a frequency vector of fixed length, which encodes the relative frequency of three consecutive amino acids of a sequence. We built a support vector machine (SVM model to predict human proteins that interact with virus proteins. In two types of viruses, human papillomaviruses (HPV and hepatitis C virus (HCV, our SVM model achieved an average accuracy above 80%, which is higher than that of another SVM model with a different representation scheme. Using the SVM model and Gene Ontology (GO annotations of proteins, we predicted new interactions between virus proteins and human proteins. Conclusions Encoding the relative frequency of amino acid triplets of a protein sequence is a simple yet powerful representation method for predicting protein-protein interactions across different species. The representation method has several advantages: (1 it enables a prediction model to achieve a better performance than other representations, (2 it generates feature vectors of fixed length regardless of the sequence length, and (3 the same representation is applicable to different types of proteins.

  17. Penilaian Esai Jawaban Bahasa Indonesia Menggunakan Metode Svm - Lsa Dengan Fitur Generik

    OpenAIRE

    Adhitia, Rama; Purwarianti, Ayu

    2009-01-01

    Paper ini mengkaji sebuah solusi untuk permasalahan penilaian jawaban esai secara otomatis dengan menggabungkan support vector machine (SVM) sebagai teknik klasifikasi teks otomatis dengan LSA sebagai USAha untuk menangani sinonim dan polisemi antar index term. Berbeda dengan sistem penilaian esai yang biasa yakni fitur yang digunakan berupa index term, fitur yang digunakan proses penilaian jawaban esai adalah berupa fitur generic yang memungkinkan pengujian model penilaian esai untuk berbaga...

  18. SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas

    OpenAIRE

    Xiaogang Ning; Xiangguo Lin; Jixian Zhang

    2013-01-01

    Object-based point cloud analysis (OBPA) is useful for information extraction from airborne LiDAR point clouds. An object-based classification method is proposed for classifying the airborne LiDAR point clouds in urban areas herein. In the process of classification, the surface growing algorithm is employed to make clustering of the point clouds without outliers, thirteen features of the geometry, radiometry, topology and echo characteristics are calculated, a support vector machine (SVM) is ...

  19. Identification of DNA-binding proteins using support vector machines and evolutionary profiles

    Directory of Open Access Journals (Sweden)

    Gromiha Michael M

    2007-11-01

    Full Text Available Abstract Background Identification of DNA-binding proteins is one of the major challenges in the field of genome annotation, as these proteins play a crucial role in gene-regulation. In this paper, we developed various SVM modules for predicting DNA-binding domains and proteins. All models were trained and tested on multiple datasets of non-redundant proteins. Results SVM models have been developed on DNAaset, which consists of 1153 DNA-binding and equal number of non DNA-binding proteins, and achieved the maximum accuracy of 72.42% and 71.59% using amino acid and dipeptide compositions, respectively. The performance of SVM model improved from 72.42% to 74.22%, when evolutionary information in form of PSSM profiles was used as input instead of amino acid composition. In addition, SVM models have been developed on DNAset, which consists of 146 DNA-binding and 250 non-binding chains/domains, and achieved the maximum accuracy of 79.80% and 86.62% using amino acid composition and PSSM profiles. The SVM models developed in this study perform better than existing methods on a blind dataset. Conclusion A highly accurate method has been developed for predicting DNA-binding proteins using SVM and PSSM profiles. This is the first study in which evolutionary information in form of PSSM profiles has been used successfully for predicting DNA-binding proteins. A web-server DNAbinder has been developed for identifying DNA-binding proteins and domains from query amino acid sequences http://www.imtech.res.in/raghava/dnabinder/.

  20. Grouped fuzzy SVM with EM-based partition of sample space for clustered microcalcification detection.

    Science.gov (United States)

    Wang, Huiya; Feng, Jun; Wang, Hongyu

    2017-07-20

    Detection of clustered microcalcification (MC) from mammograms plays essential roles in computer-aided diagnosis for early stage breast cancer. To tackle problems associated with the diversity of data structures of MC lesions and the variability of normal breast tissues, multi-pattern sample space learning is required. In this paper, a novel grouped fuzzy Support Vector Machine (SVM) algorithm with sample space partition based on Expectation-Maximization (EM) (called G-FSVM) is proposed for clustered MC detection. The diversified pattern of training data is partitioned into several groups based on EM algorithm. Then a series of fuzzy SVM are integrated for classification with each group of samples from the MC lesions and normal breast tissues. From DDSM database, a total of 1,064 suspicious regions are selected from 239 mammography, and the measurement of Accuracy, True Positive Rate (TPR), False Positive Rate (FPR) and EVL = TPR* 1-FPR are 0.82, 0.78, 0.14 and 0.72, respectively. The proposed method incorporates the merits of fuzzy SVM and multi-pattern sample space learning, decomposing the MC detection problem into serial simple two-class classification. Experimental results from synthetic data and DDSM database demonstrate that our integrated classification framework reduces the false positive rate significantly while maintaining the true positive rate.

  1. Combination of minimum enclosing balls classifier with SVM in coal-rock recognition.

    Directory of Open Access Journals (Sweden)

    QingJun Song

    Full Text Available Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB algorithm plus Support vector machine (SVM is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition.

  2. Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter.

    Science.gov (United States)

    Wang, Tianzhen; Qi, Jie; Xu, Hao; Wang, Yide; Liu, Lei; Gao, Diju

    2016-01-01

    Thanks to reduced switch stress, high quality of load wave, easy packaging and good extensibility, the cascaded H-bridge multilevel inverter is widely used in wind power system. To guarantee stable operation of system, a new fault diagnosis method, based on Fast Fourier Transform (FFT), Relative Principle Component Analysis (RPCA) and Support Vector Machine (SVM), is proposed for H-bridge multilevel inverter. To avoid the influence of load variation on fault diagnosis, the output voltages of the inverter is chosen as the fault characteristic signals. To shorten the time of diagnosis and improve the diagnostic accuracy, the main features of the fault characteristic signals are extracted by FFT. To further reduce the training time of SVM, the feature vector is reduced based on RPCA that can get a lower dimensional feature space. The fault classifier is constructed via SVM. An experimental prototype of the inverter is built to test the proposed method. Compared to other fault diagnosis methods, the experimental results demonstrate the high accuracy and efficiency of the proposed method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  3. Automated Classification and Removal of EEG Artifacts with SVM and Wavelet-ICA.

    Science.gov (United States)

    Sai, Chong Yeh; Mokhtar, Norrima; Arof, Hamzah; Cumming, Paul; Iwahashi, Masahiro

    2017-07-04

    Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain computer interface (BCI) applications. In recent years, a combination of independent component analysis (ICA) and discrete wavelet transform (DWT) has been introduced as standard technique for EEG artifact removal. However, in performing the wavelet-ICA procedure, visual inspection or arbitrary thresholding may be required for identifying artifactual components in the EEG signal. We now propose a novel approach for identifying artifactual components separated by wavelet-ICA using a pre-trained support vector machine (SVM). Our method presents a robust and extendable system that enables fully automated identification and removal of artifacts from EEG signals, without applying any arbitrary thresholding. Using test data contaminated by eye blink artifacts, we show that our method performed better in identifying artifactual components than did existing thresholding methods. Furthermore, wavelet-ICA in conjunction with SVM successfully removed target artifacts, while largely retaining the EEG source signals of interest. We propose a set of features including kurtosis, variance, Shannon's entropy and range of amplitude as training and test data of SVM to identify eye blink artifacts in EEG signals. This combinatorial method is also extendable to accommodate multiple types of artifacts present in multi-channel EEG. We envision future research to explore other descriptive features corresponding to other types of artifactual components.

  4. A hybrid feature selection method using multiclass SVM for diagnosis of erythemato-squamous disease

    Science.gov (United States)

    Maryam, Setiawan, Noor Akhmad; Wahyunggoro, Oyas

    2017-08-01

    The diagnosis of erythemato-squamous disease is a complex problem and difficult to detect in dermatology. Besides that, it is a major cause of skin cancer. Data mining implementation in the medical field helps expert to diagnose precisely, accurately, and inexpensively. In this research, we use data mining technique to developed a diagnosis model based on multiclass SVM with a novel hybrid feature selection method to diagnose erythemato-squamous disease. Our hybrid feature selection method, named ChiGA (Chi Square and Genetic Algorithm), uses the advantages from filter and wrapper methods to select the optimal feature subset from original feature. Chi square used as filter method to remove redundant features and GA as wrapper method to select the ideal feature subset with SVM used as classifier. Experiment performed with 10 fold cross validation on erythemato-squamous diseases dataset taken from University of California Irvine (UCI) machine learning database. The experimental result shows that the proposed model based multiclass SVM with Chi Square and GA can give an optimum feature subset. There are 18 optimum features with 99.18% accuracy.

  5. Wind Power Prediction Based on LS-SVM Model with Error Correction

    Directory of Open Access Journals (Sweden)

    ZHANG, Y.

    2017-02-01

    Full Text Available As conventional energy sources are non-renewable, the world's major countries are investing heavily in renewable energy research. Wind power represents the development trend of future energy, but the intermittent and volatility of wind energy are the main reasons that leads to the poor accuracy of wind power prediction. However, by analyzing the error level at different time points, it can be found that the errors of adjacent time are often approximately the same, the least square support vector machine (LS-SVM model with error correction is used to predict the wind power in this paper. According to the simulation of wind power data of two wind farms, the proposed method can effectively improve the prediction accuracy of wind power, and the error distribution is concentrated almost without deviation. The improved method proposed in this paper takes into account the error correction process of the model, which improved the prediction accuracy of the traditional model (RBF, Elman, LS-SVM. Compared with the single LS-SVM prediction model in this paper, the mean absolute error of the proposed method had decreased by 52 percent. The research work in this paper will be helpful to the reasonable arrangement of dispatching operation plan, the normal operation of the wind farm and the large-scale development as well as fully utilization of renewable energy resources.

  6. Applications of PCA and SVM-PSO Based Real-Time Face Recognition System

    Directory of Open Access Journals (Sweden)

    Ming-Yuan Shieh

    2014-01-01

    Full Text Available This paper incorporates principal component analysis (PCA with support vector machine-particle swarm optimization (SVM-PSO for developing real-time face recognition systems. The integrated scheme aims to adopt the SVM-PSO method to improve the validity of PCA based image recognition systems on dynamically visual perception. The face recognition for most human-robot interaction applications is accomplished by PCA based method because of its dimensionality reduction. However, PCA based systems are only suitable for processing the faces with the same face expressions and/or under the same view directions. Since the facial feature selection process can be considered as a problem of global combinatorial optimization in machine learning, the SVM-PSO is usually used as an optimal classifier of the system. In this paper, the PSO is used to implement a feature selection, and the SVMs serve as fitness functions of the PSO for classification problems. Experimental results demonstrate that the proposed method simplifies features effectively and obtains higher classification accuracy.

  7. A SVM framework for fault detection of the braking system in a high speed train

    Science.gov (United States)

    Liu, Jie; Li, Yan-Fu; Zio, Enrico

    2017-03-01

    In April 2015, the number of operating High Speed Trains (HSTs) in the world has reached 3603. An efficient, effective and very reliable braking system is evidently very critical for trains running at a speed around 300 km/h. Failure of a highly reliable braking system is a rare event and, consequently, informative recorded data on fault conditions are scarce. This renders the fault detection problem a classification problem with highly unbalanced data. In this paper, a Support Vector Machine (SVM) framework, including feature selection, feature vector selection, model construction and decision boundary optimization, is proposed for tackling this problem. Feature vector selection can largely reduce the data size and, thus, the computational burden. The constructed model is a modified version of the least square SVM, in which a higher cost is assigned to the error of classification of faulty conditions than the error of classification of normal conditions. The proposed framework is successfully validated on a number of public unbalanced datasets. Then, it is applied for the fault detection of braking systems in HST: in comparison with several SVM approaches for unbalanced datasets, the proposed framework gives better results.

  8. Research on Intersession Variability Compensation for MLLR-SVM Speaker Recognition

    Science.gov (United States)

    Zhong, Shan; Shan, Yuxiang; He, Liang; Liu, Jia

    One of the most important challenges in speaker recognition is intersession variability (ISV), primarily cross-channel effects. Recent NIST speaker recognition evaluations (SRE) include a multilingual scenario with training conversations involving multilingual speakers collected in a number of other languages, leading to further performance decline. One important reason for this is that more and more researchers are using phonetic clustering to introduce high level information to improve speaker recognition. But such language dependent methods do not work well in multilingual conditions. In this paper, we study both language and channel mismatch using a support vector machine (SVM) speaker recognition system. Maximum likelihood linear regression (MLLR) transforms adapting a universal background model (UBM) are adopted as features. We first introduce a novel language independent statistical binary-decision tree to reduce multi-language effects, and compare this data-driven approach with a traditional knowledge based one. We also construct a framework for channel compensation using feature-domain latent factor analysis (LFA) and MLLR supervector kernel-based nuisance attribute projection (NAP) in the model-domain. Results on the NIST SRE 2006 1conv4w-1conv4w/mic corpus show significant improvement. We also compare our compensated MLLR-SVM system with state-of-the-art cepstral Gaussian mixture and SVM systems, and combine them for a further improvement.

  9. A self-trained semisupervised SVM approach to the remote sensing land cover classification

    Science.gov (United States)

    Liu, Ying; Zhang, Bai; Wang, Li-min; Wang, Nan

    2013-09-01

    Support vector machines (SVM) are nowadays receiving increasing attention in remote sensing applications although this technique is very sensitive to the parameters setting and training set definition. Self-training is an effective semisupervised method, which can reduce the effort needed to prepare the training set by training the model with a small number of labeled examples and an additional set of unlabeled examples. In this study, a novel semisupervised SVM model that uses self-training approach is proposed to address the problem of remote sensing land cover classification. The key characteristics of this approach are that (1) the self-adaptive mutation particle swarm optimization algorithm is introduced to get the optimum parameters that improve the generalization performance of the SVM classifier, and (2) the Gustafson-Kessel fuzzy clustering algorithm is proposed for the selection of unlabeled points to reduce the impact of ineffective labels. The effectiveness of the proposed technique is evaluated firstly with samples from remote sensing data and then by identifying different land cover regions in the remote sensing imagery. Experimental results show that accuracy level is increased by applying this learning scheme, which results in the smallest generalization error compared with the other schemes.

  10. Using Multidimensional ADTPE and SVM for Optical Modulation Real-Time Recognition

    Directory of Open Access Journals (Sweden)

    Junyu Wei

    2016-01-01

    Full Text Available Based on the feature extraction of multidimensional asynchronous delay-tap plot entropy (ADTPE and multiclass classification of support vector machine (SVM, we propose a method for recognition of multiple optical modulation formats and various data rates. We firstly present the algorithm of multidimensional ADTPE, which is extracted from asynchronous delay sampling pairs of modulated optical signal. Then, a multiclass SVM is utilized for fast and accurate classification of several widely-used optical modulation formats. In addition, a simple real-time recognition scheme is designed to reduce the computation time. Compared to the existing method based on asynchronous delay-tap plot (ADTP, the theoretical analysis and simulation results show that our recognition method can effectively enhance the tolerance of transmission impairments, obtaining relatively high accuracy. Finally, it is further demonstrated that the proposed method can be integrated in an optical transport network (OTN with flexible expansion. Through simply adding the corresponding sub-SVM module in the digital signal processer (DSP, arbitrary new modulation formats can be recognized with high recognition accuracy in a short response time.

  11. Diesel Engine Valve Clearance Fault Diagnosis Based on Features Extraction Techniques and FastICA-SVM

    Science.gov (United States)

    Jing, Ya-Bing; Liu, Chang-Wen; Bi, Feng-Rong; Bi, Xiao-Yang; Wang, Xia; Shao, Kang

    2017-07-01

    Numerous vibration-based techniques are rarely used in diesel engines fault diagnosis in a direct way, due to the surface vibration signals of diesel engines with the complex non-stationary and nonlinear time-varying features. To investigate the fault diagnosis of diesel engines, fractal correlation dimension, wavelet energy and entropy as features reflecting the diesel engine fault fractal and energy characteristics are extracted from the decomposed signals through analyzing vibration acceleration signals derived from the cylinder head in seven different states of valve train. An intelligent fault detector FastICA-SVM is applied for diesel engine fault diagnosis and classification. The results demonstrate that FastICA-SVM achieves higher classification accuracy and makes better generalization performance in small samples recognition. Besides, the fractal correlation dimension and wavelet energy and entropy as the special features of diesel engine vibration signal are considered as input vectors of classifier FastICA-SVM and could produce the excellent classification results. The proposed methodology improves the accuracy of feature extraction and the fault diagnosis of diesel engines.

  12. Detection of Cross Site Scripting Attack in Wireless Networks Using n-Gram and SVM

    Directory of Open Access Journals (Sweden)

    Jun-Ho Choi

    2012-01-01

    Full Text Available Large parts of attacks targeting the web are aiming at the weak point of web application. Even though SQL injection, which is the form of XSS (Cross Site Scripting attacks, is not a threat to the system to operate the web site, it is very critical to the places that deal with the important information because sensitive information can be obtained and falsified. In this paper, the method to detect themalicious SQL injection script code which is the typical XSS attack using n-Gram indexing and SVM (Support Vector Machine is proposed. In order to test the proposed method, the test was conducted after classifying each data set as normal code and malicious code, and the malicious script code was detected by applying index term generated by n-Gram and data set generated by code dictionary to SVM classifier. As a result, when the malicious script code detection was conducted using n-Gram index term and SVM, the superior performance could be identified in detecting malicious script and the more improved results than existing methods could be seen in the malicious script code detection recall.

  13. SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals

    Directory of Open Access Journals (Sweden)

    Jiping Xiong

    2017-03-01

    Full Text Available Although wrist-type photoplethysmographic (hereafter referred to as WPPG sensor signals can measure heart rate quite conveniently, the subjects’ hand movements can cause strong motion artifacts, and then the motion artifacts will heavily contaminate WPPG signals. Hence, it is challenging for us to accurately estimate heart rate from WPPG signals during intense physical activities. The WWPG method has attracted more attention thanks to the popularity of wrist-worn wearable devices. In this paper, a mixed approach called Mix-SVM is proposed, it can use multi-channel WPPG sensor signals and simultaneous acceleration signals to measurement heart rate. Firstly, we combine the principle component analysis and adaptive filter to remove a part of the motion artifacts. Due to the strong relativity between motion artifacts and acceleration signals, the further denoising problem is regarded as a sparse signals reconstruction problem. Then, we use a spectrum subtraction method to eliminate motion artifacts effectively. Finally, the spectral peak corresponding to heart rate is sought by an SVM-based spectral analysis method. Through the public PPG database in the 2015 IEEE Signal Processing Cup, we acquire the experimental results, i.e., the average absolute error was 1.01 beat per minute, and the Pearson correlation was 0.9972. These results also confirm that the proposed Mix-SVM approach has potential for multi-channel WPPG-based heart rate estimation in the presence of intense physical exercise.

  14. Combination of minimum enclosing balls classifier with SVM in coal-rock recognition.

    Science.gov (United States)

    Song, QingJun; Jiang, HaiYan; Song, Qinghui; Zhao, XieGuang; Wu, Xiaoxuan

    2017-01-01

    Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score) feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB) algorithm plus Support vector machine (SVM) is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition.

  15. Hybrid SVM-HMM based recognition algorithm for pen-based tutoring system

    Science.gov (United States)

    Yuan, Zhenming; Pan, Hong

    2007-11-01

    Pen-based computing takes advantage of human skill with the pen, which is more than a substitute for the mouse. A hybrid SVM-HMM based recognition algorithm is presented for pen-based single stroke diagram. The algorithm includes five steps: sampling and pre-processing, segmentation, formal feature computing, SVM based feature classification, and HMM based symbol recognition. The formal feature of a stroke is composed of five static features and one dynamic feature. A group of one-to-one combinations of binary SVMs are used as feature classifiers to produce fixed length feature vectors, each of which is produced by the probability output with Sigmoid function and act as the posterior probability of observation of HMM. Finally HMMs are employed as final recognizer to recognize the unknown stroke. Based on this algorithm, a tutoring system is designed to identify the sketches of the flowchart diagrams. Experiment results show the hybrid algorithm has a good learning and recognition ability, which is benefited from combining the SVM's classification ability of static properties with the HMM's recognition ability of dynamic properties.

  16. Fault Diagnosis for Constant Deceleration Braking System of Mine Hoist based on Principal Component Analysis and SVM

    Directory of Open Access Journals (Sweden)

    Li Juan-Juan

    2017-01-01

    Full Text Available Based on AMESim simulation platform, the pressure-time curve of constant deceleration braking system is obtained in this paper firstly, by simulating three typical faults of brake, the spring stiffness decrease, the brake shoe friction coefficient decrease and brake leaking. Then pressure data on the curve for each time are seen as a variable and the curve is chosen as the fault sample, analysed by the method of Principal Component Analysis (PCA. Last, principal components or sum of variance contribution rates more than 95% are selected as sample eigenvalues and Support Vector Machine (SVM is used for fault diagnosis. Diagnosis results show that all testing faults can be identified accurately, which indicates SVM model has an extremely excellent ability to identify faults. To further verify the performance of SVM for fault identification, BP neural network is established to compare. The result shows that SVM model is more accurate than BP neural network in fault recognition.

  17. Performance Analysis of DTC-SVM Sliding Mode Controllers-Based Parameters Estimator of Electric Motor Speed Drive

    Directory of Open Access Journals (Sweden)

    Fatma Ben Salem

    2014-01-01

    Full Text Available This paper is concerned with a framework which unifies direct torque control space vector modulation (DTC-SVM and variable structure control (VSC. The result is a hybrid VSC-DTC-SVM controller design which eliminates several major limitations of the two individual controls and retains merits of both controllers. It has been shown that obtained control laws are very sensitive to variations of the stator resistance, the rotor resistance, and the mutual inductance. This paper discusses the performances of adaptive controllers of VSC-DTC-SVM monitored induction motor drive in a wide speed range and even in the presence of parameters uncertainties and mismatching disturbances. Better estimations of the stator resistance, the rotor resistance, and the mutual inductance yield improvements of induction motor performances using VSC-DTC-SVM, thereby facilitating torque ripple minimization. Simulation results verified the performances of the proposed approach.

  18. Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems

    Directory of Open Access Journals (Sweden)

    Ming-Yuan Cho

    2017-01-01

    Full Text Available Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO based support vector machine (SVM classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR method with a pseudorandom binary sequence (PRBS stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

  19. Performance Analysis of DTC-SVM Sliding Mode Controllers-Based Parameters Estimator of Electric Motor Speed Drive

    OpenAIRE

    Ben Salem, Fatma; Derbel, Nabil

    2014-01-01

    This paper is concerned with a framework which unifies direct torque control space vector modulation (DTC-SVM) and variable structure control (VSC). The result is a hybrid VSC-DTC-SVM controller design which eliminates several major limitations of the two individual controls and retains merits of both controllers. It has been shown that obtained control laws are very sensitive to variations of the stator resistance, the rotor resistance, and the mutual inductance. This paper discusses the per...

  20. Classification of different kinds of pesticide residues on lettuce based on fluorescence spectra and WT-BCC-SVM algorithm

    Science.gov (United States)

    Zhou, Xin; Jun, Sun; Zhang, Bing; Jun, Wu

    2017-07-01

    In order to improve the reliability of the spectrum feature extracted by wavelet transform, a method combining wavelet transform (WT) with bacterial colony chemotaxis algorithm and support vector machine (BCC-SVM) algorithm (WT-BCC-SVM) was proposed in this paper. Besides, we aimed to identify different kinds of pesticide residues on lettuce leaves in a novel and rapid non-destructive way by using fluorescence spectra technology. The fluorescence spectral data of 150 lettuce leaf samples of five different kinds of pesticide residues on the surface of lettuce were obtained using Cary Eclipse fluorescence spectrometer. Standard normalized variable detrending (SNV detrending), Savitzky-Golay coupled with Standard normalized variable detrending (SG-SNV detrending) were used to preprocess the raw spectra, respectively. Bacterial colony chemotaxis combined with support vector machine (BCC-SVM) and support vector machine (SVM) classification models were established based on full spectra (FS) and wavelet transform characteristics (WTC), respectively. Moreover, WTC were selected by WT. The results showed that the accuracy of training set, calibration set and the prediction set of the best optimal classification model (SG-SNV detrending-WT-BCC-SVM) were 100%, 98% and 93.33%, respectively. In addition, the results indicated that it was feasible to use WT-BCC-SVM to establish diagnostic model of different kinds of pesticide residues on lettuce leaves.

  1. Abnormal Gait Behavior Detection for Elderly Based on Enhanced Wigner-Ville Analysis and Cloud Incremental SVM Learning

    Directory of Open Access Journals (Sweden)

    Jian Luo

    2016-01-01

    Full Text Available A cloud based health care system is proposed in this paper for the elderly by providing abnormal gait behavior detection, classification, online diagnosis, and remote aid service. Intelligent mobile terminals with triaxial acceleration sensor embedded are used to capture the movement and ambulation information of elderly. The collected signals are first enhanced by a Kalman filter. And the magnitude of signal vector features is then extracted and decomposed into a linear combination of enhanced Gabor atoms. The Wigner-Ville analysis method is introduced and the problem is studied by joint time-frequency analysis. In order to solve the large-scale abnormal behavior data lacking problem in training process, a cloud based incremental SVM (CI-SVM learning method is proposed. The original abnormal behavior data are first used to get the initial SVM classifier. And the larger abnormal behavior data of elderly collected by mobile devices are then gathered in cloud platform to conduct incremental training and get the new SVM classifier. By the CI-SVM learning method, the knowledge of SVM classifier could be accumulated due to the dynamic incremental learning. Experimental results demonstrate that the proposed method is feasible and can be applied to aged care, emergency aid, and related fields.

  2. Multi-scale ancient DNA analyses confirm the western origin of Michelsberg farmers and document probable practices of human sacrifice.

    Directory of Open Access Journals (Sweden)

    Alice Beau

    Full Text Available In Europe, the Middle Neolithic is characterized by an important diversification of cultures. In northeastern France, the appearance of the Michelsberg culture has been correlated with major cultural changes and interpreted as the result of the settlement of new groups originating from the Paris Basin. This cultural transition has been accompanied by the expansion of particular funerary practices involving inhumations within circular pits and individuals in "non-conventional" positions (deposited in the pits without any particular treatment. If the status of such individuals has been highly debated, the sacrifice hypothesis has been retained for the site of Gougenheim (Alsace. At the regional level, the analysis of the Gougenheim mitochondrial gene pool (SNPs and HVR-I sequence analyses permitted us to highlight a major genetic break associated with the emergence of the Michelsberg in the region. This genetic discontinuity appeared to be linked to new affinities with farmers from the Paris Basin, correlated to a noticeable hunter-gatherer legacy. All of the evidence gathered supports (i the occidental origin of the Michelsberg groups and (ii the potential implication of this migration in the progression of the hunter-gatherer legacy from the Paris Basin to Alsace / Western Germany at the beginning of the Late Neolithic. At the local level, we noted some differences in the maternal gene pool of individuals in "conventional" vs. "non-conventional" positions. The relative genetic isolation of these sub-groups nicely echoes both their social distinction and the hypothesis of sacrifices retained for the site. Our investigation demonstrates that a multi-scale aDNA study of ancient communities offers a unique opportunity to disentangle the complex relationships between cultural and biological evolution.

  3. Disorder recognition in clinical texts using multi-label structured SVM.

    Science.gov (United States)

    Lin, Wutao; Ji, Donghong; Lu, Yanan

    2017-01-31

    Information extraction in clinical texts enables medical workers to find out problems of patients faster as well as makes intelligent diagnosis possible in the future. There has been a lot of work about disorder mention recognition in clinical narratives. But recognition of some more complicated disorder mentions like overlapping ones is still an open issue. This paper proposes a multi-label structured Support Vector Machine (SVM) based method for disorder mention recognition. We present a multi-label scheme which could be used in complicated entity recognition tasks. We performed three sets of experiments to evaluate our model. Our best F1-Score on the 2013 Conference and Labs of the Evaluation Forum data set is 0.7343. There are six types of labels in our multi-label scheme, all of which are represented by 24-bit binary numbers. The binary digits of each label contain information about different disorder mentions. Our multi-label method can recognize not only disorder mentions in the form of contiguous or discontiguous words but also mentions whose spans overlap with each other. The experiments indicate that our multi-label structured SVM model outperforms the condition random field (CRF) model for this disorder mention recognition task. The experiments show that our multi-label scheme surpasses the baseline. Especially for overlapping disorder mentions, the F1-Score of our multi-label scheme is 0.1428 higher than the baseline BIOHD1234 scheme. This multi-label structured SVM based approach is demonstrated to work well with this disorder recognition task. The novel multi-label scheme we presented is superior to the baseline and it can be used in other models to solve various types of complicated entity recognition tasks as well.

  4. Intrusion detection model using fusion of chi-square feature selection and multi class SVM

    Directory of Open Access Journals (Sweden)

    Ikram Sumaiya Thaseen

    2017-10-01

    Full Text Available Intrusion detection is a promising area of research in the domain of security with the rapid development of internet in everyday life. Many intrusion detection systems (IDS employ a sole classifier algorithm for classifying network traffic as normal or abnormal. Due to the large amount of data, these sole classifier models fail to achieve a high attack detection rate with reduced false alarm rate. However by applying dimensionality reduction, data can be efficiently reduced to an optimal set of attributes without loss of information and then classified accurately using a multi class modeling technique for identifying the different network attacks. In this paper, we propose an intrusion detection model using chi-square feature selection and multi class support vector machine (SVM. A parameter tuning technique is adopted for optimization of Radial Basis Function kernel parameter namely gamma represented by ‘ϒ’ and over fitting constant ‘C’. These are the two important parameters required for the SVM model. The main idea behind this model is to construct a multi class SVM which has not been adopted for IDS so far to decrease the training and testing time and increase the individual classification accuracy of the network attacks. The investigational results on NSL-KDD dataset which is an enhanced version of KDDCup 1999 dataset shows that our proposed approach results in a better detection rate and reduced false alarm rate. An experimentation on the computational time required for training and testing is also carried out for usage in time critical applications.

  5. [SVM-based qualitative analysis of Muscat Hamburg wine produced in Tianjin region].

    Science.gov (United States)

    Zhang, Jun; Wang, Fang; Wei, Ji-Ping; Li, Chang-Wen; Yang, Hua; Shao, Chun-Fu; Zhang, Fu-Qing; Yin, Ji-Tai; Xiao, Dong-Guang

    2011-01-01

    The purpose was to achieve the identification of Muscat Hamburg wines produced in Tianjin region through scanning and analyzing dry white wine samples of different grape varieties and regions by infrared spectroscopy technology. A support vector machine (SVM) based method was introduced to analyze infrared spectra of dry white wines. The pretreatment processes of the IR spectra were also elaborated, including baseline adjustment, noise Elimination, standard normalization and eliminating the main component of abnormal sample points. The authors selected great quantity of dry white wine samples of different grape regions including 511 Muscat Hamburg wine samples, 438 Italian Riesling wine samples, 307 Chardonnay wine samples, 29 Ugni Blanc wine samples, 44 Rkatsiteli wine samples, 31 longan wine samples and 79 ZeHong wine samples. According to different classification problems, 80% of IR spectra of the wine samples were used to establish discrimination models with SVM-based method, and the remaining 20% of IR spectra were used for the validation of the discrimination models. Experimental results showed that the proposed method is effective, since high classification accuracy, identification rate and rejecting rate were achieved: over 97% for the white wine samples of different grape varieties, meanwhile over 98% for the Muscat Hamburg wine samples produced in different regions. So the method developed in this paper played a good role in the qualitative classification and discrimination of Muscat Hamburg wines produced in Tianjin region. This novel method has a considerable potential and a rosy application future due to the expeditiousness, stability and easy-operation of FTIR method, as well as the veracity and credibility of SVM method.

  6. A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM

    Directory of Open Access Journals (Sweden)

    Chenchen Huang

    2014-01-01

    Full Text Available Feature extraction is a very important part in speech emotion recognition, and in allusion to feature extraction in speech emotion recognition problems, this paper proposed a new method of feature extraction, using DBNs in DNN to extract emotional features in speech signal automatically. By training a 5 layers depth DBNs, to extract speech emotion feature and incorporate multiple consecutive frames to form a high dimensional feature. The features after training in DBNs were the input of nonlinear SVM classifier, and finally speech emotion recognition multiple classifier system was achieved. The speech emotion recognition rate of the system reached 86.5%, which was 7% higher than the original method.

  7. Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling.

    Science.gov (United States)

    Tanabe, Kazutoshi; Lučić, Bono; Amić, Dragan; Kurita, Takio; Kaihara, Mikio; Onodera, Natsuo; Suzuki, Takahiro

    2010-11-01

    The Carcinogenicity Reliability Database (CRDB) was constructed by collecting experimental carcinogenicity data on about 1,500 chemicals from six sources, including IARC, and NTP databases, and then by ranking their reliabilities into six unified categories. A wide variety of 911 organic chemicals were selected from the database for QSAR modeling, and 1,504 kinds of different molecular descriptors were calculated, based on their 3D molecular structures as modeled by the Dragon software. Positive (carcinogenic) and negative (non-carcinogenic) chemicals containing various substructures were counted using atom and functional group count descriptors, and the statistical significance of ratios of positives to negatives was tested for those substructures. Very few were judged to be strongly related to carcinogenicity, among substructures known to be responsible for carcinogens as revealed from biomedical studies. In order to develop QSAR models for the prediction of the carcinogenicities of a wide variety of chemicals with a satisfactory performance level, the relationship between the carcinogenicity data with improved reliability and a subset of significant descriptors selected from 1,504 Dragon descriptors was analyzed with a support vector machine (SVM) method: the classification function (SVC) for weighted data in LIBSVM program was used to classify chemicals into two carcinogenic categories (positive or negative), where weights were set depending on the reliabilities of the carcinogenicity data. The quality and stability of the models presented were tested by performing a dual cross-validation procedure. A single SVM model as the first step was developed for all the 911 chemicals using 250 selected descriptors, achieving an overall accuracy level, i.e., positive and negative correct estimate, of about 70%. In order to improve the accuracy of the final model, the 911 chemicals were classified into 20 mutually overlapping subgroups according to contained substructures

  8. Time Reversal Reconstruction Algorithm Based on PSO Optimized SVM Interpolation for Photoacoustic Imaging

    Directory of Open Access Journals (Sweden)

    Mingjian Sun

    2015-01-01

    Full Text Available Photoacoustic imaging is an innovative imaging technique to image biomedical tissues. The time reversal reconstruction algorithm in which a numerical model of the acoustic forward problem is run backwards in time is widely used. In the paper, a time reversal reconstruction algorithm based on particle swarm optimization (PSO optimized support vector machine (SVM interpolation method is proposed for photoacoustics imaging. Numerical results show that the reconstructed images of the proposed algorithm are more accurate than those of the nearest neighbor interpolation, linear interpolation, and cubic convolution interpolation based time reversal algorithm, which can provide higher imaging quality by using significantly fewer measurement positions or scanning times.

  9. SVM-BALSA: Remote Homology Detection based on Bayesian Sequence Alignment

    Energy Technology Data Exchange (ETDEWEB)

    Webb-Robertson, Bobbie-Jo M.; Oehmen, Chris S.; Matzke, Melissa M.

    2005-11-10

    Using biopolymer sequence comparison methods to identify evolutionarily related proteins is one of the most common tasks in bioinformatics. Recently, support vector machines (SVMs) utilizing statistical learning theory have been employed in the problem of remote homology detection and shown to outperform iterative profile methods such as PSI-BLAST. In this study we demonstrate the utilization of a Bayesian alignment score, which accounts for the uncertainty of all possible alignments, in the SVM construction improves sensitivity compared to the traditional dynamic programming implementation.

  10. A Non-Destructive Method for Distinguishing Reindeer Antler (Rangifer tarandus from Red Deer Antler (Cervus elaphus Using X-Ray Micro-Tomography Coupled with SVM Classifiers.

    Directory of Open Access Journals (Sweden)

    Alexandre Lefebvre

    Full Text Available Over the last decade, biomedical 3D-imaging tools have gained widespread use in the analysis of prehistoric bone artefacts. While initial attempts to characterise the major categories used in osseous industry (i.e. bone, antler, and dentine/ivory have been successful, the taxonomic determination of prehistoric artefacts remains to be investigated. The distinction between reindeer and red deer antler can be challenging, particularly in cases of anthropic and/or taphonomic modifications. In addition to the range of destructive physicochemical identification methods available (mass spectrometry, isotopic ratio, and DNA analysis, X-ray micro-tomography (micro-CT provides convincing non-destructive 3D images and analyses. This paper presents the experimental protocol (sample scans, image processing, and statistical analysis we have developed in order to identify modern and archaeological antler collections (from Isturitz, France. This original method is based on bone microstructure analysis combined with advanced statistical support vector machine (SVM classifiers. A combination of six microarchitecture biomarkers (bone volume fraction, trabecular number, trabecular separation, trabecular thickness, trabecular bone pattern factor, and structure model index were screened using micro-CT in order to characterise internal alveolar structure. Overall, reindeer alveoli presented a tighter mesh than red deer alveoli, and statistical analysis allowed us to distinguish archaeological antler by species with an accuracy of 96%, regardless of anatomical location on the antler. In conclusion, micro-CT combined with SVM classifiers proves to be a promising additional non-destructive method for antler identification, suitable for archaeological artefacts whose degree of human modification and cultural heritage or scientific value has previously made it impossible (tools, ornaments, etc..

  11. Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier.

    Science.gov (United States)

    Li, Qiang; Gu, Yu; Jia, Jing

    2017-01-30

    Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method to identify multiple Chinese liquors is of positive significance. This paper presents a pattern recognition system for classifying ten brands of Chinese liquors based on multidimensional scaling (MDS) and support vector machine (SVM) algorithms in a quartz crystal microbalance (QCM)-based electronic nose (e-nose) we designed. We evaluated the comprehensive performance of the MDS-SVM classifier that predicted all ten brands of Chinese liquors individually. The prediction accuracy (98.3%) showed superior performance of the MDS-SVM classifier over the back-propagation artificial neural network (BP-ANN) classifier (93.3%) and moving average-linear discriminant analysis (MA-LDA) classifier (87.6%). The MDS-SVM classifier has reasonable reliability, good fitting and prediction (generalization) performance in classification of the Chinese liquors. Taking both application of the e-nose and validation of the MDS-SVM classifier into account, we have thus created a useful method for the classification of multiple Chinese liquors.

  12. Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier

    Directory of Open Access Journals (Sweden)

    Qiang Li

    2017-01-01

    Full Text Available Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method to identify multiple Chinese liquors is of positive significance. This paper presents a pattern recognition system for classifying ten brands of Chinese liquors based on multidimensional scaling (MDS and support vector machine (SVM algorithms in a quartz crystal microbalance (QCM-based electronic nose (e-nose we designed. We evaluated the comprehensive performance of the MDS-SVM classifier that predicted all ten brands of Chinese liquors individually. The prediction accuracy (98.3% showed superior performance of the MDS-SVM classifier over the back-propagation artificial neural network (BP-ANN classifier (93.3% and moving average-linear discriminant analysis (MA-LDA classifier (87.6%. The MDS-SVM classifier has reasonable reliability, good fitting and prediction (generalization performance in classification of the Chinese liquors. Taking both application of the e-nose and validation of the MDS-SVM classifier into account, we have thus created a useful method for the classification of multiple Chinese liquors.

  13. Bearing Fault Diagnosis Based on Improved Locality-Constrained Linear Coding and Adaptive PSO-Optimized SVM

    Directory of Open Access Journals (Sweden)

    Haodong Yuan

    2017-01-01

    Full Text Available A novel bearing fault diagnosis method based on improved locality-constrained linear coding (LLC and adaptive PSO-optimized support vector machine (SVM is proposed. In traditional LLC, each feature is encoded by using a fixed number of bases without considering the distribution of the features and the weight of the bases. To address these problems, an improved LLC algorithm based on adaptive and weighted bases is proposed. Firstly, preliminary features are obtained by wavelet packet node energy. Then, dictionary learning with class-wise K-SVD algorithm is implemented. Subsequently, based on the learned dictionary the LLC codes can be solved using the improved LLC algorithm. Finally, SVM optimized by adaptive particle swarm optimization (PSO is utilized to classify the discriminative LLC codes and thus bearing fault diagnosis is realized. In the dictionary leaning stage, other methods such as selecting the samples themselves as dictionary and K-means are also conducted for comparison. The experiment results show that the LLC codes can effectively extract the bearing fault characteristics and the improved LLC outperforms traditional LLC. The dictionary learned by class-wise K-SVD achieves the best performance. Additionally, adaptive PSO-optimized SVM can greatly enhance the classification accuracy comparing with SVM using default parameters and linear SVM.

  14. Discrimination between Alzheimer's Disease and Mild Cognitive Impairment Using SOM and PSO-SVM

    Directory of Open Access Journals (Sweden)

    Shih-Ting Yang

    2013-01-01

    Full Text Available In this study, an MRI-based classification framework was proposed to distinguish the patients with AD and MCI from normal participants by using multiple features and different classifiers. First, we extracted features (volume and shape from MRI data by using a series of image processing steps. Subsequently, we applied principal component analysis (PCA to convert a set of features of possibly correlated variables into a smaller set of values of linearly uncorrelated variables, decreasing the dimensions of feature space. Finally, we developed a novel data mining framework in combination with support vector machine (SVM and particle swarm optimization (PSO for the AD/MCI classification. In order to compare the hybrid method with traditional classifier, two kinds of classifiers, that is, SVM and a self-organizing map (SOM, were trained for patient classification. With the proposed framework, the classification accuracy is improved up to 82.35% and 77.78% in patients with AD and MCI. The result achieved up to 94.12% and 88.89% in AD and MCI by combining the volumetric features and shape features and using PCA. The present results suggest that novel multivariate methods of pattern matching reach a clinically relevant accuracy for the a priori prediction of the progression from MCI to AD.

  15. Efficient and Privacy-Preserving Online Medical Prediagnosis Framework Using Nonlinear SVM.

    Science.gov (United States)

    Zhu, Hui; Liu, Xiaoxia; Lu, Rongxing; Li, Hui

    2017-05-01

    With the advances of machine learning algorithms and the pervasiveness of network terminals, the online medical prediagnosis system, which can provide the diagnosis of healthcare provider anywhere anytime, has attracted considerable interest recently. However, the flourish of online medical prediagnosis system still faces many challenges including information security and privacy preservation. In this paper, we propose an e fficient and privacy-preserving online medical prediagnosis framework, called eDiag, by using nonlinear kernel support vector machine (SVM). With eDiag, the sensitive personal health information can be processed without privacy disclosure during online prediagnosis service. Specifically, based on an improved expression for the nonlinear SVM, an efficient and privacy-preserving classification scheme is introduced with lightweight multiparty random masking and polynomial aggregation techniques. The encrypted user query is directly operated at the service provider without decryption, and the diagnosis result can only be decrypted by user. Through extensive analysis, we show that eDiag can ensure that users' health information and healthcare provider's prediction model are kept confidential, and has significantly less computation and communication overhead than existing schemes. In addition, performance evaluations via implementing eDiag on smartphone and computer demonstrate eDiag's effectiveness in term of real online environment.

  16. Realization of SVM Algorithm for Indirect Matrix Converter and Its Application in Power Factor Control

    Directory of Open Access Journals (Sweden)

    Gang Li

    2015-01-01

    Full Text Available Compared with AC-DC-AC converter, matrix converter (MC has several advantages for its bidirectional power flow, controllable power factor, and the absence of large energy storage in dc-link. The topology of MC includes direct matrix converter (DMC and indirect matrix converter (IMC. IMC has received great attention worldwide because of its easy implementation and safe commutation. Space vector PWM (SVM algorithm for indirect matrix converter is realized on DSP and CPLD platform in this paper. The control of the rectifier and inverter in IMC can be decoupled because of the intermediate dc-link. The space vector modulation scheme for IMC is discussed and the PWM sequences for the rectifier and inverter are generated. And a two-step commutation of zero current switching (ZCS in the rectifier is achieved. Input power factor of IMC can be changed by adjusting the angle of the reference current vector. Experimental tests have been conducted on a RB-IGBT based indirect matrix converter prototype. The results verify the performance of the SVM algorithm and the ability of power factor correction.

  17. APPLICATION OF FUSION WITH SAR AND OPTICAL IMAGES IN LAND USE CLASSIFICATION BASED ON SVM

    Directory of Open Access Journals (Sweden)

    C. Bao

    2012-07-01

    Full Text Available As the increment of remote sensing data with multi-space resolution, multi-spectral resolution and multi-source, data fusion technologies have been widely used in geological fields. Synthetic Aperture Radar (SAR and optical camera are two most common sensors presently. The multi-spectral optical images express spectral features of ground objects, while SAR images express backscatter information. Accuracy of the image classification could be effectively improved fusing the two kinds of images. In this paper, Terra SAR-X images and ALOS multi-spectral images were fused for land use classification. After preprocess such as geometric rectification, radiometric rectification noise suppression and so on, the two kind images were fused, and then SVM model identification method was used for land use classification. Two different fusion methods were used, one is joining SAR image into multi-spectral images as one band, and the other is direct fusing the two kind images. The former one can raise the resolution and reserve the texture information, and the latter can reserve spectral feature information and improve capability of identifying different features. The experiment results showed that accuracy of classification using fused images is better than only using multi-spectral images. Accuracy of classification about roads, habitation and water bodies was significantly improved. Compared to traditional classification method, the method of this paper for fused images with SVM classifier could achieve better results in identifying complicated land use classes, especially for small pieces ground features.

  18. Damage Detection of Structures for Ambient Loading Based on Cross Correlation Function Amplitude and SVM

    Directory of Open Access Journals (Sweden)

    Lin-sheng Huo

    2016-01-01

    Full Text Available An effective method for the damage detection of skeletal structures which combines the cross correlation function amplitude (CCFA with the support vector machine (SVM is presented in this paper. The proposed method consists of two stages. Firstly, the data features are extracted from the CCFA, which, calculated from dynamic responses and as a representation of the modal shapes of the structure, changes when damage occurs on the structure. The data features are then input into the SVM with the one-against-one (OAO algorithm to classify the damage status of the structure. The simulation data of IASC-ASCE benchmark model and a vibration experiment of truss structure are adopted to verify the feasibility of proposed method. The results show that the proposed method is suitable for the damage identification of skeletal structures with the limited sensors subjected to ambient excitation. As the CCFA based data features are sensitive to damage, the proposed method demonstrates its reliability in the diagnosis of structures with damage, especially for those with minor damage. In addition, the proposed method shows better noise robustness and is more suitable for noisy environments.

  19. IMPROVED LS-SVM USING ACO TO ESTIMATE FLASHOVER VOLTAGE OF POLLUTED INSULATORS

    Directory of Open Access Journals (Sweden)

    SID AHMED BESSEDIK

    2017-01-01

    Full Text Available The reliability of insulators under polluted environment is one of the guiding factors in the insulation coordination of high voltage transmission lines. In order to improve understanding of the flashover phenomenon in polluted insulators, several experimental studies and mathematical approaches have been made‎ in‎ last‎ year’s.‎ In‎ this‎ paper,‎ the‎ critical flashover voltage behavior of polluted insulators has been calculated and a hybrid model between machine Learning (ML and optimization technique has been proposed. For this purpose, firstly the ant colony optimization (ACO technique is utilized to optimize the hyper-parameters needed in least squares support vector machines (LS-SVM. Then, a LS-SVM-ACO model is designed to establish a nonlinear model between the characteristics of the insulator and the critical flashover voltage. The data used to train the model and test its performance is derived from experimental measurements and a mathematical model. The results obtained from the proposed model are in good accord with other mathematical and experimental results of previous researchers.

  20. SVM and ANN Based Classification of Plant Diseases Using Feature Reduction Technique

    Directory of Open Access Journals (Sweden)

    Jagadeesh D.Pujari

    2016-06-01

    Full Text Available Computers have been used for mechanization and automation in different applications of agriculture/horticulture. The critical decision on the agricultural yield and plant protection is done with the development of expert system (decision support system using computer vision techniques. One of the areas considered in the present work is the processing of images of plant diseases affecting agriculture/horticulture crops. The first symptoms of plant disease have to be correctly detected, identified, and quantified in the initial stages. The color and texture features have been used in order to work with the sample images of plant diseases. Algorithms for extraction of color and texture features have been developed, which are in turn used to train support vector machine (SVM and artificial neural network (ANN classifiers. The study has presented a reduced feature set based approach for recognition and classification of images of plant diseases. The results reveal that SVM classifier is more suitable for identification and classification of plant diseases affecting agriculture/horticulture crops.

  1. Improving Accuracy of Intrusion Detection Model Using PCA and optimized SVM

    Directory of Open Access Journals (Sweden)

    Sumaiya Thaseen Ikram

    2016-06-01

    Full Text Available Intrusion detection is very essential for providing security to different network domains and is mostly used for locating and tracing the intruders. There are many problems with traditional intrusion detection models (IDS such as low detection capability against unknown network attack, high false alarm rate and insufficient analysis capability. Hence the major scope of the research in this domain is to develop an intrusion detection model with improved accuracy and reduced training time. This paper proposes a hybrid intrusiondetection model by integrating the principal component analysis (PCA and support vector machine (SVM. The novelty of the paper is the optimization of kernel parameters of the SVM classifier using automatic parameter selection technique. This technique optimizes the punishment factor (C and kernel parameter gamma (γ, thereby improving the accuracy of the classifier and reducing the training and testing time. The experimental results obtained on the NSL KDD and gurekddcup dataset show that the proposed technique performs better with higher accuracy, faster convergence speed and better generalization. Minimum resources are consumed as the classifier input requires reduced feature set for optimum classification. A comparative analysis of hybrid models with the proposed model is also performed.

  2. Data on Support Vector Machines (SVM model to forecast photovoltaic power

    Directory of Open Access Journals (Sweden)

    M. Malvoni

    2016-12-01

    Full Text Available The data concern the photovoltaic (PV power, forecasted by a hybrid model that considers weather variations and applies a technique to reduce the input data size, as presented in the paper entitled “Photovoltaic forecast based on hybrid pca-lssvm using dimensionality reducted data” (M. Malvoni, M.G. De Giorgi, P.M. Congedo, 2015 [1]. The quadratic Renyi entropy criteria together with the principal component analysis (PCA are applied to the Least Squares Support Vector Machines (LS-SVM to predict the PV power in the day-ahead time frame. The data here shared represent the proposed approach results. Hourly PV power predictions for 1,3,6,12, 24 ahead hours and for different data reduction sizes are provided in Supplementary material.

  3. Determination Of Gas Mixture Components Using Fluctuation Enhanced Sensing And The LS-SVM Regression Algorithm

    Directory of Open Access Journals (Sweden)

    Lentka Łukasz

    2015-09-01

    Full Text Available This paper analyses the effectiveness of determining gas concentrations by using a prototype WO3 resistive gas sensor together with fluctuation enhanced sensing. We have earlier demonstrated that this method can determine the composition of a gas mixture by using only a single sensor. In the present study, we apply Least-Squares Support-Vector-Machine-based (LS-SVM-based nonlinear regression to determine the gas concentration of each constituent in a mixture. We confirmed that the accuracy of the estimated gas concentration could be significantly improved by applying temperature change and ultraviolet irradiation of the WO3 layer. Fluctuation-enhanced sensing allowed us to predict the concentration of both component gases.

  4. An SVM Framework for Malignant Melanoma Detection Based on Optimized HOG Features

    Directory of Open Access Journals (Sweden)

    Samy Bakheet

    2017-01-01

    Full Text Available Early detection of skin cancer through improved techniques and innovative technologies has the greatest potential for significantly reducing both morbidity and mortality associated with this disease. In this paper, an effective framework of a CAD (Computer-Aided Diagnosis system for melanoma skin cancer is developed mainly by application of an SVM (Support Vector Machine model on an optimized set of HOG (Histogram of Oriented Gradient based descriptors of skin lesions. Experimental results obtained by applying the presented methodology on a large, publicly accessible dataset of dermoscopy images demonstrate that the proposed framework is a strong contender for the state-of-the-art alternatives by achieving high levels of sensitivity, specificity, and accuracy (98.21%, 96.43% and 97.32%, respectively, without sacrificing computational soundness.

  5. Deeper understanding about the genetic structure of dengue virus using SVM

    Directory of Open Access Journals (Sweden)

    Choi Subin

    2016-01-01

    Full Text Available Dengue fever, mainly found in the tropical and subtropical regions, is carried by mosquitoes. With the help of greenhouse effect, places considered to be a Dengue safe-zone are becoming more and more dangerous. Dengue fever shows similar aspects to MERS, which caused heavy casualties in South Korea; Dengue virus does not have clear treatments nor vaccines like MERS. Development of Dengue vaccine is actively investigated lately. However, it is not easy to succeed; the fact that Dengue’s 4 serotypes have different properties and that repeated infections worsen the symptoms. This research aims to analyze the 4 serotypes (DENV1, DENV2, DENV3, DENV4 using SVM and ANN algorithms to investigate the constraints in the development of Dengue’s vaccines and treatments.

  6. Semi-supervised Learning for Classification of Polarimetric SAR Images Based on SVM-Wishart

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    Hua Wen-qiang

    2015-02-01

    Full Text Available In this study, we propose a new semi-supervised classification method for Polarimetric SAR (PolSAR images, aiming at handling the issue that the number of train set is small. First, considering the scattering characters of PolSAR data, this method extracts multiple scattering features using target decomposition approach. Then, a semi-supervised learning model is established based on a co-training framework and Support Vector Machine (SVM. Both labeled and unlabeled data are utilized in this model to obtain high classification accuracy. Third, a recovery scheme based on the Wishart classifier is proposed to improve the classification performance. From the experiments conducted in this study, it is evident that the proposed method performs more effectively compared with other traditional methods when the number of train set is small.

  7. Using LS-SVM based motion recognition for smartphone indoor wireless positioning.

    Science.gov (United States)

    Pei, Ling; Liu, Jingbin; Guinness, Robert; Chen, Yuwei; Kuusniemi, Heidi; Chen, Ruizhi

    2012-01-01

    The paper presents an indoor navigation solution by combining physical motion recognition with wireless positioning. Twenty-seven simple features are extracted from the built-in accelerometers and magnetometers in a smartphone. Eight common motion states used during indoor navigation are detected by a Least Square-Support Vector Machines (LS-SVM) classification algorithm, e.g., static, standing with hand swinging, normal walking while holding the phone in hand, normal walking with hand swinging, fast walking, U-turning, going up stairs, and going down stairs. The results indicate that the motion states are recognized with an accuracy of up to 95.53% for the test cases employed in this study. A motion recognition assisted wireless positioning approach is applied to determine the position of a mobile user. Field tests show a 1.22 m mean error in "Static Tests" and a 3.53 m in "Stop-Go Tests".

  8. Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning

    Directory of Open Access Journals (Sweden)

    Ruizhi Chen

    2012-05-01

    Full Text Available The paper presents an indoor navigation solution by combining physical motion recognition with wireless positioning. Twenty-seven simple features are extracted from the built-in accelerometers and magnetometers in a smartphone. Eight common motion states used during indoor navigation are detected by a Least Square-Support Vector Machines (LS-SVM classification algorithm, e.g., static, standing with hand swinging, normal walking while holding the phone in hand, normal walking with hand swinging, fast walking, U-turning, going up stairs, and going down stairs. The results indicate that the motion states are recognized with an accuracy of up to 95.53% for the test cases employed in this study. A motion recognition assisted wireless positioning approach is applied to determine the position of a mobile user. Field tests show a 1.22 m mean error in “Static Tests” and a 3.53 m in “Stop-Go Tests”.

  9. AN IMPLEMENTATION OF EIS-SVM CLASSIFIER USING RESEARCH ARTICLES FOR TEXT CLASSIFICATION

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    B Ramesh

    2016-04-01

    Full Text Available Automatic text classification is a prominent research topic in text mining. The text pre-processing is a major role in text classifier. The efficiency of pre-processing techniques is increasing the performance of text classifier. In this paper, we are implementing ECAS stemmer, Efficient Instance Selection and Pre-computed Kernel Support Vector Machine for text classification using recent research articles. We are using better pre-processing techniques such as ECAS stemmer to find root word, Efficient Instance Selection for dimensionality reduction of text data and Pre-computed Kernel Support Vector Machine for classification of selected instances. In this experiments were performed on 750 research articles with three classes such as engineering article, medical articles and educational articles. The EIS-SVM classifier provides better performance in real-time research articles classification.

  10. Text independent writer identification based on Gabor filter and SVM classifier

    Science.gov (United States)

    Feng, Jun; Zhu, Yanhai

    2006-11-01

    Writer identification has become a hot topic in pattern recognition and machine learning research area. This paper studies on the technology of text independent writer identification based on texture analysis. At first in the preprocessing stage the uniform texture images are created from the input document. An approach for improved characters segmentation is presented based on analysis for the character elements and their topological relations. Then the 32-channel Gabor filter is utilized to extract 64 texture features of writing image by calculating the mean values and the standard deviations of filtering output images. Finally, multi-class support vector machines (SVM) classifier is adopted to fulfill the identification task. The experiment result shows that the scheme is effective and promising.

  11. A novel robust adaptive control algorithm and application to DTC-SVM of AC drives

    Directory of Open Access Journals (Sweden)

    Belkacem Sebti

    2010-01-01

    Full Text Available In this paper a new robust adaptive control algorithm for AC machine is presented. The main feature of this algorithm is that minimum synthesis is required to implement the strategy. The MCS algorithm is a significant development of MRAC and is similary based on the hyper stability theory of Popov. The hyperstability theory guarantees the global asymptotic stability of the error vector (i.e. the difference between the reference model and system states. Finally, a new approach has been successfully implemented to DTC-SVM. Discussion on theoretical aspects, such as, selection of a reference model, stability analysis, gain adaptive and steady state error are included. Results of simulations are also presented.

  12. A fast image retrieval method based on SVM and imbalanced samples in filtering multimedia message spam

    Science.gov (United States)

    Chen, Zhang; Peng, Zhenming; Peng, Lingbing; Liao, Dongyi; He, Xin

    2011-11-01

    With the swift and violent development of the Multimedia Messaging Service (MMS), it becomes an urgent task to filter the Multimedia Message (MM) spam effectively in real-time. For the fact that most MMs contain images or videos, a method based on retrieving images is given in this paper for filtering MM spam. The detection method used in this paper is a combination of skin-color detection, texture detection, and face detection, and the classifier for this imbalanced problem is a very fast multi-classification combining Support vector machine (SVM) with unilateral binary decision tree. The experiments on 3 test sets show that the proposed method is effective, with the interception rate up to 60% and the average detection time for each image less than 1 second.

  13. Research on Chinese web page SVM classifer based on information gain

    Directory of Open Access Journals (Sweden)

    PAN Zhengcai

    2013-06-01

    Full Text Available In order to improve the efficiency and accuracy of text classification,optimization and improvement are made for defects and deficiencies of the feature dimensionality reduction method and traditional information gain method in text classification of Chinese web pages.At first,part-of-speech filtering and synonyms merging processes are taken for the first feature dimension reduction of feature items.Then,an improved information gain method is proposed for feature weighting computation of feature items.Finally,the classification algorithm of Support Vector Machine (SVM is used for text classification of Chinese web pages.Both theoretical analysis and experimental results show that this method has better performance and classification results than traditional method.

  14. Protein-protein interaction site prediction in Homo sapiens and E. coli using an interaction-affinity based membership function in fuzzy SVM.

    Science.gov (United States)

    Sriwastava, Brijesh Kumar; Basu, Subhadip; Maulik, Ujjwal

    2015-10-01

    Protein-protein interaction (PPI) site prediction aids to ascertain the interface residues that participate in interaction processes. Fuzzy support vector machine (F-SVM) is proposed as an effective method to solve this problem, and we have shown that the performance of the classical SVM can be enhanced with the help of an interaction-affinity based fuzzy membership function. The performances of both SVM and F-SVM on the PPI databases of the Homo sapiens and E. coli organisms are evaluated and estimated the statistical significance of the developed method over classical SVM and other fuzzy membership-based SVM methods available in the literature. Our membership function uses the residue-level interaction affinity scores for each pair of positive and negative sequence fragments. The average AUC scores in the 10-fold cross-validation experiments are measured as 79.94% and 80.48% for the Homo sapiens and E. coli organisms respectively. On the independent test datasets, AUC scores are obtained as 76.59% and 80.17% respectively for the two organisms. In almost all cases, the developed F-SVM method improves the performances obtained by the corresponding classical SVM and the other classifiers, available in the literature.

  15. [Application of optimized parameters SVM based on photoacoustic spectroscopy method in fault diagnosis of power transformer].

    Science.gov (United States)

    Zhang, Yu-xin; Cheng, Zhi-feng; Xu, Zheng-ping; Bai, Jing

    2015-01-01

    In order to solve the problems such as complex operation, consumption for the carrier gas and long test period in traditional power transformer fault diagnosis approach based on dissolved gas analysis (DGA), this paper proposes a new method which is detecting 5 types of characteristic gas content in transformer oil such as CH4, C2H2, C2H4, C2H6 and H2 based on photoacoustic Spectroscopy and C2H2/C2H4, CH4/H2, C2H4/C2H6 three-ratios data are calculated. The support vector machine model was constructed using cross validation method under five support vector machine functions and four kernel functions, heuristic algorithms were used in parameter optimization for penalty factor c and g, which to establish the best SVM model for the highest fault diagnosis accuracy and the fast computing speed. Particles swarm optimization and genetic algorithm two types of heuristic algorithms were comparative studied in this paper for accuracy and speed in optimization. The simulation result shows that SVM model composed of C-SVC, RBF kernel functions and genetic algorithm obtain 97. 5% accuracy in test sample set and 98. 333 3% accuracy in train sample set, and genetic algorithm was about two times faster than particles swarm optimization in computing speed. The methods described in this paper has many advantages such as simple operation, non-contact measurement, no consumption for the carrier gas, long test period, high stability and sensitivity, the result shows that the methods described in this paper can instead of the traditional transformer fault diagnosis by gas chromatography and meets the actual project needs in transformer fault diagnosis.

  16. Forecasting Seizures Using Intracranial EEG Measures and SVM in Naturally Occurring Canine Epilepsy.

    Science.gov (United States)

    Brinkmann, Benjamin H; Patterson, Edward E; Vite, Charles; Vasoli, Vincent M; Crepeau, Daniel; Stead, Matt; Howbert, J Jeffry; Cherkassky, Vladimir; Wagenaar, Joost B; Litt, Brian; Worrell, Gregory A

    2015-01-01

    Management of drug resistant focal epilepsy would be greatly assisted by a reliable warning system capable of alerting patients prior to seizures to allow the patient to adjust activities or medication. Such a system requires successful identification of a preictal, or seizure-prone state. Identification of preictal states in continuous long- duration intracranial electroencephalographic (iEEG) recordings of dogs with naturally occurring epilepsy was investigated using a support vector machine (SVM) algorithm. The dogs studied were implanted with a 16-channel ambulatory iEEG recording device with average channel reference for a mean (st. dev.) of 380.4 (+87.5) days producing 220.2 (+104.1) days of intracranial EEG recorded at 400 Hz for analysis. The iEEG records had 51.6 (+52.8) seizures identified, of which 35.8 (+30.4) seizures were preceded by more than 4 hours of seizure-free data. Recorded iEEG data were stratified into 11 contiguous, non-overlapping frequency bands and binned into one-minute synchrony features for analysis. Performance of the SVM classifier was assessed using a 5-fold cross validation approach, where preictal training data were taken from 90 minute windows with a 5 minute pre-seizure offset. Analysis of the optimal preictal training time was performed by repeating the cross validation over a range of preictal windows and comparing results. We show that the optimization of feature selection varies for each subject, i.e. algorithms are subject specific, but achieve prediction performance significantly better than a time-matched Poisson random predictor (pdogs analyzed.

  17. Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM.

    Science.gov (United States)

    Dyrba, Martin; Grothe, Michel; Kirste, Thomas; Teipel, Stefan J

    2015-06-01

    Alzheimer's disease (AD) patients exhibit alterations in the functional connectivity between spatially segregated brain regions which may be related to both local gray matter (GM) atrophy as well as a decline in the fiber integrity of the underlying white matter tracts. Machine learning algorithms are able to automatically detect the patterns of the disease in image data, and therefore, constitute a suitable basis for automated image diagnostic systems. The question of which magnetic resonance imaging (MRI) modalities are most useful in a clinical context is as yet unresolved. We examined multimodal MRI data acquired from 28 subjects with clinically probable AD and 25 healthy controls. Specifically, we used fiber tract integrity as measured by diffusion tensor imaging (DTI), GM volume derived from structural MRI, and the graph-theoretical measures 'local clustering coefficient' and 'shortest path length' derived from resting-state functional MRI (rs-fMRI) to evaluate the utility of the three imaging methods in automated multimodal image diagnostics, to assess their individual performance, and the level of concordance between them. We ran the support vector machine (SVM) algorithm and validated the results using leave-one-out cross-validation. For the single imaging modalities, we obtained an area under the curve (AUC) of 80% for rs-fMRI, 87% for DTI, and 86% for GM volume. When it came to the multimodal SVM, we obtained an AUC of 82% using all three modalities, and 89% using only DTI measures and GM volume. Combined multimodal imaging data did not significantly improve classification accuracy compared to the best single measures alone. © 2015 Wiley Periodicals, Inc.

  18. An Out-of-School Practical Exercise: An Examination of Different DNA Methylation Conditions Using a Restriction Assay

    Science.gov (United States)

    Heyduck, Birgit; Harms, Ute

    2015-01-01

    Our out-of-school practical exercise was designed to bring upper secondary school students in contact with one of the most exciting and expanding topics in biology today: epigenetics. In school, students only study the basics in genetics and the respective investigation techniques as provided by the syllabus. For a practical exercise in…

  19. How Can Marketing Academics Serve Marketing Practice? The New Marketing DNA as a Model for Marketing Education

    Science.gov (United States)

    Harrigan, Paul; Hulbert, Bev

    2011-01-01

    This article seeks to address how marketing academics can best serve marketing practice through marketing education. It is contended that, where technology is driving marketing in practice, it is afforded significantly less attention in both theory and education. Thus, the marketing graduates being produced from universities are often lacking in…

  20. Elucidation of Metallic Plume and Spatter Characteristics Based on SVM During High-Power Disk Laser Welding

    Science.gov (United States)

    Gao, Xiangdong; Liu, Guiqian

    2015-01-01

    During deep penetration laser welding, there exist plume (weak plasma) and spatters, which are the results of weld material ejection due to strong laser heating. The characteristics of plume and spatters are related to welding stability and quality. Characteristics of metallic plume and spatters were investigated during high-power disk laser bead-on-plate welding of Type 304 austenitic stainless steel plates at a continuous wave laser power of 10 kW. An ultraviolet and visible sensitive high-speed camera was used to capture the metallic plume and spatter images. Plume area, laser beam path through the plume, swing angle, distance between laser beam focus and plume image centroid, abscissa of plume centroid and spatter numbers are defined as eigenvalues, and the weld bead width was used as a characteristic parameter that reflected welding stability. Welding status was distinguished by SVM (support vector machine) after data normalization and characteristic analysis. Also, PCA (principal components analysis) feature extraction was used to reduce the dimensions of feature space, and PSO (particle swarm optimization) was used to optimize the parameters of SVM. Finally a classification model based on SVM was established to estimate the weld bead width and welding stability. Experimental results show that the established algorithm based on SVM could effectively distinguish the variation of weld bead width, thus providing an experimental example of monitoring high-power disk laser welding quality.

  1. Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

    Directory of Open Access Journals (Sweden)

    Fei Wang

    2017-12-01

    Full Text Available Accurate solar photovoltaic (PV power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN and support vector machines (SVM are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples.

  2. Influence of Sampling Practices on the Appearance of DNA Image Histograms of Prostate Cells in FNAB Samples

    Directory of Open Access Journals (Sweden)

    Abdelbaset Buhmeida

    1999-01-01

    Full Text Available Twenty‐one fine needle aspiration biopsies (FNAB of the prostate, diagnostically classified as definitely malignant, were studied. The Papanicolaou or H&E stained samples were destained and then stained for DNA with the Feulgen reaction. DNA cytometry was applied after different sampling rules. The histograms varied according to the sampling rule applied. Because free cells between cell groups were easier to measure than cells in the cell groups, two sampling rules were tested in all samples: (i cells in the cell groups were measured, and (ii free cells between cell groups were measured. Abnormal histograms were more common after the sampling rule based on free cells, suggesting that abnormal patterns are best revealed through the free cells in these samples. The conclusions were independent of the applied histogram interpretation method.

  3. Microcalcification detection in full-field digital mammograms with PFCM clustering and weighted SVM-based method

    Science.gov (United States)

    Liu, Xiaoming; Mei, Ming; Liu, Jun; Hu, Wei

    2015-12-01

    Clustered microcalcifications (MCs) in mammograms are an important early sign of breast cancer in women. Their accurate detection is important in computer-aided detection (CADe). In this paper, we integrated the possibilistic fuzzy c-means (PFCM) clustering algorithm and weighted support vector machine (WSVM) for the detection of MC clusters in full-field digital mammograms (FFDM). For each image, suspicious MC regions are extracted with region growing and active contour segmentation. Then geometry and texture features are extracted for each suspicious MC, a mutual information-based supervised criterion is used to select important features, and PFCM is applied to cluster the samples into two clusters. Weights of the samples are calculated based on possibilities and typicality values from the PFCM, and the ground truth labels. A weighted nonlinear SVM is trained. During the test process, when an unknown image is presented, suspicious regions are located with the segmentation step, selected features are extracted, and the suspicious MC regions are classified as containing MC or not by the trained weighted nonlinear SVM. Finally, the MC regions are analyzed with spatial information to locate MC clusters. The proposed method is evaluated using a database of 410 clinical mammograms and compared with a standard unweighted support vector machine (SVM) classifier. The detection performance is evaluated using response receiver operating (ROC) curves and free-response receiver operating characteristic (FROC) curves. The proposed method obtained an area under the ROC curve of 0.8676, while the standard SVM obtained an area of 0.8268 for MC detection. For MC cluster detection, the proposed method obtained a high sensitivity of 92 % with a false-positive rate of 2.3 clusters/image, and it is also better than standard SVM with 4.7 false-positive clusters/image at the same sensitivity.

  4. Practical application of DNA markers for high-throughput authentication of Panax ginseng and Panax quinquefolius from commercial ginseng products.

    Science.gov (United States)

    Jung, Juyeon; Kim, Kyung Hee; Yang, Kiwoung; Bang, Kyong-Hwan; Yang, Tae-Jin

    2014-04-01

    Korean ginseng (Panax ginseng) and American ginseng (Panax quinquefolius) are widely used medicinal plants with similar morphology but different medicinal efficacy. Roots, flowers, and processed products of Korean and American ginseng can be difficult to differentiate from each other, leading to illegal trade in which one species is sold as the other. This study was carried out to develop convenient and reliable chloroplast genome-derived DNA markers for authentication of Korean and American ginseng in commercial processed products. One codominant marker could reproducibly identify both species and intentional mixtures of the two species. We further developed a set of species-unique dominant DNA markers. Each species-specific dominant marker could detect 1% cross contamination with other species by low resolution agarose gel electrophoresis or quantitative polymerase chain reaction. Both markers were successfully applied to evaluate the original species from various processed ginseng products purchased from markets in Korea and China. We believe that high-throughput application of this marker system will eradicate illegal trade and promote confident marketing for both species to increase the value of Korean as well as American ginseng in Korea and worldwide.

  5. Effects of hardware heterogeneity on the performance of SVM Alzheimer's disease classifier.

    Science.gov (United States)

    Abdulkadir, Ahmed; Mortamet, Bénédicte; Vemuri, Prashanthi; Jack, Clifford R; Krueger, Gunnar; Klöppel, Stefan

    2011-10-01

    Fully automated machine learning methods based on structural magnetic resonance imaging (MRI) data can assist radiologists in the diagnosis of Alzheimer's disease (AD). These algorithms require large data sets to learn the separation of subjects with and without AD. Training and test data may come from heterogeneous hardware settings, which can potentially affect the performance of disease classification. A total of 518 MRI sessions from 226 healthy controls and 191 individuals with probable AD from the multicenter Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to investigate whether grouping data by acquisition hardware (i.e. vendor, field strength, coil system) is beneficial for the performance of a support vector machine (SVM) classifier, compared to the case where data from different hardware is mixed. We compared the change of the SVM decision value resulting from (a) changes in hardware against the effect of disease and (b) changes resulting simply from rescanning the same subject on the same machine. Maximum accuracy of 87% was obtained with a training set of all 417 subjects. Classifiers trained with 95 subjects in each diagnostic group and acquired with heterogeneous scanner settings had an empirical detection accuracy of 84.2±2.4% when tested on an independent set of the same size. These results mirror the accuracy reported in recent studies. Encouragingly, classifiers trained on images acquired with homogenous and heterogeneous hardware settings had equivalent cross-validation performances. Two scans of the same subject acquired on the same machine had very similar decision values and were generally classified into the same group. Higher variation was introduced when two acquisitions of the same subject were performed on two scanners with different field strengths. The variation was unbiased and similar for both diagnostic groups. The findings of the study encourage the pooling of data from different sites to increase the number of

  6. Ancient DNA

    DEFF Research Database (Denmark)

    Willerslev, Eske; Cooper, Alan

    2004-01-01

    ancient DNA, palaeontology, palaeoecology, archaeology, population genetics, DNA damage and repair......ancient DNA, palaeontology, palaeoecology, archaeology, population genetics, DNA damage and repair...

  7. An Automatic Traffic Sign Detection and Recognition System Based on Colour Segmentation, Shape Matching, and SVM

    Directory of Open Access Journals (Sweden)

    Safat B. Wali

    2015-01-01

    Full Text Available The main objective of this study is to develop an efficient TSDR system which contains an enriched dataset of Malaysian traffic signs. The developed technique is invariant in variable lighting, rotation, translation, and viewing angle and has a low computational time with low false positive rate. The development of the system has three working stages: image preprocessing, detection, and recognition. The system demonstration using a RGB colour segmentation and shape matching followed by support vector machine (SVM classifier led to promising results with respect to the accuracy of 95.71%, false positive rate (0.9%, and processing time (0.43 s. The area under the receiver operating characteristic (ROC curves was introduced to statistically evaluate the recognition performance. The accuracy of the developed system is relatively high and the computational time is relatively low which will be helpful for classifying traffic signs especially on high ways around Malaysia. The low false positive rate will increase the system stability and reliability on real-time application.

  8. Identification of potential ACAT-2 selective inhibitors using pharmacophore, SVM and SVR from Chinese herbs.

    Science.gov (United States)

    Qiao, Lian-Sheng; Zhang, Xian-Bao; Jiang, Lu-di; Zhang, Yan-Ling; Li, Gong-Yu

    2016-11-01

    Acyl-coenzyme A cholesterol acyltransferase (ACAT) plays an important role in maintaining cellular and organismal cholesterol homeostasis. Two types of ACAT isozymes with different functions exist in mammals, named ACAT-1 and ACAT-2. Numerous studies showed that ACAT-2 selective inhibitors are effective for the treatment of hypercholesterolemia and atherosclerosis. However, as a typical endoplasmic reticulum protein, ACAT-2 protein has not been purified and revealed, so combinatorial ligand-based methods might be the optimal strategy for discovering the ACAT-2 selective inhibitors. In this study, selective pharmacophore models of ACAT-1 inhibitors and ACAT-2 inhibitors were built, respectively. The optimal pharmacophore model for each subtype was identified and utilized as queries for the Traditional Chinese Medicine Database screening. A total of 180 potential ACAT-2 selective inhibitors were obtained, which were identified using an ACAT-2 pharmacophore and not by our ACAT-1 model. Selective SVM model and bioactive SVR model were generated for further identification of the obtained ACAT-2 inhibitors. Ten compounds were finally obtained with predicted inhibitory activities toward ACAT-2. Hydrogen bond acceptor, 2D autocorrelations, GETAWAY descriptors, and BCUT descriptors were identified as key structural features for selectivity and activity of ACAT-2 inhibitors. This study provides a reasonable ligand-based approach to discover potential ACAT-2 selective inhibitors from Chinese herbs, which could help in further screening and development of ACAT-2 selective inhibitors.

  9. FUSION OF NON-THERMAL AND THERMAL SATELLITE IMAGES BY BOOSTED SVM CLASSIFIERS FOR CLOUD DETECTION

    Directory of Open Access Journals (Sweden)

    N. Ghasemian

    2017-09-01

    Full Text Available The goal of ensemble learning methods like Bagging and Boosting is to improve the classification results of some weak classifiers gradually. Usually, Boosting algorithms show better results than Bagging. In this article, we have examined the possibility of fusion of non-thermal and thermal bands of Landsat 8 satellite images for cloud detection by using the boosting method. We used SVM as a base learner and the performance of two kinds of Boosting methods including AdaBoost.M1 and σ Boost was compared on remote sensing images of Landsat 8 satellite. We first extracted the co-occurrence matrix features of non-thermal and thermal bands separately and then used PCA method for feature selection. In the next step AdaBoost.M1 and σ Boost algorithms were applied on non-thermal and thermal bands and finally, the classifiers were fused using majority voting. Also, we showed that by changing the regularization parameter (C the result of σ Boost algorithm can significantly change and achieve overall accuracy and cloud producer accuracy of 74%, and 0.53 kappa coefficient that shows better results in comparison to AdaBoost.M1.

  10. Iterative Reweighted Noninteger Norm Regularizing SVM for Gene Expression Data Classification

    Directory of Open Access Journals (Sweden)

    Jianwei Liu

    2013-01-01

    Full Text Available Support vector machine is an effective classification and regression method that uses machine learning theory to maximize the predictive accuracy while avoiding overfitting of data. L2 regularization has been commonly used. If the training dataset contains many noise variables, L1 regularization SVM will provide a better performance. However, both L1 and L2 are not the optimal regularization method when handing a large number of redundant values and only a small amount of data points is useful for machine learning. We have therefore proposed an adaptive learning algorithm using the iterative reweighted p-norm regularization support vector machine for 0 < p ≤ 2. A simulated data set was created to evaluate the algorithm. It was shown that a p value of 0.8 was able to produce better feature selection rate with high accuracy. Four cancer data sets from public data banks were used also for the evaluation. All four evaluations show that the new adaptive algorithm was able to achieve the optimal prediction error using a p value less than L1 norm. Moreover, we observe that the proposed Lp penalty is more robust to noise variables than the L1 and L2 penalties.

  11. Iterative reweighted noninteger norm regularizing SVM for gene expression data classification.

    Science.gov (United States)

    Liu, Jianwei; Li, Shuang Cheng; Luo, Xionglin

    2013-01-01

    Support vector machine is an effective classification and regression method that uses machine learning theory to maximize the predictive accuracy while avoiding overfitting of data. L2 regularization has been commonly used. If the training dataset contains many noise variables, L1 regularization SVM will provide a better performance. However, both L1 and L2 are not the optimal regularization method when handing a large number of redundant values and only a small amount of data points is useful for machine learning. We have therefore proposed an adaptive learning algorithm using the iterative reweighted p-norm regularization support vector machine for 0 < p ≤ 2. A simulated data set was created to evaluate the algorithm. It was shown that a p value of 0.8 was able to produce better feature selection rate with high accuracy. Four cancer data sets from public data banks were used also for the evaluation. All four evaluations show that the new adaptive algorithm was able to achieve the optimal prediction error using a p value less than L1 norm. Moreover, we observe that the proposed Lp penalty is more robust to noise variables than the L1 and L2 penalties.

  12. Fusion of Non-Thermal and Thermal Satellite Images by Boosted Svm Classifiers for Cloud Detection

    Science.gov (United States)

    Ghasemian, N.; Akhoondzadeh, M.

    2017-09-01

    The goal of ensemble learning methods like Bagging and Boosting is to improve the classification results of some weak classifiers gradually. Usually, Boosting algorithms show better results than Bagging. In this article, we have examined the possibility of fusion of non-thermal and thermal bands of Landsat 8 satellite images for cloud detection by using the boosting method. We used SVM as a base learner and the performance of two kinds of Boosting methods including AdaBoost.M1 and σ Boost was compared on remote sensing images of Landsat 8 satellite. We first extracted the co-occurrence matrix features of non-thermal and thermal bands separately and then used PCA method for feature selection. In the next step AdaBoost.M1 and σ Boost algorithms were applied on non-thermal and thermal bands and finally, the classifiers were fused using majority voting. Also, we showed that by changing the regularization parameter (C) the result of σ Boost algorithm can significantly change and achieve overall accuracy and cloud producer accuracy of 74%, and 0.53 kappa coefficient that shows better results in comparison to AdaBoost.M1.

  13. Simultaneous localization of lumbar vertebrae and intervertebral discs with SVM-based MRF.

    Science.gov (United States)

    Oktay, Ayse Betul; Akgul, Yusuf Sinan

    2013-09-01

    This paper presents a method for localizing and labeling the lumbar vertebrae and intervertebral discs in mid-sagittal MR image slices. The approach is based on a Markov-chain-like graphical model of the ordered discs and vertebrae in the lumbar spine. The graphical model is formulated by combining local image features and semiglobal geometrical information. The local image features are extracted from the image by employing pyramidal histogram of oriented gradients (PHOG) and a novel descriptor that we call image projection descriptor (IPD). These features are trained with support vector machines (SVM) and each pixel in the target image is locally assigned a score. These local scores are combined with the semiglobal geometrical information like the distance ratio and angle between the neighboring structures under the Markov random field (MRF) framework. An exact localization of discs and vertebrae is inferred from the MRF by finding a maximum a posteriori solution efficiently using dynamic programming. As a result of the novel features introduced, our system can scale-invariantly localize discs and vertebra at the same time even in the existence of missing structures. The proposed system is tested and validated on a clinical lumbar spine MR image dataset containing 80 subjects of which 64 have disc- and vertebra-related diseases and abnormalities. The experiments show that our system is successful even in abnormal cases and our results are comparable to the state of the art.

  14. SVM to detect the presence of visitors in a smart home environment.

    Science.gov (United States)

    Petersen, Johanna; Larimer, Nicole; Kaye, Jeffrey A; Pavel, Misha; Hayes, Tamara L

    2012-01-01

    With the rising age of the population, there is increased need to help elderly maintain their independence. Smart homes, employing passive sensor networks and pervasive computing techniques, enable the unobtrusive assessment of activities and behaviors of the elderly which can be useful for health state assessment and intervention. Due to the multiple health benefits associated with socializing, accurately tracking whether an individual has visitors to their home is one of the more important aspects of elders' behaviors that could be assessed with smart home technology. With this goal, we have developed a preliminary SVM model to identify periods where untagged visitors are present in the home. Using the dwell time, number of sensor firings, and number of transitions between major living spaces (living room, dining room, kitchen and bathroom) as features in the model, and self report from two subjects as ground truth, we were able to accurately detect the presence of visitors in the home with a sensitivity and specificity of 0.90 and 0.89 for subject 1, and of 0.67 and 0.78 for subject 2, respectively. These preliminary data demonstrate the feasibility of detecting visitors with in-home sensor data, but highlight the need for more advanced modeling techniques so the model performs well for all subjects and all types of visitors.

  15. STUDY COMPARISON OF SVM-, K-NN- AND BACKPROPAGATION-BASED CLASSIFIER FOR IMAGE RETRIEVAL

    Directory of Open Access Journals (Sweden)

    Muhammad Athoillah

    2015-03-01

    Full Text Available Classification is a method for compiling data systematically according to the rules that have been set previously. In recent years classification method has been proven to help many people’s work, such as image classification, medical biology, traffic light, text classification etc. There are many methods to solve classification problem. This variation method makes the researchers find it difficult to determine which method is best for a problem. This framework is aimed to compare the ability of classification methods, such as Support Vector Machine (SVM, K-Nearest Neighbor (K-NN, and Backpropagation, especially in study cases of image retrieval with five category of image dataset. The result shows that K-NN has the best average result in accuracy with 82%. It is also the fastest in average computation time with 17,99 second during retrieve session for all categories class. The Backpropagation, however, is the slowest among three of them. In average it needed 883 second for training session and 41,7 second for retrieve session.

  16. Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach.

    Science.gov (United States)

    Al-Shargie, Fares; Tang, Tong Boon; Badruddin, Nasreen; Kiguchi, Masashi

    2017-10-18

    Mental stress has been identified as one of the major contributing factors that leads to various diseases such as heart attack, depression, and stroke. To avoid this, stress quantification is important for clinical intervention and disease prevention. This study aims to investigate the feasibility of exploiting electroencephalography (EEG) signals to discriminate between different stress levels. We propose a new assessment protocol whereby the stress level is represented by the complexity of mental arithmetic (MA) task for example, at three levels of difficulty, and the stressors are time pressure and negative feedback. Using 18-male subjects, the experimental results showed that there were significant differences in EEG response between the control and stress conditions at different levels of MA task with p values < 0.001. Furthermore, we found a significant reduction in alpha rhythm power from one stress level to another level, p values < 0.05. In comparison, results from self-reporting questionnaire NASA-TLX approach showed no significant differences between stress levels. In addition, we developed a discriminant analysis method based on multiclass support vector machine (SVM) with error-correcting output code (ECOC). Different stress levels were detected with an average classification accuracy of 94.79%. The lateral index (LI) results further showed dominant right prefrontal cortex (PFC) to mental stress (reduced alpha rhythm). The study demonstrated the feasibility of using EEG in classifying multilevel mental stress and reported alpha rhythm power at right prefrontal cortex as a suitable index.

  17. Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

    Directory of Open Access Journals (Sweden)

    S. Ganapathy

    2012-01-01

    Full Text Available Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set.

  18. Intelligent agent-based intrusion detection system using enhanced multiclass SVM.

    Science.gov (United States)

    Ganapathy, S; Yogesh, P; Kannan, A

    2012-01-01

    Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set.

  19. Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

    Science.gov (United States)

    Ganapathy, S.; Yogesh, P.; Kannan, A.

    2012-01-01

    Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set. PMID:23056036

  20. DisArticle: a web server for SVM-based discrimination of articles on traditional medicine.

    Science.gov (United States)

    Kim, Sang-Kyun; Nam, SeJin; Kim, SangHyun

    2017-01-28

    Much research has been done in Northeast Asia to show the efficacy of traditional medicine. While MEDLINE contains many biomedical articles including those on traditional medicine, it does not categorize those articles by specific research area. The aim of this study was to provide a method that searches for articles only on traditional medicine in Northeast Asia, including traditional Chinese medicine, from among the articles in MEDLINE. This research established an SVM-based classifier model to identify articles on traditional medicine. The TAK + HM classifier, trained with the features of title, abstract, keywords, herbal data, and MeSH, has a precision of 0.954 and a recall of 0.902. In particular, the feature of herbal data significantly increased the performance of the classifier. By using the TAK + HM classifier, a total of about 108,000 articles were discriminated as articles on traditional medicine from among all articles in MEDLINE. We also built a web server called DisArticle ( http://informatics.kiom.re.kr/disarticle ), in which users can search for the articles and obtain statistical data. Because much evidence-based research on traditional medicine has been published in recent years, it has become necessary to search for articles on traditional medicine exclusively in literature databases. DisArticle can help users to search for and analyze the research trends in traditional medicine.

  1. Comparison between SARS CoV and MERS CoV Using Apriori Algorithm, Decision Tree, SVM

    Directory of Open Access Journals (Sweden)

    Jang Seongpil

    2016-01-01

    Full Text Available MERS (Middle East Respiratory Syndrome is a worldwide disease these days. The number of infected people is 1038(08/03/2015 in Saudi Arabia and 186(08/03/2015 in South Korea. MERS is all over the world including Europe and the fatality rate is 38.8%, East Asia and the Middle East. The MERS is also known as a cousin of SARS (Severe Acute Respiratory Syndrome because both diseases show similar symptoms such as high fever and difficulty in breathing. This is why we compared MERS with SARS. We used data of the spike glycoprotein from NCBI. As a way of analyzing the protein, apriori algorithm, decision tree, SVM were used, and particularly SVM was iterated by normal, polynomial, and sigmoid. The result came out that the MERS and the SARS are alike but also different in some way.

  2. A Novel Feature Extraction Approach Using Window Function Capturing and QPSO-SVM for Enhancing Electronic Nose Performance

    Directory of Open Access Journals (Sweden)

    Xiuzhen Guo

    2015-06-01

    Full Text Available In this paper, a novel feature extraction approach which can be referred to as moving window function capturing (MWFC has been proposed to analyze signals of an electronic nose (E-nose used for detecting types of infectious pathogens in rat wounds. Meanwhile, a quantum-behaved particle swarm optimization (QPSO algorithm is implemented in conjunction with support vector machine (SVM for realizing a synchronization optimization of the sensor array and SVM model parameters. The results prove the efficacy of the proposed method for E-nose feature extraction, which can lead to a higher classification accuracy rate compared to other established techniques. Meanwhile it is interesting to note that different classification results can be obtained by changing the types, widths or positions of windows. By selecting the optimum window function for the sensor response, the performance of an E-nose can be enhanced.

  3. Energy Management in Wireless Sensor Networks Based on Naive Bayes, MLP, and SVM Classifications: A Comparative Study

    Directory of Open Access Journals (Sweden)

    Abdulaziz Y. Barnawi

    2016-01-01

    Full Text Available Maximizing wireless sensor networks (WSNs lifetime is a primary objective in the design of these networks. Intelligent energy management models can assist designers to achieve this objective. These models aim to reduce the number of selected sensors to report environmental measurements and, hence, achieve higher energy efficiency while maintaining the desired level of accuracy in the reported measurement. In this paper, we present a comparative study of three intelligent models based on Naive Bayes, Multilayer Perceptrons (MLP, and Support Vector Machine (SVM classifiers. Simulation results show that Linear-SVM selects sensors that produce higher energy efficiency compared to those selected by MLP and Naive Bayes for the same WSNs Lifetime Extension Factor.

  4. DISEÑO Y EVALUACIÓN DE UN CLASIFICADOR DE TEXTURAS BASADO EN LS-SVM

    Directory of Open Access Journals (Sweden)

    Beitmantt Cárdenas Quintero

    2013-07-01

    Full Text Available Evaluar el desempeño y el costo computacional de diferentes arquitecturas y metodologías Least Square Support Vector Machine (LS-SVM ante la segmentación de imágenes por textura y a partir de dichos resultados postular un modelo de un clasificador de texturas LS-SVM.  Metodología: Ante un problema de clasificación binaria representado por la segmentación  de 32 imágenes, organizadas en 4 grupos y formadas por pares de texturas típicas (granito/corteza, ladrillo/tapicería, madera/mármol, tejido/pelaje, se mide y compara el desempeño y el costo computacional de dos tipos de núcleo (Radial / Polinomial, dos funciones de optimización (mínimo local / búsqueda exhaustiva y dos funciones de costo (validación cruzada aleatoria / Validación cruzada dejando al menos uno en una LS-SVM que toma como entrada los pixeles que conforman la vecindad cruz del pixel a evaluar (no se hace extracción de características. Resultados: LS-SVM como clasificador de texturas, presenta mejor desempeño y exige menor costo computacional cuando utiliza un kernel de base radial y una función de optimización basada en un algoritmo de búsqueda de mínimos locales acompañado de una función de costo que use validación cruzada aleatoria.

  5. Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples

    Directory of Open Access Journals (Sweden)

    Hong Men

    2018-01-01

    Full Text Available Paraffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraffin samples. The original feature set was optimized using Principal Component Analysis (PCA and Partial Least Squares (PLS. Support Vector Machine (SVM, Random Forest (RF, and Extreme Learning Machine (ELM were applied to three different feature data sets for classification and level assessment of paraffin. For classification, the model based on SVM, with an accuracy rate of 100%, was superior to that based on RF, with an accuracy rate of 98.33–100%, and ELM, with an accuracy rate of 98.01–100%. For level assessment, the R2 related to the training set was above 0.97 and the R2 related to the test set was above 0.87. Through comprehensive comparison, the generalization of the model based on ELM was superior to those based on SVM and RF. The scoring errors for the three models were 0.0016–0.3494, lower than the error of 0.5–1.0 measured by industry standard experts, meaning these methods have a higher prediction accuracy for scoring paraffin level.

  6. Computer-Aided Lung Nodule Recognition by SVM Classifier Based on Combination of Random Undersampling and SMOTE

    Directory of Open Access Journals (Sweden)

    Yuan Sui

    2015-01-01

    Full Text Available In lung cancer computer-aided detection/diagnosis (CAD systems, classification of regions of interest (ROI is often used to detect/diagnose lung nodule accurately. However, problems of unbalanced datasets often have detrimental effects on the performance of classification. In this paper, both minority and majority classes are resampled to increase the generalization ability. We propose a novel SVM classifier combined with random undersampling (RU and SMOTE for lung nodule recognition. The combinations of the two resampling methods not only achieve a balanced training samples but also remove noise and duplicate information in the training sample and retain useful information to improve the effective data utilization, hence improving performance of SVM algorithm for pulmonary nodules classification under the unbalanced data. Eight features including 2D and 3D features are extracted for training and classification. Experimental results show that for different sizes of training datasets our RU-SMOTE-SVM classifier gets the highest classification accuracy among the four kinds of classifiers, and the average classification accuracy is more than 92.94%.

  7. Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples

    Science.gov (United States)

    Men, Hong; Fu, Songlin; Yang, Jialin; Cheng, Meiqi; Shi, Yan

    2018-01-01

    Paraffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraffin samples. The original feature set was optimized using Principal Component Analysis (PCA) and Partial Least Squares (PLS). Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM) were applied to three different feature data sets for classification and level assessment of paraffin. For classification, the model based on SVM, with an accuracy rate of 100%, was superior to that based on RF, with an accuracy rate of 98.33–100%, and ELM, with an accuracy rate of 98.01–100%. For level assessment, the R2 related to the training set was above 0.97 and the R2 related to the test set was above 0.87. Through comprehensive comparison, the generalization of the model based on ELM was superior to those based on SVM and RF. The scoring errors for the three models were 0.0016–0.3494, lower than the error of 0.5–1.0 measured by industry standard experts, meaning these methods have a higher prediction accuracy for scoring paraffin level. PMID:29346328

  8. Practical identification of human originated Lactobacillus species by amplified ribosomal DNA restriction analysis (ARDRA) for probiotic use.

    Science.gov (United States)

    Öztürk, Mehmet; Meterelliyöz, Merve

    2015-08-01

    Probiotics are gaining popularity and increasing the importance of their accurate speciation. Lactobacillus species are commonly used as probiotic strains mostly of clinical importance. Present knowledge indicates that at least 14 Lactobacillus species are associated with the human intestinal tract. Currently, researchers are interested in developing efficient techniques for screening and selecting probiotics bacteria, but unfortunately most of these methods are time-consuming, labor-intensive and costly. The aim of this study is to develop reliable, rapid and accurate method to identify 14 references Lactobacillus species that could have been found in the human alimentary tract by 16S ribosomal DNA restriction analysis. In this study, to develop an effective method for the genotype-based identification of the reference Lactobacillus species, 1.5 kb of 16S rRNA nucleotide sequences of 14 Lactobacillus were collected from the Gene Bank aligned, in silico restricted and analyzed in respect to their 16S-rRNA restriction fragment polymorphism. In silico restriction profiles of 16S-rRNA indicated that FspBI, HinfI and DraI restriction enzymes (RE) are convenient for differentiation of 14 Lactobacillus species in human intestinal tract except Lb. casei and Lb. paracasei. The patterns of our experimental findings obtained from 16S PCR-ARDRA completely confirmed our in silico patterns. The present work demonstrated that 16S PCR-ARDRA method with FspBI, HinfI and DraI RE is a rapid, accurate and reliable method for the identification of Lactobacillus species from human alimentary tract, especially during the identification of large numbers of isolates and any laboratory equipped with a thermo cycler for probiotic use.

  9. Approche de sélection d’attributs pour la classification basée sur l’algorithme RFE-SVM

    OpenAIRE

    Slimani, yahya; Essegir, Mohamed Amir; Samb, Mouhamadou Lamine; Camara, Fodé; Ndiaye, Samba

    2014-01-01

    International audience; The feature selection for classification is a very active research field in data mining and optimization. Its combinatorial nature requires the development of specific techniques (such as filters, wrappers, genetic algorithms, and so on) or hybrid approaches combining several optimization methods. In this context, the support vector machine recursive feature elimination (SVM-RFE), is distinguished as one of the most effective methods. However, the RFE-SVM algorithm is ...

  10. Segmentasi Citra menggunakan Support Vector Machine (SVM dan Ellipsoid Region Search Strategy (ERSS Arimoto Entropy berdasarkan Ciri Warna dan Tekstur

    Directory of Open Access Journals (Sweden)

    Lukman Hakim

    2016-02-01

    Full Text Available Abstrak Segmentasi citra merupakan suatu metode penting dalam pengolahan citra digital yang bertujuan membagi citra menjadi beberapa region yang homogen berdasarkan kriteria kemiripan tertentu. Salah satu syarat utama yang harus dimiliki suatu metode segmentasi citra yaitu menghasilkan citra boundary yang optimal.Untuk memenuhi syarat tersebut suatu metode segmentasi membutuhkan suatu klasifikasi piksel citra yang dapat memisahkan piksel secara linier dan non-linear. Pada penelitian ini, penulis mengusulkan metode segmentasi citra menggunakan SVM dan entropi Arimoto berbasis ERSS sehingga tahan terhadap derau dan mempunyai kompleksitas yang rendah untuk menghasilkan citra boundary yang optimal. Pertama, ekstraksi ciri warna dengan local homogeneity dan ciri tekstur dengan menggunakan Gray Level Co-occurrence Matrix (GLCM yang menghasilkan beberapa fitur. Kedua, pelabelan dengan Arimoto berbasis ERSS yang digunakan sebagai kelas dalam klasifikasi. Ketiga, hasil ekstraksi fitur dan training kemudian diklasifikasi berdasarkan label dengan SVM yang telah di-training. Dari percobaan yang dilakukan menunjukkan hasil segmentasi kurang optimal dengan akurasi 69 %. Reduksi fitur perlu dilakukan untuk menghasilkan citra yang tersegmentasi dengan baik. Kata kunci: segmentasi citra, support vector machine, ERSS Arimoto Entropy, ekstraksi ciri. Abstract Image segmentation is an important tool in image processing that divides an image into homogeneous regions based on certain similarity criteria, which ideally should be meaning-full for a certain purpose. Optimal boundary is one of the main criteria that an image segmentation method should has. A classification method that can partitions pixel linearly or non-linearly is needed by an image segmentation method. We propose a color image segmentation using Support Vector Machine (SVM classification and ERSS Arimoto entropy thresholding to get optimal boundary of segmented image that noise-free and low complexity

  11. SVM-based classification of LV wall motion in cardiac MRI with the assessment of STE

    Science.gov (United States)

    Mantilla, Juan; Garreau, Mireille; Bellanger, Jean-Jacques; Paredes, José Luis

    2015-01-01

    In this paper, we propose an automated method to classify normal/abnormal wall motion in Left Ventricle (LV) function in cardiac cine-Magnetic Resonance Imaging (MRI), taking as reference, strain information obtained from 2D Speckle Tracking Echocardiography (STE). Without the need of pre-processing and by exploiting all the images acquired during a cardiac cycle, spatio-temporal profiles are extracted from a subset of radial lines from the ventricle centroid to points outside the epicardial border. Classical Support Vector Machines (SVM) are used to classify features extracted from gray levels of the spatio-temporal profile as well as their representations in the Wavelet domain under the assumption that the data may be sparse in that domain. Based on information obtained from radial strain curves in 2D-STE studies, we label all the spatio-temporal profiles that belong to a particular segment as normal if the peak systolic radial strain curve of this segment presents normal kinesis, or abnormal if the peak systolic radial strain curve presents hypokinesis or akinesis. For this study, short-axis cine- MR images are collected from 9 patients with cardiac dyssynchrony for which we have the radial strain tracings at the mid-papilary muscle obtained by 2D STE; and from one control group formed by 9 healthy subjects. The best classification performance is obtained with the gray level information of the spatio-temporal profiles using a RBF kernel with 91.88% of accuracy, 92.75% of sensitivity and 91.52% of specificity.

  12. Automatic schizophrenic discrimination on fNIRS by using complex brain network analysis and SVM.

    Science.gov (United States)

    Song, Hong; Chen, Lei; Gao, RuiQi; Bogdan, Iordachescu Ilie Mihaita; Yang, Jian; Wang, Shuliang; Dong, Wentian; Quan, Wenxiang; Dang, Weimin; Yu, Xin

    2017-12-20

    Schizophrenia is a kind of serious mental illness. Due to the lack of an objective physiological data supporting and a unified data analysis method, doctors can only rely on the subjective experience of the data to distinguish normal people and patients, which easily lead to misdiagnosis. In recent years, functional Near-Infrared Spectroscopy (fNIRS) has been widely used in clinical diagnosis, it can get the hemoglobin concentration through the variation of optical intensity. Firstly, the prefrontal brain networks were constructed based on oxy-Hb signals from 52-channel fNIRS data of schizophrenia and healthy controls. Then, Complex Brain Network Analysis (CBNA) was used to extract features from the prefrontal brain networks. Finally, a classier based on Support Vector Machine (SVM) is designed and trained to discriminate schizophrenia from healthy controls. We recruited a sample which contains 34 healthy controls and 42 schizophrenia patients to do the one-back memory task. The hemoglobin response was measured in the prefrontal cortex during the task using a 52-channel fNIRS system. The experimental results indicate that the proposed method can achieve a satisfactory classification with the accuracy of 85.5%, 92.8% for schizophrenia samples and 76.5% for healthy controls. Also, our results suggested that fNIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia. Our results suggested that, using the appropriate classification method, fNIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia.

  13. AI-based (ANN and SVM) statistical downscaling methods for precipitation estimation under climate change scenarios

    Science.gov (United States)

    Mehrvand, Masoud; Baghanam, Aida Hosseini; Razzaghzadeh, Zahra; Nourani, Vahid

    2017-04-01

    Since statistical downscaling methods are the most largely used models to study hydrologic impact studies under climate change scenarios, nonlinear regression models known as Artificial Intelligence (AI)-based models such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been used to spatially downscale the precipitation outputs of Global Climate Models (GCMs). The study has been carried out using GCM and station data over GCM grid points located around the Peace-Tampa Bay watershed weather stations. Before downscaling with AI-based model, correlation coefficient values have been computed between a few selected large-scale predictor variables and local scale predictands to select the most effective predictors. The selected predictors are then assessed considering grid location for the site in question. In order to increase AI-based downscaling model accuracy pre-processing has been developed on precipitation time series. In this way, the precipitation data derived from various GCM data analyzed thoroughly to find the highest value of correlation coefficient between GCM-based historical data and station precipitation data. Both GCM and station precipitation time series have been assessed by comparing mean and variances over specific intervals. Results indicated that there is similar trend between GCM and station precipitation data; however station data has non-stationary time series while GCM data does not. Finally AI-based downscaling model have been applied to several GCMs with selected predictors by targeting local precipitation time series as predictand. The consequences of recent step have been used to produce multiple ensembles of downscaled AI-based models.

  14. Application of ANFIS and SVM Systems in Order to Estimate Monthly Reference Crop Evapotranspiration in the Northwest of Iran

    Directory of Open Access Journals (Sweden)

    F. Ahmadi

    2016-10-01

    Full Text Available Introduction Crop evapotranspiration modeling process mainly performs with empirical methods, aerodynamic and energy balance. In these methods, the evapotranspiration is calculated based on the average values of meteorological parameters at different time steps. The linear models didn’t have a good performance in this field due to high variability of evapotranspiration and the researchers have turned to the use of nonlinear and intelligent models. For accurate estimation of this hydrologic variable, it should be spending much time and money to measure many data (19. Materials and Methods Recently the new hybrid methods have been developed by combining some of methods such as artificial neural networks, fuzzy logic and evolutionary computation, that called Soft Computing and Intelligent Systems. These soft techniques are used in various fields of engineering. A fuzzy neurosis is a hybrid system that incorporates the decision ability of fuzzy logic with the computational ability of neural network, which provides a high capability for modeling and estimating. Basically, the Fuzzy part is used to classify the input data set and determines the degree of membership (that each number can be laying between 0 and 1 and decisions for the next activity made based on a set of rules and move to the next stage. Adaptive Neuro-Fuzzy Inference Systems (ANFIS includes some parts of a typical fuzzy expert system which the calculations at each step is performed by the hidden layer neurons and the learning ability of the neural network has been created to increase the system information (9. SVM is a one of supervised learning methods which used for classification and regression affairs. This method was developed by Vapink (15 based on statistical learning theory. The SVM is a method for binary classification in an arbitrary characteristic space, so it is suitable for prediction problems (12. The SVM is originally a two-class Classifier that separates the classes

  15. Geographical traceability of wild Boletus edulis based on data fusion of FT-MIR and ICP-AES coupled with data mining methods (SVM)

    Science.gov (United States)

    Li, Yun; Zhang, Ji; Li, Tao; Liu, Honggao; Li, Jieqing; Wang, Yuanzhong

    2017-04-01

    In this work, the data fusion strategy of Fourier transform mid infrared (FT-MIR) spectroscopy and inductively coupled plasma-atomic emission spectrometry (ICP-AES) was used in combination with Support Vector Machine (SVM) to determine the geographic origin of Boletus edulis collected from nine regions of Yunnan Province in China. Firstly, competitive adaptive reweighted sampling (CARS) was used for selecting an optimal combination of key wavenumbers of second derivative FT-MIR spectra, and thirteen elements were sorted with variable importance in projection (VIP) scores. Secondly, thirteen subsets of multi-elements with the best VIP score were generated and each subset was used to fuse with FT-MIR. Finally, the classification models were established by SVM, and the combination of parameter C and γ (gamma) of SVM models was calculated by the approaches of grid search (GS) and genetic algorithm (GA). The results showed that both GS-SVM and GA-SVM models achieved good performances based on the #9 subset and the prediction accuracy in calibration and validation sets of the two models were 81.40% and 90.91%, correspondingly. In conclusion, it indicated that the data fusion strategy of FT-MIR and ICP-AES coupled with the algorithm of SVM can be used as a reliable tool for accurate identification of B. edulis, and it can provide a useful way of thinking for the quality control of edible mushrooms.

  16. Polsar Land Cover Classification Based on Hidden Polarimetric Features in Rotation Domain and Svm Classifier

    Science.gov (United States)

    Tao, C.-S.; Chen, S.-W.; Li, Y.-Z.; Xiao, S.-P.

    2017-09-01

    Land cover classification is an important application for polarimetric synthetic aperture radar (PolSAR) data utilization. Rollinvariant polarimetric features such as H / Ani / α / Span are commonly adopted in PolSAR land cover classification. However, target orientation diversity effect makes PolSAR images understanding and interpretation difficult. Only using the roll-invariant polarimetric features may introduce ambiguity in the interpretation of targets' scattering mechanisms and limit the followed classification accuracy. To address this problem, this work firstly focuses on hidden polarimetric feature mining in the rotation domain along the radar line of sight using the recently reported uniform polarimetric matrix rotation theory and the visualization and characterization tool of polarimetric coherence pattern. The former rotates the acquired polarimetric matrix along the radar line of sight and fully describes the rotation characteristics of each entry of the matrix. Sets of new polarimetric features are derived to describe the hidden scattering information of the target in the rotation domain. The latter extends the traditional polarimetric coherence at a given rotation angle to the rotation domain for complete interpretation. A visualization and characterization tool is established to derive new polarimetric features for hidden information exploration. Then, a classification scheme is developed combing both the selected new hidden polarimetric features in rotation domain and the commonly used roll-invariant polarimetric features with a support vector machine (SVM) classifier. Comparison experiments based on AIRSAR and multi-temporal UAVSAR data demonstrate that compared with the conventional classification scheme which only uses the roll-invariant polarimetric features, the proposed classification scheme achieves both higher classification accuracy and better robustness. For AIRSAR data, the overall classification accuracy with the proposed

  17. POLSAR LAND COVER CLASSIFICATION BASED ON HIDDEN POLARIMETRIC FEATURES IN ROTATION DOMAIN AND SVM CLASSIFIER

    Directory of Open Access Journals (Sweden)

    C.-S. Tao

    2017-09-01

    Full Text Available Land cover classification is an important application for polarimetric synthetic aperture radar (PolSAR data utilization. Rollinvariant polarimetric features such as H / Ani / α / Span are commonly adopted in PolSAR land cover classification. However, target orientation diversity effect makes PolSAR images understanding and interpretation difficult. Only using the roll-invariant polarimetric features may introduce ambiguity in the interpretation of targets’ scattering mechanisms and limit the followed classification accuracy. To address this problem, this work firstly focuses on hidden polarimetric feature mining in the rotation domain along the radar line of sight using the recently reported uniform polarimetric matrix rotation theory and the visualization and characterization tool of polarimetric coherence pattern. The former rotates the acquired polarimetric matrix along the radar line of sight and fully describes the rotation characteristics of each entry of the matrix. Sets of new polarimetric features are derived to describe the hidden scattering information of the target in the rotation domain. The latter extends the traditional polarimetric coherence at a given rotation angle to the rotation domain for complete interpretation. A visualization and characterization tool is established to derive new polarimetric features for hidden information exploration. Then, a classification scheme is developed combing both the selected new hidden polarimetric features in rotation domain and the commonly used roll-invariant polarimetric features with a support vector machine (SVM classifier. Comparison experiments based on AIRSAR and multi-temporal UAVSAR data demonstrate that compared with the conventional classification scheme which only uses the roll-invariant polarimetric features, the proposed classification scheme achieves both higher classification accuracy and better robustness. For AIRSAR data, the overall classification accuracy

  18. Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design.

    Science.gov (United States)

    Moghram, Basem Ameen; Nabil, Emad; Badr, Amr

    2018-01-01

    T-cell epitope structure identification is a significant challenging immunoinformatic problem within epitope-based vaccine design. Epitopes or antigenic peptides are a set of amino acids that bind with the Major Histocompatibility Complex (MHC) molecules. The aim of this process is presented by Antigen Presenting Cells to be inspected by T-cells. MHC-molecule-binding epitopes are responsible for triggering the immune response to antigens. The epitope's three-dimensional (3D) molecular structure (i.e., tertiary structure) reflects its proper function. Therefore, the identification of MHC class-II epitopes structure is a significant step towards epitope-based vaccine design and understanding of the immune system. In this paper, we propose a new technique using a Genetic Algorithm for Predicting the Epitope Structure (GAPES), to predict the structure of MHC class-II epitopes based on their sequence. The proposed Elitist-based genetic algorithm for predicting the epitope's tertiary structure is based on Ab-Initio Empirical Conformational Energy Program for Peptides (ECEPP) Force Field Model. The developed secondary structure prediction technique relies on Ramachandran Plot. We used two alignment algorithms: the ROSS alignment and TM-Score alignment. We applied four different alignment approaches to calculate the similarity scores of the dataset under test. We utilized the support vector machine (SVM) classifier as an evaluation of the prediction performance. The prediction accuracy and the Area Under Receiver Operating Characteristic (ROC) Curve (AUC) were calculated as measures of performance. The calculations are performed on twelve similarity-reduced datasets of the Immune Epitope Data Base (IEDB) and a large dataset of peptide-binding affinities to HLA-DRB1*0101. The results showed that GAPES was reliable and very accurate. We achieved an average prediction accuracy of 93.50% and an average AUC of 0.974 in the IEDB dataset. Also, we achieved an accuracy of 95

  19. DNA Extraction Techniques for Use in Education

    Science.gov (United States)

    Hearn, R. P.; Arblaster, K. E.

    2010-01-01

    DNA extraction provides a hands-on introduction to DNA and enables students to gain real life experience and practical knowledge of DNA. Students gain a sense of ownership and are more enthusiastic when they use their own DNA. A cost effective, simple protocol for DNA extraction and visualization was devised. Buccal mucosal epithelia provide a…

  20. Novel SVM-based technique to improve rainfall estimation over the Mediterranean region (north of Algeria) using the multispectral MSG SEVIRI imagery

    Science.gov (United States)

    Sehad, Mounir; Lazri, Mourad; Ameur, Soltane

    2017-03-01

    In this work, a new rainfall estimation technique based on the high spatial and temporal resolution of the Spinning Enhanced Visible and Infra Red Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) is presented. This work proposes efficient scheme rainfall estimation based on two multiclass support vector machine (SVM) algorithms: SVM_D for daytime and SVM_N for night time rainfall estimations. Both SVM models are trained using relevant rainfall parameters based on optical, microphysical and textural cloud proprieties. The cloud parameters are derived from the Spectral channels of the SEVIRI MSG radiometer. The 3-hourly and daily accumulated rainfall are derived from the 15 min-rainfall estimation given by the SVM classifiers for each MSG observation image pixel. The SVMs were trained with ground meteorological radar precipitation scenes recorded from November 2006 to March 2007 over the north of Algeria located in the Mediterranean region. Further, the SVM_D and SVM_N models were used to estimate 3-hourly and daily rainfall using data set gathered from November 2010 to March 2011 over north Algeria. The results were validated against collocated rainfall observed by rain gauge network. Indeed, the statistical scores given by correlation coefficient, bias, root mean square error and mean absolute error, showed good accuracy of rainfall estimates by the present technique. Moreover, rainfall estimates of our technique were compared with two high accuracy rainfall estimates methods based on MSG SEVIRI imagery namely: random forests (RF) based approach and an artificial neural network (ANN) based technique. The findings of the present technique indicate higher correlation coefficient (3-hourly: 0.78; daily: 0.94), and lower mean absolute error and root mean square error values. The results show that the new technique assign 3-hourly and daily rainfall with good and better accuracy than ANN technique and (RF) model.

  1. [Selection of Characteristic Wavelengths Using SPA and Qualitative Discrimination of Mildew Degree of Corn Kernels Based on SVM].

    Science.gov (United States)

    Yuan, Ying; Wang, Wei; Chu, Xuan; Xi, Ming-jie

    2016-01-01

    The feasibility of Fourier transform near infrared (FT-NIR) spectroscopy with spectral range between 833 and 2 500 nm to detect the moldy corn kernels with different levels of mildew was verified in this paper. Firstly, to avoid the influence of noise, moving average smoothing was used for spectral data preprocessing after four common pretreatment methods were compared. Then to improve the prediction performance of the model, SPXY (sample set partitioning based on joint x-y distance) was selected and used for sample set partition. Furthermore, in order to reduce the dimensions of the original spectral data, successive projection algorithm (SPA) was adopted and ultimately 7 characteristic wavelengths were extracted, the characteristic wave-lengths were 833, 927, 1 208, 1 337, 1 454, 1 861, 2 280 nm. The experimental results showed when the spectrum data of the 7 characteristic wavelengths were taken as the input of SVM, the radial basic function (RBF) used as the kernel function, and kernel parameter C = 7 760 469, γ = 0.017 003, the classification accuracies of the established SVM model were 97.78% and 93.33% for the training and testing sets respectively. In addition, the independent validation set was selected in the same standard, and used to verify the model. At last, the classification accuracy of 91.11% for the independent validation set was achieved. The result indicated that it is feasible to identify and classify different degree of moldy corn grain kernels using SPA and SVM, and characteristic wavelengths selected by SPA in this paper also lay a foundation for the online NIR detection of mildew corn kernels.

  2. Klasifikasi Topik Keluhan Pelanggan Berdasarkan Tweet dengan Menggunakan Penggabungan Feature Hasil Ekstraksi pada Metode Support Vector Machine (SVM

    Directory of Open Access Journals (Sweden)

    Enda Esyudha Pratama

    2015-12-01

    Full Text Available Pemanfaatan twitter sebagai layanan customer serevice perusahaan sudah mulai banyak digunakan, tak terkecuali Speedy. Mekanisme yang ada saat ini untuk proses klasifikasi bentuk dan jenis keluhan serta informasi tentang jumlah keluhan lewat twitter masih dilakukan secara manual. Belum lagi data twitter yang bersifat tidak terstruktur tentunya akan menyulitkan untuk dilakukan analisa dan penggalian informasi dari data tersebut. Berdasarkan permasalahan tersebut, penelitian ini bertujuan untuk memproses data teks dari tweet pengguna twitteryang masuk ke akun @TelkomSpeedy untuk diolah menjadi informasi. Informasi tersebut nantinya digunakan untuk klasifikasi bentuk dan jenis keluhan. Merujuk pada beberapa penelitian terkait, salah satu metode klasifikasi yang paling baik untuk digunakan adalah metode Support Vector Machine (SVM. Konsep dari SVM dapat dijelaskan secara sederhana sebagai usaha mencari hyperplane yang dapat memisahkan dataset sesuai dengan kelasnya. Kelas yang digunakan dalam penelitian kali ini berdasarkan topik keluhan pelanggan yaitu billing, pemasangan/instalasi, putus (disconnect, dan lambat. Faktor penting lainnya dalam hal klasifikasi adalah penentuan feature atau atribut kata yang akan digunakan. Metode feature selection yang digunakan pada penlitian ini adalah term frequency (TF, document frequency (DF, information gain, dan chi-square. Pada penelitian ini juga dilakukan metode penggabungan feature yang telah dihasilkan dari beberapa metode feature selection sebelumnya. Dari hasil penelitian menunjukan bahwa SVM mampu melakukan klasifikasi keluhan dengan baik, hal ini dibuktikan dengan akurasi 82,50% untuk klasifikasi bentuk keluhan dan 86,67% untuk klasifikasi jenis keluhan. Sedangkan untuk kombinasi penggunaan feature dapat meningkatkan akurasi menjadi 83,33% untuk bentuk keluhan dan 89,17% untuk jenis keluhan.   Kata Kunci—customer service, klasifikasi topik keluhan, penggabungan feature, support vector machine

  3. KOMPARASI MODEL SUPPORT VECTOR MACHINES (SVM DAN NEURAL NETWORK UNTUK MENGETAHUI TINGKAT AKURASI PREDIKSI TERTINGGI HARGA SAHAM

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    R. Hadapiningradja Kusumodestoni

    2017-09-01

    Full Text Available There are many types of investments to make money, one of which is in the form of shares. Shares is a trading company dealing with securities in the global capital markets. Stock Exchange or also called stock market is actually the activities of private companies in the form of buying and selling investments. To avoid losses in investing, we need a model of predictive analysis with high accuracy and supported by data - lots of data and accurately. The correct techniques in the analysis will be able to reduce the risk for investors in investing. There are many models used in the analysis of stock price movement prediction, in this study the researchers used models of neural networks (NN and a model of support vector machine (SVM. Based on the background of the problems that have been mentioned in the previous description it can be formulated the problem as follows: need an algorithm that can predict stock prices, and need a high accuracy rate by adding a data set on the prediction, two algorithms will be investigated expected results last researchers can deduce where the algorithm accuracy rate predictions are the highest or accurate, then the purpose of this study was to mengkomparasi or compare between the two algorithms are algorithms Neural Network algorithm and Support Vector Machine which later on the end result has an accuracy rate forecast stock prices highest to see the error value RMSEnya. After doing research using the model of neural network and model of support vector machine (SVM to predict the stock using the data value of the shares on the stock index hongkong dated July 20, 2016 at 16:26 pm until the date of 15 September 2016 at 17:40 pm as many as 729 data sets within an interval of 5 minute through a process of training, learning, and then continue the process of testing so the result is that by using a neural network model of the prediction accuracy of 0.503 +/- 0.009 (micro 503 while using the model of support vector machine

  4. Using Generalized Entropies and OC-SVM with Mahalanobis Kernel for Detection and Classification of Anomalies in Network Traffic

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    Jayro Santiago-Paz

    2015-09-01

    Full Text Available Network anomaly detection and classification is an important open issue in network security. Several approaches and systems based on different mathematical tools have been studied and developed, among them, the Anomaly-Network Intrusion Detection System (A-NIDS, which monitors network traffic and compares it against an established baseline of a “normal” traffic profile. Then, it is necessary to characterize the “normal” Internet traffic. This paper presents an approach for anomaly detection and classification based on Shannon, Rényi and Tsallis entropies of selected features, and the construction of regions from entropy data employing the Mahalanobis distance (MD, and One Class Support Vector Machine (OC-SVM with different kernels (Radial Basis Function (RBF and Mahalanobis Kernel (MK for “normal” and abnormal traffic. Regular and non-regular regions built from “normal” traffic profiles allow anomaly detection, while the classification is performed under the assumption that regions corresponding to the attack classes have been previously characterized. Although this approach allows the use of as many features as required, only four well-known significant features were selected in our case. In order to evaluate our approach, two different data sets were used: one set of real traffic obtained from an Academic Local Area Network (LAN, and the other a subset of the 1998 MIT-DARPA set. For these data sets, a True positive rate up to 99.35%, a True negative rate up to 99.83% and a False negative rate at about 0.16% were yielded. Experimental results show that certain q-values of the generalized entropies and the use of OC-SVM with RBF kernel improve the detection rate in the detection stage, while the novel inclusion of MK kernel in OC-SVM and k-temporal nearest neighbors improve accuracy in classification. In addition, the results show that using the Box-Cox transformation, the Mahalanobis distance yielded high detection rates with

  5. Identifying 1 Method of Meat Containing Excessive Moisture Based on hyperspectral and SVM Multi-Information Fusion

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    Guo Peiyuan

    2016-01-01

    Full Text Available In this paper, a quick and accurate detection method which can identify whether the meat contain excessive moisture is mentioned. By using near-infrared spectroscopy measurement and SVM Multi-Information Fusion, the meat moisture content model has been established. In order to improve the accuracy of NIR measurement predicted model and to reduce the measurement sensitivity, utilizing image information and the PH value data as the parameters of the meat moisture content model. The study concluded that the theory and method can be further extended to the detection of other related meat agricultural products.

  6. An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications.

    Directory of Open Access Journals (Sweden)

    Fei Ye

    Full Text Available This paper proposes a new support vector machine (SVM optimization scheme based on an improved chaotic fly optimization algorithm (FOA with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm's performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem.

  7. An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications

    Science.gov (United States)

    Lou, Xin Yuan; Sun, Lin Fu

    2017-01-01

    This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm’s performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem. PMID:28369096

  8. Intelligent Optimized Combined Model Based on GARCH and SVM for Forecasting Electricity Price of New South Wales, Australia

    Directory of Open Access Journals (Sweden)

    Yi Yang

    2014-01-01

    Full Text Available Daily electricity price forecasting plays an essential role in electrical power system operation and planning. The accuracy of forecasting electricity price can ensure that consumers minimize their electricity costs and make producers maximize their profits and avoid volatility. However, the fluctuation of electricity price depends on other commodities and there is a very complicated randomization in its evolution process. Therefore, in recent years, although large number of forecasting methods have been proposed and researched in this domain, it is very difficult to forecast electricity price with only one traditional model for different behaviors of electricity price. In this paper, we propose an optimized combined forecasting model by ant colony optimization algorithm (ACO based on the generalized autoregressive conditional heteroskedasticity (GARCH model and support vector machine (SVM to improve the forecasting accuracy. First, both GARCH model and SVM are developed to forecast short-term electricity price of New South Wales in Australia. Then, ACO algorithm is applied to determine the weight coefficients. Finally, the forecasting errors by three models are analyzed and compared. The experiment results demonstrate that the combined model makes accuracy higher than the single models.

  9. a Comparison Study of Different Kernel Functions for Svm-Based Classification of Multi-Temporal Polarimetry SAR Data

    Science.gov (United States)

    Yekkehkhany, B.; Safari, A.; Homayouni, S.; Hasanlou, M.

    2014-10-01

    In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

  10. A COMPARISON STUDY OF DIFFERENT KERNEL FUNCTIONS FOR SVM-BASED CLASSIFICATION OF MULTI-TEMPORAL POLARIMETRY SAR DATA

    Directory of Open Access Journals (Sweden)

    B. Yekkehkhany

    2014-10-01

    Full Text Available In this paper, a framework is developed based on Support Vector Machines (SVM for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF. The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

  11. A Hybrid Prediction Method of Thermal Extension Error for Boring Machine Based on PCA and LS-SVM

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    Cheng Qiang

    2017-01-01

    Full Text Available Thermal extension error of boring bar in z-axis is one of the key factors that have a bad influence on the machining accuracy of boring machine, so how to exactly establish the relationship between the thermal extension length and temperature and predict the changing rule of thermal error are the premise of thermal extension error compensation. In this paper, a prediction method of thermal extension length of boring bar in boring machine is proposed based on principal component analysis (PCA and least squares support vector machine (LS-SVM model. In order to avoid the multiple correlation and coupling among the great amount temperature input variables, firstly, PCA is introduced to extract the principal components of temperature data samples. Then, LS-SVM is used to predict the changing tendency of the thermally induced thermal extension error of boring bar. Finally, experiments are conducted on a boring machine, the application results show that Boring bar axial thermal elongation error residual value dropped below 5 μm and minimum residual error is only 0.5 μm. This method not only effectively improve the efficiency of the temperature data acquisition and analysis, and improve the modeling accuracy and robustness.

  12. A Study on SVM Based on the Weighted Elitist Teaching-Learning-Based Optimization and Application in the Fault Diagnosis of Chemical Process

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    Cao Junxiang

    2015-01-01

    Full Text Available Teaching-Learning-Based Optimization (TLBO is a new swarm intelligence optimization algorithm that simulates the class learning process. According to such problems of the traditional TLBO as low optimizing efficiency and poor stability, this paper proposes an improved TLBO algorithm mainly by introducing the elite thought in TLBO and adopting different inertia weight decreasing strategies for elite and ordinary individuals of the teacher stage and the student stage. In this paper, the validity of the improved TLBO is verified by the optimizations of several typical test functions and the SVM optimized by the weighted elitist TLBO is used in the diagnosis and classification of common failure data of the TE chemical process. Compared with the SVM combining other traditional optimizing methods, the SVM optimized by the weighted elitist TLBO has a certain improvement in the accuracy of fault diagnosis and classification.

  13. QSAR study of anthranilic acid sulfonamides as inhibitors of methionine aminopeptidase-2 using LS-SVM and GRNN based on principal components.

    Science.gov (United States)

    Shahlaei, Mohsen; Sabet, Razieh; Ziari, Maryam Bahman; Moeinifard, Behzad; Fassihi, Afshin; Karbakhsh, Reza

    2010-10-01

    Quantitative relationships between molecular structure and methionine aminopeptidase-2 inhibitory activity of a series of cytotoxic anthranilic acid sulfonamide derivatives were discovered. We have demonstrated the detailed application of two efficient nonlinear methods for evaluation of quantitative structure-activity relationships of the studied compounds. Components produced by principal component analysis as input of developed nonlinear models were used. The performance of the developed models namely PC-GRNN and PC-LS-SVM were tested by several validation methods. The resulted PC-LS-SVM model had a high statistical quality (R(2)=0.91 and R(CV)(2)=0.81) for predicting the cytotoxic activity of the compounds. Comparison between predictability of PC-GRNN and PC-LS-SVM indicates that later method has higher ability to predict the activity of the studied molecules. Copyright (c) 2010 Elsevier Masson SAS. All rights reserved.

  14. DNA barcoding fishes.

    Science.gov (United States)

    Weigt, Lee A; Driskell, Amy C; Baldwin, Carole C; Ormos, Andrea

    2012-01-01

    This chapter is an overview of the techniques for DNA barcoding of fishes from field collection to DNA sequence analysis. Recommendations for modifications of field protocols and best tissue sampling practices are made. A variety of DNA extraction protocols is provided, including high-throughput robot-assisted methods. A pair of well-tested forward and reverse primers for PCR amplification and sequencing are presented. These primers have been successfully used for DNA barcode on a wide array of marine fish taxa and also work well in most freshwater and cartilaginous fishes. Recipes and cycling protocols for both PCR amplification and sequencing and cleanup methods for the reaction products are provided. A method for the consistent production of high-quality DNA barcodes from DNA sequence data is given and stringent guidelines for judging the quality of raw sequence data are laid out.

  15. Using self-organizing map (SOM) and support vector machine (SVM) for classification of selectivity of ACAT inhibitors.

    Science.gov (United States)

    Wang, Ling; Wang, Maolin; Yan, Aixia; Dai, Bin

    2013-02-01

    Using a self-organizing map (SOM) and support vector machine, two classification models were built to predict whether a compound is a selective inhibitor toward the two Acyl-coenzyme A: cholesterol acyltransferase (ACAT) isozymes, ACAT-1 and ACAT-2. A dataset of 97 ACAT inhibitors was collected. For each molecule, the global descriptors, 2D and 3D property autocorrelation descriptors and autocorrelation of surface properties were calculated from the program ADRIANA.Code. The prediction accuracies of the models (based on the training/ test set splitting by SOM method) for the test sets are 88.9 % for SOM1, 92.6 % for SVM1 model. In addition, the extended connectivity fingerprints (ECFP_4) for all the molecules were calculated and the structure-activity relationship of selective ACAT inhibitors was summarized, which may help find important structural features of inhibitors relating to the selectivity of ACAT isozymes.

  16. Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification.

    Science.gov (United States)

    Younghak Shin; Balasingham, Ilangko

    2017-07-01

    Colonoscopy is a standard method for screening polyps by highly trained physicians. Miss-detected polyps in colonoscopy are potential risk factor for colorectal cancer. In this study, we investigate an automatic polyp classification framework. We aim to compare two different approaches named hand-craft feature method and convolutional neural network (CNN) based deep learning method. Combined shape and color features are used for hand craft feature extraction and support vector machine (SVM) method is adopted for classification. For CNN approach, three convolution and pooling based deep learning framework is used for classification purpose. The proposed framework is evaluated using three public polyp databases. From the experimental results, we have shown that the CNN based deep learning framework shows better classification performance than the hand-craft feature based methods. It achieves over 90% of classification accuracy, sensitivity, specificity and precision.

  17. SVM-based prediction of propeptide cleavage sites in spider toxins identifies toxin innovation in an Australian tarantula.

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    Emily S W Wong

    Full Text Available Spider neurotoxins are commonly used as pharmacological tools and are a popular source of novel compounds with therapeutic and agrochemical potential. Since venom peptides are inherently toxic, the host spider must employ strategies to avoid adverse effects prior to venom use. It is partly for this reason that most spider toxins encode a protective proregion that upon enzymatic cleavage is excised from the mature peptide. In order to identify the mature toxin sequence directly from toxin transcripts, without resorting to protein sequencing, the propeptide cleavage site in the toxin precursor must be predicted bioinformatically. We evaluated different machine learning strategies (support vector machines, hidden Markov model and decision tree and developed an algorithm (SpiderP for prediction of propeptide cleavage sites in spider toxins. Our strategy uses a support vector machine (SVM framework that combines both local and global sequence information. Our method is superior or comparable to current tools for prediction of propeptide sequences in spider toxins. Evaluation of the SVM method on an independent test set of known toxin sequences yielded 96% sensitivity and 100% specificity. Furthermore, we sequenced five novel peptides (not used to train the final predictor from the venom of the Australian tarantula Selenotypus plumipes to test the accuracy of the predictor and found 80% sensitivity and 99.6% 8-mer specificity. Finally, we used the predictor together with homology information to predict and characterize seven groups of novel toxins from the deeply sequenced venom gland transcriptome of S. plumipes, which revealed structural complexity and innovations in the evolution of the toxins. The precursor prediction tool (SpiderP is freely available on ArachnoServer (http://www.arachnoserver.org/spiderP.html, a web portal to a comprehensive relational database of spider toxins. All training data, test data, and scripts used are available from

  18. SVM classifier to predict genes important for self-renewal and pluripotency of mouse embryonic stem cells

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    Xu Huilei

    2010-12-01

    Full Text Available Abstract Background Mouse embryonic stem cells (mESCs are derived from the inner cell mass of a developing blastocyst and can be cultured indefinitely in-vitro. Their distinct features are their ability to self-renew and to differentiate to all adult cell types. Genes that maintain mESCs self-renewal and pluripotency identity are of interest to stem cell biologists. Although significant steps have been made toward the identification and characterization of such genes, the list is still incomplete and controversial. For example, the overlap among candidate self-renewal and pluripotency genes across different RNAi screens is surprisingly small. Meanwhile, machine learning approaches have been used to analyze multi-dimensional experimental data and integrate results from many studies, yet they have not been applied to specifically tackle the task of predicting and classifying self-renewal and pluripotency gene membership. Results For this study we developed a classifier, a supervised machine learning framework for predicting self-renewal and pluripotency mESCs stemness membership genes (MSMG using support vector machines (SVM. The data used to train the classifier was derived from mESCs-related studies using mRNA microarrays, measuring gene expression in various stages of early differentiation, as well as ChIP-seq studies applied to mESCs profiling genome-wide binding of key transcription factors, such as Nanog, Oct4, and Sox2, to the regulatory regions of other genes. Comparison to other classification methods using the leave-one-out cross-validation method was employed to evaluate the accuracy and generality of the classification. Finally, two sets of candidate genes from genome-wide RNA interference screens are used to test the generality and potential application of the classifier. Conclusions Our results reveal that an SVM approach can be useful for prioritizing genes for functional validation experiments and complement the analyses of high

  19. Epileptic seizure classifications of single-channel scalp EEG data using wavelet-based features and SVM.

    Science.gov (United States)

    Janjarasjitt, Suparerk

    2017-02-13

    In this study, wavelet-based features of single-channel scalp EEGs recorded from subjects with intractable seizure are examined for epileptic seizure classification. The wavelet-based features extracted from scalp EEGs are simply based on detail and approximation coefficients obtained from the discrete wavelet transform. Support vector machine (SVM), one of the most commonly used classifiers, is applied to classify vectors of wavelet-based features of scalp EEGs into either seizure or non-seizure class. In patient-based epileptic seizure classification, a training data set used to train SVM classifiers is composed of wavelet-based features of scalp EEGs corresponding to the first epileptic seizure event. Overall, the excellent performance on patient-dependent epileptic seizure classification is obtained with the average accuracy, sensitivity, and specificity of, respectively, 0.9687, 0.7299, and 0.9813. The vector composed of two wavelet-based features of scalp EEGs provide the best performance on patient-dependent epileptic seizure classification in most cases, i.e., 19 cases out of 24. The wavelet-based features corresponding to the 32-64, 8-16, and 4-8 Hz subbands of scalp EEGs are the mostly used features providing the best performance on patient-dependent classification. Furthermore, the performance on both patient-dependent and patient-independent epileptic seizure classifications are also validated using tenfold cross-validation. From the patient-independent epileptic seizure classification validated using tenfold cross-validation, it is shown that the best classification performance is achieved using the wavelet-based features corresponding to the 64-128 and 4-8 Hz subbands of scalp EEGs.

  20. Classification of EEG-P300 Signals Extracted from Brain Activities in BCI Systems Using ν-SVM and BLDA Algorithms

    Directory of Open Access Journals (Sweden)

    Ali MOMENNEZHAD

    2014-06-01

    Full Text Available In this paper, a linear predictive coding (LPC model is used to improve classification accuracy, convergent speed to maximum accuracy, and maximum bitrates in brain computer interface (BCI system based on extracting EEG-P300 signals. First, EEG signal is filtered in order to eliminate high frequency noise. Then, the parameters of filtered EEG signal are extracted using LPC model. Finally, the samples are reconstructed by LPC coefficients and two classifiers, a Bayesian Linear discriminant analysis (BLDA, and b the υ-support vector machine (υ-SVM are applied in order to classify. The proposed algorithm performance is compared with fisher linear discriminant analysis (FLDA. Results show that the efficiency of our algorithm in improving classification accuracy and convergent speed to maximum accuracy are much better. As example at the proposed algorithms, respectively BLDA with LPC model and υ-SVM with LPC model with8 electrode configuration for subject S1 the total classification accuracy is improved as 9.4% and 1.7%. And also, subject 7 at BLDA and υ-SVM with LPC model algorithms (LPC+BLDA and LPC+ υ-SVM after block 11th converged to maximum accuracy but Fisher Linear Discriminant Analysis (FLDA algorithm did not converge to maximum accuracy (with the same configuration. So, it can be used as a promising tool in designing BCI systems.

  1. Automatic SVM classification of sudden cardiac death and pump failure death from autonomic and repolarization ECG markers.

    Science.gov (United States)

    Ramírez, Julia; Monasterio, Violeta; Mincholé, Ana; Llamedo, Mariano; Lenis, Gustavo; Cygankiewicz, Iwona; Bayés de Luna, Antonio; Malik, Marek; Martínez, Juan Pablo; Laguna, Pablo; Pueyo, Esther

    2015-01-01

    Considering the rates of sudden cardiac death (SCD) and pump failure death (PFD) in chronic heart failure (CHF) patients and the cost-effectiveness of their preventing treatments, identification of CHF patients at risk is an important challenge. In this work, we studied the prognostic performance of the combination of an index potentially related to dispersion of repolarization restitution (Δα), an index quantifying T-wave alternans (IAA) and the slope of heart rate turbulence (TS) for classification of SCD and PFD. Holter ECG recordings of 597 CHF patients with sinus rhythm enrolled in the MUSIC study were analyzed and Δα, IAA and TS were obtained. A strategy was implemented using support vector machines (SVM) to classify patients in three groups: SCD victims, PFD victims and other patients (the latter including survivors and victims of non-cardiac causes). Cross-validation was used to evaluate the performance of the implemented classifier. Δα and IAA, dichotomized at 0.035 (dimensionless) and 3.73 μV, respectively, were the ECG markers most strongly associated with SCD, while TS, dichotomized at 2.5 ms/RR, was the index most strongly related to PFD. When separating SCD victims from the rest of patients, the individual marker with best performance was Δα≥0.035, which, for a fixed specificity (Sp) of 90%, showed a sensitivity (Se) value of 10%, while the combination of Δα and IAA increased Se to 18%. For separation of PFD victims from the rest of patients, the best individual marker was TS ≤ 2.5 ms/RR, which, for Sp=90%, showed a Se of 26%, this value being lower than Se=34%, produced by the combination of Δα and TS. Furthermore, when performing SVM classification into the three reported groups, the optimal combination of risk markers led to a maximum Sp of 79% (Se=18%) for SCD and Sp of 81% (Se=14%) for PFD. The results shown in this work suggest that it is possible to efficiently discriminate SCD and PFD in a population of CHF patients using ECG

  2. A multitemporal probabilistic error correction approach to SVM classification of alpine glacier exploiting sentinel-1 images (Conference Presentation)

    Science.gov (United States)

    Callegari, Mattia; Marin, Carlo; Notarnicola, Claudia; Carturan, Luca; Covi, Federico; Galos, Stephan; Seppi, Roberto

    2016-10-01

    In mountain regions and their forelands, glaciers are key source of melt water during the middle and late ablation season, when most of the winter snow has already melted. Furthermore, alpine glaciers are recognized as sensitive indicators of climatic fluctuations. Monitoring glacier extent changes and glacier surface characteristics (i.e. snow, firn and bare ice coverage) is therefore important for both hydrological applications and climate change studies. Satellite remote sensing data have been widely employed for glacier surface classification. Many approaches exploit optical data, such as from Landsat. Despite the intuitive visual interpretation of optical images and the demonstrated capability to discriminate glacial surface thanks to the combination of different bands, one of the main disadvantages of available high-resolution optical sensors is their dependence on cloud conditions and low revisit time frequency. Therefore, operational monitoring strategies relying only on optical data have serious limitations. Since SAR data are insensitive to clouds, they are potentially a valid alternative to optical data for glacier monitoring. Compared to past SAR missions, the new Sentinel-1 mission provides much higher revisit time frequency (two acquisitions each 12 days) over the entire European Alps, and this number will be doubled once the Sentinel1-b will be in orbit (April 2016). In this work we present a method for glacier surface classification by exploiting dual polarimetric Sentinel-1 data. The method consists of a supervised approach based on Support Vector Machine (SVM). In addition to the VV and VH signals, we tested the contribution of local incidence angle, extracted from a digital elevation model and orbital information, as auxiliary input feature in order to account for the topographic effects. By exploiting impossible temporal transition between different classes (e.g. if at a given date one pixel is classified as rock it cannot be classified as

  3. Mitochondrial DNA.

    Science.gov (United States)

    Wright, Russell G.; Bottino, Paul J.

    1986-01-01

    Provides background information for teachers on mitochondrial DNA, pointing out that it may have once been a free-living organism. Includes a ready-to-duplicate exercise titled "Using Microchondrial DNA to Measure Evolutionary Distance." (JN)

  4. Fault Diagnosis of Rotating Machinery Based on Multisensor Information Fusion Using SVM and Time-Domain Features

    Directory of Open Access Journals (Sweden)

    Ling-li Jiang

    2014-01-01

    Full Text Available Multisensor information fusion, when applied to fault diagnosis, the time-space scope, and the quantity of information are expanded compared to what could be acquired by a single sensor, so the diagnostic object can be described more comprehensively. This paper presents a methodology of fault diagnosis in rotating machinery using multisensor information fusion that all the features are calculated using vibration data in time domain to constitute fusional vector and the support vector machine (SVM is used for classification. The effectiveness of the presented methodology is tested by three case studies: diagnostic of faulty gear, rolling bearing, and identification of rotor crack. For each case study, the sensibilities of the features are analyzed. The results indicate that the peak factor is the most sensitive feature in the twelve time-domain features for identifying gear defect, and the mean, amplitude square, root mean square, root amplitude, and standard deviation are all sensitive for identifying gear, rolling bearing, and rotor crack defect comparatively.

  5. Dimensionality of ICA in resting-state fMRI investigated by feature optimized classification of independent components with SVM

    Science.gov (United States)

    Wang, Yanlu; Li, Tie-Qiang

    2015-01-01

    Different machine learning algorithms have recently been used for assisting automated classification of independent component analysis (ICA) results from resting-state fMRI data. The success of this approach relies on identification of artifact components and meaningful functional networks. A limiting factor of ICA is the uncertainty of the number of independent components (NIC). We aim to develop a framework based on support vector machines (SVM) and optimized feature-selection for automated classification of independent components (ICs) and use the framework to investigate the effects of input NIC on the ICA results. Seven different resting-state fMRI datasets were studied. 18 features were devised by mimicking the empirical criteria for manual evaluation. The five most significant (p NIC. Through tracking, we demonstrate that incrementing NIC affects most ICs when NIC NIC is incremented beyond NIC > 40. For a given IC, its changes with increasing NIC are individually specific irrespective whether the component is a potential resting-state functional network or an artifact component. Using FOCIS, we investigated experimentally the ICA dimensionality of resting-state fMRI datasets and found that the input NIC can critically affect the ICA results of resting-state fMRI data. PMID:26005413

  6. Prediction of healthy blood with data mining classification by using Decision Tree, Naive Baysian and SVM approaches

    Science.gov (United States)

    Khalilinezhad, Mahdieh; Minaei, Behrooz; Vernazza, Gianni; Dellepiane, Silvana

    2015-03-01

    Data mining (DM) is the process of discovery knowledge from large databases. Applications of data mining in Blood Transfusion Organizations could be useful for improving the performance of blood donation service. The aim of this research is the prediction of healthiness of blood donors in Blood Transfusion Organization (BTO). For this goal, three famous algorithms such as Decision Tree C4.5, Naïve Bayesian classifier, and Support Vector Machine have been chosen and applied to a real database made of 11006 donors. Seven fields such as sex, age, job, education, marital status, type of donor, results of blood tests (doctors' comments and lab results about healthy or unhealthy blood donors) have been selected as input to these algorithms. The results of the three algorithms have been compared and an error cost analysis has been performed. According to this research and the obtained results, the best algorithm with low error cost and high accuracy is SVM. This research helps BTO to realize a model from blood donors in each area in order to predict the healthy blood or unhealthy blood of donors. This research could be useful if used in parallel with laboratory tests to better separate unhealthy blood.

  7. Real-time human pose estimation and gesture recognition from depth images using superpixels and SVM classifier.

    Science.gov (United States)

    Kim, Hanguen; Lee, Sangwon; Lee, Dongsung; Choi, Soonmin; Ju, Jinsun; Myung, Hyun

    2015-05-26

    In this paper, we present human pose estimation and gesture recognition algorithms that use only depth information. The proposed methods are designed to be operated with only a CPU (central processing unit), so that the algorithm can be operated on a low-cost platform, such as an embedded board. The human pose estimation method is based on an SVM (support vector machine) and superpixels without prior knowledge of a human body model. In the gesture recognition method, gestures are recognized from the pose information of a human body. To recognize gestures regardless of motion speed, the proposed method utilizes the keyframe extraction method. Gesture recognition is performed by comparing input keyframes with keyframes in registered gestures. The gesture yielding the smallest comparison error is chosen as a recognized gesture. To prevent recognition of gestures when a person performs a gesture that is not registered, we derive the maximum allowable comparison errors by comparing each registered gesture with the other gestures. We evaluated our method using a dataset that we generated. The experiment results show that our method performs fairly well and is applicable in real environments.

  8. Real-Time Human Pose Estimation and Gesture Recognition from Depth Images Using Superpixels and SVM Classifier

    Directory of Open Access Journals (Sweden)

    Hanguen Kim

    2015-05-01

    Full Text Available In this paper, we present human pose estimation and gesture recognition algorithms that use only depth information. The proposed methods are designed to be operated with only a CPU (central processing unit, so that the algorithm can be operated on a low-cost platform, such as an embedded board. The human pose estimation method is based on an SVM (support vector machine and superpixels without prior knowledge of a human body model. In the gesture recognition method, gestures are recognized from the pose information of a human body. To recognize gestures regardless of motion speed, the proposed method utilizes the keyframe extraction method. Gesture recognition is performed by comparing input keyframes with keyframes in registered gestures. The gesture yielding the smallest comparison error is chosen as a recognized gesture. To prevent recognition of gestures when a person performs a gesture that is not registered, we derive the maximum allowable comparison errors by comparing each registered gesture with the other gestures. We evaluated our method using a dataset that we generated. The experiment results show that our method performs fairly well and is applicable in real environments.

  9. Dynamic partial reconfiguration implementation of the SVM/KNN multi-classifier on FPGA for bioinformatics application.

    Science.gov (United States)

    Hussain, Hanaa M; Benkrid, Khaled; Seker, Huseyin

    2015-01-01

    Bioinformatics data tend to be highly dimensional in nature thus impose significant computational demands. To resolve limitations of conventional computing methods, several alternative high performance computing solutions have been proposed by scientists such as Graphical Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs). The latter have shown to be efficient and high in performance. In recent years, FPGAs have been benefiting from dynamic partial reconfiguration (DPR) feature for adding flexibility to alter specific regions within the chip. This work proposes combing the use of FPGAs and DPR to build a dynamic multi-classifier architecture that can be used in processing bioinformatics data. In bioinformatics, applying different classification algorithms to the same dataset is desirable in order to obtain comparable, more reliable and consensus decision, but it can consume long time when performed on conventional PC. The DPR implementation of two common classifiers, namely support vector machines (SVMs) and K-nearest neighbor (KNN) are combined together to form a multi-classifier FPGA architecture which can utilize specific region of the FPGA to work as either SVM or KNN classifier. This multi-classifier DPR implementation achieved at least ~8x reduction in reconfiguration time over the single non-DPR classifier implementation, and occupied less space and hardware resources than having both classifiers. The proposed architecture can be extended to work as an ensemble classifier.

  10. SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas

    Directory of Open Access Journals (Sweden)

    Xiaogang Ning

    2013-07-01

    Full Text Available Object-based point cloud analysis (OBPA is useful for information extraction from airborne LiDAR point clouds. An object-based classification method is proposed for classifying the airborne LiDAR point clouds in urban areas herein. In the process of classification, the surface growing algorithm is employed to make clustering of the point clouds without outliers, thirteen features of the geometry, radiometry, topology and echo characteristics are calculated, a support vector machine (SVM is utilized to classify the segments, and connected component analysis for 3D point clouds is proposed to optimize the original classification results. Three datasets with different point densities and complexities are employed to test our method. Experiments suggest that the proposed method is capable of making a classification of the urban point clouds with the overall classification accuracy larger than 92.34% and the Kappa coefficient larger than 0.8638, and the classification accuracy is promoted with the increasing of the point density, which is meaningful for various types of applications.

  11. Modeling DNA

    Science.gov (United States)

    Robertson, Carol

    2016-01-01

    Deoxyribonucleic acid (DNA) is life's most amazing molecule. It carries the genetic instructions that almost every organism needs to develop and reproduce. In the human genome alone, there are some three billion DNA base pairs. The most difficult part of teaching DNA structure, however, may be getting students to visualize something as small as a…

  12. Robust Non-Linear Direct Torque and Flux Control of Adjustable Speed Sensorless PMSM Drive Based on SVM Using a PI Predictive Controller

    Directory of Open Access Journals (Sweden)

    F. Naceri

    2010-01-01

    Full Text Available This paper presents a new sensorless direct torque control method for voltage inverter – fed PMSM. The control methodis used a modified Direct Torque Control scheme with constant inverter switching frequency using Space Vector Modulation(DTC-SVM. The variation of stator and rotor resistance due to changes in temperature or frequency deteriorates theperformance of DTC-SVM controller by introducing errors in the estimated flux linkage and the electromagnetic torque.As a result, this approach will not be suitable for high power drives such as those used in tractions, as they require goodtorque control performance at considerably lower frequency. A novel stator resistance estimator is proposed. The estimationmethod is implemented using the Extended Kalman Filter. Finally extensive simulation results are presented to validate theproposed technique. The system is tested at different speeds and a very satisfactory performance has been achieved.

  13. Máquinas de soporte vectorial (svm) para la detección de nódulos pulmonares en tomografía axial computarizada (tac)

    OpenAIRE

    Ledezma Garrido, Willmar

    2012-01-01

    El cáncer de pulmón es uno de los mas comunes en el mundo y los nódulos pulmonares son su principal indicador de alerta temprana para su diagnóstico. Se presenta un proyecto para la detección de nódulos pulmonares con el uso de máquinas de soporte vectorial (svm), usando el kernel función de base radial gausiana (RBFG), previo a la aplicación de la svm, se hace un trabajo de procesamiento de imágenes que incluye la extracción de la región de interés y extracción de las características que ide...

  14. Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers

    Energy Technology Data Exchange (ETDEWEB)

    Salahshoor, Karim [Department of Instrumentation and Automation, Petroleum University of Technology, Tehran (Iran, Islamic Republic of); Kordestani, Mojtaba; Khoshro, Majid S. [Department of Control Engineering, Islamic Azad University South Tehran branch (Iran, Islamic Republic of)

    2010-12-15

    The subject of FDD (fault detection and diagnosis) has gained widespread industrial interest in machine condition monitoring applications. This is mainly due to the potential advantage to be achieved from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a new FDD scheme for condition machinery of an industrial steam turbine using a data fusion methodology. Fusion of a SVM (support vector machine) classifier with an ANFIS (adaptive neuro-fuzzy inference system) classifier, integrated into a common framework, is utilized to enhance the fault detection and diagnostic tasks. For this purpose, a multi-attribute data is fused into aggregated values of a single attribute by OWA (ordered weighted averaging) operators. The simulation studies indicate that the resulting fusion-based scheme outperforms the individual SVM and ANFIS systems to detect and diagnose incipient steam turbine faults. (author)

  15. ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment

    Science.gov (United States)

    Quej, Victor H.; Almorox, Javier; Arnaldo, Javier A.; Saito, Laurel

    2017-03-01

    Daily solar radiation is an important variable in many models. In this paper, the accuracy and performance of three soft computing techniques (i.e., adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and support vector machine (SVM) were assessed for predicting daily horizontal global solar radiation from measured meteorological variables in the Yucatán Peninsula, México. Model performance was assessed with statistical indicators such as root mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The performance assessment indicates that the SVM technique with requirements of daily maximum and minimum air temperature, extraterrestrial solar radiation and rainfall has better performance than the other techniques and may be a promising alternative to the usual approaches for predicting solar radiation.

  16. DNA Methylation and Cancer Diagnosis

    Science.gov (United States)

    Delpu, Yannick; Cordelier, Pierre; Cho, William C.; Torrisani, Jérôme

    2013-01-01

    DNA methylation is a major epigenetic modification that is strongly involved in the physiological control of genome expression. DNA methylation patterns are largely modified in cancer cells and can therefore be used to distinguish cancer cells from normal tissues. This review describes the main technologies available for the detection and the discovery of aberrantly methylated DNA patterns. It also presents the different sources of biological samples suitable for DNA methylation studies. We discuss the interest and perspectives on the use of DNA methylation measurements for cancer diagnosis through examples of methylated genes commonly documented in the literature. The discussion leads to our consideration for why DNA methylation is not commonly used in clinical practice through an examination of the main requirements that constitute a reliable biomarker. Finally, we describe the main DNA methylation inhibitors currently used in clinical trials and those that exhibit promising results. PMID:23873296

  17. Discrimination of Single Base Pair Differences Among Individual DNA Molecules Using a Nanopore

    Science.gov (United States)

    Vercoutere, Wenonah; DeGuzman, Veronica

    2003-01-01

    The protein toxin alpha-hemolysin form nanometer scale channels across lipid membranes. Our lab uses a single channel in an artificial lipid bilayer in a patch clamp device to capture and examine individual DNA molecules. This nanopore detector used with a support vector machine (SVM) can analyze DNA hairpin molecules on the millisecond time scale. We distinguish duplex stem length, base pair mismatches, loop length, and single base pair differences. The residual current fluxes also reveal structural molecular dynamics elements. DNA end-fraying (terminal base pair dissociation) can be observed as near full blockades, or spikes, in current. This technique can be used to investigate other biological processes dependent on DNA end-fraying, such as the processing of HIV DNA by HIV integrase.

  18. Impact of genetic structures on haploid genome-based quantification of genetically modified DNA: theoretical considerations, experimental data in MON 810 maize kernels (Zea mays L.) and some practical applications.

    Science.gov (United States)

    Zhang, David; Corlet, Aurélie; Fouilloux, Stephane

    2008-06-01

    Real-time Polymerase Chain Reaction (PCR) based assays are widely used to estimate the content of genetically modified (GM) materials in food, feed and seed. It has been known that the genetic structures of the analyte can significantly influence the GM content expressed by the haploid genome (HG) % estimated using real-time PCR assays; this kind of influence is also understood as the impact of biological factors. The influence was first simulated at theoretical level using maize as a model. We then experimentally assessed the impact of biological factors on quantitative results, analysing by quantitative real-time PCR six maize MON 810 hybrid kernels with different genetic structures: (1) hemizygous from transgenic male parent, (2) hemizygous from transgenic female parent and (3) homozygous at the transgenic locus. The results obtained in the present study showed clear influences of biological factors on GM DNA quantification: 1% of GM materials by weight (wt) for the three genetic structures contained 0.39, 0.55 and 1.0% of GM DNA by HG respectively, from quantitative real-time PCR analyses. The relationships between GM wt% and GM HG% can be empirically established as: (1) in the case of the presence of a single GM trait: GM HG% = GM wt% x (0.5 +/- 0.167Y), where Y is the endosperm DNA content (%) in the total DNA of a maize kernel, (2) in the case of the presence of multiple GM traits: GM HG% = N x GM wt% x (0.5 +/- 0.167Y), where N is the number of GM traits (stacked or not) present in an unknown sample. This finding can be used by stakeholders related to GMO for empirical prediction from one unit of expression to another in the monitoring of seed and grain production chains. Practical equations have also been suggested for haploid copy number calculations, using hemizygous GM materials for calibration curves.

  19. QSAR studies of the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by multiple linear regression (MLR) and support vector machine (SVM).

    Science.gov (United States)

    Qin, Zijian; Wang, Maolin; Yan, Aixia

    2017-07-01

    In this study, quantitative structure-activity relationship (QSAR) models using various descriptor sets and training/test set selection methods were explored to predict the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by using a multiple linear regression (MLR) and a support vector machine (SVM) method. 512 HCV NS3/4A protease inhibitors and their IC 50 values which were determined by the same FRET assay were collected from the reported literature to build a dataset. All the inhibitors were represented with selected nine global and 12 2D property-weighted autocorrelation descriptors calculated from the program CORINA Symphony. The dataset was divided into a training set and a test set by a random and a Kohonen's self-organizing map (SOM) method. The correlation coefficients (r 2 ) of training sets and test sets were 0.75 and 0.72 for the best MLR model, 0.87 and 0.85 for the best SVM model, respectively. In addition, a series of sub-dataset models were also developed. The performances of all the best sub-dataset models were better than those of the whole dataset models. We believe that the combination of the best sub- and whole dataset SVM models can be used as reliable lead designing tools for new NS3/4A protease inhibitors scaffolds in a drug discovery pipeline. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Application of chemometric tools for automatic classification and profile extraction of DNA samples in forensic tasks.

    Science.gov (United States)

    Talavera Bustamante, Isneri; Silva Mata, Francisco; Hernández González, Noslen; González Gazapo, Ricardo; Palau, Juan; Ferreira, Marcia M Castro

    2007-07-09

    In this paper a method for the automatic DNA spots classification and extraction of profiles associated in DNA polyacrylamide gel electrophoresis is presented and it integrates the use of image processing techniques and chemometrics tools. A software which implements this method was developed; for feature extraction a combination of a PCA analysis and a C4.5 decision tree were used. To obtain good results in the profile extraction only DNA spots are useful; therefore, it was necessary to solve a two-class classification problem among DNA spots and no-DNA spots. In order to perform the classification process with high velocity, effectiveness and robustness, comparative classification studies among support vector machine (SVM), K-NN and PLS-DA classifiers were made. The best results obtained with the SVM classifier demonstrated the advantages attributed to it in the literature as a two-class classifier. A Sequential Cluster Leader Algorithm and another one developed for the restoration of pattern missing spots were needed to conclude the profiles extraction step. The experimental results show that this method has a very effective computational behavior and effectiveness, and provide a very useful tool to decrease the time and increase the quality of the specialist responses.

  1. Dimensionality of ICA in resting-state fMRI investigated by feature optimized classification of independent components with SVM.

    Science.gov (United States)

    Wang, Yanlu; Li, Tie-Qiang

    2015-01-01

    Different machine learning algorithms have recently been used for assisting automated classification of independent component analysis (ICA) results from resting-state fMRI data. The success of this approach relies on identification of artifact components and meaningful functional networks. A limiting factor of ICA is the uncertainty of the number of independent components (NIC). We aim to develop a framework based on support vector machines (SVM) and optimized feature-selection for automated classification of independent components (ICs) and use the framework to investigate the effects of input NIC on the ICA results. Seven different resting-state fMRI datasets were studied. 18 features were devised by mimicking the empirical criteria for manual evaluation. The five most significant (p ICA results. The classification results obtained using FOCIS and previously published FSL-FIX were compared against manually evaluated results. On average the false negative rate in identifying artifact contaminated ICs for FOCIS and FSL-FIX were 98.27 and 92.34%, respectively. The number of artifact and functional network components increased almost linearly with the input NIC. Through tracking, we demonstrate that incrementing NIC affects most ICs when NIC 40. For a given IC, its changes with increasing NIC are individually specific irrespective whether the component is a potential resting-state functional network or an artifact component. Using FOCIS, we investigated experimentally the ICA dimensionality of resting-state fMRI datasets and found that the input NIC can critically affect the ICA results of resting-state fMRI data.

  2. Multi-class clustering of cancer subtypes through SVM based ensemble of pareto-optimal solutions for gene marker identification.

    Science.gov (United States)

    Mukhopadhyay, Anirban; Bandyopadhyay, Sanghamitra; Maulik, Ujjwal

    2010-11-12

    With the advancement of microarray technology, it is now possible to study the expression profiles of thousands of genes across different experimental conditions or tissue samples simultaneously. Microarray cancer datasets, organized as samples versus genes fashion, are being used for classification of tissue samples into benign and malignant or their subtypes. They are also useful for identifying potential gene markers for each cancer subtype, which helps in successful diagnosis of particular cancer types. In this article, we have presented an unsupervised cancer classification technique based on multiobjective genetic clustering of the tissue samples. In this regard, a real-coded encoding of the cluster centers is used and cluster compactness and separation are simultaneously optimized. The resultant set of near-Pareto-optimal solutions contains a number of non-dominated solutions. A novel approach to combine the clustering information possessed by the non-dominated solutions through Support Vector Machine (SVM) classifier has been proposed. Final clustering is obtained by consensus among the clusterings yielded by different kernel functions. The performance of the proposed multiobjective clustering method has been compared with that of several other microarray clustering algorithms for three publicly available benchmark cancer datasets. Moreover, statistical significance tests have been conducted to establish the statistical superiority of the proposed clustering method. Furthermore, relevant gene markers have been identified using the clustering result produced by the proposed clustering method and demonstrated visually. Biological relationships among the gene markers are also studied based on gene ontology. The results obtained are found to be promising and can possibly have important impact in the area of unsupervised cancer classification as well as gene marker identification for multiple cancer subtypes.

  3. Detecting brain structural changes as biomarker from magnetic resonance images using a local feature based SVM approach.

    Science.gov (United States)

    Chen, Ye; Storrs, Judd; Tan, Lirong; Mazlack, Lawrence J; Lee, Jing-Huei; Lu, Long J

    2014-01-15

    Detecting brain structural changes from magnetic resonance (MR) images can facilitate early diagnosis and treatment of neurological and psychiatric diseases. Many existing methods require an accurate deformation registration, which is difficult to achieve and therefore prevents them from obtaining high accuracy. We develop a novel local feature based support vector machine (SVM) approach to detect brain structural changes as potential biomarkers. This approach does not require deformation registration and thus is less influenced by artifacts such as image distortion. We represent the anatomical structures based on scale invariant feature transform (SIFT). Likelihood scores calculated using feature-based morphometry is used as the criterion to categorize image features into three classes (healthy, patient and noise). Regional SVMs are trained to classify the three types of image features in different brain regions. Only healthy and patient features are used to predict the disease status of new brain images. An ensemble classifier is built from the regional SVMs to obtain better prediction accuracy. We apply this approach to 3D MR images of Alzheimer's disease, Parkinson's disease and bipolar disorder. The classification accuracy ranges between 70% and 87%. The highly predictive disease-related regions, which represent significant anatomical differences between the healthy and diseased, are shown in heat maps. The common and disease-specific brain regions are identified by comparing the highly predictive regions in each disease. All of the top-ranked regions are supported by literature. Thus, this approach will be a promising tool for assisting automatic diagnosis and advancing mechanism studies of neurological and psychiatric diseases. Copyright © 2013 Elsevier B.V. All rights reserved.

  4. Supervised DNA Barcodes species classification: analysis, comparisons and results.

    Science.gov (United States)

    Weitschek, Emanuel; Fiscon, Giulia; Felici, Giovanni

    2014-04-11

    Specific fragments, coming from short portions of DNA (e.g., mitochondrial, nuclear, and plastid sequences), have been defined as DNA Barcode and can be used as markers for organisms of the main life kingdoms. Species classification with DNA Barcode sequences has been proven effective on different organisms. Indeed, specific gene regions have been identified as Barcode: COI in animals, rbcL and matK in plants, and ITS in fungi. The classification problem assigns an unknown specimen to a known species by analyzing its Barcode. This task has to be supported with reliable methods and algorithms. In this work the efficacy of supervised machine learning methods to classify species with DNA Barcode sequences is shown. The Weka software suite, which includes a collection of supervised classification methods, is adopted to address the task of DNA Barcode analysis. Classifier families are tested on synthetic and empirical datasets belonging to the animal, fungus, and plant kingdoms. In particular, the function-based method Support Vector Machines (SVM), the rule-based RIPPER, the decision tree C4.5, and the Naïve Bayes method are considered. Additionally, the classification results are compared with respect to ad-hoc and well-established DNA Barcode classification methods. A software that converts the DNA Barcode FASTA sequences to the Weka format is released, to adapt different input formats and to allow the execution of the classification procedure. The analysis of results on synthetic and real datasets shows that SVM and Naïve Bayes outperform on average the other considered classifiers, although they do not provide a human interpretable classification model. Rule-based methods have slightly inferior classification performances, but deliver the species specific positions and nucleotide assignments. On synthetic data the supervised machine learning methods obtain superior classification performances with respect to the traditional DNA Barcode classification methods. On

  5. Analysis of Human Papillomavirus Using Datamining - Apriori, Decision Tree, and Support Vector Machine (SVM) and its Application Field

    OpenAIRE

    Cho Younghoon; Burm Seungwon; Choi Nayoung; Yoon Taeseon

    2016-01-01

    Human Papillomavirus(HPV) has various types (compared to other viruses) and plays a key role in evoking diverse diseases, especially cervical cancer. In this study, we aim to distinguish the features of HPV of different degree of fatality by analyzing their DNA sequences. We used Decision Tree Algorithm, Apriori Algorithm, and Support Vector Machine in our experiment. By analyzing their DNA sequences, we discovered some relationships between certain types of HPV, especially on the most fatal ...

  6. DNA-based hybrid catalysis.

    Science.gov (United States)

    Rioz-Martínez, Ana; Roelfes, Gerard

    2015-04-01

    In the past decade, DNA-based hybrid catalysis has merged as a promising novel approach to homogeneous (asymmetric) catalysis. A DNA hybrid catalysts comprises a transition metal complex that is covalently or supramolecularly bound to DNA. The chiral microenvironment and the second coordination sphere interactions provided by the DNA are key to achieve high enantioselectivities and, often, additional rate accelerations in catalysis. Nowadays, current efforts are focused on improved designs, understanding the origin of the enantioselectivity and DNA-induced rate accelerations, expanding the catalytic scope of the concept and further increasing the practicality of the method for applications in synthesis. Herein, the recent developments will be reviewed and the perspectives for the emerging field of DNA-based hybrid catalysis will be discussed. Copyright © 2015 Elsevier Ltd. All rights reserved.

  7. DNA glue

    DEFF Research Database (Denmark)

    Filichev, Vyacheslav V; Astakhova, Irina V.; Malakhov, Andrei D.

    2008-01-01

    Significant alterations in thermal stability of parallel DNA triplexes and antiparallel duplexes were observed upon changing the attachment of ethynylpyrenes from para to ortho in the structure of phenylmethylglycerol inserted as a bulge into DNA (TINA). Insertions of two ortho-TINAs as a pseudo...

  8. DNA Vaccines

    Indian Academy of Sciences (India)

    research interests include: eukaryotic gene expres- sion and infectious diseases. Keywords. DNA vaccine, immune response, antibodies, infectious diseases. GENERAL I ... T -cells: Lymphocytes that differentiate primarily in the thymus and are central to the control and ... enhance DNA delivery into skeletal muscle.

  9. PREDICTION OF CHROMATIN STATES USING DNA SEQUENCE PROPERTIES

    KAUST Repository

    Bahabri, Rihab R.

    2013-06-01

    Activities of DNA are to a great extent controlled epigenetically through the internal struc- ture of chromatin. This structure is dynamic and is influenced by different modifications of histone proteins. Various combinations of epigenetic modification of histones pinpoint to different functional regions of the DNA determining the so-called chromatin states. How- ever, the characterization of chromatin states by the DNA sequence properties remains largely unknown. In this study we aim to explore whether DNA sequence patterns in the human genome can characterize different chromatin states. Using DNA sequence motifs we built binary classifiers for each chromatic state to eval- uate whether a given genomic sequence is a good candidate for belonging to a particular chromatin state. Of four classification algorithms (C4.5, Naive Bayes, Random Forest, and SVM) used for this purpose, the decision tree based classifiers (C4.5 and Random Forest) yielded best results among those we evaluated. Our results suggest that in general these models lack sufficient predictive power, although for four chromatin states (insulators, het- erochromatin, and two types of copy number variation) we found that presence of certain motifs in DNA sequences does imply an increased probability that such a sequence is one of these chromatin states.

  10. PERBANDINGAN TINGKAT PENGENALAN CITRA DIABETIC RETINOPATHY PADA KOMBINASI PRINCIPLE COMPONENT DARI 4 CIRI BERBASIS METODE SVM (SUPPORT VECTOR MACHINE

    Directory of Open Access Journals (Sweden)

    Sari Ayu Wulandari

    2016-06-01

    Full Text Available Perbedaan pigmentasi mempengaruhi me­­­­tode pengenalan pola citra retinopati di­a­betik beserta set­ting poinnya. Di­butuhkan sebuah pe­rangkat lunak, yang mampu menjadi alat bantu pengenalan citra retinopati diabetik. Telah dilakukan penelitian tentang pe­nge­nalan po­la citra retinopati dia­be­tik, dengan meng­gunakan citra kanal ku­ning (Yello­w, dengan menggunakan filter gabor dan ciri yang diambil dari tiap citra ada­lah ciri rerata (Means, variasi Varians, skewness dan entropy, yang dilanjutkan de­ngan ekstraksi ciri  PCA (Principle Com­­ponent Analysis. Pada ekstraksi ci­ri PCA, Matriks hasil PCA meru­pakan ma­triks bujur sangkar, yang jumlah ko­lom­nya, sama dengan jumlah ciri. Pe­ne­li­tian menggunakan 4 ciri, dengan de­mi­­kian, terdapat 4 buah PC (Principle Com­ponent, PC1, PC2, PC3 dan PC4. Pada artikel ini akan dibahas mengenai tingkat akurasi tertinggi dari peng­gunaan pasangan PC. Tingkat aku­ra­si, dihitung dengan meng­gu­­nakan mo­del linear dari SVM. Model de­ngan akurasi tertinggi dan tercepat ada­lah model pasangan PC1 dan PC2, yang mempunyai akurasi citra pem­be­lajaran tertinggi yaitu 100% dan waktu terce­pat, yang secara eksplisit diperli­hat­kan pada jumlah support vektor ter­kecil, yaitu 2. Pasa­ngan yang mempu­nyai ting­kat akurasi terburuk adalah PC3 dan PC4. Pengenalan turun pada citra pengu­jian, yaitu hanya 93,75%, hal ini disebabkan oleh pelebaran daerah ca­ku­pan. Pelebaran daerah cakupan ke­mungkinan disebabkan oleh pemi­lihan nilai rerata pada PCA, sebelum matriks reduksi. Pada penelitian berikutnya, bi­sa dilakukan dengan menggunakan pencarian nilai standart deviasi atau varians, dengan begitu, akan diketahui matriks reduksi yang mewakili sebaran angka pada matriks.

  11. Comparison Algorithm Kernels on Support Vector Machine (SVM To Compare The Trend Curves with Curves Online Forex Trading

    Directory of Open Access Journals (Sweden)

    irfan abbas

    2017-01-01

    Full Text Available At this time, the players Forex Trading generally still use the data exchange in the form of a Forex Trading figures from different sources. Thus they only receive or know the data rate of a Forex Trading prevailing at the time just so difficult to analyze or predict exchange rate movements future. Forex players usually use the indicators to enable them to analyze and memperdiksi future value. Indicator is a decision making tool. Trading forex is trading currency of a country, the other country's currency. Trading took place globally between the financial centers of the world with the involvement of the world's major banks as the major transaction. Trading Forex offers profitable investment type with a small capital and high profit, with relatively small capital can earn profits doubled. This is due to the forex trading systems exist leverage which the invested capital will be doubled if the predicted results of buy / sell is accurate, but Trading Forex having high risk level, but by knowing the right time to trade (buy or sell, the losses can be avoided. Traders who invest in the foreign exchange market is expected to have the ability to analyze the circumstances and situations in predicting the difference in currency exchange rates. Forex price movements that form the pattern (curve up and down greatly assist traders in making decisions. The movement of the curve used as an indicator in the decision to purchase (buy or sell (sell. This study compares (Comparation type algorithm kernel on Support Vector Machine (SVM to predict the movement of the curve in live time trading forex using the data GBPUSD, 1H. Results of research on the study of the results and discussion can be concluded that the Kernel Dot, Kernel Multiquaric, Kernel Neural inappropriately used for data is non-linear in the case of data forex to follow the pattern of trend curves, because curves generated curved linear (straight and then to type of kernel is the closest curve

  12. Combining high-speed SVM learning with CNN feature encoding for real-time target recognition in high-definition video for ISR missions

    Science.gov (United States)

    Kroll, Christine; von der Werth, Monika; Leuck, Holger; Stahl, Christoph; Schertler, Klaus

    2017-05-01

    For Intelligence, Surveillance, Reconnaissance (ISR) missions of manned and unmanned air systems typical electrooptical payloads provide high-definition video data which has to be exploited with respect to relevant ground targets in real-time by automatic/assisted target recognition software. Airbus Defence and Space is developing required technologies for real-time sensor exploitation since years and has combined the latest advances of Deep Convolutional Neural Networks (CNN) with a proprietary high-speed Support Vector Machine (SVM) learning method into a powerful object recognition system with impressive results on relevant high-definition video scenes compared to conventional target recognition approaches. This paper describes the principal requirements for real-time target recognition in high-definition video for ISR missions and the Airbus approach of combining an invariant feature extraction using pre-trained CNNs and the high-speed training and classification ability of a novel frequency-domain SVM training method. The frequency-domain approach allows for a highly optimized implementation for General Purpose Computation on a Graphics Processing Unit (GPGPU) and also an efficient training of large training samples. The selected CNN which is pre-trained only once on domain-extrinsic data reveals a highly invariant feature extraction. This allows for a significantly reduced adaptation and training of the target recognition method for new target classes and mission scenarios. A comprehensive training and test dataset was defined and prepared using relevant high-definition airborne video sequences. The assessment concept is explained and performance results are given using the established precision-recall diagrams, average precision and runtime figures on representative test data. A comparison to legacy target recognition approaches shows the impressive performance increase by the proposed CNN+SVM machine-learning approach and the capability of real-time high

  13. Predicting the Metabolic Sites by Flavin-Containing Monooxygenase on Drug Molecules Using SVM Classification on Computed Quantum Mechanics and Circular Fingerprints Molecular Descriptors.

    Directory of Open Access Journals (Sweden)

    Chien-Wei Fu

    Full Text Available As an important enzyme in Phase I drug metabolism, the flavin-containing monooxygenase (FMO also metabolizes some xenobiotics with soft nucleophiles. The site of metabolism (SOM on a molecule is the site where the metabolic reaction is exerted by an enzyme. Accurate prediction of SOMs on drug molecules will assist the search for drug leads during the optimization process. Here, some quantum mechanics features such as the condensed Fukui function and attributes from circular fingerprints (called Molprint2D are computed and classified using the support vector machine (SVM for predicting some potential SOMs on a series of drugs that can be metabolized by FMO enzymes. The condensed Fukui function fA- representing the nucleophilicity of central atom A and the attributes from circular fingerprints accounting the influence of neighbors on the central atom. The total number of FMO substrates and non-substrates collected in the study is 85 and they are equally divided into the training and test sets with each carrying roughly the same number of potential SOMs. However, only N-oxidation and S-oxidation features were considered in the prediction since the available C-oxidation data was scarce. In the training process, the LibSVM package of WEKA package and the option of 10-fold cross validation are employed. The prediction performance on the test set evaluated by accuracy, Matthews correlation coefficient and area under ROC curve computed are 0.829, 0.659, and 0.877 respectively. This work reveals that the SVM model built can accurately predict the potential SOMs for drug molecules that are metabolizable by the FMO enzymes.

  14. A Comparative Study between SVM and Fuzzy Inference System for the Automatic Prediction of Sleep Stages and the Assessment of Sleep Quality

    Directory of Open Access Journals (Sweden)

    John Gialelis

    2015-11-01

    Full Text Available This paper compares two supervised learning algorithms for predicting the sleep stages based on the human brain activity. The first step of the presented work regards feature extraction from real human electroencephalography (EEG data together with its corresponding sleep stages that are utilized for training a support vector machine (SVM, and a fuzzy inference system (FIS algorithm. Then, the trained algorithms are used to predict the sleep stages of real human patients. Extended comparison results are demonstrated which indicate that both classifiers could be utilized as a basis for an unobtrusive sleep quality assessment.

  15. Performance of svm, k-nn and nbc classifiers for text-independent speaker identification with and without modelling through merging models

    Directory of Open Access Journals (Sweden)

    Yussouf Nahayo

    2016-04-01

    Full Text Available This paper proposes some methods of robust text-independent speaker identification based on Gaussian Mixture Model (GMM. We implemented a combination of GMM model with a set of classifiers such as Support Vector Machine (SVM, K-Nearest Neighbour (K-NN, and Naive Bayes Classifier (NBC. In order to improve the identification rate, we developed a combination of hybrid systems by using validation technique. The experiments were performed on the dialect DR1 of the TIMIT corpus. The results have showed a better performance for the developed technique compared to the individual techniques.

  16. Estudio de un sistema de reconocimiento biométrico mediante firma manuscrita online basado en SVM usando Análisis Formal de Conceptos

    OpenAIRE

    Mendaza Ormaza, Aitor; Miguel Hurtado, Óscar; Sánchez Reillo, Raúl; Valverde Albacete, Francisco José; Peláez Moreno, Carmen

    2010-01-01

    10 pages, 8 figures.-- Contributed to: V Jornadas de Reconocimiento Biométrico de Personas (JRBP 2010, Huesca, Spain, Sep 2-3, 2010). En el presente artículo se pretende estudiar las prestaciones de un sistema de reconocimiento biométrico mediante firma manuscrita usando la teoría de Análisis Formal de Conceptos (FCA). Se usará la modalidad online de la firma manuscrita, con un algoritmo basado en Máquinas de Vectores Soporte (SVM). Para analizar el desempeño del sistema se realizará un es...

  17. Analysis of Human Papillomavirus Using Datamining - Apriori, Decision Tree, and Support Vector Machine (SVM and its Application Field

    Directory of Open Access Journals (Sweden)

    Cho Younghoon

    2016-01-01

    Full Text Available Human Papillomavirus(HPV has various types (compared to other viruses and plays a key role in evoking diverse diseases, especially cervical cancer. In this study, we aim to distinguish the features of HPV of different degree of fatality by analyzing their DNA sequences. We used Decision Tree Algorithm, Apriori Algorithm, and Support Vector Machine in our experiment. By analyzing their DNA sequences, we discovered some relationships between certain types of HPV, especially on the most fatal types, 16 and 18. Moreover, we concluded that it would be possible for scientists to develop more potent HPV cures by applying these relationships and features that HPV virus exhibit.

  18. DNA methylation

    DEFF Research Database (Denmark)

    Williams, Kristine; Christensen, Jesper; Helin, Kristian

    2012-01-01

    DNA methylation is involved in key cellular processes, including X-chromosome inactivation, imprinting and transcriptional silencing of specific genes and repetitive elements. DNA methylation patterns are frequently perturbed in human diseases such as imprinting disorders and cancer. The recent...... discovery that the three members of the TET protein family can convert 5-methylcytosine (5mC) into 5-hydroxymethylcytosine (5hmC) has provided a potential mechanism leading to DNA demethylation. Moreover, the demonstration that TET2 is frequently mutated in haematopoietic tumours suggests that the TET...... proteins are important regulators of cellular identity. Here, we review the current knowledge regarding the function of the TET proteins, and discuss various mechanisms by which they contribute to transcriptional control. We propose that the TET proteins have an important role in regulating DNA methylation...

  19. DNA data

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Raw DNA chromatogram data produced by the ABI 373, 377, 3130 and 3730 automated sequencing machines in ABI format. These are from fish (primarily Sebastes spp.,...

  20. DNA nanotechnology

    Science.gov (United States)

    Seeman, Nadrian C.; Sleiman, Hanadi F.

    2018-01-01

    DNA is the molecule that stores and transmits genetic information in biological systems. The field of DNA nanotechnology takes this molecule out of its biological context and uses its information to assemble structural motifs and then to connect them together. This field has had a remarkable impact on nanoscience and nanotechnology, and has been revolutionary in our ability to control molecular self-assembly. In this Review, we summarize the approaches used to assemble DNA nanostructures and examine their emerging applications in areas such as biophysics, diagnostics, nanoparticle and protein assembly, biomolecule structure determination, drug delivery and synthetic biology. The introduction of orthogonal interactions into DNA nanostructures is discussed, and finally, a perspective on the future directions of this field is presented.

  1. DNA Chip

    Indian Academy of Sciences (India)

    involved in the pathology of schizophrenia. In the human ge- nome, the ratio between coding and non-coding DNA is very low (less than 3% of the human .... construction of a Tm-specific chip, i.e. all the oligos/cDNA on the chip will hybridize at the same temperature. The techniques available are still not able to create a chip ...

  2. DNA expressions - A formal notation for DNA

    NARCIS (Netherlands)

    Vliet, Rudy van

    2015-01-01

    We describe a formal notation for DNA molecules that may contain nicks and gaps. The resulting DNA expressions denote formal DNA molecules. Different DNA expressions may denote the same molecule. Such DNA expressions are called equivalent. We examine which DNA expressions are minimal, which

  3. Pre-cancer risk assessment in habitual smokers from DIC images of oral exfoliative cells using active contour and SVM analysis.

    Science.gov (United States)

    Dey, Susmita; Sarkar, Ripon; Chatterjee, Kabita; Datta, Pallab; Barui, Ananya; Maity, Santi P

    2017-04-01

    Habitual smokers are known to be at higher risk for developing oral cancer, which is increasing at an alarming rate globally. Conventionally, oral cancer is associated with high mortality rates, although recent reports show the improved survival outcomes by early diagnosis of disease. An effective prediction system which will enable to identify the probability of cancer development amongst the habitual smokers, is thus expected to benefit sizable number of populations. Present work describes a non-invasive, integrated method for early detection of cellular abnormalities based on analysis of different cyto-morphological features of exfoliative oral epithelial cells. Differential interference contrast (DIC) microscopy provides a potential optical tool as this mode provides a pseudo three dimensional (3-D) image with detailed morphological and textural features obtained from noninvasive, label free epithelial cells. For segmentation of DIC images, gradient vector flow snake model active contour process has been adopted. To evaluate cellular abnormalities amongst habitual smokers, the selected morphological and textural features of epithelial cells are compared with the non-smoker (-ve control group) group and clinically diagnosed pre-cancer patients (+ve control group) using support vector machine (SVM) classifier. Accuracy of the developed SVM based classification has been found to be 86% with 80% sensitivity and 89% specificity in classifying the features from the volunteers having smoking habit. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. A Fast SVM-Based Tongue’s Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis

    Directory of Open Access Journals (Sweden)

    Nur Diyana Kamarudin

    2017-01-01

    Full Text Available In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye’s ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue’s multicolour classification based on a support vector machine (SVM whose support vectors are reduced by our proposed k-means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k-means clustering is used to cluster a tongue image into four clusters of image background (black, deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds.

  5. Effect of Substrate and Culture Conditions on the Production of Amylase and Pullulanase by Thermophilic Clostridium thermosulforegenes SVM17 in Solid State Fermentation

    Directory of Open Access Journals (Sweden)

    Seenayya, G.

    2011-01-01

    Full Text Available The endo acting enzyme with dual specificity towards α-1,4- and α-1,6-glycosidic linkages are named as amylopullulanase. The production of extracellular thermostable amylopullulanase by Clostridium thermosulfurogenes SVM17 was investigated in solid state fermentation (SSF. Coarse type wheat bran was found to be the best substrate among ten easily available complex organic substrates evaluated. The production of enzyme reached a peak in 72 h. A high level of enzyme was produced in wheat bran moistened with PYE medium with a moisture content of 73 %. The optimum temperature and pH for amylopullulanase production was 60 °C and 7.5, respectively. An inoculum size of 20 % resulted in maximum production of amylopullulanase. Under the optimum conditions the strain showed a maximum of 17,227 and 21,526 U of amylase and pullulanase activity, respectively per kilogram of bacterial bran (BB. The enzyme production was high in SSF than that in SmF. The use of SSF for the production of thermostable amylopullulanase by C. thermosulfurogenes SVM17 could, therefore led to reduction in the overall cost of enzyme production.

  6. In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches

    Directory of Open Access Journals (Sweden)

    Zhijun Liao

    2016-01-01

    Full Text Available Gamma-aminobutyric acid type-A receptors (GABAARs belong to multisubunit membrane spanning ligand-gated ion channels (LGICs which act as the principal mediators of rapid inhibitory synaptic transmission in the human brain. Therefore, the category prediction of GABAARs just from the protein amino acid sequence would be very helpful for the recognition and research of novel receptors. Based on the proteins’ physicochemical properties, amino acids composition and position, a GABAAR classifier was first constructed using a 188-dimensional (188D algorithm at 90% cd-hit identity and compared with pseudo-amino acid composition (PseAAC and ProtrWeb web-based algorithms for human GABAAR proteins. Then, four classifiers including gradient boosting decision tree (GBDT, random forest (RF, a library for support vector machine (libSVM, and k-nearest neighbor (k-NN were compared on the dataset at cd-hit 40% low identity. This work obtained the highest correctly classified rate at 96.8% and the highest specificity at 99.29%. But the values of sensitivity, accuracy, and Matthew’s correlation coefficient were a little lower than those of PseAAC and ProtrWeb; GBDT and libSVM can make a little better performance than RF and k-NN at the second dataset. In conclusion, a GABAAR classifier was successfully constructed using only the protein sequence information.

  7. Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM and Artificial Neural Network (ANN

    Directory of Open Access Journals (Sweden)

    Maria Grazia De Giorgi

    2014-08-01

    Full Text Available A high penetration of wind energy into the electricity market requires a parallel development of efficient wind power forecasting models. Different hybrid forecasting methods were applied to wind power prediction, using historical data and numerical weather predictions (NWP. A comparative study was carried out for the prediction of the power production of a wind farm located in complex terrain. The performances of Least-Squares Support Vector Machine (LS-SVM with Wavelet Decomposition (WD were evaluated at different time horizons and compared to hybrid Artificial Neural Network (ANN-based methods. It is acknowledged that hybrid methods based on LS-SVM with WD mostly outperform other methods. A decomposition of the commonly known root mean square error was beneficial for a better understanding of the origin of the differences between prediction and measurement and to compare the accuracy of the different models. A sensitivity analysis was also carried out in order to underline the impact that each input had in the network training process for ANN. In the case of ANN with the WD technique, the sensitivity analysis was repeated on each component obtained by the decomposition.

  8. The system evaluation for report writing skills of summary by HGA-SVM with Ontology: Medical case study in problem based learning

    Science.gov (United States)

    Yenaeng, Sasikanchana; Saelee, Somkid; Samai, Wirachai

    2018-01-01

    The system evaluation for report writing skills of summary by Hybrid Genetic Algorithm-Support Vector Machines (HGA-SVM) with Ontology of Medical Case Study in Problem Based Learning (PBL) is a system was developed as a guideline of scoring for the facilitators or medical teacher. The essay answers come from medical student of medical education courses in the nervous system motion and Behavior I and II subject, a third year medical student 20 groups of 9-10 people, the Faculty of Medicine in Prince of Songkla University (PSU). The audit committee have the opinion that the ratings of individual facilitators are inadequate, this system to solve such problems. In this paper proposes a development of the system evaluation for report writing skills of summary by HGA-SVM with Ontology of medical case study in PBL which the mean scores of machine learning score and humans (facilitators) score were not different at the significantly level .05 all 3 essay parts contain problem essay part, hypothesis essay part and learning objective essay part. The result show that, the average score all 3 essay parts that were not significantly different from the rate at the level of significance .05.

  9. What Is Mitochondrial DNA?

    Science.gov (United States)

    ... DNA What is mitochondrial DNA? What is mitochondrial DNA? Although most DNA is packaged in chromosomes within ... proteins. For more information about mitochondria and mitochondrial DNA: Molecular Expressions, a web site from the Florida ...

  10. HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features

    Directory of Open Access Journals (Sweden)

    Rianon Zaman

    2017-01-01

    Full Text Available DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.

  11. [DNA extraction from bones and teeth using AutoMate Express forensic DNA extraction system].

    Science.gov (United States)

    Gao, Lin-Lin; Xu, Nian-Lai; Xie, Wei; Ding, Shao-Cheng; Wang, Dong-Jing; Ma, Li-Qin; Li, You-Ying

    2013-04-01

    To explore a new method in order to extract DNA from bones and teeth automatically. Samples of 33 bones and 15 teeth were acquired by freeze-mill method and manual method, respectively. DNA materials were extracted and quantified from the triturated samples by AutoMate Express forensic DNA extraction system. DNA extraction from bones and teeth were completed in 3 hours using the AutoMate Express forensic DNA extraction system. There was no statistical difference between the two methods in the DNA concentration of bones. Both bones and teeth got the good STR typing by freeze-mill method, and the DNA concentration of teeth was higher than those by manual method. AutoMate Express forensic DNA extraction system is a new method to extract DNA from bones and teeth, which can be applied in forensic practice.

  12. Authentication of forensic DNA samples.

    Science.gov (United States)

    Frumkin, Dan; Wasserstrom, Adam; Davidson, Ariane; Grafit, Arnon

    2010-02-01

    Over the past twenty years, DNA analysis has revolutionized forensic science, and has become a dominant tool in law enforcement. Today, DNA evidence is key to the conviction or exoneration of suspects of various types of crime, from theft to rape and murder. However, the disturbing possibility that DNA evidence can be faked has been overlooked. It turns out that standard molecular biology techniques such as PCR, molecular cloning, and recently developed whole genome amplification (WGA), enable anyone with basic equipment and know-how to produce practically unlimited amounts of in vitro synthesized (artificial) DNA with any desired genetic profile. This artificial DNA can then be applied to surfaces of objects or incorporated into genuine human tissues and planted in crime scenes. Here we show that the current forensic procedure fails to distinguish between such samples of blood, saliva, and touched surfaces with artificial DNA, and corresponding samples with in vivo generated (natural) DNA. Furthermore, genotyping of both artificial and natural samples with Profiler Plus((R)) yielded full profiles with no anomalies. In order to effectively deal with this problem, we developed an authentication assay, which distinguishes between natural and artificial DNA based on methylation analysis of a set of genomic loci: in natural DNA, some loci are methylated and others are unmethylated, while in artificial DNA all loci are unmethylated. The assay was tested on natural and artificial samples of blood, saliva, and touched surfaces, with complete success. Adopting an authentication assay for casework samples as part of the forensic procedure is necessary for maintaining the high credibility of DNA evidence in the judiciary system.

  13. Molecular Programming with DNA

    Science.gov (United States)

    Winfree, Erik

    2009-05-01

    Information can be stored in molecules and processed by molecular reactions. Molecular information processing is at the heart of all biological systems; might it soon also be at the heart of non-biological synthetic chemical systems? Perhaps yes. One technological approach comes from DNA nanotechnology and DNA computing, where DNA is used as a non-biological informational polymer that can be rationally designed to create a rich class of molecular systems -- for example, DNA molecules that self-assemble precisely, that fold into complex nanoscale objects, that act as mechanical actuators and molecular motors, and that make decisions based on digital and analog logic. I will argue that to fully exploit their design potential, we will need to invent programming languages for specifying the behavior of information-based molecular systems, to create theoretical tools for understanding and analyzing the behavior of molecular programs, to develop compilers that automate the design of molecules with the desired behaviors, and to expand experimental techniques so that the implementation and debugging of complex molecular systems becomes as commonplace and practical as computer programming.

  14. Thermal stability of DNA functionalized gold nanoparticles.

    Science.gov (United States)

    Li, Feng; Zhang, Hongquan; Dever, Brittany; Li, Xing-Fang; Le, X Chris

    2013-11-20

    Therapeutic uses of DNA functionalized gold nanoparticles (DNA-AuNPs) have shown great potential and exciting opportunities for disease diagnostics and treatment. Maintaining stable conjugation between DNA oligonucleotides and gold nanoparticles under thermally stressed conditions is one of the critical aspects for any of the practical applications. We systematically studied the thermal stability of DNA-AuNPs as affected by organosulfur anchor groups and packing densities. Using a fluorescence assay to determine the kinetics of releasing DNA molecules from DNA-AuNPs, we observed an opposite trend between the temperature-induced and chemical-induced release of DNA from DNA-AuNPs when comparing the DNA-AuNPs that were constructed with different anchor groups. Specifically, the bidentate Au-S bond formed with cyclic disulfide was thermally less stable than those formed with thiol or acyclic disulfide. However, the same bidentate Au-S bond was chemically more stable under the treatment of competing thiols (mercaptohexanol or dithiothreitol). DNA packing density on AuNPs influenced the thermal stability of DNA-AuNPs at 37 °C, but this effect was minimum as temperature increased to 85 °C. With the improved understanding from these results, we were able to design a strategy to enhance the stability of DNA-AuNPs by conjugating double-stranded DNA to AuNPs through multiple thiol anchors.

  15. Using an Integrated Group Decision Method Based on SVM, TFN-RS-AHP, and TOPSIS-CD for Cloud Service Supplier Selection

    Directory of Open Access Journals (Sweden)

    Lian-hui Li

    2017-01-01

    Full Text Available To solve the cloud service supplier selection problem under the background of cloud computing emergence, an integrated group decision method is proposed. The cloud service supplier selection index framework is built from two perspectives of technology and technology management. Support vector machine- (SVM- based classification model is applied for the preliminary screening to reduce the number of candidate suppliers. A triangular fuzzy number-rough sets-analytic hierarchy process (TFN-RS-AHP method is designed to calculate supplier’s index value by expert’s wisdom and experience. The index weight is determined by criteria importance through intercriteria correlation (CRITIC. The suppliers are evaluated by the improved TOPSIS replacing Euclidean distance with connection distance (TOPSIS-CD. An electric power enterprise’s case is given to illustrate the correctness and feasibility of the proposed method.

  16. Research on big data risk assessment of major transformer defects and faults fusing power grid, equipment and environment based on SVM

    Science.gov (United States)

    Guo, Lijuan; Yan, Haijun; Gao, Wensheng; Chen, Yun; Hao, Yongqi

    2018-01-01

    With the development of power big data, considering the wider power system data, the appropriate large data analysis method can be used to mine the potential law and value of power big data. On the basis of considering all kinds of monitoring data and defects and fault records of main transformer, the paper integrates the power grid, equipment as well as environment data and uses SVM as the main algorithm to evaluate the risk of the main transformer. It gets and compares the evaluation results under different modes, and proves that the risk assessment algorithms and schemes have certain effectiveness. This paper provides a new idea for data fusion of smart grid, and provides a reference for further big data evaluation of power grid equipment.

  17. A two-dimensional matrix image based feature extraction method for classification of sEMG: A comparative analysis based on SVM, KNN and RBF-NN.

    Science.gov (United States)

    Wen, Tingxi; Zhang, Zhongnan; Qiu, Ming; Zeng, Ming; Luo, Weizhen

    2017-01-01

    The computer mouse is an important human-computer interaction device. But patients with physical finger disability are unable to operate this device. Surface EMG (sEMG) can be monitored by electrodes on the skin surface and is a reflection of the neuromuscular activities. Therefore, we can control limbs auxiliary equipment by utilizing sEMG classification in order to help the physically disabled patients to operate the mouse. To develop a new a method to extract sEMG generated by finger motion and apply novel features to classify sEMG. A window-based data acquisition method was presented to extract signal samples from sEMG electordes. Afterwards, a two-dimensional matrix image based feature extraction method, which differs from the classical methods based on time domain or frequency domain, was employed to transform signal samples to feature maps used for classification. In the experiments, sEMG data samples produced by the index and middle fingers at the click of a mouse button were separately acquired. Then, characteristics of the samples were analyzed to generate a feature map for each sample. Finally, the machine learning classification algorithms (SVM, KNN, RBF-NN) were employed to classify these feature maps on a GPU. The study demonstrated that all classifiers can identify and classify sEMG samples effectively. In particular, the accuracy of the SVM classifier reached up to 100%. The signal separation method is a convenient, efficient and quick method, which can effectively extract the sEMG samples produced by fingers. In addition, unlike the classical methods, the new method enables to extract features by enlarging sample signals' energy appropriately. The classical machine learning classifiers all performed well by using these features.

  18. Predicting Protein-DNA Binding Residues by Weightedly Combining Sequence-Based Features and Boosting Multiple SVMs.

    Science.gov (United States)

    Hu, Jun; Li, Yang; Zhang, Ming; Yang, Xibei; Shen, Hong-Bin; Yu, Dong-Jun

    2017-01-01

    Protein-DNA interactions are ubiquitous in a wide variety of biological processes. Correctly locating DNA-binding residues solely from protein sequences is an important but challenging task for protein function annotations and drug discovery, especially in the post-genomic era where large volumes of protein sequences have quickly accumulated. In this study, we report a new predictor, named TargetDNA, for targeting protein-DNA binding residues from primary sequences. TargetDNA uses a protein's evolutionary information and its predicted solvent accessibility as two base features and employs a centered linear kernel alignment algorithm to learn the weights for weightedly combining the two features. Based on the weightedly combined feature, multiple initial predictors with SVM as classifiers are trained by applying a random under-sampling technique to the original dataset, the purpose of which is to cope with the severe imbalance phenomenon that exists between the number of DNA-binding and non-binding residues. The final ensembled predictor is obtained by boosting the multiple initially trained predictors. Experimental simulation results demonstrate that the proposed TargetDNA achieves a high prediction performance and outperforms many existing sequence-based protein-DNA binding residue predictors. The TargetDNA web server and datasets are freely available at http://csbio.njust.edu.cn/bioinf/TargetDNA/ for academic use.

  19. Oxidative DNA glycosylases: recipes from cloning to characterization.

    Science.gov (United States)

    Bandaru, Viswanath; Blaisdell, Jeffrey O; Wallace, Susan S

    2006-01-01

    As new organisms are being sequenced on a daily basis, new DNA glycosylases that recognize DNA damage can be easily identified in an effort to understand both their phylogenetics and substrate specificities. As a practical matter, existing bacterial and human homologs need to be readily available as laboratory reagents in order to compare the activities of the novel enzymes to existing enzymes. This chapter attempts to provide a primer for cloning, expression, and assay procedures for bacterial and human DNA glycosylases that recognize oxidative DNA damages. These methodologies can be translated readily to novel DNA glycosylases or to DNA glycosylases that recognize other types of DNA damages.

  20. THE APPLICATION OF SUPPORT VECTOR MACHINE (SVM USING CIELAB COLOR MODEL, COLOR INTENSITY AND COLOR CONSTANCY AS FEATURES FOR ORTHO IMAGE CLASSIFICATION OF BENTHIC HABITATS IN HINATUAN, SURIGAO DEL SUR, PHILIPPINES

    Directory of Open Access Journals (Sweden)

    J. E. Cubillas

    2016-06-01

    Full Text Available This study demonstrates the application of CIELAB, Color intensity, and One Dimensional Scalar Constancy as features for image recognition and classifying benthic habitats in an image with the coastal areas of Hinatuan, Surigao Del Sur, Philippines as the study area. The study area is composed of four datasets, namely: (a Blk66L005, (b Blk66L021, (c Blk66L024, and (d Blk66L0114. SVM optimization was performed in Matlab® software with the help of Parallel Computing Toolbox to hasten the SVM computing speed. The image used for collecting samples for SVM procedure was Blk66L0114 in which a total of 134,516 sample objects of mangrove, possible coral existence with rocks, sand, sea, fish pens and sea grasses were collected and processed. The collected samples were then used as training sets for the supervised learning algorithm and for the creation of class definitions. The learned hyper-planes separating one class from another in the multi-dimensional feature space can be thought of as a super feature which will then be used in developing the C (classifier rule set in eCognition® software. The classification results of the sampling site yielded an accuracy of 98.85% which confirms the reliability of remote sensing techniques and analysis employed to orthophotos like the CIELAB, Color Intensity and One dimensional scalar constancy and the use of SVM classification algorithm in classifying benthic habitats.

  1. Experiences of social harm and changes in sexual practices among volunteers who had completed a phase I/II HIV vaccine trial employing HIV-1 DNA priming and HIV-1 MVA boosting in Dar es Salaam, Tanzania.

    Directory of Open Access Journals (Sweden)

    Edith A M Tarimo

    Full Text Available BACKGROUND: Volunteers in phase I/II HIV vaccine trials are assumed to be at low risk of acquiring HIV infection and are expected to have normal lives in the community. However, during participation in the trials, volunteers may encounter social harm and changes in their sexual behaviours. The current study aimed to study persistence of social harm and changes in sexual practices over time among phase I/II HIV vaccine immunogenicity (HIVIS03 trial volunteers in Dar es Salaam, Tanzania. METHODS AND RESULTS: A descriptive prospective cohort study was conducted among 33 out of 60 volunteers of HIVIS03 trial in Dar es Salaam, Tanzania, who had received three HIV-1 DNA injections boosted with two HIV-1 MVA doses. A structured interview was administered to collect data. Analysis was carried out using SPSS and McNemars' chi-square (χ2 was used to test the association within-subjects. Participants reported experiencing negative comments from their colleagues about the trial; but such comments were less severe during the second follow up visits (χ2 = 8.72; P<0.001. Most of the comments were associated with discrimination (χ2 = 26.72; P<0.001, stigma (χ2 = 6.06; P<0.05, and mistrust towards the HIV vaccine trial (χ2 = 4.9; P<0.05. Having a regular sexual partner other than spouse or cohabitant declined over the two follow-up periods (χ2 = 4.45; P<0.05. CONCLUSION: Participants in the phase I/II HIV vaccine trial were likely to face negative comments from relatives and colleagues after the end of the trial, but those comments decreased over time. In this study, the inherent sexual practice of having extra sexual partners other than spouse declined over time. Therefore, prolonged counselling and support appears important to minimize risky sexual behaviour among volunteers after participation in HIV Vaccine trials.

  2. How We Make DNA Origami.

    Science.gov (United States)

    Wagenbauer, Klaus F; Engelhardt, Floris A S; Stahl, Evi; Hechtl, Vera K; Stömmer, Pierre; Seebacher, Fabian; Meregalli, Letizia; Ketterer, Philip; Gerling, Thomas; Dietz, Hendrik

    2017-10-05

    DNA origami has attracted substantial attention since its invention ten years ago, due to the seemingly infinite possibilities that it affords for creating customized nanoscale objects. Although the basic concept of DNA origami is easy to understand, using custom DNA origami in practical applications requires detailed know-how for designing and producing the particles with sufficient quality and for preparing them at appropriate concentrations with the necessary degree of purity in custom environments. Such know-how is not readily available for newcomers to the field, thus slowing down the rate at which new applications outside the field of DNA nanotechnology may emerge. To foster faster progress, we share in this article the experience in making and preparing DNA origami that we have accumulated over recent years. We discuss design solutions for creating advanced structural motifs including corners and various types of hinges that expand the design space for the more rigid multilayer DNA origami and provide guidelines for preventing undesired aggregation and on how to induce specific oligomerization of multiple DNA origami building blocks. In addition, we provide detailed protocols and discuss the expected results for five key methods that allow efficient and damage-free preparation of DNA origami. These methods are agarose-gel purification, filtration through molecular cut-off membranes, PEG precipitation, size-exclusion chromatography, and ultracentrifugation-based sedimentation. The guide for creating advanced design motifs and the detailed protocols with their experimental characterization that we describe here should lower the barrier for researchers to accomplish the full DNA origami production workflow. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. DNA Microarrays

    Science.gov (United States)

    Nguyen, C.; Gidrol, X.

    Genomics has revolutionised biological and biomedical research. This revolution was predictable on the basis of its two driving forces: the ever increasing availability of genome sequences and the development of new technology able to exploit them. Up until now, technical limitations meant that molecular biology could only analyse one or two parameters per experiment, providing relatively little information compared with the great complexity of the systems under investigation. This gene by gene approach is inadequate to understand biological systems containing several thousand genes. It is essential to have an overall view of the DNA, RNA, and relevant proteins. A simple inventory of the genome is not sufficient to understand the functions of the genes, or indeed the way that cells and organisms work. For this purpose, functional studies based on whole genomes are needed. Among these new large-scale methods of molecular analysis, DNA microarrays provide a way of studying the genome and the transcriptome. The idea of integrating a large amount of data derived from a support with very small area has led biologists to call these chips, borrowing the term from the microelectronics industry. At the beginning of the 1990s, the development of DNA chips on nylon membranes [1, 2], then on glass [3] and silicon [4] supports, made it possible for the first time to carry out simultaneous measurements of the equilibrium concentration of all the messenger RNA (mRNA) or transcribed RNA in a cell. These microarrays offer a wide range of applications, in both fundamental and clinical research, providing a method for genome-wide characterisation of changes occurring within a cell or tissue, as for example in polymorphism studies, detection of mutations, and quantitative assays of gene copies. With regard to the transcriptome, it provides a way of characterising differentially expressed genes, profiling given biological states, and identifying regulatory channels.

  4. Monitoring endangered freshwater biodiversity using environmental DNA

    DEFF Research Database (Denmark)

    Thomsen, Philip Francis; Kielgast, Jos; Iversen, Lars Lønsmann

    2012-01-01

    that depend on practical and taxonomic expertise, which is rapidly declining. Here, we show that a diversity of rare and threatened freshwater animals-representing amphibians, fish, mammals, insects and crustaceans-can be detected and quantified based on DNA obtained directly from small water samples of lakes...... of amphibians and fish can be detected by high-throughput sequencing of DNA extracted from pond water. Our findings underpin the ubiquitous nature of DNA traces in the environment and establish environmental DNA as a tool for monitoring rare and threatened species across a wide range of taxonomic groups....

  5. DNA Microarrays in Herbal Drug Research

    Directory of Open Access Journals (Sweden)

    Preeti Chavan

    2006-01-01

    Full Text Available Natural products are gaining increased applications in drug discovery and development. Being chemically diverse they are able to modulate several targets simultaneously in a complex system. Analysis of gene expression becomes necessary for better understanding of molecular mechanisms. Conventional strategies for expression profiling are optimized for single gene analysis. DNA microarrays serve as suitable high throughput tool for simultaneous analysis of multiple genes. Major practical applicability of DNA microarrays remains in DNA mutation and polymorphism analysis. This review highlights applications of DNA microarrays in pharmacodynamics, pharmacogenomics, toxicogenomics and quality control of herbal drugs and extracts.

  6. DNA Methyltransferase Activity Assays: Advances and Challenges.

    Science.gov (United States)

    Poh, Wan Jun; Wee, Cayden Pang Pee; Gao, Zhiqiang

    2016-01-01

    DNA methyltransferases (MTases), a family of enzymes that catalyse the methylation of DNA, have a profound effect on gene regulation. A large body of evidence has indicated that DNA MTase is potentially a predictive biomarker closely associated with genetic disorders and genetic diseases like cancer. Given the attention bestowed onto DNA MTases in molecular biology and medicine, highly sensitive detection of DNA MTase activity is essential in determining gene regulation, epigenetic modification, clinical diagnosis and therapeutics. Conventional techniques such as isotope labelling are effective, but they often require laborious sample preparation, isotope labelling, sophisticated equipment and large amounts of DNA, rendering them unsuitable for uses at point-of-care. Simple, portable, highly sensitive and low-cost assays are urgently needed for DNA MTase activity screening. In most recent technological advances, many alternative DNA MTase activity assays such as fluorescent, electrochemical, colorimetric and chemiluminescent assays have been proposed. In addition, many of them are coupled with nanomaterials and/or enzymes to significantly enhance their sensitivity. Herein we review the progress in the development of DNA MTase activity assays with an emphasis on assay mechanism and performance with some discussion on challenges and perspectives. It is hoped that this article will provide a broad coverage of DNA MTase activity assays and their latest developments and open new perspectives toward the development of DNA MTase activity assays with much improved performance for uses in molecular biology and clinical practice.

  7. Production and pharmaceutical formulation of plasmid DNA vaccines

    NARCIS (Netherlands)

    van der Heijden, I.

    2013-01-01

    Research leading to the thesis ‘Production and pharmaceutical formulation of plasmid DNA vaccines‘ can be divided into two parts. The first part describes the development of a Good Manufacturing Practice (GMP) compliant plasmid DNA production process of pDNA vaccines for the treatment of Human

  8. Dynamics and Control of DNA Sequence Amplification

    CERN Document Server

    Marimuthu, Karthikeyan

    2014-01-01

    DNA amplification is the process of replication of a specified DNA sequence \\emph{in vitro} through time-dependent manipulation of its external environment. A theoretical framework for determination of the optimal dynamic operating conditions of DNA amplification reactions, for any specified amplification objective, is presented based on first-principles biophysical modeling and control theory. Amplification of DNA is formulated as a problem in control theory with optimal solutions that can differ considerably from strategies typically used in practice. Using the Polymerase Chain Reaction (PCR) as an example, sequence-dependent biophysical models for DNA amplification are cast as control systems, wherein the dynamics of the reaction are controlled by a manipulated input variable. Using these control systems, we demonstrate that there exists an optimal temperature cycling strategy for geometric amplification of any DNA sequence and formulate optimal control problems that can be used to derive the optimal tempe...

  9. Relaxed specificity of prokaryotic DNA methyltransferases results in DNA site-specific modification of RNA/DNA heteroduplexes.

    Science.gov (United States)

    Wons, Ewa; Mruk, Iwona; Kaczorowski, Tadeusz

    2015-11-01

    RNA/DNA hybrid duplexes regularly occur in nature, for example in transcriptional R loops. Their susceptibility to modification by DNA-specific or RNA-specific enzymes is, thus, a biologically relevant question, which, in addition, has possible biotechnological implications. In this study, we investigated the activity of four isospecific DNA methyltransferases (M.EcoVIII, M.LlaCI, M.HindIII, M.BstZ1II) toward an RNA/DNA duplex carrying one 5'-AAGCUU-3'/3'-TTCGAA-5' target sequence. The analyzed enzymes belong to the β-group of adenine N6-methyltransferases and recognize the palindromic DNA sequence 5'-AAGCTT-3'/3'-TTCGAA-5'. Under standard conditions, none of these isospecific enzymes could detectibly methylate the RNA/DNA duplex. However, the addition of agents that generally relax specificity, such as dimethyl sulfoxide (DMSO) and glycerol, resulted in substantial methylation of the RNA/DNA duplex by M.EcoVIII and M.LlaCI. Only the DNA strand of the RNA/DNA duplex was methylated. The same was not observed for M.HindIII or M.BstZ1II. This is, to our knowledge, the first report that demonstrates such activity by prokaryotic DNA methyltransferases. Possible applications of these findings in a laboratory practice are also discussed.

  10. Efficiency of methylated DNA immunoprecipitation bisulphite sequencing for whole-genome DNA methylation analysis.

    Science.gov (United States)

    Jeong, Hae Min; Lee, Sangseon; Chae, Heejoon; Kim, RyongNam; Kwon, Mi Jeong; Oh, Ensel; Choi, Yoon-La; Kim, Sun; Shin, Young Kee

    2016-08-01

    We compared four common methods for measuring DNA methylation levels and recommended the most efficient method in terms of cost and coverage. The DNA methylation status of liver and stomach tissues was profiled using four different methods, whole-genome bisulphite sequencing (WG-BS), targeted bisulphite sequencing (Targeted-BS), methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylated DNA immunoprecipitation bisulphite sequencing (MeDIP-BS). We calculated DNA methylation levels using each method and compared the results. MeDIP-BS yielded the most similar DNA methylation profile to WG-BS, with 20 times less data, suggesting remarkable cost savings and coverage efficiency compared with the other methods. MeDIP-BS is a practical cost-effective method for analyzing whole-genome DNA methylation that is highly accurate at base-pair resolution.

  11. DNA barcode goes two-dimensions: DNA QR code web server.

    Science.gov (United States)

    Liu, Chang; Shi, Linchun; Xu, Xiaolan; Li, Huan; Xing, Hang; Liang, Dong; Jiang, Kun; Pang, Xiaohui; Song, Jingyuan; Chen, Shilin

    2012-01-01

    The DNA barcoding technology uses a standard region of DNA sequence for species identification and discovery. At present, "DNA barcode" actually refers to DNA sequences, which are not amenable to information storage, recognition, and retrieval. Our aim is to identify the best symbology that can represent DNA barcode sequences in practical applications. A comprehensive set of sequences for five DNA barcode markers ITS2, rbcL, matK, psbA-trnH, and CO1 was used as the test data. Fifty-three different types of one-dimensional and ten two-dimensional barcode symbologies were compared based on different criteria, such as coding capacity, compression efficiency, and error detection ability. The quick response (QR) code was found to have the largest coding capacity and relatively high compression ratio. To facilitate the further usage of QR code-based DNA barcodes, a web server was developed and is accessible at http://qrfordna.dnsalias.org. The web server allows users to retrieve the QR code for a species of interests, convert a DNA sequence to and from a QR code, and perform species identification based on local and global sequence similarities. In summary, the first comprehensive evaluation of various barcode symbologies has been carried out. The QR code has been found to be the most appropriate symbology for DNA barcode sequences. A web server has also been constructed to allow biologists to utilize QR codes in practical DNA barcoding applications.

  12. H-DROP: an SVM based helical domain linker predictor trained with features optimized by combining random forest and stepwise selection.

    Science.gov (United States)

    Ebina, Teppei; Suzuki, Ryosuke; Tsuji, Ryotaro; Kuroda, Yutaka

    2014-08-01

    Domain linker prediction is attracting much interest as it can help identifying novel domains suitable for high throughput proteomics analysis. Here, we report H-DROP, an SVM-based Helical Domain linker pRediction using OPtimal features. H-DROP is, to the best of our knowledge, the first predictor for specifically and effectively identifying helical linkers. This was made possible first because a large training dataset became available from IS-Dom, and second because we selected a small number of optimal features from a huge number of potential ones. The training helical linker dataset, which included 261 helical linkers, was constructed by detecting helical residues at the boundary regions of two independent structural domains listed in our previously reported IS-Dom dataset. 45 optimal feature candidates were selected from 3,000 features by random forest, which were further reduced to 26 optimal features by stepwise selection. The prediction sensitivity and precision of H-DROP were 35.2 and 38.8%, respectively. These values were over 10.7% higher than those of control methods including our previously developed DROP, which is a coil linker predictor, and PPRODO, which is trained with un-differentiated domain boundary sequences. Overall, these results indicated that helical linkers can be predicted from sequence information alone by using a strictly curated training data set for helical linkers and carefully selected set of optimal features. H-DROP is available at http://domserv.lab.tuat.ac.jp.

  13. The role of the continuous wavelet transform in mineral identification using hyperspectral imaging in the long-wave infrared by using SVM classifier

    Science.gov (United States)

    Sojasi, Saeed; Yousefi, Bardia; Liaigre, Kévin; Ibarra-Castanedo, Clemente; Beaudoin, Georges; Maldague, Xavier P. V.; Huot, François; Chamberland, Martin

    2017-05-01

    Hyperspectral imaging (HSI) in the long-wave infrared spectrum (LWIR) provides spectral and spatial information concerning the emissivity of the surface of materials, which can be used for mineral identification. For this, an endmember, which is the purest form of a mineral, is used as reference. All pure minerals have specific spectral profiles in the electromagnetic wavelength, which can be thought of as the mineral's fingerprint. The main goal of this paper is the identification of minerals by LWIR hyperspectral imaging using a machine learning scheme. The information of hyperspectral imaging has been recorded from the energy emitted from the mineral's surface. Solar energy is the source of energy in remote sensing, while a heating element is the energy source employed in laboratory experiments. Our work contains three main steps where the first step involves obtaining the spectral signatures of pure (single) minerals with a hyperspectral camera, in the long-wave infrared (7.7 to 11.8 μm), which measures the emitted radiance from the minerals' surface. The second step concerns feature extraction by applying the continuous wavelet transform (CWT) and finally we use support vector machine classifier with radial basis functions (SVM-RBF) for classification/identification of minerals. The overall accuracy of classification in our work is 90.23+/- 2.66%. In conclusion, based on CWT's ability to capture the information of signals can be used as a good marker for classification and identification the minerals substance.

  14. Determining the Relationship between U.S. County-Level Adult Obesity Rate and Multiple Risk Factors by PLS Regression and SVM Modeling Approaches

    Directory of Open Access Journals (Sweden)

    Chau-Kuang Chen

    2015-02-01

    Full Text Available Data from the Center for Disease Control (CDC has shown that the obesity rate doubled among adults within the past two decades. This upsurge was the result of changes in human behavior and environment. Partial least squares (PLS regression and support vector machine (SVM models were conducted to determine the relationship between U.S. county-level adult obesity rate and multiple risk factors. The outcome variable was the adult obesity rate. The 23 risk factors were categorized into four domains of the social ecological model including biological/behavioral factor, socioeconomic status, food environment, and physical environment. Of the 23 risk factors related to adult obesity, the top eight significant risk factors with high normalized importance were identified including physical inactivity, natural amenity, percent of households receiving SNAP benefits, and percent of all restaurants being fast food. The study results were consistent with those in the literature. The study showed that adult obesity rate was influenced by biological/behavioral factor, socioeconomic status, food environment, and physical environment embedded in the social ecological theory. By analyzing multiple risk factors of obesity in the communities, may lead to the proposal of more comprehensive and integrated policies and intervention programs to solve the population-based problem.

  15. Clearing muddied waters: Capture of environmental DNA from turbid waters.

    Science.gov (United States)

    Williams, Kelly E; Huyvaert, Kathryn P; Piaggio, Antoinette J

    2017-01-01

    Understanding the differences in efficiencies of various methods to concentrate, extract, and amplify environmental DNA (eDNA) is vital for best performance of eDNA detection. Aquatic systems vary in characteristics such as turbidity, eDNA concentration, and inhibitor load, thus affecting eDNA capture efficiency. Application of eDNA techniques to the detection of terrestrial invasive or endangered species may require sampling at intermittent water sources that are used for drinking and cooling; these water bodies may often be stagnant and turbid. We present our best practices technique for the detection of wild pig eDNA in water samples, a protocol that will have wide applicability to the detection of elusive vertebrate species. We determined the best practice for eDNA capture in a turbid water system was to concentrate DNA from a 15 mL water sample via centrifugation, purify DNA with the DNeasy mericon Food kit, and remove inhibitors with Zymo Inhibitor Removal Technology columns. Further, we compared the sensitivity of conventional PCR to quantitative PCR and found that quantitative PCR was more sensitive in detecting lower concentrations of eDNA. We show significant differences in efficiencies among methods in each step of eDNA capture, emphasizing the importance of optimizing best practices for the system of interest.

  16. Synthesis of DNA

    Science.gov (United States)

    Mariella, Jr., Raymond P.

    2008-11-18

    A method of synthesizing a desired double-stranded DNA of a predetermined length and of a predetermined sequence. Preselected sequence segments that will complete the desired double-stranded DNA are determined. Preselected segment sequences of DNA that will be used to complete the desired double-stranded DNA are provided. The preselected segment sequences of DNA are assembled to produce the desired double-stranded DNA.

  17. Molecular Analysis of Circulating Cell-Free DNA from Lung Cancer Patients in Routine Laboratory Practice: A Cross-Platform Comparison of Three Different Molecular Methods for Mutation Detection.

    Science.gov (United States)

    Bartels, Stephan; Persing, Sascha; Hasemeier, Britta; Schipper, Elisa; Kreipe, Hans; Lehmann, Ulrich

    2017-09-01

    Circulating cell-free DNA (cfDNA), which is isolated from blood plasma, represents a noninvasive source for the detection of mutations conferring resistance against epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors in non-small-cell lung cancer patients. In advanced disease stages, performing regular biopsies is often not possible because of the general health condition of the patients. Furthermore, a biopsy of a single tumor lesion or metastasis may not reflect the heterogeneous genotype of the tumor and its metastases. Plasma cfDNA represents an alternative material for molecular monitoring of patients under therapy. Herein, we present a cross-platform comparison of three different molecular methods [digital PCR, next-generation sequencing (NGS), and quantitative PCR] to detect clinically relevant mutations in cfDNA. We validated our workflow with commercially available cfDNA reference material (5.0%, 1.0%, and 0.1% mutation frequency, respectively). Digital PCR and NGS detect reliably 0.1% allele frequency of the EGFR p.T790M mutation. Furthermore, we analyzed 55 cfDNA preparations from patients with lung cancer to compare reliability and sensitivity of the three methods under routine conditions and achieved 96.0% concordance of p.T790M results. A limit of detection for mutation calling using digital PCR (>0.1%) and NGS (>0.2%) was established. In total, 62.5% of known primary EGFR mutations were successfully detected in cfDNA. In 56.0% of the patients with detectable EGFR primary mutations, we identified a resistance conferring the EGFR p.T790M mutation. Copyright © 2017 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.

  18. Practice changing practices

    DEFF Research Database (Denmark)

    Thomsen, Rikke; Buch, Anders

    2016-01-01

    study about the practices of a study administration unit in a university college in Denmark. The study includes ten weeks of participation observation study and five qualitative interviews, both in the central part and in three local study administrations. Managerial initiated organizational change...... makes work intelligible. This provides possibilities for action and change. We claim that the practice-based studies can become an integrated part of doing critical action research, and we investigate and reflect upon our stewardship of the practices we engaged in....

  19. An SVM-Based Classifier for Estimating the State of Various Rotating Components in Agro-Industrial Machinery with a Vibration Signal Acquired from a Single Point on the Machine Chassis

    Directory of Open Access Journals (Sweden)

    Ruben Ruiz-Gonzalez

    2014-11-01

    Full Text Available The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM-based system is employed. Experimental tests evaluated this system by acquiring vibration data from a single point of an agricultural harvester, while varying several of its working conditions. The whole process included two major steps. Initially, the vibration data were preprocessed through twelve feature extraction algorithms, after which the Exhaustive Search method selected the most suitable features. Secondly, the SVM-based system accuracy was evaluated by using Leave-One-Out cross-validation, with the selected features as the input data. The results of this study provide evidence that (i accurate estimation of the status of various rotating components in agro-industrial machinery is possible by processing the vibration signal acquired from a single point on the machine structure; (ii the vibration signal can be acquired with a uniaxial accelerometer, the orientation of which does not significantly affect the classification accuracy; and, (iii when using an SVM classifier, an 85% mean cross-validation accuracy can be reached, which only requires a maximum of seven features as its input, and no significant improvements are noted between the use of either nonlinear or linear kernels.

  20. Independent component analysis of Alzheimer's DNA microarray gene expression data

    Directory of Open Access Journals (Sweden)

    Vanderburg Charles R

    2009-01-01

    Full Text Available Abstract Background Gene microarray technology is an effective tool to investigate the simultaneous activity of multiple cellular pathways from hundreds to thousands of genes. However, because data in the colossal amounts generated by DNA microarray technology are usually complex, noisy, high-dimensional, and often hindered by low statistical power, their exploitation is difficult. To overcome these problems, two kinds of unsupervised analysis methods for microarray data: principal component analysis (PCA and independent component analysis (ICA have been developed to accomplish the task. PCA projects the data into a new space spanned by the principal components that are mutually orthonormal to each other. The constraint of mutual orthogonality and second-order statistics technique within PCA algorithms, however, may not be applied to the biological systems studied. Extracting and characterizing the most informative features of the biological signals, however, require higher-order statistics. Results ICA is one of the unsupervised algorithms that can extract higher-order statistical structures from data and has been applied to DNA microarray gene expression data analysis. We performed FastICA method on DNA microarray gene expression data from Alzheimer's disease (AD hippocampal tissue samples and consequential gene clustering. Experimental results showed that the ICA method can improve the clustering results of AD samples and identify significant genes. More than 50 significant genes with high expression levels in severe AD were extracted, representing immunity-related protein, metal-related protein, membrane protein, lipoprotein, neuropeptide, cytoskeleton protein, cellular binding protein, and ribosomal protein. Within the aforementioned categories, our method also found 37 significant genes with low expression levels. Moreover, it is worth noting that some oncogenes and phosphorylation-related proteins are expressed in low levels. In

  1. MAPPING OF HIGH VALUE CROPS THROUGH AN OBJECT-BASED SVM MODEL USING LIDAR DATA AND ORTHOPHOTO IN AGUSAN DEL NORTE PHILIPPINES

    Directory of Open Access Journals (Sweden)

    R. J. Candare

    2016-06-01

    Full Text Available This research describes the methods involved in the mapping of different high value crops in Agusan del Norte Philippines using LiDAR. This project is part of the Phil-LiDAR 2 Program which aims to conduct a nationwide resource assessment using LiDAR. Because of the high resolution data involved, the methodology described here utilizes object-based image analysis and the use of optimal features from LiDAR data and Orthophoto. Object-based classification was primarily done by developing rule-sets in eCognition. Several features from the LiDAR data and Orthophotos were used in the development of rule-sets for classification. Generally, classes of objects can't be separated by simple thresholds from different features making it difficult to develop a rule-set. To resolve this problem, the image-objects were subjected to Support Vector Machine learning. SVMs have gained popularity because of their ability to generalize well given a limited number of training samples. However, SVMs also suffer from parameter assignment issues that can significantly affect the classification results. More specifically, the regularization parameter C in linear SVM has to be optimized through cross validation to increase the overall accuracy. After performing the segmentation in eCognition, the optimization procedure as well as the extraction of the equations of the hyper-planes was done in Matlab. The learned hyper-planes separating one class from another in the multi-dimensional feature-space can be thought of as super-features which were then used in developing the classifier rule set in eCognition. In this study, we report an overall classification accuracy of greater than 90% in different areas.

  2. An S-Transform and Support Vector Machine (SVM-Based Online Method for Diagnosing Broken Strands in Transmission Lines

    Directory of Open Access Journals (Sweden)

    Caxin Sun

    2011-08-01

    Full Text Available During their long-term outdoor field service, overhead transmission lines will be exposed to strikes by lightning, corrosion by chemical contaminants, ice-shedding, wind vibration of conductors, line galloping, external destructive forces and so on, which will generally cause a series of latent faults such as aluminum strand fracture. This may lead to broken transmission lines which will have a very strong impact on the safe operation of power grids that if the latent faults cannot be recognized and fixed as soon as possible. The detection of broken strands in transmission lines using inspection robots equipped with suitable detectors is a method with good prospects. In this paper, a method for detecting broken strands in transmission lines using an eddy current transducer (ECT carried by a robot is developed, and an approach for identifying broken strands in transmission lines based on an S-transform is proposed. The proposed approach utilizes the S-transform to extract the module and phase information at each frequency point from detection signals. Through module phase and comparison, the characteristic frequency points are ascertained, and the fault information of the detection signal is constructed. The degree of confidence of broken strand identification is defined by the Shannon fuzzy entropy (SFE-BSICD. The proposed approach combines module information while utilizing phase information, SFE-BSICD, and the energy, so the reliability is greatly improved. These characteristic qualities of broken strands in transmission lines are used as the input of a multi-classification SVM, allowing the number of broken strands to be determined. Through experimental field verification, it can be shown that the proposed approach displays high accuracy and the SFE-BSICD is defined reasonably.

  3. Osmylated DNA, a novel concept for sequencing DNA using nanopores.

    Science.gov (United States)

    Kanavarioti, Anastassia

    2015-03-27

    Saenger sequencing has led the advances in molecular biology, while faster and cheaper next generation technologies are urgently needed. A newer approach exploits nanopores, natural or solid-state, set in an electrical field, and obtains base sequence information from current variations due to the passage of a ssDNA molecule through the pore. A hurdle in this approach is the fact that the four bases are chemically comparable to each other which leads to small differences in current obstruction. 'Base calling' becomes even more challenging because most nanopores sense a short sequence and not individual bases. Perhaps sequencing DNA via nanopores would be more manageable, if only the bases were two, and chemically very different from each other; a sequence of 1s and 0s comes to mind. Osmylated DNA comes close to such a sequence of 1s and 0s. Osmylation is the addition of osmium tetroxide bipyridine across the C5-C6 double bond of the pyrimidines. Osmylation adds almost 400% mass to the reactive base, creates a sterically and electronically notably different molecule, labeled 1, compared to the unreactive purines, labeled 0. If osmylated DNA were successfully sequenced, the result would be a sequence of osmylated pyrimidines (1), and purines (0), and not of the actual nucleobases. To solve this problem we studied the osmylation reaction with short oligos and with M13mp18, a long ssDNA, developed a UV-vis assay to measure extent of osmylation, and designed two protocols. Protocol A uses mild conditions and yields osmylated thymidines (1), while leaving the other three bases (0) practically intact. Protocol B uses harsher conditions and effectively osmylates both pyrimidines, but not the purines. Applying these two protocols also to the complementary of the target polynucleotide yields a total of four osmylated strands that collectively could define the actual base sequence of the target DNA.

  4. [Forensic Validation of the Goldeneye™ DNA ID 25A Kit].

    Science.gov (United States)

    Sun, Yao-dong; Cao, Li-ping

    2015-08-01

    To test and estimate the forensic application of Goldeneye™ DNA ID 25A Kit. The kit was validated by a series of tests for accuracy, sensitivity, consistency, peak height balance, stability, and mixed samples through measured blood samples and other samples in routine casework. The peak height balance of the different loci was ≥ 42%. The genotyping results of the positive control DNA was accurate. The complete STR genotyping result could be obtained from 0.125 ng positive control DNA. Goldeneye™ DNA ID 25A Kit is suitable for criminal cases and DNA database in forensic practice.

  5. ex vivo DNA assembly

    Directory of Open Access Journals (Sweden)

    Adam B Fisher

    2013-10-01

    Full Text Available Even with decreasing DNA synthesis costs there remains a need for inexpensive, rapid and reliable methods for assembling synthetic DNA into larger constructs or combinatorial libraries. Advances in cloning techniques have resulted in powerful in vitro and in vivo assembly of DNA. However, monetary and time costs have limited these approaches. Here, we report an ex vivo DNA assembly method that uses cellular lysates derived from a commonly used laboratory strain of Escherichia coli for joining double-stranded DNA with short end homologies embedded within inexpensive primers. This method concurrently shortens the time and decreases costs associated with current DNA assembly methods.

  6. DNA tagged microparticles

    Science.gov (United States)

    Farquar, George Roy; Leif, Roald N; Wheeler, Elizabeth

    2015-05-05

    A simulant that includes a carrier and DNA encapsulated in the carrier. Also a method of making a simulant including the steps of providing a carrier and encapsulating DNA in the carrier to produce the simulant.

  7. DNA computing models

    CERN Document Server

    Ignatova, Zoya; Zimmermann, Karl-Heinz

    2008-01-01

    In this excellent text, the reader is given a comprehensive introduction to the field of DNA computing. The book emphasizes computational methods to tackle central problems of DNA computing, such as controlling living cells, building patterns, and generating nanomachines.

  8. Modeling DNA Replication.

    Science.gov (United States)

    Bennett, Joan

    1998-01-01

    Recommends the use of a model of DNA made out of Velcro to help students visualize the steps of DNA replication. Includes a materials list, construction directions, and details of the demonstration using the model parts. (DDR)

  9. [Application of DNA labeling technology in forensic botany].

    Science.gov (United States)

    Znang, Xian; Li, Jing-Lin; Zhang, Xiang-Yu

    2008-12-01

    Forensic botany is a study of judicial plant evidence. Recently, researches on DNA labeling technology have been a mainstream of forensic botany. The article systematically reviews various types of DNA labeling techniques in forensic botany with enumerated practical cases, as well as the potential forensic application of each individual technique. The advantages of the DNA labeling technology over traditional morphological taxonomic methods are also summarized.

  10. Replicating animal mitochondrial DNA

    Directory of Open Access Journals (Sweden)

    Emily A. McKinney

    2013-01-01

    Full Text Available The field of mitochondrial DNA (mtDNA replication has been experiencing incredible progress in recent years, and yet little is certain about the mechanism(s used by animal cells to replicate this plasmid-like genome. The long-standing strand-displacement model of mammalian mtDNA replication (for which single-stranded DNA intermediates are a hallmark has been intensively challenged by a new set of data, which suggests that replication proceeds via coupled leading-and lagging-strand synthesis (resembling bacterial genome replication and/or via long stretches of RNA intermediates laid on the mtDNA lagging-strand (the so called RITOLS. The set of proteins required for mtDNA replication is small and includes the catalytic and accessory subunits of DNA polymerase y, the mtDNA helicase Twinkle, the mitochondrial single-stranded DNA-binding protein, and the mitochondrial RNA polymerase (which most likely functions as the mtDNA primase. Mutations in the genes coding for the first three proteins are associated with human diseases and premature aging, justifying the research interest in the genetic, biochemical and structural properties of the mtDNA replication machinery. Here we summarize these properties and discuss the current models of mtDNA replication in animal cells.

  11. DNA damage response

    NARCIS (Netherlands)

    G. Giglia-Mari (Giuseppina); A. Zotter (Angelika); W. Vermeulen (Wim)

    2011-01-01

    textabstractStructural changes to DNA severely affect its functions, such as replication and transcription, and play a major role in age-related diseases and cancer. A complicated and entangled network ofDNA damage response (DDR) mechanisms, including multiple DNA repair pathways, damage tolerance

  12. DNA Repair Systems

    Indian Academy of Sciences (India)

    nal factors such as UV radiation, high energy radiation such as X-. Keywords. DNA repair, DNA damage, base excision repair, nucleotide exci- sion repair, methlyl-directed mis- match repair, Nobel Prize. rays and gamma rays, mutagenic chemicals and viruses. Different types of DNA ... be especially important in plants.

  13. The DNA bank network: the start from a german initiative.

    Science.gov (United States)

    Gemeinholzer, Birgit; Dröge, Gabriele; Zetzsche, Holger; Haszprunar, Gerhard; Klenk, Hans-Peter; Güntsch, Anton; Berendsohn, Walter G; Wägele, Johann-Wolfgang

    2011-03-01

    The explicit aim of the DNA Bank Network is to close the divide between biological specimen collections and molecular sequence databases. It provides a technically optimized DNA and tissue collection service facility in the interest of all biological research, with access to well-documented DNA-containing samples and voucher specimens as well as to corresponding molecular data stored in public sequence databases. The Network enables scientists to (i) query and order DNA samples of organisms collected from natural habitats via a shared Web portal, (ii) store DNA samples for reference under optimal conditions after project completion or data publication, (iii) obtain DNA material to conduct new studies or to extend and complement previous investigations, and (iv) support good scientific practice as the deposition of DNA samples and related specimens facilitates the verification of published results.

  14. Fast phylogenetic DNA barcoding

    DEFF Research Database (Denmark)

    Terkelsen, Kasper Munch; Boomsma, Wouter Krogh; Willerslev, Eske

    2008-01-01

    We present a heuristic approach to the DNA assignment problem based on phylogenetic inferences using constrained neighbour joining and non-parametric bootstrapping. We show that this method performs as well as the more computationally intensive full Bayesian approach in an analysis of 500 insect...... DNA sequences obtained from GenBank. We also analyse a previously published dataset of environmental DNA sequences from soil from New Zealand and Siberia, and use these data to illustrate the fact that statistical approaches to the DNA assignment problem allow for more appropriate criteria...... for determining the taxonomic level at which a particular DNA sequence can be assigned....

  15. DNA: Structure and function

    DEFF Research Database (Denmark)

    Sinden, Richard R.; E. Pearson, Christopher; N. Potaman, Vladimir

    1998-01-01

    This chapter discusses the structure and function of DNA. DNA occupies a critical role in cells, because it is the source of all intrinsic genetic information. Chemically, DNA is a very stable molecule, a characteristic important for a macromolecule that may have to persist in an intact form...... for a long period of time before its information is accessed by the cell. Although DNA plays a critical role as an informational storage molecule, it is by no means as unexciting as a computer tape or disk drive. The structure of the DNA described by Watson and Crick in 1953 is a right handed helix of two...

  16. Archaeal DNA replication.

    Science.gov (United States)

    Kelman, Lori M; Kelman, Zvi

    2014-01-01

    DNA replication is essential for all life forms. Although the process is fundamentally conserved in the three domains of life, bioinformatic, biochemical, structural, and genetic studies have demonstrated that the process and the proteins involved in archaeal DNA replication are more similar to those in eukaryal DNA replication than in bacterial DNA replication, but have some archaeal-specific features. The archaeal replication system, however, is not monolithic, and there are some differences in the replication process between different species. In this review, the current knowledge of the mechanisms governing DNA replication in Archaea is summarized. The general features of the replication process as well as some of the differences are discussed.

  17. DNA structure and function.

    Science.gov (United States)

    Travers, Andrew; Muskhelishvili, Georgi

    2015-06-01

    The proposal of a double-helical structure for DNA over 60 years ago provided an eminently satisfying explanation for the heritability of genetic information. But why is DNA, and not RNA, now the dominant biological information store? We argue that, in addition to its coding function, the ability of DNA, unlike RNA, to adopt a B-DNA structure confers advantages both for information accessibility and for packaging. The information encoded by DNA is both digital - the precise base specifying, for example, amino acid sequences - and analogue. The latter determines the sequence-dependent physicochemical properties of DNA, for example, its stiffness and susceptibility to strand separation. Most importantly, DNA chirality enables the formation of supercoiling under torsional stress. We review recent evidence suggesting that DNA supercoiling, particularly that generated by DNA translocases, is a major driver of gene regulation and patterns of chromosomal gene organization, and in its guise as a promoter of DNA packaging enables DNA to act as an energy store to facilitate the passage of translocating enzymes such as RNA polymerase. © 2015 FEBS.

  18. DNA fragmentation in spermatozoa

    DEFF Research Database (Denmark)

    Rex, A S; Aagaard, J.; Fedder, J

    2017-01-01

    Sperm DNA Fragmentation has been extensively studied for more than a decade. In the 1940s the uniqueness of the spermatozoa protein complex which stabilizes the DNA was discovered. In the fifties and sixties, the association between unstable chromatin structure and subfertility was investigated....... In the seventies, the impact of induced DNA damage was investigated. In the 1980s the concept of sperm DNA fragmentation as related to infertility was introduced as well as the first DNA fragmentation test: the Sperm Chromatin Structure Assay (SCSA). The terminal deoxynucleotidyl transferase nick end labelling...... (TUNEL) test followed by others was introduced in the nineties. The association between DNA fragmentation in spermatozoa and pregnancy loss has been extensively investigated spurring the need for a therapeutic tool for these patients. This gave rise to an increased interest in the aetiology of DNA damage...

  19. Biophysics of DNA

    CERN Document Server

    Vologodskii, Alexander

    2015-01-01

    Surveying the last sixty years of research, this book describes the physical properties of DNA in the context of its biological functioning. It is designed to enable both students and researchers of molecular biology, biochemistry and physics to better understand the biophysics of DNA, addressing key questions and facilitating further research. The chapters integrate theoretical and experimental approaches, emphasising throughout the importance of a quantitative knowledge of physical properties in building and analysing models of DNA functioning. For example, the book shows how the relationship between DNA mechanical properties and the sequence specificity of DNA-protein binding can be analyzed quantitatively by using our current knowledge of the physical and structural properties of DNA. Theoretical models and experimental methods in the field are critically considered to enable the reader to engage effectively with the current scientific literature on the physical properties of DNA.

  20. Effects of DNA mass on multiple displacement whole genome amplification and genotyping performance

    Directory of Open Access Journals (Sweden)

    Haque Kashif A

    2005-09-01

    Full Text Available Abstract Background Whole genome amplification (WGA promises to eliminate practical molecular genetic analysis limitations associated with genomic DNA (gDNA quantity. We evaluated the performance of multiple displacement amplification (MDA WGA using gDNA extracted from lymphoblastoid cell lines (N = 27 with a range of starting gDNA input of 1–200 ng into the WGA reaction. Yield and composition analysis of whole genome amplified DNA (wgaDNA was performed using three DNA quantification methods (OD, PicoGreen® and RT-PCR. Two panels of N = 15 STR (using the AmpFlSTR® Identifiler® panel and N = 49 SNP (TaqMan® genotyping assays were performed on each gDNA and wgaDNA sample in duplicate. gDNA and wgaDNA masses of 1, 4 and 20 ng were used in the SNP assays to evaluate the effects of DNA mass on SNP genotyping assay performance. A total of N = 6,880 STR and N = 56,448 SNP genotype attempts provided adequate power to detect differences in STR and SNP genotyping performance between gDNA and wgaDNA, and among wgaDNA produced from a range of gDNA templates inputs. Results The proportion of double-stranded wgaDNA and human-specific PCR amplifiable wgaDNA increased with increased gDNA input into the WGA reaction. Increased amounts of gDNA input into the WGA reaction improved wgaDNA genotyping performance. Genotype completion or genotype concordance rates of wgaDNA produced from all gDNA input levels were observed to be reduced compared to gDNA, although the reduction was not always statistically significant. Reduced wgaDNA genotyping performance was primarily due to the increased variance of allelic amplification, resulting in loss of heterozygosity or increased undetermined genotypes. MDA WGA produces wgaDNA from no template control samples; such samples exhibited substantial false-positive genotyping rates. Conclusion The amount of gDNA input into the MDA WGA reaction is a critical determinant of genotyping performance of wgaDNA. At least 10 ng of

  1. DNA origami: the art of folding DNA.

    Science.gov (United States)

    Saccà, Barbara; Niemeyer, Christof M

    2012-01-02

    The advent of DNA origami technology greatly simplified the design and construction of nanometer-sized DNA objects. The self-assembly of a DNA-origami structure is a straightforward process in which a long single-stranded scaffold (often from the phage M13mp18) is folded into basically any desired shape with the help of a multitude of short helper strands. This approach enables the ready generation of objects with an addressable surface area of a few thousand nm(2) and with a single "pixel" resolution of about 6 nm. The process is rapid, puts low demands on experimental conditions, and delivers target products in high yields. These features make DNA origami the method of choice in structural DNA nanotechnology when two- and three-dimensional objects are desired. This Minireview summarizes recent advances in the design of DNA origami nanostructures, which open the door to numerous exciting applications. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  2. DNA methylation in plants.

    Science.gov (United States)

    Vanyushin, B F

    2006-01-01

    DNA in plants is highly methylated, containing 5-methylcytosine (m5C) and N6-methyladenine (m6A); m5C is located mainly in symmetrical CG and CNG sequences but it may occur also in other non-symmetrical contexts. m6A but not m5C was found in plant mitochondrial DNA. DNA methylation in plants is species-, tissue-, organelle- and age-specific. It is controlled by phytohormones and changes on seed germination, flowering and under the influence of various pathogens (viral, bacterial, fungal). DNA methylation controls plant growth and development, with particular involvement in regulation of gene expression and DNA replication. DNA replication is accompanied by the appearance of under-methylated, newly formed DNA strands including Okazaki fragments; asymmetry of strand DNA methylation disappears until the end of the cell cycle. A model for regulation of DNA replication by methylation is suggested. Cytosine DNA methylation in plants is more rich and diverse compared with animals. It is carried out by the families of specific enzymes that belong to at least three classes of DNA methyltransferases. Open reading frames (ORF) for adenine DNA methyltransferases are found in plant and animal genomes, and a first eukaryotic (plant) adenine DNA methyltransferase (wadmtase) is described; the enzyme seems to be involved in regulation of the mitochondria replication. Like in animals, DNA methylation in plants is closely associated with histone modifications and it affects binding of specific proteins to DNA and formation of respective transcription complexes in chromatin. The same gene (DRM2) in Arabidopsis thaliana is methylated both at cytosine and adenine residues; thus, at least two different, and probably interdependent, systems of DNA modification are present in plants. Plants seem to have a restriction-modification (R-M) system. RNA-directed DNA methylation has been observed in plants; it involves de novo methylation of almost all cytosine residues in a region of siRNA-DNA

  3. DNA-based materials and their device applications (Conference Presentation)

    Science.gov (United States)

    Rau, Ileana; Kajzar, François; Grote, James G.

    2016-10-01

    In the last decade a lot of interest was paid to DNA materials in view of their practical applications in photonics and in electronics. This aspect is especially due to the fact that this polymer is eco-friendly, originating from renewable resources and can be obtained from any animal or vegetable waste. In this respect many studies have shown that DNA is an intriguing biopolymer which can find applications in many fields. In this paper we will review and discuss the functionalization of DNA and some practical applications.

  4. Combined DNA extraction and antibody elution from filter papers for the assessment of malaria transmission intensity in epidemiological studies

    National Research Council Canada - National Science Library

    Baidjoe, Amrish; Stone, Will; Ploemen, Ivo; Shagari, Shehu; Grignard, Lynn; Osoti, Victor; Makori, Euniah; Stevenson, Jennifer; Kariuki, Simon; Sutherland, Colin; Sauerwein, Robert; Cox, Jonathan; Drakeley, Chris; Bousema, Teun

    2013-01-01

    .... Filter paper blood spots are commonly used a source of both DNA and antibodies. To enhance the operational practicability of malaria surveys, a method is presented for combined DNA extraction and antibody elution...

  5. DNA replication and cancer

    DEFF Research Database (Denmark)

    Boyer, Anne-Sophie; Walter, David; Sørensen, Claus Storgaard

    2016-01-01

    A dividing cell has to duplicate its DNA precisely once during the cell cycle to preserve genome integrity avoiding the accumulation of genetic aberrations that promote diseases such as cancer. A large number of endogenous impacts can challenge DNA replication and cells harbor a battery of pathways...... to promote genome integrity during DNA replication. This includes suppressing new replication origin firing, stabilization of replicating forks, and the safe restart of forks to prevent any loss of genetic information. Here, we describe mechanisms by which oncogenes can interfere with DNA replication thereby...... causing DNA replication stress and genome instability. Further, we describe cellular and systemic responses to these insults with a focus on DNA replication restart pathways. Finally, we discuss the therapeutic potential of exploiting intrinsic replicative stress in cancer cells for targeted therapy....

  6. Eukaryotic DNA Replication Fork.

    Science.gov (United States)

    Burgers, Peter M J; Kunkel, Thomas A

    2017-06-20

    This review focuses on the biogenesis and composition of the eukaryotic DNA replication fork, with an emphasis on the enzymes that synthesize DNA and repair discontinuities on the lagging strand of the replication fork. Physical and genetic methodologies aimed at understanding these processes are discussed. The preponderance of evidence supports a model in which DNA polymerase ε (Pol ε) carries out the bulk of leading strand DNA synthesis at an undisturbed replication fork. DNA polymerases α and δ carry out the initiation of Okazaki fragment synthesis and its elongation and maturation, respectively. This review also discusses alternative proposals, including cellular processes during which alternative forks may be utilized, and new biochemical studies with purified proteins that are aimed at reconstituting leading and lagging strand DNA synthesis separately and as an integrated replication fork.

  7. MITOCHONDRIAL DNA- REVOLUTIONARY EVOLUTION

    Directory of Open Access Journals (Sweden)

    Vaidhehi Narayan Nayak

    2017-07-01

    Full Text Available BACKGROUND Mitochondrion, the sausage-shaped organelle residing in the cytoplasm of all eukaryotic cells, apart from being the power house, represents endosymbiotic evolution of a free living organism to intracellular structure. Anthropologically, mitochondrial DNA is the fossilised source to trace the human ancestry particularly of maternal lineage. This article attempts to highlight the various biological functions of mitochondrial DNA (mtDNA with a note on its forensic application.

  8. [Theory? Practice?].

    Science.gov (United States)

    Medina Moya, José Luis

    2005-04-01

    This second article about the difficult balance between academic and professional development in pre-European Spain poses the issue of the theory-practice, or learning-treatment, relationship as the historical dissolution of the practical in the theoretical.

  9. PDNAsite: Identification of DNA-binding Site from Protein Sequence by Incorporating Spatial and Sequence Context.

    Science.gov (United States)

    Zhou, Jiyun; Xu, Ruifeng; He, Yulan; Lu, Qin; Wang, Hongpeng; Kong, Bing

    2016-06-10

    Protein-DNA interactions are involved in many fundamental biological processes essential for cellular function. Most of the existing computational approaches employed only the sequence context of the target residue for its prediction. In the present study, for each target residue, we applied both the spatial context and the sequence context to construct the feature space. Subsequently, Latent Semantic Analysis (LSA) was applied to remove the redundancies in the feature space. Finally, a predictor (PDNAsite) was developed through the integration of the support vector machines (SVM) classifier and ensemble learning. Results on the PDNA-62 and the PDNA-224 datasets demonstrate that features extracted from spatial context provide more information than those from sequence context and the combination of them gives more performance gain. An analysis of the number of binding sites in the spatial context of the target site indicates that the interactions between binding sites next to each other are important for protein-DNA recognition and their binding ability. The comparison between our proposed PDNAsite method and the existing methods indicate that PDNAsite outperforms most of the existing methods and is a useful tool for DNA-binding site identification. A web-server of our predictor (http://hlt.hitsz.edu.cn:8080/PDNAsite/) is made available for free public accessible to the biological research community.

  10. DNA ELECTROPHORESIS AT SURFACES

    Energy Technology Data Exchange (ETDEWEB)

    RAFAILOVICH, MIRIAM; SOKOLOV, JONATHAN; GERSAPPE, DILIP

    2003-09-01

    During this year we performed two major projects: I. We developed a detailed theoretical model which complements our experiments on surface DNA electrophoresis. We found that it was possible to enhance the separation of DNA chains by imposing a chemical nanoscale pattern on the surface. This approach utilized the surface interaction effect of the DNA chains with the substrate and is a refinement to our previous method in which DNA chains were separated on homogeneous flat surfaces. By introducing the nano-patterns on the surface, the conformational changes of DNA chains of different lengths can be amplified, which results in the different friction strengths with the substrate surface. Our results also show that, when compared to the DNA electrophoresis performed on homogeneous flat surfaces, nanopatterned surfaces offer a larger window in choosing different surface interactions to achieve separation. II. In collaboration with a large international manufacturer of skin care products we also embarked on a project involving photo toxicity of titanium dioxide nanoparticles, which are a key ingredient in sunscreen and cosmetic lotions. The results clearly implicated the nanoparticles in catalyzing damage to chromosomal DNA. We then used this knowledge to develop a polymer/anti-oxidant coating which prevented the photocatalytic reaction on DNA while still retaining the UV absorptive properties of the nanoparticles. The standard gel electrophoresis was not sufficient in determining the extent of the DNA damage. The conclusions of this study were based predominantly on analysis obtained with the surface electrophoresis method.

  11. DNA origami nanopores.

    Science.gov (United States)

    Bell, Nicholas A W; Engst, Christian R; Ablay, Marc; Divitini, Giorgio; Ducati, Caterina; Liedl, Tim; Keyser, Ulrich F

    2012-01-11

    We demonstrate the assembly of functional hybrid nanopores for single molecule sensing by inserting DNA origami structures into solid-state nanopores. In our experiments, single artificial nanopores based on DNA origami are repeatedly inserted in and ejected from solid-state nanopores with diameters around 15 nm. We show that these hybrid nanopores can be employed for the detection of λ-DNA molecules. Our approach paves the way for future development of adaptable single-molecule nanopore sensors based on the combination of solid-state nanopores and DNA self-assembly. © 2011 American Chemical Society

  12. DNA transformation via local heat shock

    Science.gov (United States)

    Li, Sha; Meadow Anderson, L.; Yang, Jui-Ming; Lin, Liwei; Yang, Haw

    2007-07-01

    This work describes transformation of foreign DNA into bacterial host cells by local heat shock using a microfluidic system with on-chip, built-in platinum heaters. Plasmid DNA encoding ampicillin resistance and a fluorescent protein can be effectively transformed into the DH5α chemically competent E. coli using this device. Results further demonstrate that only one-thousandth of volume is required to obtain transformation efficiencies as good as or better than conventional practices. As such, this work complements other lab-on-a-chip technologies for potential gene cloning/therapy and protein expression applications.

  13. Clinical Practice

    DEFF Research Database (Denmark)

    Contarino, Maria Fiorella; Van Den Dool, Joost; Balash, Yacov

    2017-01-01

    issues still remain open in the clinical practice. We performed a systematic review of the literature on botulinum toxin treatment for CD based on a question-oriented approach, with the aim to provide practical recommendations for the treating clinicians. Key questions from the clinical practice were...... of anticoagulants, neurological comorbidities) should also be further explored....

  14. Monitoring endangered freshwater biodiversity using environmental DNA.

    Science.gov (United States)

    Thomsen, Philip Francis; Kielgast, Jos; Iversen, Lars L; Wiuf, Carsten; Rasmussen, Morten; Gilbert, M Thomas P; Orlando, Ludovic; Willerslev, Eske

    2012-06-01

    Freshwater ecosystems are among the most endangered habitats on Earth, with thousands of animal species known to be threatened or already extinct. Reliable monitoring of threatened organisms is crucial for data-driven conservation actions but remains a challenge owing to nonstandardized methods that depend on practical and taxonomic expertise, which is rapidly declining. Here, we show that a diversity of rare and threatened freshwater animals--representing amphibians, fish, mammals, insects and crustaceans--can be detected and quantified based on DNA obtained directly from small water samples of lakes, ponds and streams. We successfully validate our findings in a controlled mesocosm experiment and show that DNA becomes undetectable within 2 weeks after removal of animals, indicating that DNA traces are near contemporary with presence of the species. We further demonstrate that entire faunas of amphibians and fish can be detected by high-throughput sequencing of DNA extracted from pond water. Our findings underpin the ubiquitous nature of DNA traces in the environment and establish environmental DNA as a tool for monitoring rare and threatened species across a wide range of taxonomic groups. © 2011 Blackwell Publishing Ltd.

  15. Interdisciplinary Practices in iSchools

    DEFF Research Database (Denmark)

    Madsen, Dorte; Ho, Shuyan Mary

    2014-01-01

    Interdisciplinarity is in the DNA of the iSchools. This workshop invites you to discuss how inter-disciplinarity plays out in theory and practice. The workshop addresses the uniqueness of the iSchools, provides an interactive framework to discuss and reflect on interdisciplinary practice. It sugg...

  16. Probing DNA-DNA electrostatic friction in tight superhelical DNA plies.

    Science.gov (United States)

    Cherstvy, A G

    2009-04-23

    We estimate theoretically the strength of DNA-DNA electrostatic friction forces emerging upon a slow drag of one DNA over another one in a close juxtaposition. For ideally helical DNA duplexes, this friction occurs due to correlations in electrostatic potential near the DNA surface. The latter originate from the intrinsic helicity of DNA phosphates and adsorbed cations on a scale of 3.4 nm. They produce positive-negative charge interlocking along the DNA-DNA contact. For realistic nonideally helical DNAs, where electrostatic potential barriers become decorrelated due to accumulation of mismatches in DNA structure, DNA-DNA frictional forces are strongly impeded. We discuss possibilities of probing the DNA-DNA intermolecular interactions in strongly confined DNA superhelical plies, as obtained in single-molecule experiments.

  17. nDNA-Prot: identification of DNA-binding proteins based on unbalanced classification.

    Science.gov (United States)

    Song, Li; Li, Dapeng; Zeng, Xiangxiang; Wu, Yunfeng; Guo, Li; Zou, Quan

    2014-09-08

    DNA-binding proteins are vital for the study of cellular processes. In recent genome engineering studies, the identification of proteins with certain functions has become increasingly important and needs to be performed rapidly and efficiently. In previous years, several approaches have been developed to improve the identification of DNA-binding proteins. However, the currently available resources are insufficient to accurately identify these proteins. Because of this, the previous research has been limited by the relatively unbalanced accuracy rate and the low identification success of the current methods. In this paper, we explored the practicality of modelling DNA binding identification and simultaneously employed an ensemble classifier, and a new predictor (nDNA-Prot) was designed. The presented framework is comprised of two stages: a 188-dimension feature extraction method to obtain the protein structure and an ensemble classifier designated as imDC. Experiments using different datasets showed that our method is more successful than the traditional methods in identifying DNA-binding proteins. The identification was conducted using a feature that selected the minimum Redundancy and Maximum Relevance (mRMR). An accuracy rate of 95.80% and an Area Under the Curve (AUC) value of 0.986 were obtained in a cross validation. A test dataset was tested in our method and resulted in an 86% accuracy, versus a 76% using iDNA-Prot and a 68% accuracy using DNA-Prot. Our method can help to accurately identify DNA-binding proteins, and the web server is accessible at http://datamining.xmu.edu.cn/~songli/nDNA. In addition, we also predicted possible DNA-binding protein sequences in all of the sequences from the UniProtKB/Swiss-Prot database.

  18. DNA barcoding of Cryptosporidium.

    Science.gov (United States)

    Šlapeta, Jan

    2017-11-08

    Cryptosporidium spp. (Apicomplexa) causing cryptosporidiosis are of medical and veterinary significance. The genus Cryptosporidium has benefited from the application of what is considered a DNA-barcoding approach, even before the term 'DNA barcoding' was formally coined. Here, the objective to define the DNA barcode diversity of Cryptosporidium infecting mammals is reviewed and considered to be accomplished. Within the Cryptosporidium literature, the distinction between DNA barcoding and DNA taxonomy is indistinct. DNA barcoding and DNA taxonomy are examined using the latest additions to the growing spectrum of named Cryptosporidium species and within-species and between-species identity is revisited. Ease and availability of whole-genome DNA sequencing of the relatively small Cryptosporidium genome offer an initial perspective on the intra-host diversity. The opportunity emerges to apply a metagenomic approach to purified field/clinical Cryptosporidum isolates. The outstanding question remains a reliable definition of Cryptosporidium phenotype. The complementary experimental infections and metagenome approach will need to be applied simultaneously to address Cryptosporidium phenotype with carefully chosen clinical evaluations enabling identification of virulence factors.

  19. Actinomycin and DNA transcription.

    OpenAIRE

    Sobell, H M

    1985-01-01

    Recent advances in understanding how actinomycin binds to DNA have suggested its mechanism of action. Actinomycin binds to a premelted DNA conformation present within the transcriptional complex. This immobilizes the complex, interfering with the elongation of growing RNA chains. The model has a number of implications for understanding RNA synthesis.

  20. Actinomycin and DNA transcription.

    Science.gov (United States)

    Sobell, H M

    1985-01-01

    Recent advances in understanding how actinomycin binds to DNA have suggested its mechanism of action. Actinomycin binds to a premelted DNA conformation present within the transcriptional complex. This immobilizes the complex, interfering with the elongation of growing RNA chains. The model has a number of implications for understanding RNA synthesis. Images PMID:2410919

  1. Characterization of muntjac DNA

    Energy Technology Data Exchange (ETDEWEB)

    Davis, R.C.

    1981-05-27

    Sister chromatid exchange (SCE) in muntjac chromosomes is generally proportional to the chromosomal DNA content, but the SCE frequency is reduced in the heterochromatic neck region of the X chromosome. The physical properties of muntjac DNA and the kinetics of repair of UV damage in muntjac heterochromatin and euchromatin were examined and compared with the distribution of sister chromatid exchange.

  2. Whose DNA is this?

    DEFF Research Database (Denmark)

    Taroni, Franco; Biedermann, Alex; Vuille, Joëlle

    2013-01-01

    evoked during the international conference "The hidden side of DNA profiles. Artifacts, errors and uncertain evidence" held in Rome (April 27th to 28th, 2012). Indeed, despite the fact that this conference brought together some of the world's leading forensic DNA specialists, it appeared clearly...

  3. Extended DNA Tile Actuators

    DEFF Research Database (Denmark)

    Kristiansen, Martin; Kryger, Mille; Zhang, Zhao

    2012-01-01

    A dynamic linear DNA tile actuator is expanded to three new structures of higher complexity. The original DNA actuator was constructed from a central roller strand which hybridizes with two piston strands by forming two half-crossover junctions. A linear expansion of the actuator is obtained...

  4. Actinomycin and DNA transcription

    Energy Technology Data Exchange (ETDEWEB)

    Sobell, H.M.

    1985-08-01

    Recent advances in understanding how actinomycin binds to DNA have suggested its mechanism of action. Actinomycin binds to a premelted DNA conformation present within the transcriptional complex. This immobilizes the complex, interfering with the elongation of growing RNA chains. The model has a number of implications for understanding RNA synthesis.

  5. DNA-cell conjugates

    Science.gov (United States)

    Hsiao, Shih-Chia; Francis, Matthew B.; Bertozzi, Carolyn; Mathies, Richard; Chandra, Ravi; Douglas, Erik; Twite, Amy; Toriello, Nicholas; Onoe, Hiroaki

    2016-05-03

    The present invention provides conjugates of DNA and cells by linking the DNA to a native functional group on the cell surface. The cells can be without cell walls or can have cell walls. The modified cells can be linked to a substrate surface and used in assay or bioreactors.

  6. [Microsatellite DNA analysis as a tool for forensic paternity testing (DNA paternity testing)].

    Science.gov (United States)

    Veselinović, Igor

    2006-01-01

    MICROSATELLITE ANALYSIS: By using serological or HLA-testing, the alleged father can be excluded as the biological father, but, regardless of the degree of probability, positive paternity results cannot be obtained without DNA testing. According to the results of the National Human Genome Project, human genome consists of approximately 30.000 genes. The vast majority of human DNA is not organized in genes and has no genetic expression or visible function. Non-coding DNA contains genetic markers important for human identification. Short tandem repeats, or STRs, are a class of microsatellites consisting of tandemly repeated sequences of 2 to 6 base pair length monomers. Most of the microsatellites show a high degree of polymorphism, which can be evaluated by PCR technique, and used in criminalistics, forensic identification and parentage testing. A source of DNA in parentage testing are blood samples or buccal swabs which are routinelly used. Amplification of isolated DNA can be performed in 25-30 cycles by PCR, and fragments are separated by capillary electrophoresis. The probability of paternity of 99.99% or higher corresponds to the paternity "practically proven", indicating that the alleged father is the biological father. Such results can be obtained only by DNA testing. DNA-testing laboratories are required to conduct validation of laboratory facilities, equipment and staff and are subject to permanent control by the society.

  7. Premeltons in DNA.

    Science.gov (United States)

    Sobell, Henry M

    2016-03-01

    Premeltons are examples of emergent-structures (i.e., structural-solitons) that arise spontaneously in DNA due to the presence of nonlinear-excitations in its structure. They are of two kinds: B-B (or A-A) premeltons form at specific DNA-regions to nucleate site-specific DNA melting. These are stationary and, being globally-nontopological, undergo breather-motions that allow drugs and dyes to intercalate into DNA. B-A (or A-B) premeltons, on the other hand, are mobile, and being globally-topological, act as phase-boundaries transforming B- into A-DNA during the structural phase-transition. They are not expected to undergo breather motions. A key feature of both types of premeltons is the presence of an intermediate structural-form in their central regions (proposed as being a transition-state intermediate in DNA-melting and in the B- to A-transition), which differs from either A- or B-DNA. Called beta-DNA, this is both metastable and hyperflexible--and contains an alternating sugar-puckering pattern along the polymer backbone combined with the partial unstacking (in its lower energy-forms) of every-other base-pair. Beta-DNA is connected to either B- or to A-DNA on either side by boundaries possessing a gradation of nonlinear structural-change, these being called the kink and the antikink regions. The presence of premeltons in DNA leads to a unifying theory to understand much of DNA physical chemistry and molecular biology. In particular, premeltons are predicted to define the 5' and 3' ends of genes in naked-DNA and DNA in active-chromatin, this having important implications for understanding physical aspects of the initiation, elongation and termination of RNA-synthesis during transcription. For these and other reasons, the model will be of broader interest to the general-audience working in these areas. The model explains a wide variety of data, and carries with it a number of experimental predictions--all readily testable--as will be described in this review.

  8. DNA repair protocols

    DEFF Research Database (Denmark)

    Bjergbæk, Lotte

    In its 3rd edition, this Methods in Molecular Biology(TM) book covers the eukaryotic response to genomic insult including advanced protocols and standard techniques in the field of DNA repair. Offers expert guidance for DNA repair, recombination, and replication. Current knowledge of the mechanisms...... recent advanced protocols as well as standard techniques used in the field of DNA repair. Both mammalian and non-mammalian model organisms are covered in the book, and many of the techniques can be applied with only minor modifications to other systems than the one described. Written in the highly...... that regulate DNA repair has grown significantly over the past years with technology advances such as RNA interference, advanced proteomics and microscopy as well as high throughput screens. The third edition of DNA Repair Protocols covers various aspects of the eukaryotic response to genomic insult including...

  9. Advances in DNA photonics

    Science.gov (United States)

    Heckman, Emily M.; Aga, Roberto S.; Fehrman Cory, Emily M.; Ouchen, Fahima; Lesko, Alyssa; Telek, Brian; Lombardi, Jack; Bartsch, Carrie M.; Grote, James G.

    2012-10-01

    In this paper we present our current research in exploring a DNA biopolymer for photonics applications. A new processing technique has been adopted that employs a modified soxhlet-dialysis (SD) rinsing technique to completely remove excess ionic contaminants from the DNA biopolymer, resulting in a material with greater mechanical stability and enhanced performance reproducibility. This newly processed material has been shown to be an excellent material for cladding layers in poled polymer electro-optic (EO) waveguide modulator applications. Thin film poling results are reported for materials using the DNA biopolymer as a cladding layer, as are results for beam steering devices also using the DNA biopolymer. Finally, progress on fabrication of a Mach Zehnder EO modulator with DNA biopolymer claddings using nanoimprint lithography techniques is reported.

  10. DNA repair protocols

    DEFF Research Database (Denmark)

    Bjergbæk, Lotte

    In its 3rd edition, this Methods in Molecular Biology(TM) book covers the eukaryotic response to genomic insult including advanced protocols and standard techniques in the field of DNA repair. Offers expert guidance for DNA repair, recombination, and replication. Current knowledge of the mechanisms...... that regulate DNA repair has grown significantly over the past years with technology advances such as RNA interference, advanced proteomics and microscopy as well as high throughput screens. The third edition of DNA Repair Protocols covers various aspects of the eukaryotic response to genomic insult including...... recent advanced protocols as well as standard techniques used in the field of DNA repair. Both mammalian and non-mammalian model organisms are covered in the book, and many of the techniques can be applied with only minor modifications to other systems than the one described. Written in the highly...

  11. DNA repair protocols

    DEFF Research Database (Denmark)

    Bjergbæk, Lotte

    that regulate DNA repair has grown significantly over the past years with technology advances such as RNA interference, advanced proteomics and microscopy as well as high throughput screens. The third edition of DNA Repair Protocols covers various aspects of the eukaryotic response to genomic insult including......In its 3rd edition, this Methods in Molecular Biology(TM) book covers the eukaryotic response to genomic insult including advanced protocols and standard techniques in the field of DNA repair. Offers expert guidance for DNA repair, recombination, and replication. Current knowledge of the mechanisms...... recent advanced protocols as well as standard techniques used in the field of DNA repair. Both mammalian and non-mammalian model organisms are covered in the book, and many of the techniques can be applied with only minor modifications to other systems than the one described. Written in the highly...

  12. Practical capillary electrophoresis

    CERN Document Server

    Weinberger, Robert

    2000-01-01

    In the 1980s, capillary electrophoresis (CE) joined high-performance liquid chromatography (HPLC) as the most powerful separation technique available to analytical chemists and biochemists. Published research using CE grew from 48 papers in the year of commercial introduction (1988) to 1200 in 1997. While only a dozen major pharmaceutical and biotech companies have reduced CE to routine practice, the applications market is showing real or potential growth in key areas, particularly in the DNA marketplace for genomic mapping and forensic identification. For drug development involving small molecules (including chiral separations), one CE instrument can replace 10 liquid chromatographs in terms of speed of analysis. CE also uses aqueous rather than organic solvents and is thus environmentally friendlier than HPLC. The second edition of Practical Capillary Electrophoresis has been extensively reorganized and rewritten to reflect modern usage in the field, with an emphasis on commercially available apparatus and ...

  13. Fidelity of DNA polymerases in DNA amplification

    Energy Technology Data Exchange (ETDEWEB)

    Keohavong, P.; Thilly, W.G. (Massachusetts Institute of Technology, Cambridge (USA))

    1989-12-01

    Denaturing gradient gel electrophoresis (DGGE) was used to separate and isolate the products of DNA amplification by polymerase chain reaction (PCR). The strategy permitted direct enumeration and identification of point mutations created by T4, modified T7, Klenow fragment of polymerase I, and Thermus aquaticus (Tag) DNA polymerases. Incorrectly synthesized sequences were separated from the wild type by DGGE as mutant/wild-type heteroduplexes and the heteroduplex fraction was used to calculate the average error rate (mutations per base duplication). The error rate induced in the 104-base-pair low-temperature melting domain of exon 3 of the human hypoxanthine/guanine phosphoribosyltransferase (HPRT) gene was {approx} 3.4 {times} 10{sup {minus}5} for modified T7, 1.3 {times} 10{sup {minus}4} for Klenow fragment, and 2.1 {times} 10{sup {minus}4} for Taq polymerases after a 10{sup 6}-fold amplification. The error rate for T4 DNA polymerase was not more than 3 {times} 10{sup {minus}6} error per base duplication. The predominant mutations were sequenced and found to be transitions of G{center dot}C to A{center dot}T for T4 and modified T7 DNA polymerases, and A{center dot}T to G{center dot}C for Taq polymerase. Klenow fragment induced both possible transitions and deletions of 2 and 4 base pairs.

  14. DNA markers in plant improvement: an overview.

    Science.gov (United States)

    Kumar, L S

    1999-09-01

    The progress made in DNA marker technology has been tremendous and exciting. DNA markers have provided valuable tools in various analyses ranging from phylogenetic analysis to the positional cloning of genes. The development of high-density molecular maps which has been facilitated by PCR-based markers, have made the mapping and tagging of almost any trait possible. Marker-assisted selection has the potential to deploy favorable gene combinations for disease control. Comparative studies between incompatible species using these markers has resulted in synteny maps which are useful not only in predicting genome organization and evolution but also have practical application in plant breeding. DNA marker technology has found application in fingerprinting genotypes, in determining seed purity, in systematic sampling of germplasm, and in phylogenetic analysis. This review discusses the use of this technology for the genetic improvement of plants.

  15. DNA mini-barcodes.

    Science.gov (United States)

    Hajibabaei, Mehrdad; McKenna, Charly

    2012-01-01

    Conventional DNA barcoding uses an approximately 650 bp DNA barcode of the mitochondrial gene COI for species identification in animal groups. Similar size fragments from chloroplast genes have been proposed as barcode markers for plants. While PCR amplification and sequencing of a 650 bp fragment is consistent in freshly collected and well-preserved specimens, it is difficult to obtain a full-length barcode in older museum specimens and samples which have been preserved in formalin or similar DNA-unfriendly preservatives. A comparable issue may prevent effective DNA-based authentication and testing in processed biological materials, such as food products, pharmaceuticals, and nutraceuticals. In these cases, shorter DNA sequences-mini-barcodes-have been robustly recovered and shown to be effective in identifying majority of specimens to a species level. Furthermore, short DNA regions can be utilized via high-throughput sequencing platforms providing an inexpensive and comprehensive means of large-scale species identification. These properties of mini-barcodes, coupled with the availability of standardized and universal primers make mini-barcodes a feasible option for DNA barcode analysis in museum samples and applied diagnostic and environmental biodiversity analysis.

  16. Quantitive DNA Fiber Mapping

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Chun-Mei; Wang, Mei; Greulich-Bode, Karin M.; Weier, Jingly F.; Weier, Heinz-Ulli G.

    2008-01-28

    Several hybridization-based methods used to delineate single copy or repeated DNA sequences in larger genomic intervals take advantage of the increased resolution and sensitivity of free chromatin, i.e., chromatin released from interphase cell nuclei. Quantitative DNA fiber mapping (QDFM) differs from the majority of these methods in that it applies FISH to purified, clonal DNA molecules which have been bound with at least one end to a solid substrate. The DNA molecules are then stretched by the action of a receding meniscus at the water-air interface resulting in DNA molecules stretched homogeneously to about 2.3 kb/{micro}m. When non-isotopically, multicolor-labeled probes are hybridized to these stretched DNA fibers, their respective binding sites are visualized in the fluorescence microscope, their relative distance can be measured and converted into kilobase pairs (kb). The QDFM technique has found useful applications ranging from the detection and delineation of deletions or overlap between linked clones to the construction of high-resolution physical maps to studies of stalled DNA replication and transcription.

  17. HIV DNA Integration

    Science.gov (United States)

    Craigie, Robert; Bushman, Frederic D.

    2012-01-01

    Retroviruses are distinguished from other viruses by two characteristic steps in the viral replication cycle. The first is reverse transcription, which results in the production of a double-stranded DNA copy of the viral RNA genome, and the second is integration, which results in covalent attachment of the DNA copy to host cell DNA. The initial catalytic steps of the integration reaction are performed by the virus-encoded integrase (IN) protein. The chemistry of the IN-mediated DNA breaking and joining steps is well worked out, and structures of IN-DNA complexes have now clarified how the overall complex assembles. Methods developed during these studies were adapted for identification of IN inhibitors, which received FDA approval for use in patients in 2007. At the chromosomal level, HIV integration is strongly favored in active transcription units, which may promote efficient viral gene expression after integration. HIV IN binds to the cellular factor LEDGF/p75, which promotes efficient infection and tethers IN to favored target sites. The HIV integration machinery must also interact with many additional host factors during infection, including nuclear trafficking and pore proteins during nuclear entry, histones during initial target capture, and DNA repair proteins during completion of the DNA joining steps. Models for some of the molecular mechanisms involved have been proposed, but important details remain to be clarified. PMID:22762018

  18. DNA Import into Mitochondria.

    Science.gov (United States)

    Konstantinov, Yu M; Dietrich, A; Weber-Lotfi, F; Ibrahim, N; Klimenko, E S; Tarasenko, V I; Bolotova, T A; Koulintchenko, M V

    2016-10-01

    In recent decades, it has become evident that the condition for normal functioning of mitochondria in higher eukaryotes is the presence of membrane transport systems of macromolecules (proteins and nucleic acids). Natural competence of the mitochondria in plants, animals, and yeasts to actively uptake DNA may be directly related to horizontal gene transfer into these organelles occurring at much higher rate compared to the nuclear and chloroplast genomes. However, in contrast with import of proteins and tRNAs, little is known about the biological role and molecular mechanism underlying import of DNA into eukaryotic mitochondria. In this review, we discuss current state of investigations in this area, particularly specificity of DNA import into mitochondria and its features in plants, animals, and yeasts; a tentative mechanism of DNA import across the mitochondrial outer and inner membranes; experimental data evidencing several existing, but not yet fully understood mechanisms of DNA transfer into mitochondria. Currently available data regarding transport of informational macromolecules (DNA, RNA, and proteins) into the mitochondria do not rule out that the mechanism of protein and tRNA import as well as tRNA and DNA import into the mitochondria may partially overlap.

  19. Regulating DNA Self-assembly by DNA-Surface Interactions.

    Science.gov (United States)

    Liu, Longfei; Li, Yulin; Wang, Yong; Zheng, Jianwei; Mao, Chengde

    2017-12-14

    DNA self-assembly provides a powerful approach for preparation of nanostructures. It is often studied in bulk solution and involves only DNA-DNA interactions. When confined to surfaces, DNA-surface interactions become an additional, important factor to DNA self-assembly. However, the way in which DNA-surface interactions influence DNA self-assembly is not well studied. In this study, we showed that weak DNA-DNA interactions could be stabilized by DNA-surface interactions to allow large DNA nanostructures to form. In addition, the assembly can be conducted isothermally at room temperature in as little as 5 seconds. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  20. DNA Bending elasticity

    Science.gov (United States)

    Sivak, David Alexander

    DNA bending elasticity on length scales of tens of basepairs is of critical importance in numerous biological contexts. Even the simplest models of DNA bending admit of few simple analytic results, thus there is a need for numerical methods to calculate experimental observables, such as distance distributions, forces, FRET efficiencies, and timescales of particular large-scale motions. We have implemented and helped develop a coarse-grained representation of DNA and various other covalently-linked groups that allows simple calculation of such observables for varied experimental systems. The simple freely-jointed chain (FJC) model and extremely coarse resolution proved useful in understanding DNA threading through nanopores, identifying steric occlusion by other parts of the chain as a prime culprit for slower capture as distance to the pore decreased. Enhanced sampling techniques of a finer resolution discrete wormlike chain (WLC) model permitted calculation of cyclization rates for small chains and identified the ramifications of a thermodynamically-sound treatment of thermal melts. Adding treatment of double-stranded DNA's helical nature and single-stranded DNA provided a model system that helped demonstrate the importance of statistical fluctuations in even highly-stressed DNA mini-loops, and allowed us to verify that even these constructs show no evidence of excitation-induced softening. Additional incorporation of salt-sensitivity to the model allowed us to calculate forces and FRET efficiencies for such mini-loops and their uncircularized precursors, thereby furthering the understanding of the nature of IHF binding and bending of its recognition sequence. Adding large volume-excluding spheres linked to the ends of the dsDNA permits calculation of distance distributions and thus small-angle X-ray scattering, whereby we demonstrated the validity of the WLC in describing bending fluctuations in DNA chains as short as 42 bp. We also make important connections

  1. "Artifactual" arsenate DNA

    DEFF Research Database (Denmark)

    Nielsen, Peter E

    2012-01-01

    The recent claim by Wolfe-Simon et al. that the Halomonas bacterial strain GFAJ-1 when grown in arsenate-containing medium with limiting phosphate is able to substitute phosphate with arsenate in biomolecules including nucleic acids and in particular DNA(1) arose much skepticism, primarily due...... to the very limited chemical stability of arsenate esters (see ref. 2 and references therein). A major part of the criticisms was concerned with the insufficient (bio)chemical evidence in the Wolfe-Simon study for the actual chemical incorporation of arsenate in DNA (and/or RNA). Redfield et al. now present...... evidence that the identification of arsenate DNA was artifactual....

  2. Apoptosis and DNA Methylation

    Directory of Open Access Journals (Sweden)

    Richard R. Meehan

    2011-04-01

    Full Text Available Epigenetic mechanisms assist in maintaining gene expression patterns and cellular properties in developing and adult tissues. The molecular pathology of disease states frequently includes perturbation of DNA and histone methylation patterns, which can activate apoptotic pathways associated with maintenance of genome integrity. This perspective focuses on the pathways linking DNA methyltransferases and methyl-CpG binding proteins to apoptosis, and includes new bioinformatic analyses to characterize the evolutionary origin of two G/T mismatch-specific thymine DNA glycosylases, MBD4 and TDG.

  3. The changing epitome of species identification - DNA barcoding.

    Science.gov (United States)

    Ajmal Ali, M; Gyulai, Gábor; Hidvégi, Norbert; Kerti, Balázs; Al Hemaid, Fahad M A; Pandey, Arun K; Lee, Joongku

    2014-07-01

    The discipline taxonomy (the science of naming and classifying organisms, the original bioinformatics and a basis for all biology) is fundamentally important in ensuring the quality of life of future human generation on the earth; yet over the past few decades, the teaching and research funding in taxonomy have declined because of its classical way of practice which lead the discipline many a times to a subject of opinion, and this ultimately gave birth to several problems and challenges, and therefore the taxonomist became an endangered race in the era of genomics. Now taxonomy suddenly became fashionable again due to revolutionary approaches in taxonomy called DNA barcoding (a novel technology to provide rapid, accurate, and automated species identifications using short orthologous DNA sequences). In DNA barcoding, complete data set can be obtained from a single specimen irrespective to morphological or life stage characters. The core idea of DNA barcoding is based on the fact that the highly conserved stretches of DNA, either coding or non coding regions, vary at very minor degree during the evolution within the species. Sequences suggested to be useful in DNA barcoding include cytoplasmic mitochondrial DNA (e.g. cox1) and chloroplast DNA (e.g. rbcL, trnL-F, matK, ndhF, and atpB rbcL), and nuclear DNA (ITS, and house keeping genes e.g. gapdh). The plant DNA barcoding is now transitioning the epitome of species identification; and thus, ultimately helping in the molecularization of taxonomy, a need of the hour. The 'DNA barcodes' show promise in providing a practical, standardized, species-level identification tool that can be used for biodiversity assessment, life history and ecological studies, forensic analysis, and many more.

  4. The changing epitome of species identification – DNA barcoding

    Science.gov (United States)

    Ajmal Ali, M.; Gyulai, Gábor; Hidvégi, Norbert; Kerti, Balázs; Al Hemaid, Fahad M.A.; Pandey, Arun K.; Lee, Joongku

    2014-01-01

    The discipline taxonomy (the science of naming and classifying organisms, the original bioinformatics and a basis for all biology) is fundamentally important in ensuring the quality of life of future human generation on the earth; yet over the past few decades, the teaching and research funding in taxonomy have declined because of its classical way of practice which lead the discipline many a times to a subject of opinion, and this ultimately gave birth to several problems and challenges, and therefore the taxonomist became an endangered race in the era of genomics. Now taxonomy suddenly became fashionable again due to revolutionary approaches in taxonomy called DNA barcoding (a novel technology to provide rapid, accurate, and automated species identifications using short orthologous DNA sequences). In DNA barcoding, complete data set can be obtained from a single specimen irrespective to morphological or life stage characters. The core idea of DNA barcoding is based on the fact that the highly conserved stretches of DNA, either coding or non coding regions, vary at very minor degree during the evolution within the species. Sequences suggested to be useful in DNA barcoding include cytoplasmic mitochondrial DNA (e.g. cox1) and chloroplast DNA (e.g. rbcL, trnL-F, matK, ndhF, and atpB rbcL), and nuclear DNA (ITS, and house keeping genes e.g. gapdh). The plant DNA barcoding is now transitioning the epitome of species identification; and thus, ultimately helping in the molecularization of taxonomy, a need of the hour. The ‘DNA barcodes’ show promise in providing a practical, standardized, species-level identification tool that can be used for biodiversity assessment, life history and ecological studies, forensic analysis, and many more. PMID:24955007

  5. Best practices

    NARCIS (Netherlands)

    Baar, J.; Roestel, van A.J.J.

    2004-01-01

    In het convenant gewasbescherming wordt het zichtbaar maken van 'best practices' benoemd als één van de acties om meer innovatie een een beter management te krijgen. Het ministerie van LNV gaf in april 2003 het Praktijkonderzoek Plant en Omgeving (PPO) de opdracht om deze 'best practices' van

  6. Practicing Humanities

    DEFF Research Database (Denmark)

    Gimmler, Antje

    2016-01-01

    and self-reflective democracy. Contemporary humanities have adopted a new orientation towards practices, and it is not clear how this fits with the ideals of ‘Bildung’ and ‘pure science’. A possible theoretical framework for this orientation towards practices could be found in John Dewey’s pragmatic...

  7. Ophthalmic practice

    Directory of Open Access Journals (Sweden)

    Sue Stevens

    2005-03-01

    Full Text Available Eye health workers carry out many basic routine procedures. Sometimes bad practice develops and this, in turn, may lead to new members of staff learning unsafe methods. Community Eye Health Journal plans to run a series on practical procedures, when applicable, relating to the theme.

  8. Services of DNA barcoding in different fields.

    Science.gov (United States)

    Muhammad Tahir, Hafiz; Akhtar, Samreen

    2016-11-01

    DNA barcoding is a new master key for species identification and has greatly accelerated the pace of species discovery. In this novel and cost-effective technique, a short DNA sequence from a standard region of mitochondrial "CO1" gene called "barcode" is used. At present, researchers all over the world are utilizing this powerful tool for investigating biodiversity, differentiating cryptic species, testing food authenticity, identifying parasites, vectors, insect pests, and predators, monitoring of illegal trade of animals and their products, and identifying forensically important insects. In addition, this technique can potentially be used to monitor quality of drinking water, quickly identify the indicator species of lakes, rivers, and streams, identify species with harmful attributes or medicinal properties, monitor smuggling of endangered plants and animals and their products, and disease investigations. Despite non-favorable criticism from a few researchers, DNA barcoding has achieved immense popularity in the scientific community, especially among biologists. The present review provides an overview of DNA barcoding and its practical applications. The limitation, future prospective and main informative platforms for DNA barcoding have also been discussed.

  9. Archaeal DNA polymerases in biotechnology.

    Science.gov (United States)

    Zhang, Likui; Kang, Manyu; Xu, Jiajun; Huang, Yanchao

    2015-08-01

    DNA polymerase (pol) is a ubiquitous enzyme that synthesizes DNA strands in all living cells. In vitro, DNA pol is used for DNA manipulation, including cloning, PCR, site-directed mutagenesis, sequencing, and several other applications. Family B archaeal DNA pols have been widely used for molecular biological methods. Biochemical and structural studies reveal that each archaeal DNA pol has different characteristics with respect to fidelity, processivity and thermostability. Due to their high fidelity and strong thermostability, family B archaeal DNA pols have the extensive application on high-fidelity PCR, DNA sequencing, and site-directed mutagenesis while family Y archaeal DNA pols have the potential for error-prone PCR and random mutagenesis because of their low fidelity and strong thermostability. This information combined with mutational analysis has been used to construct novel DNA pols with altered properties that enhance their use as biotechnological reagents. In this review, we focus on the development and use of family B archaeal DNA pols.

  10. Close encounters with DNA

    Science.gov (United States)

    Maffeo, C.; Yoo, J.; Comer, J.; Wells, D. B.; Luan, B.; Aksimentiev, A.

    2014-01-01

    Over the past ten years, the all-atom molecular dynamics method has grown in the scale of both systems and processes amenable to it and in its ability to make quantitative predictions about the behavior of experimental systems. The field of computational DNA research is no exception, witnessing a dramatic increase in the size of systems simulated with atomic resolution, the duration of individual simulations and the realism of the simulation outcomes. In this topical review, we describe the hallmark physical properties of DNA from the perspective of all-atom simulations. We demonstrate the amazing ability of such simulations to reveal the microscopic physical origins of experimentally observed phenomena and we review the frustrating limitations associated with imperfections of present atomic force fields and inadequate sampling. The review is focused on the following four physical properties of DNA: effective electric charge, response to an external mechanical force, interaction with other DNA molecules and behavior in an external electric field. PMID:25238560

  11. Kink solitons in DNA

    CERN Document Server

    Zdravković, S; Daniel, M

    2012-01-01

    We here examine the nonlinear dynamics of artificial homogeneous DNA chain relying on the plain-base rotator model. It is shown that such dynamics can exhibit kink and antikink solitons of sine-Gordon type. In that respect we propose possible experimental assays based on single molecule micromanipulation techniques. The aim of these experiments is to excite the rotational waves and to determine their speeds along excited DNA. We propose that these experiments should be conducted either for the case of double stranded (DS) or single stranded (SS) DNA. A key question is to compare the corresponding velocities of the rotational waves indicating which one is bigger. The ratio of these velocities appears to be related with the sign of the model parameter representing ratio of the hydrogen-bonding and the covalent-bonding interaction within the considered DNA chain.

  12. DNA Microarray Technology

    Science.gov (United States)

    Skip to main content DNA Microarray Technology Enter Search Term(s): Español Research Funding An Overview Bioinformatics Current Grants Education and Training Funding Extramural Research News Features Funding Divisions Funding ...

  13. Retroviral DNA Integration

    Science.gov (United States)

    2016-01-01

    The integration of a DNA copy of the viral RNA genome into host chromatin is the defining step of retroviral replication. This enzymatic process is catalyzed by the virus-encoded integrase protein, which is conserved among retroviruses and LTR-retrotransposons. Retroviral integration proceeds via two integrase activities: 3′-processing of the viral DNA ends, followed by the strand transfer of the processed ends into host cell chromosomal DNA. Herein we review the molecular mechanism of retroviral DNA integration, with an emphasis on reaction chemistries and architectures of the nucleoprotein complexes involved. We additionally discuss the latest advances on anti-integrase drug development for the treatment of AIDS and the utility of integrating retroviral vectors in gene therapy applications. PMID:27198982

  14. DNA sequencing conference, 2

    Energy Technology Data Exchange (ETDEWEB)

    Cook-Deegan, R.M. [Georgetown Univ., Kennedy Inst. of Ethics, Washington, DC (United States); Venter, J.C. [National Inst. of Neurological Disorders and Strokes, Bethesda, MD (United States); Gilbert, W. [Harvard Univ., Cambridge, MA (United States); Mulligan, J. [Stanford Univ., CA (United States); Mansfield, B.K. [Oak Ridge National Lab., TN (United States)

    1991-06-19

    This conference focused on DNA sequencing, genetic linkage mapping, physical mapping, informatics and bioethics. Several were used to study this sequencing and mapping. This article also discusses computer hardware and software aiding in the mapping of genes.

  15. HPV DNA test

    Science.gov (United States)

    ... test; Cancer of cervix - HPV DNA test References Hacker NF. Cervical dysplasia and cancer. In: Hacker NF, Gambone JC, Hobel CJ, eds. Hacker and Moore's Essentials of Obstetrics and Gynecology . 6th ...

  16. Making DNA Fingerprints.

    Science.gov (United States)

    Nunley, Kathie F.

    1996-01-01

    Presents an activity to simulate electrophoresis using everyday items. Uses adding machine paper to construct a set of DNA fingerprints that can be used to solve crime cases designed by students in any biology class. (JRH)

  17. Ratchet Nanofiltration of DNA

    Science.gov (United States)

    Thomas, Joel D. P.; Joswiak, Mark N.; Olson, Daniel W.; Park, Sung-Gyu

    2013-01-01

    The DNA nanofilter is a microfabricated electrophoretic separation device consisting of a periodic array of thin slits (circa 60 nm) separated by deeper wells (circa 320 nm). We demonstrate that this device can act as a tuneable, clog-free filter when operating in a low frequency, asymmetric field inversion mode. This filtration occurs by using the asymmetric field inversion to achieve bi-directional migration of short (less than 1000 bp) DNA. Moreover, similar ratchet-type operation can improve separations when compared to a constant field separation in the same device. These modes of operation enhance the utility of the DNA nanofilter as a component of integrated lab-on-a-chip devices. The experimental data confirm theoretical predictions for the bidirectional transport of DNA in entropy-based separations. PMID:23896739

  18. DNA-Origami

    DEFF Research Database (Denmark)

    Voigt, Niels Vinther; Tørring, Thomas; Gothelf, Kurt Vesterager

    2010-01-01

    DNA-nanostrukturer giver nye muligheder for studier af individuelle molekyler. Ved at udnytte DNAs unikke selvsamlende egenskaber kan man designe systemer, hvorpå der kan studeres kemiske reaktioner, fluoroforer og biiomolekyler på enkeltmolekyle-niveau....

  19. Supercoiling DNA Locates Mismatches

    Science.gov (United States)

    Dittmore, Andrew; Brahmachari, Sumitabha; Takagi, Yasuharu; Marko, John F.; Neuman, Keir C.

    2017-10-01

    We present a method of detecting sequence defects by supercoiling DNA with magnetic tweezers. The method is sensitive to a single mismatched base pair in a DNA sequence of several thousand base pairs. We systematically compare DNA molecules with 0 to 16 adjacent mismatches at 1 M monovalent salt and 3.6 pN force and show that under these conditions, a single plectoneme forms and is stably pinned at the defect. We use these measurements to estimate the energy and degree of end-loop kinking at defects. From this, we calculate the relative probability of plectoneme pinning at the mismatch under physiologically relevant conditions. Based on this estimate, we propose that DNA supercoiling could contribute to mismatch and damage sensing in vivo.

  20. DNA Sampling Hook

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The DNA Sampling Hook is a significant improvement on a method of obtaining a tissue sample from a live fish in situ from an aquatic environment. A tissue sample...

  1. Gomphid DNA sequence data

    Data.gov (United States)

    U.S. Environmental Protection Agency — DNA sequence data for several genetic loci. This dataset is not publicly accessible because: It's already publicly available on GenBank. It can be accessed through...

  2. DNA from keratinous tissue

    DEFF Research Database (Denmark)

    Bengtsson, Camilla F.; Olsen, Maja E.; Brandt, Luise Ørsted

    2011-01-01

    Keratinous tissues such as nail, hair, horn, scales and feather have been used as a source of DNA for over 20 years. Particular benefits of such tissues include the ease with which they can be sampled, the relative stability of DNA in such tissues once sampled, and, in the context of ancient...... genetic analyses, the fact that sampling generally causes minimal visual damage to valuable specimens. Even when freshly sampled, however, the DNA quantity and quality in the fully keratinized parts of such tissues is extremely poor in comparison to other tissues such as blood and muscle – although little...... systematic research has been undertaken to characterize how such degradation may relate to sample source. In this review paper we present the current understanding of the quality and limitations of DNA in two key keratinous tissues, nail and hair. The findings indicate that although some fragments of nuclear...

  3. Patterning nanocrystals using DNA

    Energy Technology Data Exchange (ETDEWEB)

    Williams, Shara Carol [Univ. of California, Berkeley, CA (United States)

    2003-01-01

    One of the goals of nanotechnology is to enable programmed self-assembly of patterns made of various materials with nanometer-sized control. This dissertation describes the results of experiments templating arrangements of gold and semiconductor nanocrystals using 2'-deoxyribonucleic acid (DNA). Previously, simple DNA-templated linear arrangements of two and three nanocrystals structures have been made.[1] Here, we have sought to assemble larger and more complex nanostructures. Gold-DNA conjugates with 50 to 100 bases self-assembled into planned arrangements using strands of DNA containing complementary base sequences. We used two methods to increase the complexity of the arrangements: using branched synthetic doublers within the DNA covalent backbone to create discrete nanocrystal groupings, and incorporating the nanocrystals into a previously developed DNA lattice structure [2][3] that self-assembles from tiles made of DNA double-crossover molecules to create ordered nanoparticle arrays. In the first project, the introduction of a covalently-branched synthetic doubler reagent into the backbone of DNA strands created a branched DNA ''trimer.'' This DNA trimer templated various structures that contained groupings of three and four gold nanoparticles, giving promising, but inconclusive transmission electron microscopy (TEM) results. Due to the presence of a variety of possible structures in the reaction mixtures, and due to the difficulty of isolating the desired structures, the TEM and gel electrophoresis results for larger structures having four particles, and for structures containing both 5 and 10 nm gold nanoparticles were inconclusive. Better results may come from using optical detection methods, or from improved sample preparation. In the second project, we worked toward making two-dimensional ordered arrays of nanocrystals. We replicated and improved upon previous results for making DNA lattices, increasing the size of the lattices

  4. Alternative Okazaki Fragment Ligation Pathway by DNA Ligase III

    Directory of Open Access Journals (Sweden)

    Hiroshi Arakawa

    2015-06-01

    -ligase to a universal DNA ligase, which can potentially substitute or backup the repair and replication functions of all other DNA ligases in the cell nucleus. Thus, the old model of functionally dedicated DNA ligases is now replaced by one in which only Lig4 remains dedicated to C-NHEJ, with Lig1 and Lig3 showing an astounding functional flexibility and interchangeability for practically all nuclear ligation functions. The underlying mechanisms of Lig3 versus Lig1 utilization in DNA repair and replication are expected to be partly different and remain to be elucidated.

  5. Das DNA-Puzzle

    Science.gov (United States)

    Kirchner, Stefan

    Im Jahre 1953 wurde von James Watson und Francis Crick erstmalig der strukturelle Aufbau der sogenannten DNA (Desoxyribonukleinsäure) beschrieben, welche das Erbgut jedes Lebewesens enthält. Der wesentliche Teil des Erbguts wird dabei durch eine sehr lange Folge der vier Basen Adenin (A), Cytosin (C), Guanin (G) und Thymin (T) codiert. Seit einigen Jahren ist es möglich, die Folge der vier Basen zu einer gegebenen DNA zu bestimmen. Biologen bezeichnen diesen Vorgang als Sequenzierung.

  6. PDA: Pooled DNA analyzer

    Directory of Open Access Journals (Sweden)

    Lin Chin-Yu

    2006-04-01

    Full Text Available Abstract Background Association mapping using abundant single nucleotide polymorphisms is a powerful tool for identifying disease susceptibility genes for complex traits and exploring possible genetic diversity. Genotyping large numbers of SNPs individually is performed routinely but is cost prohibitive for large-scale genetic studies. DNA pooling is a reliable and cost-saving alternative genotyping method. However, no software has been developed for complete pooled-DNA analyses, including data standardization, allele frequency estimation, and single/multipoint DNA pooling association tests. This motivated the development of the software, 'PDA' (Pooled DNA Analyzer, to analyze pooled DNA data. Results We develop the software, PDA, for the analysis of pooled-DNA data. PDA is originally implemented with the MATLAB® language, but it can also be executed on a Windows system without installing the MATLAB®. PDA provides estimates of the coefficient of preferential amplification and allele frequency. PDA considers an extended single-point association test, which can compare allele frequencies between two DNA pools constructed under different experimental conditions. Moreover, PDA also provides novel chromosome-wide multipoint association tests based on p-value combinations and a sliding-window concept. This new multipoint testing procedure overcomes a computational bottleneck of conventional haplotype-oriented multipoint methods in DNA pooling analyses and can handle data sets having a large pool size and/or large numbers of polymorphic markers. All of the PDA functions are illustrated in the four bona fide examples. Conclusion PDA is simple to operate and does not require that users have a strong statistical background. The software is available at http://www.ibms.sinica.edu.tw/%7Ecsjfann/first%20flow/pda.htm.

  7. Celebrating DNA's Repair Crew.

    Science.gov (United States)

    Kunkel, Thomas A

    2015-12-03

    This year, the Nobel Prize in Chemistry has been awarded to Tomas Lindahl, Aziz Sancar, and Paul Modrich for their seminal studies of the mechanisms by which cells from bacteria to man repair DNA damage that is generated by normal cellular metabolism and stress from the environment. These studies beautifully illustrate the remarkable power of DNA repair to influence life from evolution through disease susceptibility. Copyright © 2015 Elsevier Inc. All rights reserved.

  8. Application of DNA-based methods in forensic entomology.

    Science.gov (United States)

    Wells, Jeffrey D; Stevens, Jamie R

    2008-01-01

    A forensic entomological investigation can benefit from a variety of widely practiced molecular genotyping methods. The most commonly used is DNA-based specimen identification. Other applications include the identification of insect gut contents and the characterization of the population genetic structure of a forensically important insect species. The proper application of these procedures demands that the analyst be technically expert. However, one must also be aware of the extensive list of standards and expectations that many legal systems have developed for forensic DNA analysis. We summarize the DNA techniques that are currently used in, or have been proposed for, forensic entomology and review established genetic analyses from other scientific fields that address questions similar to those in forensic entomology. We describe how accepted standards for forensic DNA practice and method validation are likely to apply to insect evidence used in a death or other forensic entomological investigation.

  9. Variations in brain DNA

    Directory of Open Access Journals (Sweden)

    Jesus eAvila

    2014-11-01

    Full Text Available It is assumed that DNA sequences are conserved in the diverse cell types present in a multicellular organism like the human being. Thus, in order to compare the sequences in the genome of DNA from different individuals, nucleic acid is commonly isolated from a single tissue. In this regard, blood cells are widely used for this purpose because of their availability. Thus blood DNA has been used to study genetic familiar diseases that affect other tissues and organs, such as the liver, heart, and brain. While this approach is valid for the identification of familial diseases in which mutations are present in parental germinal cells and, therefore, in all the cells of a given organism, it is not suitable to identify sporadic diseases in which mutations might occur in specific somatic cells. This review addresses somatic DNA variations in different tissues or cells (mainly in the brain of single individuals and discusses whether the dogma of DNA invariance between cell types is indeed correct. We will also discuss how single nucleotide somatic variations arise, focusing on the presence of specific DNA mutations in the brain.

  10. Intravesicle Isothermal DNA Replication

    Directory of Open Access Journals (Sweden)

    Ross Lindsey A

    2011-04-01

    Full Text Available Abstract Background Bacterial and viral DNA replication was previously reconstituted in vitro from component parts 1234. Significant advances in building minimal cell-like structures also have been made recently 567. Combining the two approaches would further attempts to build a minimal cell-like structure capable of undergoing evolution by combining membrane encapsulation and genome replication. Towards this end, we attempted to use purified genomic replication protein components from thermophilic bacterial sources to copy strands of DNA isothermally within lipid vesicles. Findings Bacterial replication components (such as helicases and DNA polymerases are compatible with methods for the generation of lipid vesicles. Encapsulation inside phospholipid vesicles does not inhibit the activity of bacterial DNA genome replication machinery. Further the described system is efficient at isothermally amplifying short segments of DNA within phospholipid vesicles. Conclusions Herein we show that bacterial isothermal DNA replication machinery is functional inside of phospholipid vesicles, suggesting that replicating cellular mimics can be built from purified bacterial components.

  11. DNA Polymerase Gamma in Mitochondrial DNA Replication and Repair

    Directory of Open Access Journals (Sweden)

    William C. Copeland

    2003-01-01

    Full Text Available Mutations in mitochondrial DNA (mtDNA are associated with aging, and they can cause tissue degeneration and neuromuscular pathologies known as mitochondrial diseases. Because DNA polymerase γ (pol γ is the enzyme responsible for replication and repair of mitochondrial DNA, the burden of faithful duplication of mitochondrial DNA, both in preventing spontaneous errors and in DNA repair synthesis, falls on pol γ. Investigating the biological functions of pol γ and its inhibitors aids our understanding of the sources of mtDNA mutations. In animal cells, pol γ is composed of two subunits, a larger catalytic subunit of 125–140 kDa and second subunit of 35–55 kDa. The catalytic subunit contains DNA polymerase activity, 3’-5’ exonuclease activity, and a 5’-dRP lyase activity. The accessory subunit is required for highly processive DNA synthesis and increases the affinity of pol gamma to the DNA.

  12. Ribonucleotide triggered DNA damage and RNA-DNA damage responses.

    Science.gov (United States)

    Wallace, Bret D; Williams, R Scott

    2014-01-01

    Research indicates that the transient contamination of DNA with ribonucleotides exceeds all other known types of DNA damage combined. The consequences of ribose incorporation into DNA, and the identity of protein factors operating in this RNA-DNA realm to protect genomic integrity from RNA-triggered events are emerging. Left unrepaired, the presence of ribonucleotides in genomic DNA impacts cellular proliferation and is associated with chromosome instability, gross chromosomal rearrangements, mutagenesis, and production of previously unrecognized forms of ribonucleotide-triggered DNA damage. Here, we highlight recent findings on the nature and structure of DNA damage arising from ribonucleotides in DNA, and the identification of cellular factors acting in an RNA-DNA damage response (RDDR) to counter RNA-triggered DNA damage.

  13. Practical theories

    DEFF Research Database (Denmark)

    Jensen, Klaus Bruhn

    2016-01-01

    This article revisits the place of normative and other practical issues in the wider conceptual architecture of communication theory, building on the tradition of philosophical pragmatism. The article first characterizes everyday concepts of communication as the accumulated outcome of natural...... evolution and history: practical resources for human existence and social coexistence. Such practical concepts have served as the point of departure for diverse theoretical conceptions of what communication is. The second part of the article highlights the past neglect and current potential of normative...

  14. Supramolecular Complexes of DNA

    Science.gov (United States)

    Zuber, G.; Scherman, D.

    Deoxyribose nucleic acid or DNA is a linear polymer in the form of a double strand, synthesised by sequential polymerisation of a large number of units chosen from among the nucleic bases called purines (adenosine A and guanosine G) and pyrimidines (cytosine C and thymidine T). DNA contains all the genetic information required for life. It exists in the form of a limited number (a few dozen) of very big molecules, called chromosomes. This genetic information is first of all transcribed. In this process, a restricted fragment of the DNA called a gene is copied in the form of ribonucleic acid, or RNA. This RNA is itself a polymer, but with a single strand in which the sequence of nucleic acids is schematically analogous to the sequence on one of the two strands of the transcribed DNA. Finally, this RNA is translated into a protein, yet another linear polymer. The proteins make up the main part of the active constituents ensuring the survival of the cell. Any loss of information, either by mutation or by deletion of the DNA, will cause an imbalance in the cell's metabolism that may in turn lead to incurable pathologies. Several strategies have been developed to reduce the consequences of such genetic deficiencies or, more generally, to act, by amplifying or suppressing them, on the mechanisms leading from the reading of the genetic information to the production of proteins: Strategies aiming to introduce synthetic DNA or RNA, which selectively block the expression of certain genes, are now being studied by an increasing number of research scientists and pharmacologists. They use antisense oligodeoxyribonucleotides or interfering oligoribonucleotides and they already have clinical applications. This kind of therapy is often called gene pharmacology. Other, more ambitious strategies aim to repair in situ mutated or incomplete DNA within the chromosomes themselves, by introducing short sequences of DNA or RNA which recognise and take the place of mutations. This is the

  15. The room temperature preservation of filtered environmental DNA samples and assimilation into a phenol–chloroform–isoamyl alcohol DNA extraction

    Science.gov (United States)

    Renshaw, Mark A; Olds, Brett P; Jerde, Christopher L; McVeigh, Margaret M; Lodge, David M

    2015-01-01

    Current research targeting filtered macrobial environmental DNA (eDNA) often relies upon cold ambient temperatures at various stages, including the transport of water samples from the field to the laboratory and the storage of water and/or filtered samples in the laboratory. This poses practical limitations for field collections in locations where refrigeration and frozen storage is difficult or where samples must be transported long distances for further processing and screening. This study demonstrates the successful preservation of eDNA at room temperature (20 °C) in two lysis buffers, CTAB and Longmire's, over a 2-week period of time. Moreover, the preserved eDNA samples were seamlessly integrated into a phenol–chloroform–isoamyl alcohol (PCI) DNA extraction protocol. The successful application of the eDNA extraction to multiple filter membrane types suggests the methods evaluated here may be broadly applied in future eDNA research. Our results also suggest that for many kinds of studies recently reported on macrobial eDNA, detection probabilities could have been increased, and at a lower cost, by utilizing the Longmire's preservation buffer with a PCI DNA extraction. PMID:24834966

  16. The room temperature preservation of filtered environmental DNA samples and assimilation into a phenol-chloroform-isoamyl alcohol DNA extraction.

    Science.gov (United States)

    Renshaw, Mark A; Olds, Brett P; Jerde, Christopher L; McVeigh, Margaret M; Lodge, David M

    2015-01-01

    Current research targeting filtered macrobial environmental DNA (eDNA) often relies upon cold ambient temperatures at various stages, including the transport of water samples from the field to the laboratory and the storage of water and/or filtered samples in the laboratory. This poses practical limitations for field collections in locations where refrigeration and frozen storage is difficult or where samples must be transported long distances for further processing and screening. This study demonstrates the successful preservation of eDNA at room temperature (20 °C) in two lysis buffers, CTAB and Longmire's, over a 2-week period of time. Moreover, the preserved eDNA samples were seamlessly integrated into a phenol-chloroform-isoamyl alcohol (PCI) DNA extraction protocol. The successful application of the eDNA extraction to multiple filter membrane types suggests the methods evaluated here may be broadly applied in future eDNA research. Our results also suggest that for many kinds of studies recently reported on macrobial eDNA, detection probabilities could have been increased, and at a lower cost, by utilizing the Longmire's preservation buffer with a PCI DNA extraction. © 2014 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

  17. [From fingerprints to DNA tags].

    Science.gov (United States)

    Sajantila, Antti

    2010-01-01

    Decisions concerning individuals are made based on the DNA fingerprinting technique, e.g. in the courts of law. Currently applied DNA markers and the technique associated with their analysis have changed significantly from those of the original DNA fingerprint. The digital nature of today's DNA tag determination has enabled the application of powerful DNA registers in crime investigations. Application of DNA tags has expanded from paternity, crime and cadaver identification to the prediction of external features such as hair or eye color in criminal investigation. In the early 1990's pioneering work on PCR-based DNA identification was carried out in Finland.

  18. Practicing induction:

    DEFF Research Database (Denmark)

    Sprogøe, Jonas; Rohde, Nicolas

    2009-01-01

    We claim that induction potentially triggers both individual and organizational learning and by drawing on practice-based theory we discuss how the interplay between individual and organization, what we call a generative dance, ignites both kinds of learning....

  19. Social Practices

    DEFF Research Database (Denmark)

    Schmidt, Kirsten

    2013-01-01

    The present understanding of LCM as a product management system supported by a number of tools and methods does not pay attention to the importance of social practices that the employees develop in relation to the systematic approach. A new conceptual model of LCM including the social practices...... is presented and discussed from theoretical and empirical perspectives. Theoretically, the analyses cover the formalized structures related to the division of labor and the coordination of the tasks on the one hand, and the social practices as meanings, values and priorities on the other hand. A larger Danish...... company serves as case for the empirical analyses of the formalized structures and their interaction with the social practices developed by the employees over time....

  20. Advising Practices.

    Science.gov (United States)

    Kopenhaver, Lillian Lodge

    1981-01-01

    Suggests that those who work with collegiate student publications must understand and practice freedom of the press on their campuses. Provides evidence for this argument from a survey of college publications advisers. (RL)

  1. Modelling Practice

    DEFF Research Database (Denmark)

    Cameron, Ian; Gani, Rafiqul

    2011-01-01

    requirements; model construction; model solution; model verification; model validation and finally model deployment and maintenance. Within the adopted methodology, each step is discussedthrough the consideration of key issues and questions relevant to the modelling activity. Practical advice, based on many...

  2. Best practices

    National Research Council Canada - National Science Library

    Zaring, David

    2006-01-01

    ..., touching every aspect of administrative law. This paper describes and evaluates best practices rulemaking, tracking its origins in business management, its adoption by the public sector, and analyzing how it works in that sector...

  3. Cyclization of short DNA fragments

    Science.gov (United States)

    Lam, Pui-Man; Zhen, Yi

    2017-09-01

    From the per unit length free energy for DNA under tension, we have calculated an effective contour length dependent persistence length for short DNA. This effective persistence length results from the enhanced fluctuations in short DNA. It decreases for shorter DNA, making shorter DNA more flexible. The results of the J-factor calculated using this effective persistence length are in good agreement with experimental data.

  4. Local chromatin microenvironment determines DNMT activity : from DNA methyltransferase to DNA demethylase or DNA dehydroxymethylase

    NARCIS (Netherlands)

    van der Wijst, Monique G. P.; Venkiteswaran, Muralidhar; Chen, Hui; Xu, Guo-Liang; Plosch, Torsten; Rots, Marianne G.

    2015-01-01

    Insights on active DNA demethylation disproved the original assumption that DNA methylation is a stable epigenetic modification. Interestingly, mammalian DNA methyltransferases 3A and 3B (DNMT-3A and -3B) have also been reported to induce active DNA demethylation, in addition to their well-known

  5. Characterization of denaturation and renaturation of DNA for DNA hybridization.

    Science.gov (United States)

    Wang, Xiaofang; Lim, Hyun Jeong; Son, Ahjeong

    2014-01-01

    The present study was designed to systematically characterize the denaturation and the renaturation of double stranded DNA (dsDNA), which is suitable for DNA hybridization. A series of physical and chemical denaturation methods were implemented on well-defined 86-bp dsDNA fragment. The degree of each denaturation was measured and the most suitable denaturation method was determined. DNA renaturation tendency was also investigated for the suggested denaturation method. Heating, beads mill, and sonication bath did not show any denaturation for 30 minutes. However probe sonication fully denatured DNA in 5 minutes. 1 mol/L sodium hydroxide (alkaline treatment) and 60% dimethyl sulfoxide (DMSO) treatment fully denatured DNA in 2-5 minutes. Among all the physical methods applied, the direct probe sonication was the most effective way to denature the DNA fragments. Among chemical methods, 60% DMSO was the most adequate denaturation method since it does not cause full renaturation during DNA hybridization.

  6. [Bacteriophage DNA reptation].

    Science.gov (United States)

    Gabashvili, I S; Grosberg, A Iu

    1991-01-01

    The kinetics of reptation process of dsDNA leaving the phage head is analysed theoretically. It is assumed that the process is caused by DNA free energy decrease when it is leaving the head (DNA has to be in a globular state) for its surroundings where it is transformed into a coil state. For the analysis we have used the results of previous paper on equilibrium theory of DNA intraphage globule. Three possible cases for the ejection process friction are considered: friction in the tail-part channel, that of DNA segments with each other in the whole globule volume (it is essential for the collective way of the globule decondensation with simultaneous movement of all the loops--the first type way), the globule friction with internal capsid surface (it is most essential for the decondensation by the way of the globule rotation as a whole "spool"--the second type way). The first way would correspond to the greatest ejection time. The known experimental data on distinguishing ejection kinetics for phages with short and long tail-parts allow us to formulate arguments in favor of realization of the second way in nature.

  7. Eukaryotic DNA Replicases

    KAUST Repository

    Zaher, Manal S.

    2014-11-21

    The current model of the eukaryotic DNA replication fork includes three replicative DNA polymerases, polymerase α/primase complex (Pol α), polymerase δ (Pol δ), and polymerase ε (Pol ε). The primase synthesizes 8–12 nucleotide RNA primers that are extended by the DNA polymerization activity of Pol α into 30–35 nucleotide RNA-DNA primers. Replication factor C (RFC) opens the polymerase clamp-like processivity factor, proliferating cell nuclear antigen (PCNA), and loads it onto the primer-template. Pol δ utilizes PCNA to mediate highly processive DNA synthesis, while Pol ε has intrinsic high processivity that is modestly stimulated by PCNA. Pol ε replicates the leading strand and Pol δ replicates the lagging strand in a division of labor that is not strict. The three polymerases are comprised of multiple subunits and share unifying features in their large catalytic and B subunits. The remaining subunits are evolutionarily not related and perform diverse functions. The catalytic subunits are members of family B, which are distinguished by their larger sizes due to inserts in their N- and C-terminal regions. The sizes of these inserts vary among the three polymerases, and their functions remain largely unknown. Strikingly, the quaternary structures of Pol α, Pol δ, and Pol ε are arranged similarly. The catalytic subunits adopt a globular structure that is linked via its conserved C-terminal region to the B subunit. The remaining subunits are linked to the catalytic and B subunits in a highly flexible manner.

  8. Pathways of DNA Demethylation.

    Science.gov (United States)

    Dean, Wendy

    2016-01-01

    The regulation of the genome relies on the epigenome to instruct, define and restrict the activities of growth and development. Among the cohort of epigenetic instructions, DNA methylation is perhaps the best understood. In most mammals, cycles of the addition and removal of DNA methylation constitute phases of reprogramming when the developing embryo must negotiate lineage defining and developmental commitment events. In these instances, the DNA methylation instruction is often removed, thereby allowing a change in permission for future development and a return to a more plastic and pluripotent state. Because of this, the germ line, upon demethylation, can give rise to gametes that are fully functional across generations and poised for totipotency. This return to a less differentiated state can also be achieved experimentally. The loss of DNA methylation constitutes one of the significant barriers to induced pluripotency and is a prerequisite for the generation of iPS cells. Taking fully differentiated cells, such as skin cells, and turning back the developmental clock heralded a technological breakthrough discovery in 2006 (Takahashi and Yamanaka 2006) with unprecedented promise in regenerative medicine. In this chapter, the mechanistic possibilities for DNA demethylation will be described in the context of natural and experimentally induced epigenetic reprogramming. The balance of the maintenance of this heritable mark together with its timely removal is essential for lifelong health and may be a key in our understanding of ageing.

  9. DNA as a language

    Energy Technology Data Exchange (ETDEWEB)

    Trifonov, E.N. [Weizmann Inst. of Science, Rehovot (Israel). Dept. of Structural Biology

    1993-12-31

    The classical triplet code is not the, only code carried by the sequences. They contain, for example, the gene-splicing code, transcription codes and many other codes. By analyzing a large volume of the nucleotide sequences available, i.e., by performing various computer experiments with the sequences, one can decipher them and extract from them valuable biological information. At the DNA level there are at least two more codes -- the DNA shape code and the chromatin code. The overall DNA shape is sequence-dependent and can be described by a set of angles characteristic for various dinucleotide elements -- codons of the DNA shape code. The chromatin code provides instructions for histone obtainers where along the DNA to form the nucleosomes. This code is expressed as positional periodicity of, primarily, AA and TT dinucleotides. A new RNA code has been described -- the translation framing code. The frame seems to be maintained by a synchronizing pattern GCUGCUGCU... hidden in mRNA. Most enigmatic of all is, perhaps, the gene-splicing code. An interesting recent development indicates that the gene-splicing pattern in the sequences and the nucleosomal pattern have some common features. This has to do with superposition of the patterns that is characteristic for the sequence language in general which carries simultaneously many codes in one and the same text.

  10. DNA replication stress restricts ribosomal DNA copy number.

    Science.gov (United States)

    Salim, Devika; Bradford, William D; Freeland, Amy; Cady, Gillian; Wang, Jianmin; Pruitt, Steven C; Gerton, Jennifer L

    2017-09-01

    Ribosomal RNAs (rRNAs) in budding yeast are encoded by ~100-200 repeats of a 9.1kb sequence arranged in tandem on chromosome XII, the ribosomal DNA (rDNA) locus. Copy number of rDNA repeat units in eukaryotic cells is maintained far in excess of the requirement for ribosome biogenesis. Despite the importance of the repeats for both ribosomal and non-ribosomal functions, it is currently not known how "normal" copy number is determined or maintained. To identify essential genes involved in the maintenance of rDNA copy number, we developed a droplet digital PCR based assay to measure rDNA copy number in yeast and used it to screen a yeast conditional temperature-sensitive mutant collection of essential genes. Our screen revealed that low rDNA copy number is associated with compromised DNA replication. Further, subculturing yeast under two separate conditions of DNA replication stress selected for a contraction of the rDNA array independent of the replication fork blocking protein, Fob1. Interestingly, cells with a contracted array grew better than their counterparts with normal copy number under conditions of DNA replication stress. Our data indicate that DNA replication stresses select for a smaller rDNA array. We speculate that this liberates scarce replication factors for use by the rest of the genome, which in turn helps cells complete DNA replication and continue to propagate. Interestingly, tumors from mini chromosome maintenance 2 (MCM2)-deficient mice also show a loss of rDNA repeats. Our data suggest that a reduction in rDNA copy number may indicate a history of DNA replication stress, and that rDNA array size could serve as a diagnostic marker for replication stress. Taken together, these data begin to suggest the selective pressures that combine to yield a "normal" rDNA copy number.

  11. DNA replication stress restricts ribosomal DNA copy number.

    Directory of Open Access Journals (Sweden)

    Devika Salim

    2017-09-01

    Full Text Available Ribosomal RNAs (rRNAs in budding yeast are encoded by ~100-200 repeats of a 9.1kb sequence arranged in tandem on chromosome XII, the ribosomal DNA (rDNA locus. Copy number of rDNA repeat units in eukaryotic cells is maintained far in excess of the requirement for ribosome biogenesis. Despite the importance of the repeats for both ribosomal and non-ribosomal functions, it is currently not known how "normal" copy number is determined or maintained. To identify essential genes involved in the maintenance of rDNA copy number, we developed a droplet digital PCR based assay to measure rDNA copy number in yeast and used it to screen a yeast conditional temperature-sensitive mutant collection of essential genes. Our screen revealed that low rDNA copy number is associated with compromised DNA replication. Further, subculturing yeast under two separate conditions of DNA replication stress selected for a contraction of the rDNA array independent of the replication fork blocking protein, Fob1. Interestingly, cells with a contracted array grew better than their counterparts with normal copy number under conditions of DNA replication stress. Our data indicate that DNA replication stresses select for a smaller rDNA array. We speculate that this liberates scarce replication factors for use by the rest of the genome, which in turn helps cells complete DNA replication and continue to propagate. Interestingly, tumors from mini chromosome maintenance 2 (MCM2-deficient mice also show a loss of rDNA repeats. Our data suggest that a reduction in rDNA copy number may indicate a history of DNA replication stress, and that rDNA array size could serve as a diagnostic marker for replication stress. Taken together, these data begin to suggest the selective pressures that combine to yield a "normal" rDNA copy number.

  12. No increase in free fetal DNA level in ectopic pregnancy: A preliminary study.

    Science.gov (United States)

    Kömürcü Karuserci, Özge; Uğur, Mete Gürol; Balat, Özcan; Sucu, Seyhun

    2017-09-01

    The aim of this study was to diagnose ectopic pregnancy in the early period by measuring cell-free fetal DNA (cffDNA) levels in maternal blood using spectrophotometry. Thirty patients with ectopic pregnancy and 30 patients with first trimester intrauterine pregnancy were enrolled in this prospective controlled study. cffDNA levels in maternal serum were measured using spectrophotometry. There were no differences between the two groups in terms of cffDNA absorbance levels. Spectrophotometry is not suitable for measuring cffDNA levels to diagnose ectopic pregnancies in the early period. Practical and cost-effective methods should be found or larger patient series should be investigated.

  13. Transposon facilitated DNA sequencing

    Energy Technology Data Exchange (ETDEWEB)

    Berg, D.E.; Berg, C.M.; Huang, H.V.

    1990-01-01

    The purpose of this research is to investigate and develop methods that exploit the power of bacterial transposable elements for large scale DNA sequencing: Our premise is that the use of transposons to put primer binding sites randomly in target DNAs should provide access to all portions of large DNA fragments, without the inefficiencies of methods involving random subcloning and attendant repetitive sequencing, or of sequential synthesis of many oligonucleotide primers that are used to match systematically along a DNA molecule. Two unrelated bacterial transposons, Tn5 and {gamma}{delta}, are being used because they have both proven useful for molecular analyses, and because they differ sufficiently in mechanism and specificity of transposition to merit parallel development.

  14. DNA Topoisomerases in Transcription

    DEFF Research Database (Denmark)

    Rødgaard, Morten Terpager

    2015-01-01

    This Ph.D. thesis summarizes the main results of my studies on the interplay between DNA topoisomerases and transcription. The work was performed from 2011 to 2015 at Aarhus University in the Laboratory of Genome Research, and was supervised by associate professor Anni H. Andersen. Most of the ex......This Ph.D. thesis summarizes the main results of my studies on the interplay between DNA topoisomerases and transcription. The work was performed from 2011 to 2015 at Aarhus University in the Laboratory of Genome Research, and was supervised by associate professor Anni H. Andersen. Most...... topoisomerase-DNA cleavage complex. The second study is an investigation of how topoisomerases influence gene regulation by keeping the genome in an optimal topological state....

  15. DNA methylation in obesity

    Directory of Open Access Journals (Sweden)

    Małgorzata Pokrywka

    2014-11-01

    Full Text Available The number of overweight and obese people is increasing at an alarming rate, especially in the developed and developing countries. Obesity is a major risk factor for diabetes, cardiovascular disease, and cancer, and in consequence for premature death. The development of obesity results from the interplay of both genetic and environmental factors, which include sedentary life style and abnormal eating habits. In the past few years a number of events accompanying obesity, affecting expression of genes which are not directly connected with the DNA base sequence (e.g. epigenetic changes, have been described. Epigenetic processes include DNA methylation, histone modifications such as acetylation, methylation, phosphorylation, ubiquitination, and sumoylation, as well as non-coding micro-RNA (miRNA synthesis. In this review, the known changes in the profile of DNA methylation as a factor affecting obesity and its complications are described.

  16. Aplicación de las Máquinas de Soporte Vectorial (SVM al diagnóstico clínico de la Enfermedad de Párkinson y el Temblor Esencial

    Directory of Open Access Journals (Sweden)

    Roberto González

    2017-10-01

    Full Text Available Resumen: Los enfermos de Párkinson (EP y de temblor esencial (TE suponen un porcentaje importante de la casuística clínica en los trastornos del movimiento, que impiden a los sujetos afectados el llevar una vida normal, produciendo discapacidad física y una no menos importante exclusión social en muchos de los casos. Las vías de tratamiento son dispares, de ahí que sea crítico acertar con precisión en el diagnóstico en las etapas iniciales de la enfermedad. Hasta la actualidad, los profesionales y expertos en medicina, utilizan unas escalas cualitativas para diferenciar la patología y su grado de afectación. Dichas escalas también se utilizan para efectuar un seguimiento clínico y registrar la historia del paciente. En este trabajo se propone la utilización de clasificadores binarios centrados en las Máquinas de Soporte Vectorial (SVM para obtener un diagnóstico diferencial entre las dos patologías de temblor mencionadas. Abstract: Parkinson's Disease (PD and Essential Tremor (ET patients represent a significant percentage of the clinical cases in movement disorders pathologies, which prevents to the affected people leading a normal life. A physical disability results and important social exclusion in many cases are produced. The treatment methods are very differents, so it is critical hitting with the diagnosis in the early stages of these diseases. Until today, professionals and experts in medicine, use qualitative scales to differentiate pathology cases and its level of affectation. These scales are used to follow up clinically and register the patient's history. This work proposes the use of binary classifiers focused on the Vector Support Machines (SVM to obtain a differential diagnosis between the essential tremor and Parkinson's disease. Palabras clave: Ayuda al diagnóstico, clasificadores binarios, clasificación Párkinson-Esencial, medida objetiva del temblor, análisis de patrones, extracción de caracter

  17. Genomics Education in Practice: Evaluation of a Mobile Lab Design

    Science.gov (United States)

    Van Mil, Marc H. W.; Boerwinkel, Dirk Jan; Buizer-Voskamp, Jacobine E.; Speksnijder, Annelies; Waarlo, Arend Jan

    2010-01-01

    Dutch genomics research centers have developed the "DNA labs on the road" to bridge the gap between modern genomics research practice and secondary-school curriculum in the Netherlands. These mobile DNA labs offer upper-secondary students the opportunity to experience genomics research through experiments with laboratory equipment that…

  18. Repeated DNA sequences in fungi

    Energy Technology Data Exchange (ETDEWEB)

    Dutta, S.K.

    1974-11-01

    Several fungal species, representatives of all broad groups like basidiomycetes, ascomycetes and phycomycetes, were examined for the nature of repeated DNA sequences by DNA:DNA reassociation studies using hydroxyapatite chromatography. All of the fungal species tested contained 10 to 20 percent repeated DNA sequences. There are approximately 100 to 110 copies of repeated DNA sequences of approximately 4 x 10/sup 7/ daltons piece size of each. Repeated DNA sequence homoduplexes showed on average 5/sup 0/C difference of T/sub e/50 (temperature at which 50 percent duplexes dissociate) values from the corresponding homoduplexes of unfractionated whole DNA. It is suggested that a part of repetitive sequences in fungi constitutes mitochondrial DNA and a part of it constitutes nuclear DNA. (auth)

  19. Prototyping Practice

    DEFF Research Database (Denmark)

    Ramsgaard Thomsen, Mette; Tamke, Martin

    2015-01-01

    This paper examines the role of the prototyping in digital architecture. During the past decade, a new research field has emerged exploring the digital technology’s impact on the way we think, design and build our environment. In this practice the prototype, the pavilion, installation...... or demonstrator, has become a shared research tool. This paper asks how this practice has formed by tracing the different roles of the prototype from ideation and design, to analysis and evaluation. Taking point of departure in CITA’s own prototyping practice, we explore the relationships between physical...... and digital prototyping as a particular means of validation and verification. Here, a breadth of physical prototypes take on varying roles, in turn informing, testing and proving the research enquiry. The paper addresses how we can differentiate between these modes of prototyping and how....

  20. DNA-abzymes in autoimmune diseases in clinic and experiment

    Directory of Open Access Journals (Sweden)

    T E Naumova

    2003-01-01

    Full Text Available DNA-abzymes enzymes in autoimmune diseases in clinic and experiment T.E. Naumova, O.M. Durova, A.G. Gabibov, Z.S. Alekberova, S. V. Suchkov DNA-hydrolyzing autoantibodies (AAB or DNA-abzymes can be found in autoimmune diseases in clinic and experiment. Technology of serum express screening for presence of DNA abzymes is described. Comparative study of DNA-hydrolising activity in patients with different forms of systemic and organ-specific autoimmune diseases was performed. Blood of clinically healthy donors was usually free of IgG DNA-abzymes. DNA-abzymes were most often revealed in patients with systemic lupus erythematosus (SLE and rheumatoid arthritis (RA less often in patients with organ-specific forms of autoimmune disturbances. The results of the study confirm the hypothesis of autoimmune origin of IgG DNA abzymes and demonstrate the possibility to use them in clinical practice for monitoring to disease activity in SLE and RA.

  1. Site Practice

    DEFF Research Database (Denmark)

    Wahedi, Haseebullah

    2016-01-01

    different practices in the construction phase. The research is based on an ethnographic study of a case in Denmark. The empirical data were collected through direct observations and semi-structured interviews with site managers, contract managers, foremen and craftsmen. Findings revealed...... that the construction phase comprises several communities and practices, leading to various uses of the drawings. The results indicated that the craftsmen used drawings to position themselves in the correct location, and that the site managers and contract managers used them as management tools and legal documents...

  2. All-Electrical Graphene DNA Sensor Array.

    Science.gov (United States)

    Abbott, Jeffrey; Ham, Donhee; Xu, Guangyu

    2017-01-01

    Electrical sensing of biomolecules has been an important pursuit due to the label-free operation and chip-scale construct such sensing modality can enable. In particular, electrical biomolecular sensors based on nanomaterials such as semiconductor nanowires, carbon nanotubes, and graphene have demonstrated high sensitivity, which in the case of nanowires and carbon nanotubes can surpass typical optical detection sensitivity. Among these nanomaterials, graphene is well suited for a practical candidate for implementing a large-scale array of biomolecular sensors, as its two-dimensional morphology is readily compatible with industry standard top-down fabrication techniques. In our recent work published in 2014 Nature Communications, we demonstrated these benefits by creating DNA sensor arrays from chemical vapor deposition (CVD) graphene. The present chapter, which is a review of this recent work, outlines procedures demonstrating the use of individual graphene sites of the array in dual roles--electrophoretic electrodes for site specific probe DNA immobilization and field effect transistor (FET) sensors for detection of target DNA hybridization. The 100 fM detection sensitivity achieved in 7 out of 8 graphene FET sensors in the array combined with the alternative use of the graphene channels as electrophoretic electrodes for probe deposition represent steps toward creating an all-electrical multiplexed DNA array.

  3. Attomolar DNA detection with chiral nanorod assemblies

    Science.gov (United States)

    Ma, Wei; Kuang, Hua; Xu, Liguang; Ding, Li; Xu, Chuanlai; Wang, Libing; Kotov, Nicholas A.

    2013-10-01

    Nanoscale plasmonic assemblies display exceptionally strong chiral optical activity. So far, their structural design was primarily driven by challenges related to metamaterials whose practical applications are remote. Here we demonstrate that gold nanorods assembled by the polymerase chain reaction into DNA-bridged chiral systems have promising analytical applications. The chiroplasmonic activity of side-by-side assembled patterns is attributed to a 7-9 degree twist between the nanorod axes. This results in a strong polarization rotation that matches theoretical expectations. The amplitude of the bisignate ‘wave’ in the circular dichroism spectra of side-by-side assemblies demonstrates excellent linearity with the amount of target DNA. The limit of detection for DNA using side-by-side assemblies is as low as 3.7 aM. This chiroplasmonic method may be particularly useful for biological analytes larger than 2-5 nm which are difficult to detect by methods based on plasmon coupling and ‘hot spots’. Circular polarization increases for inter-nanorod gaps between 2 and 20 nm when plasmonic coupling rapidly decreases. Reaching the attomolar limit of detection for simple and reliable bioanalysis of oligonucleotides may have a crucial role in DNA biomarker detection for early diagnostics of different diseases, forensics and environmental monitoring.

  4. Relationships between 16S-23S rRNA gene internal transcribed spacer DNA and genomic DNA similarities in the taxonomy of phototrophic bacteria

    Science.gov (United States)

    Okamura, K.; Hisada, T.; Takata, K.; Hiraishi, A.

    2013-04-01

    Rapid and accurate identification of microbial species is essential task in microbiology and biotechnology. In prokaryotic systematics, genomic DNA-DNA hybridization is the ultimate tool to determine genetic relationships among bacterial strains at the species level. However, a practical problem in this assay is that the experimental procedure is laborious and time-consuming. In recent years, information on the 16S-23S rRNA gene internal transcribed spacer (ITS) region has been used to classify bacterial strains at the species and intraspecies levels. It is unclear how much information on the ITS region can reflect the genome that contain it. In this study, therefore, we evaluate the quantitative relationship between ITS DNA and entire genomic DNA similarities. For this, we determined ITS sequences of several species of anoxygenic phototrophic bacteria belonging to the order Rhizobiales, and compared with DNA-DNA relatedness among these species. There was a high correlation between the two genetic markers. Based on the regression analysis of this relationship, 70% DNA-DNA relatedness corresponded to 92% ITS sequence similarity. This suggests the usefulness of the ITS sequence similarity as a criterion for determining the genospecies of the phototrophic bacteria. To avoid the effects of polymorphism bias of ITS on similarities, PCR products from all loci of ITS were used directly as genetic probes for comparison. The results of ITS DNA-DNA hybridization coincided well with those of genomic DNA-DNA relatedness. These collective data indicate that the whole ITS DNA-DNA similarity can be used as an alternative to genomic DNA-DNA similarity.

  5. DNA Nanotechnology for Cancer Therapy.

    Science.gov (United States)

    Kumar, Vinit; Palazzolo, Stefano; Bayda, Samer; Corona, Giuseppe; Toffoli, Giuseppe; Rizzolio, Flavio

    2016-01-01

    DNA nanotechnology is an emerging and exciting field, and represents a forefront frontier for the biomedical field. The specificity of the interactions between complementary base pairs makes DNA an incredible building material for programmable and very versatile two- and three-dimensional nanostructures called DNA origami. Here, we analyze the DNA origami and DNA-based nanostructures as a drug delivery system. Besides their physical-chemical nature, we dissect the critical factors such as stability, loading capability, release and immunocompatibility, which mainly limit in vivo applications. Special attention was dedicated to highlighting the boundaries to be overcome to bring DNA nanostructures closer to the bedside of patients.

  6. Novel extraction method of genomic DNA suitable for long-fragment amplification from small amounts of milk.

    Science.gov (United States)

    Liu, Y F; Gao, J L; Yang, Y F; Ku, T; Zan, L S

    2014-11-01

    Isolation of genomic DNA is a prerequisite for assessment of milk quality. As a source of genomic DNA, milk somatic cells from milking ruminants are practical, animal friendly, and cost-effective sources. Extracting DNA from milk can avoid the stress response caused by blood and tissue sampling of cows. In this study, we optimized a novel DNA extraction method for amplifying long (>1,000 bp) DNA fragments and used it to evaluate the isolation of DNA from small amounts of milk. The techniques used for the separation of milk somatic cell were explored and combined with a sodium dodecyl sulfate (SDS)-phenol method for optimizing DNA extraction from milk. Spectrophotometry was used to determine the concentration and purity of the extracted DNA. Gel electrophoresis and DNA amplification technologies were used for to determine DNA size and quality. The DNA of 112 cows was obtained from milk (samples of 13 ± 1 mL) and the corresponding optical density ratios at 260:280 nm were between 1.65 and 1.75. Concentrations were between 12 and 45 μg/μL and DNA size and quality were acceptable. The specific PCR amplification of 1,019- and 729-bp bovine DNA fragments was successfully carried out. This novel method can be used as a practical, fast, and economical mean for long genomic DNA extraction from a small amount of milk. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

  7. Practical Latex

    CERN Document Server

    Grätzer, George

    2014-01-01

    Accessible at 200+ pages to all who want to learn the practical usages of LaTeX Avoids technical subjects like font usage Friendly and easy to read, with many examples included Extra source materials include sample LaTeX files and suggestions for further reading

  8. Practical Metallurgy

    DEFF Research Database (Denmark)

    Bissacco, Giuliano; Deganello, Davide

    2003-01-01

    This is the report regards the activities carried out during the practical metatllurgy course concerning material properties and structures and their testing & analysis methods. During the course several aspects of the materials have been considered and different techniques have been analyzed....

  9. Practical Statistics

    CERN Document Server

    Lyons, L.

    2016-01-01

    Accelerators and detectors are expensive, both in terms of money and human effort. It is thus important to invest effort in performing a good statistical anal- ysis of the data, in order to extract the best information from it. This series of five lectures deals with practical aspects of statistical issues that arise in typical High Energy Physics analyses.

  10. Nearshoring Practices

    DEFF Research Database (Denmark)

    Slepniov, Dmitrij; Brazinskas, Sigitas; Wæhrens, Brian Vejrum

    2013-01-01

    Purpose: The principal objective of this paper is to unravel and assess current nearshoring practices and their outlook in the Baltic region. Design/methodology/approach: We draw on the offshoring and outsourcing literature and use mixed methods of enquiry, including case studies, survey techniqu...

  11. DNA ligase I selectively affects DNA synthesis by DNA polymerases delta and epsilon suggesting differential functions in DNA replication and repair.

    OpenAIRE

    Mossi, R; Ferrari, E; Hübscher, U

    1998-01-01

    The joining of single-stranded breaks in double-stranded DNA is an essential step in many important processes such as DNA replication, DNA repair, and genetic recombination. Several data implicate a role for DNA ligase I in DNA replication, probably coordinated by the action of other enzymes and proteins. Since both DNA polymerases delta and epsilon show multiple functions in different DNA transactions, we investigated the effect of DNA ligase I on various DNA synthesis events catalyzed by th...

  12. Automated DNA Sequencing System

    Energy Technology Data Exchange (ETDEWEB)

    Armstrong, G.A.; Ekkebus, C.P.; Hauser, L.J.; Kress, R.L.; Mural, R.J.

    1999-04-25

    Oak Ridge National Laboratory (ORNL) is developing a core DNA sequencing facility to support biological research endeavors at ORNL and to conduct basic sequencing automation research. This facility is novel because its development is based on existing standard biology laboratory equipment; thus, the development process is of interest to the many small laboratories trying to use automation to control costs and increase throughput. Before automation, biology Laboratory personnel purified DNA, completed cycle sequencing, and prepared 96-well sample plates with commercially available hardware designed specifically for each step in the process. Following purification and thermal cycling, an automated sequencing machine was used for the sequencing. A technician handled all movement of the 96-well sample plates between machines. To automate the process, ORNL is adding a CRS Robotics A- 465 arm, ABI 377 sequencing machine, automated centrifuge, automated refrigerator, and possibly an automated SpeedVac. The entire system will be integrated with one central controller that will direct each machine and the robot. The goal of this system is to completely automate the sequencing procedure from bacterial cell samples through ready-to-be-sequenced DNA and ultimately to completed sequence. The system will be flexible and will accommodate different chemistries than existing automated sequencing lines. The system will be expanded in the future to include colony picking and/or actual sequencing. This discrete event, DNA sequencing system will demonstrate that smaller sequencing labs can achieve cost-effective the laboratory grow.

  13. Field Deployable DNA analyzer

    Energy Technology Data Exchange (ETDEWEB)

    Wheeler, E; Christian, A; Marion, J; Sorensen, K; Arroyo, E; Vrankovich, G; Hara, C; Nguyen, C

    2005-02-09

    This report details the feasibility of a field deployable DNA analyzer. Steps for swabbing cells from surfaces and extracting DNA in an automatable way are presented. Since enzymatic amplification reactions are highly sensitive to environmental contamination, sample preparation is a crucial step to make an autonomous deployable instrument. We perform sample clean up and concentration in a flow through packed bed. For small initial samples, whole genome amplification is performed in the packed bed resulting in enough product for subsequent PCR amplification. In addition to DNA, which can be used to identify a subject, protein is also left behind, the analysis of which can be used to determine exposure to certain substances, such as radionuclides. Our preparative step for DNA analysis left behind the protein complement as a waste stream; we determined to learn if the proteins themselves could be analyzed in a fieldable device. We successfully developed a two-step lateral flow assay for protein analysis and demonstrate a proof of principle assay.

  14. DNA tagged microparticles

    Energy Technology Data Exchange (ETDEWEB)

    Farquar, George R.; Leif, Roald N.; Wheeler, Elizabeth

    2016-03-22

    In one embodiment, a product includes a plurality of particles, each particle including: a carrier that includes a non-toxic material; and at least one DNA barcode coupled to the carrier, where the particles each have a diameter in a range from about 1 nanometer to about 100 microns.

  15. Application of PQR Theory for control of a 3-phase 4-wire 4-legs shunt active power filter in the aß?-axes using 3D-SVM technique

    Directory of Open Access Journals (Sweden)

    Ali CHEBABHI

    2015-05-01

    Full Text Available This article discusses and compares two control strategies applied to a 3-phase 4-wire 4-leg shunt active power filter. These two control strategies, including the cross-vector theory called CV theory and the direct method called PQR theory, are based on the instantaneous control of active and reactive power. On one hand, it is shown that, in some cases, cross-vector theory requires elimination of the zero sequence currents in a 3-phase 4-wire 4-leg shunt active power filter, which needs a power storage element, and on the other hand pretreatment system voltage is necessary to obtain compensated sinusoidal current and a degree of freedom. By relying on the cross-vector theory, the PQR theory is used to extract and remove harmonic currents components. In this control technique, there are two internal current control loops and an external voltage control loop, these control loops have been realized by PI controllers when applied 3D-SVM of switching technique. We choose as criteria for comparison the transient and the Total Harmonic Distortion in the line current. A series of simulations in MATLAB/ Simulink environment have been presented and discussed to show the performance of the two control strategies.

  16. Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoothing and Artificial Intelligence Models (ANN, SVM: The Case of Greek Electricity Market

    Directory of Open Access Journals (Sweden)

    George P. Papaioannou

    2016-08-01

    Full Text Available In this work we propose a new hybrid model, a combination of the manifold learning Principal Components (PC technique and the traditional multiple regression (PC-regression, for short and medium-term forecasting of daily, aggregated, day-ahead, electricity system-wide load in the Greek Electricity Market for the period 2004–2014. PC-regression is shown to effectively capture the intraday, intraweek and annual patterns of load. We compare our model with a number of classical statistical approaches (Holt-Winters exponential smoothing of its generalizations Error-Trend-Seasonal, ETS models, the Seasonal Autoregressive Moving Average with exogenous variables, Seasonal Autoregressive Integrated Moving Average with eXogenous (SARIMAX model as well as with the more sophisticated artificial intelligence models, Artificial Neural Networks (ANN and Support Vector Machines (SVM. Using a number of criteria for measuring the quality of the generated in-and out-of-sample forecasts, we have concluded that the forecasts of our hybrid model outperforms the ones generated by the other model, with the SARMAX model being the next best performing approach, giving comparable results. Our approach contributes to studies aimed at providing more accurate and reliable load forecasting, prerequisites for an efficient management of modern power systems.

  17. Pheochromocytomas and paragangliomas: from DNA to the daily clinical practice

    NARCIS (Netherlands)

    B-J. Petri (Bart-Jeroen)

    2010-01-01

    textabstractThe neuroendocrine system is a diffuse system in which the nervous system and the hormones of the endocrine glands interact. The neuroendocrine organs of the sympathetic and parasympathetic autonomic nervous system are called paraganglia. These organs usually manifest as anatomically

  18. Fungal DNA barcoding.

    Science.gov (United States)

    Xu, Jianping

    2016-11-01

    Fungi are ubiquitous in both natural and human-made environments. They play important roles in the health of plants, animals, and humans, and in broad ecosystem functions. Thus, having an efficient species-level identification system could significantly enhance our ability to treat fungal diseases and to monitor the spatial and temporal patterns of fungal distributions and migrations. DNA barcoding is a potent approach for rapid identification of fungal specimens, generating novel species hypothesis, and guiding biodiversity and ecological studies. In this mini-review, I briefly summarize (i) the history of DNA sequence-based fungal identification; (ii) the emergence of the ITS region as the consensus primary fungal barcode; (iii) the use of the ITS barcodes to address a variety of issues on fungal diversity from local to global scales, including generating a large number of species hypothesis; and (iv) the problems with the ITS barcode region and the approaches to overcome these problems. Similar to DNA barcoding research on plants and animals, significant progress has been achieved over the last few years in terms of both the questions being addressed and the foundations being laid for future research endeavors. However, significant challenges remain. I suggest three broad areas of research to enhance the usefulness of fungal DNA barcoding to meet the current and future challenges: (i) develop a common set of primers and technologies that allow the amplification and sequencing of all fungi at both the primary and secondary barcode loci; (ii) compile a centralized reference database that includes all recognized fungal species as well as species hypothesis, and allows regular updates from the research community; and (iii) establish a consensus set of new species recognition criteria based on barcode DNA sequences that can be applied across the fungal kingdom.

  19. Predicting and analyzing DNA-binding domains using a systematic approach to identifying a set of informative physicochemical and biochemical properties

    Science.gov (United States)

    2011-01-01

    Background Existing methods of predicting DNA-binding proteins used valuable features of physicochemical properties to design support vector machine (SVM) based classifiers. Generally, selection of physicochemical properties and determination of their corresponding feature vectors rely mainly on known properties of binding mechanism and experience of designers. However, there exists a troublesome problem for designers that some different physicochemical properties have similar vectors of representing 20 amino acids and some closely related physicochemical properties have dissimilar vectors. Results This study proposes a systematic approach (named Auto-IDPCPs) to automatically identify a set of physicochemical and biochemical properties in the AAindex database to design SVM-based classifiers for predicting and analyzing DNA-binding domains/proteins. Auto-IDPCPs consists of 1) clustering 531 amino acid indices in AAindex into 20 clusters using a fuzzy c-means algorithm, 2) utilizing an efficient genetic algorithm based optimization method IBCGA to select an informative feature set of size m to represent sequences, and 3) analyzing the selected features to identify related physicochemical properties which may affect the binding mechanism of DNA-binding domains/proteins. The proposed Auto-IDPCPs identified m=22 features of properties belonging to five clusters for predicting DNA-binding domains with a five-fold cross-validation accuracy of 87.12%, which is promising compared with the accuracy of 86.62% of the existing method PSSM-400. For predicting DNA-binding sequences, the accuracy of 75.50% was obtained using m=28 features, where PSSM-400 has an accuracy of 74.22%. Auto-IDPCPs and PSSM-400 have accuracies of 80.73% and 82.81%, respectively, applied to an independent test data set of DNA-binding domains. Some typical physicochemical properties discovered are hydrophobicity, secondary structure, charge, solvent accessibility, polarity, flexibility, normalized Van Der

  20. The dynamic interplay between DNA topoisomerases and DNA topology.

    Science.gov (United States)

    Seol, Yeonee; Neuman, Keir C

    2016-11-01

    Topological properties of DNA influence its structure and biochemical interactions. Within the cell, DNA topology is constantly in flux. Transcription and other essential processes, including DNA replication and repair, not only alter the topology of the genome but also introduce additional complications associated with DNA knotting and catenation. These topological perturbations are counteracted by the action of topoisomerases, a specialized class of highly conserved and essential enzymes that actively regulate the topological state of the genome. This dynamic interplay among DNA topology, DNA processing enzymes, and DNA topoisomerases is a pervasive factor that influences DNA metabolism in vivo. Building on the extensive structural and biochemical characterization over the past four decades that has established the fundamental mechanistic basis of topoisomerase activity, scientists have begun to explore the unique roles played by DNA topology in modulating and influencing the activity of topoisomerases. In this review we survey established and emerging DNA topology-dependent protein-DNA interactions with a focus on in vitro measurements of the dynamic interplay between DNA topology and topoisomerase activity.