WorldWideScience

Sample records for transcriptional network classifiers

  1. Arabic Handwriting Recognition Using Neural Network Classifier

    African Journals Online (AJOL)

    pc

    2018-03-05

    Mar 5, 2018 ... an OCR using Neural Network classifier preceded by a set of preprocessing .... Artificial Neural Networks (ANNs), which we adopt in this research, consist of ... advantage and disadvantages of each technique. In [9],. Khemiri ...

  2. Optical Neural Network Classifier Architectures

    National Research Council Canada - National Science Library

    Getbehead, Mark

    1998-01-01

    We present an adaptive opto-electronic neural network hardware architecture capable of exploiting parallel optics to realize real-time processing and classification of high-dimensional data for Air...

  3. Logarithmic learning for generalized classifier neural network.

    Science.gov (United States)

    Ozyildirim, Buse Melis; Avci, Mutlu

    2014-12-01

    Generalized classifier neural network is introduced as an efficient classifier among the others. Unless the initial smoothing parameter value is close to the optimal one, generalized classifier neural network suffers from convergence problem and requires quite a long time to converge. In this work, to overcome this problem, a logarithmic learning approach is proposed. The proposed method uses logarithmic cost function instead of squared error. Minimization of this cost function reduces the number of iterations used for reaching the minima. The proposed method is tested on 15 different data sets and performance of logarithmic learning generalized classifier neural network is compared with that of standard one. Thanks to operation range of radial basis function included by generalized classifier neural network, proposed logarithmic approach and its derivative has continuous values. This makes it possible to adopt the advantage of logarithmic fast convergence by the proposed learning method. Due to fast convergence ability of logarithmic cost function, training time is maximally decreased to 99.2%. In addition to decrease in training time, classification performance may also be improved till 60%. According to the test results, while the proposed method provides a solution for time requirement problem of generalized classifier neural network, it may also improve the classification accuracy. The proposed method can be considered as an efficient way for reducing the time requirement problem of generalized classifier neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.

  4. Neural Network Classifiers for Local Wind Prediction.

    Science.gov (United States)

    Kretzschmar, Ralf; Eckert, Pierre; Cattani, Daniel; Eggimann, Fritz

    2004-05-01

    This paper evaluates the quality of neural network classifiers for wind speed and wind gust prediction with prediction lead times between +1 and +24 h. The predictions were realized based on local time series and model data. The selection of appropriate input features was initiated by time series analysis and completed by empirical comparison of neural network classifiers trained on several choices of input features. The selected input features involved day time, yearday, features from a single wind observation device at the site of interest, and features derived from model data. The quality of the resulting classifiers was benchmarked against persistence for two different sites in Switzerland. The neural network classifiers exhibited superior quality when compared with persistence judged on a specific performance measure, hit and false-alarm rates.

  5. Neural Network Classifier Based on Growing Hyperspheres

    Czech Academy of Sciences Publication Activity Database

    Jiřina Jr., Marcel; Jiřina, Marcel

    2000-01-01

    Roč. 10, č. 3 (2000), s. 417-428 ISSN 1210-0552. [Neural Network World 2000. Prague, 09.07.2000-12.07.2000] Grant - others:MŠMT ČR(CZ) VS96047; MPO(CZ) RP-4210 Institutional research plan: AV0Z1030915 Keywords : neural network * classifier * hyperspheres * big -dimensional data Subject RIV: BA - General Mathematics

  6. Design of Robust Neural Network Classifiers

    DEFF Research Database (Denmark)

    Larsen, Jan; Andersen, Lars Nonboe; Hintz-Madsen, Mads

    1998-01-01

    This paper addresses a new framework for designing robust neural network classifiers. The network is optimized using the maximum a posteriori technique, i.e., the cost function is the sum of the log-likelihood and a regularization term (prior). In order to perform robust classification, we present...... a modified likelihood function which incorporates the potential risk of outliers in the data. This leads to the introduction of a new parameter, the outlier probability. Designing the neural classifier involves optimization of network weights as well as outlier probability and regularization parameters. We...... suggest to adapt the outlier probability and regularisation parameters by minimizing the error on a validation set, and a simple gradient descent scheme is derived. In addition, the framework allows for constructing a simple outlier detector. Experiments with artificial data demonstrate the potential...

  7. Transcriptional networks controlling adipocyte differentiation

    DEFF Research Database (Denmark)

    Siersbæk, R; Mandrup, Susanne

    2011-01-01

    " of the transcription factor networks operating at specific time points during adipogenesis. Using such global "snapshots," we have demonstrated that dramatic remodeling of the chromatin template occurs within the first few hours following adipogenic stimulation and that many of the early transcription factors bind...... in a cooperative fashion to transcription factor hotspots. Such hotspots are likely to represent key chromatin nodes, where many adipogenic signaling pathways converge to drive the adipogenic transcriptional reprogramming....

  8. Ensemble of classifiers based network intrusion detection system performance bound

    CSIR Research Space (South Africa)

    Mkuzangwe, Nenekazi NP

    2017-11-01

    Full Text Available This paper provides a performance bound of a network intrusion detection system (NIDS) that uses an ensemble of classifiers. Currently researchers rely on implementing the ensemble of classifiers based NIDS before they can determine the performance...

  9. Revealing effective classifiers through network comparison

    Science.gov (United States)

    Gallos, Lazaros K.; Fefferman, Nina H.

    2014-11-01

    The ability to compare complex systems can provide new insight into the fundamental nature of the processes captured, in ways that are otherwise inaccessible to observation. Here, we introduce the n-tangle method to directly compare two networks for structural similarity, based on the distribution of edge density in network subgraphs. We demonstrate that this method can efficiently introduce comparative analysis into network science and opens the road for many new applications. For example, we show how the construction of a “phylogenetic tree” across animal taxa according to their social structure can reveal commonalities in the behavioral ecology of the populations, or how students create similar networks according to the University size. Our method can be expanded to study many additional properties, such as network classification, changes during time evolution, convergence of growth models, and detection of structural changes during damage.

  10. Neural network classifier of attacks in IP telephony

    Science.gov (United States)

    Safarik, Jakub; Voznak, Miroslav; Mehic, Miralem; Partila, Pavol; Mikulec, Martin

    2014-05-01

    Various types of monitoring mechanism allow us to detect and monitor behavior of attackers in VoIP networks. Analysis of detected malicious traffic is crucial for further investigation and hardening the network. This analysis is typically based on statistical methods and the article brings a solution based on neural network. The proposed algorithm is used as a classifier of attacks in a distributed monitoring network of independent honeypot probes. Information about attacks on these honeypots is collected on a centralized server and then classified. This classification is based on different mechanisms. One of them is based on the multilayer perceptron neural network. The article describes inner structure of used neural network and also information about implementation of this network. The learning set for this neural network is based on real attack data collected from IP telephony honeypot called Dionaea. We prepare the learning set from real attack data after collecting, cleaning and aggregation of this information. After proper learning is the neural network capable to classify 6 types of most commonly used VoIP attacks. Using neural network classifier brings more accurate attack classification in a distributed system of honeypots. With this approach is possible to detect malicious behavior in a different part of networks, which are logically or geographically divided and use the information from one network to harden security in other networks. Centralized server for distributed set of nodes serves not only as a collector and classifier of attack data, but also as a mechanism for generating a precaution steps against attacks.

  11. Classifying emotion in Twitter using Bayesian network

    Science.gov (United States)

    Surya Asriadie, Muhammad; Syahrul Mubarok, Mohamad; Adiwijaya

    2018-03-01

    Language is used to express not only facts, but also emotions. Emotions are noticeable from behavior up to the social media statuses written by a person. Analysis of emotions in a text is done in a variety of media such as Twitter. This paper studies classification of emotions on twitter using Bayesian network because of its ability to model uncertainty and relationships between features. The result is two models based on Bayesian network which are Full Bayesian Network (FBN) and Bayesian Network with Mood Indicator (BNM). FBN is a massive Bayesian network where each word is treated as a node. The study shows the method used to train FBN is not very effective to create the best model and performs worse compared to Naive Bayes. F1-score for FBN is 53.71%, while for Naive Bayes is 54.07%. BNM is proposed as an alternative method which is based on the improvement of Multinomial Naive Bayes and has much lower computational complexity compared to FBN. Even though it’s not better compared to FBN, the resulting model successfully improves the performance of Multinomial Naive Bayes. F1-Score for Multinomial Naive Bayes model is 51.49%, while for BNM is 52.14%.

  12. A convolutional neural network neutrino event classifier

    International Nuclear Information System (INIS)

    Aurisano, A.; Sousa, A.; Radovic, A.; Vahle, P.; Rocco, D.; Pawloski, G.; Himmel, A.; Niner, E.; Messier, M.D.; Psihas, F.

    2016-01-01

    Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.

  13. Using Neural Networks to Classify Digitized Images of Galaxies

    Science.gov (United States)

    Goderya, S. N.; McGuire, P. C.

    2000-12-01

    Automated classification of Galaxies into Hubble types is of paramount importance to study the large scale structure of the Universe, particularly as survey projects like the Sloan Digital Sky Survey complete their data acquisition of one million galaxies. At present it is not possible to find robust and efficient artificial intelligence based galaxy classifiers. In this study we will summarize progress made in the development of automated galaxy classifiers using neural networks as machine learning tools. We explore the Bayesian linear algorithm, the higher order probabilistic network, the multilayer perceptron neural network and Support Vector Machine Classifier. The performance of any machine classifier is dependant on the quality of the parameters that characterize the different groups of galaxies. Our effort is to develop geometric and invariant moment based parameters as input to the machine classifiers instead of the raw pixel data. Such an approach reduces the dimensionality of the classifier considerably, and removes the effects of scaling and rotation, and makes it easier to solve for the unknown parameters in the galaxy classifier. To judge the quality of training and classification we develop the concept of Mathews coefficients for the galaxy classification community. Mathews coefficients are single numbers that quantify classifier performance even with unequal prior probabilities of the classes.

  14. Balanced sensitivity functions for tuning multi-dimensional Bayesian network classifiers

    NARCIS (Netherlands)

    Bolt, J.H.; van der Gaag, L.C.

    Multi-dimensional Bayesian network classifiers are Bayesian networks of restricted topological structure, which are tailored to classifying data instances into multiple dimensions. Like more traditional classifiers, multi-dimensional classifiers are typically learned from data and may include

  15. Transforming Musical Signals through a Genre Classifying Convolutional Neural Network

    Science.gov (United States)

    Geng, S.; Ren, G.; Ogihara, M.

    2017-05-01

    Convolutional neural networks (CNNs) have been successfully applied on both discriminative and generative modeling for music-related tasks. For a particular task, the trained CNN contains information representing the decision making or the abstracting process. One can hope to manipulate existing music based on this 'informed' network and create music with new features corresponding to the knowledge obtained by the network. In this paper, we propose a method to utilize the stored information from a CNN trained on musical genre classification task. The network was composed of three convolutional layers, and was trained to classify five-second song clips into five different genres. After training, randomly selected clips were modified by maximizing the sum of outputs from the network layers. In addition to the potential of such CNNs to produce interesting audio transformation, more information about the network and the original music could be obtained from the analysis of the generated features since these features indicate how the network 'understands' the music.

  16. Specificity and robustness in transcription control networks.

    Science.gov (United States)

    Sengupta, Anirvan M; Djordjevic, Marko; Shraiman, Boris I

    2002-02-19

    Recognition by transcription factors of the regulatory DNA elements upstream of genes is the fundamental step in controlling gene expression. How does the necessity to provide stability with respect to mutation constrain the organization of transcription control networks? We examine the mutation load of a transcription factor interacting with a set of n regulatory response elements as a function of the factor/DNA binding specificity and conclude on theoretical grounds that the optimal specificity decreases with n. The predicted correlation between variability of binding sites (for a given transcription factor) and their number is supported by the genomic data for Escherichia coli. The analysis of E. coli genomic data was carried out using an algorithm suggested by the biophysical model of transcription factor/DNA binding. Complete results of the search for candidate transcription factor binding sites are available at http://www.physics.rockefeller.edu/~boris/public/search_ecoli.

  17. Transcription regulatory networks analysis using CAGE

    KAUST Repository

    Tegnér, Jesper N.

    2009-10-01

    Mapping out cellular networks in general and transcriptional networks in particular has proved to be a bottle-neck hampering our understanding of biological processes. Integrative approaches fusing computational and experimental technologies for decoding transcriptional networks at a high level of resolution is therefore of uttermost importance. Yet, this is challenging since the control of gene expression in eukaryotes is a complex multi-level process influenced by several epigenetic factors and the fine interplay between regulatory proteins and the promoter structure governing the combinatorial regulation of gene expression. In this chapter we review how the CAGE data can be integrated with other measurements such as expression, physical interactions and computational prediction of regulatory motifs, which together can provide a genome-wide picture of eukaryotic transcriptional regulatory networks at a new level of resolution. © 2010 by Pan Stanford Publishing Pte. Ltd. All rights reserved.

  18. Classifying Radio Galaxies with the Convolutional Neural Network

    International Nuclear Information System (INIS)

    Aniyan, A. K.; Thorat, K.

    2017-01-01

    We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff–Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ∼200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

  19. Classifying Radio Galaxies with the Convolutional Neural Network

    Energy Technology Data Exchange (ETDEWEB)

    Aniyan, A. K.; Thorat, K. [Department of Physics and Electronics, Rhodes University, Grahamstown (South Africa)

    2017-06-01

    We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff–Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ∼200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

  20. Classifying Radio Galaxies with the Convolutional Neural Network

    Science.gov (United States)

    Aniyan, A. K.; Thorat, K.

    2017-06-01

    We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff-Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ˜200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

  1. Analysis of FOXO transcriptional networks

    NARCIS (Netherlands)

    van der Vos, K.E.

    2010-01-01

    The PI3K-PKB-FOXO signalling module plays a pivotal role in a wide variety of cellular processes, including proliferation, survival, differentiation and metabolism. Inappropriate activation of this network is frequently observed in human cancer and causes uncontrolled proliferation and survival. In

  2. Reconstructing transcriptional regulatory networks through genomics data

    OpenAIRE

    Sun, Ning; Zhao, Hongyu

    2009-01-01

    One central problem in biology is to understand how gene expression is regulated under different conditions. Microarray gene expression data and other high throughput data have made it possible to dissect transcriptional regulatory networks at the genomics level. Owing to the very large number of genes that need to be studied, the relatively small number of data sets available, the noise in the data and the different natures of the distinct data types, network inference presents great challen...

  3. The transcriptional regulatory network of Mycobacterium tuberculosis.

    Directory of Open Access Journals (Sweden)

    Joaquín Sanz

    Full Text Available Under the perspectives of network science and systems biology, the characterization of transcriptional regulatory (TR networks beyond the context of model organisms offers a versatile tool whose potential remains yet mainly unexplored. In this work, we present an updated version of the TR network of Mycobacterium tuberculosis (M.tb, which incorporates newly characterized transcriptional regulations coming from 31 recent, different experimental works available in the literature. As a result of the incorporation of these data, the new network doubles the size of previous data collections, incorporating more than a third of the entire genome of the bacterium. We also present an exhaustive topological analysis of the new assembled network, focusing on the statistical characterization of motifs significances and the comparison with other model organisms. The expanded M.tb transcriptional regulatory network, considering its volume and completeness, constitutes an important resource for diverse tasks such as dynamic modeling of gene expression and signaling processes, computational reliability determination or protein function prediction, being the latter of particular relevance, given that the function of only a small percent of the proteins of M.tb is known.

  4. Classifying images using restricted Boltzmann machines and convolutional neural networks

    Science.gov (United States)

    Zhao, Zhijun; Xu, Tongde; Dai, Chenyu

    2017-07-01

    To improve the feature recognition ability of deep model transfer learning, we propose a hybrid deep transfer learning method for image classification based on restricted Boltzmann machines (RBM) and convolutional neural networks (CNNs). It integrates learning abilities of two models, which conducts subject classification by exacting structural higher-order statistics features of images. While the method transfers the trained convolutional neural networks to the target datasets, fully-connected layers can be replaced by restricted Boltzmann machine layers; then the restricted Boltzmann machine layers and Softmax classifier are retrained, and BP neural network can be used to fine-tuned the hybrid model. The restricted Boltzmann machine layers has not only fully integrated the whole feature maps, but also learns the statistical features of target datasets in the view of the biggest logarithmic likelihood, thus removing the effects caused by the content differences between datasets. The experimental results show that the proposed method has improved the accuracy of image classification, outperforming other methods on Pascal VOC2007 and Caltech101 datasets.

  5. Classifying magnetic resonance image modalities with convolutional neural networks

    Science.gov (United States)

    Remedios, Samuel; Pham, Dzung L.; Butman, John A.; Roy, Snehashis

    2018-02-01

    Magnetic Resonance (MR) imaging allows the acquisition of images with different contrast properties depending on the acquisition protocol and the magnetic properties of tissues. Many MR brain image processing techniques, such as tissue segmentation, require multiple MR contrasts as inputs, and each contrast is treated differently. Thus it is advantageous to automate the identification of image contrasts for various purposes, such as facilitating image processing pipelines, and managing and maintaining large databases via content-based image retrieval (CBIR). Most automated CBIR techniques focus on a two-step process: extracting features from data and classifying the image based on these features. We present a novel 3D deep convolutional neural network (CNN)- based method for MR image contrast classification. The proposed CNN automatically identifies the MR contrast of an input brain image volume. Specifically, we explored three classification problems: (1) identify T1-weighted (T1-w), T2-weighted (T2-w), and fluid-attenuated inversion recovery (FLAIR) contrasts, (2) identify pre vs postcontrast T1, (3) identify pre vs post-contrast FLAIR. A total of 3418 image volumes acquired from multiple sites and multiple scanners were used. To evaluate each task, the proposed model was trained on 2137 images and tested on the remaining 1281 images. Results showed that image volumes were correctly classified with 97.57% accuracy.

  6. Learning Bayesian network classifiers for credit scoring using Markov Chain Monte Carlo search

    NARCIS (Netherlands)

    Baesens, B.; Egmont-Petersen, M.; Castelo, R.; Vanthienen, J.

    2001-01-01

    In this paper, we will evaluate the power and usefulness of Bayesian network classifiers for credit scoring. Various types of Bayesian network classifiers will be evaluated and contrasted including unrestricted Bayesian network classifiers learnt using Markov Chain Monte Carlo (MCMC) search.

  7. Transcriptional delay stabilizes bistable gene networks.

    Science.gov (United States)

    Gupta, Chinmaya; López, José Manuel; Ott, William; Josić, Krešimir; Bennett, Matthew R

    2013-08-02

    Transcriptional delay can significantly impact the dynamics of gene networks. Here we examine how such delay affects bistable systems. We investigate several stochastic models of bistable gene networks and find that increasing delay dramatically increases the mean residence times near stable states. To explain this, we introduce a non-Markovian, analytically tractable reduced model. The model shows that stabilization is the consequence of an increased number of failed transitions between stable states. Each of the bistable systems that we simulate behaves in this manner.

  8. Transcriptional networks in epithelial-mesenchymal transition.

    Directory of Open Access Journals (Sweden)

    Christo Venkov

    Full Text Available Epithelial-mesenchymal transition (EMT changes polarized epithelial cells into migratory phenotypes associated with loss of cell-cell adhesion molecules and cytoskeletal rearrangements. This form of plasticity is seen in mesodermal development, fibroblast formation, and cancer metastasis.Here we identify prominent transcriptional networks active during three time points of this transitional process, as epithelial cells become fibroblasts. DNA microarray in cultured epithelia undergoing EMT, validated in vivo, were used to detect various patterns of gene expression. In particular, the promoter sequences of differentially expressed genes and their transcription factors were analyzed to identify potential binding sites and partners. The four most frequent cis-regulatory elements (CREs in up-regulated genes were SRY, FTS-1, Evi-1, and GC-Box, and RNA inhibition of the four transcription factors, Atf2, Klf10, Sox11, and SP1, most frequently binding these CREs, establish their importance in the initiation and propagation of EMT. Oligonucleotides that block the most frequent CREs restrain EMT at early and intermediate stages through apoptosis of the cells.Our results identify new transcriptional interactions with high frequency CREs that modulate the stability of cellular plasticity, and may serve as targets for modulating these transitional states in fibroblasts.

  9. Diagnostic Classifiers: Revealing how Neural Networks Process Hierarchical Structure

    NARCIS (Netherlands)

    Veldhoen, S.; Hupkes, D.; Zuidema, W.

    2016-01-01

    We investigate how neural networks can be used for hierarchical, compositional semantics. To this end, we define the simple but nontrivial artificial task of processing nested arithmetic expressions and study whether different types of neural networks can learn to add and subtract. We find that

  10. Controllability analysis of transcriptional regulatory networks reveals circular control patterns among transcription factors

    DEFF Research Database (Denmark)

    Österlund, Tobias; Bordel, Sergio; Nielsen, Jens

    2015-01-01

    % for the human network. The high controllability (low number of drivers needed to control the system) in yeast, mouse and human is due to the presence of internal loops in their regulatory networks where the TFs regulate each other in a circular fashion. We refer to these internal loops as circular control...... motifs (CCM). The E. coli transcriptional regulatory network, which does not have any CCMs, shows a hierarchical structure of the transcriptional regulatory network in contrast to the eukaryal networks. The presence of CCMs also has influence on the stability of these networks, as the presence of cycles...

  11. Selection Shapes Transcriptional Logic and Regulatory Specialization in Genetic Networks.

    Science.gov (United States)

    Fogelmark, Karl; Peterson, Carsten; Troein, Carl

    2016-01-01

    Living organisms need to regulate their gene expression in response to environmental signals and internal cues. This is a computational task where genes act as logic gates that connect to form transcriptional networks, which are shaped at all scales by evolution. Large-scale mutations such as gene duplications and deletions add and remove network components, whereas smaller mutations alter the connections between them. Selection determines what mutations are accepted, but its importance for shaping the resulting networks has been debated. To investigate the effects of selection in the shaping of transcriptional networks, we derive transcriptional logic from a combinatorially powerful yet tractable model of the binding between DNA and transcription factors. By evolving the resulting networks based on their ability to function as either a simple decision system or a circadian clock, we obtain information on the regulation and logic rules encoded in functional transcriptional networks. Comparisons are made between networks evolved for different functions, as well as with structurally equivalent but non-functional (neutrally evolved) networks, and predictions are validated against the transcriptional network of E. coli. We find that the logic rules governing gene expression depend on the function performed by the network. Unlike the decision systems, the circadian clocks show strong cooperative binding and negative regulation, which achieves tight temporal control of gene expression. Furthermore, we find that transcription factors act preferentially as either activators or repressors, both when binding multiple sites for a single target gene and globally in the transcriptional networks. This separation into positive and negative regulators requires gene duplications, which highlights the interplay between mutation and selection in shaping the transcriptional networks.

  12. Strategies for Transporting Data Between Classified and Unclassified Networks

    Science.gov (United States)

    2016-03-01

    datagram protocol (UDP) must be used. The UDP is typically used when speed is a higher priority than data integrity, such as in music or video streaming ...and the exit point of data are separate and can be tightly controlled. This does effectively prevent the comingling of data and is used in industry to...perform functions such as streaming video and audio from secure to insecure networks (ref. 1). A second disadvantage lies in the fact that the

  13. Constructing and Classifying Email Networks from Raw Forensic Images

    Science.gov (United States)

    2016-09-01

    AUC value will be closer to 1. Figure 2.6 compares 3 different ROC curves. Figure 2.6. ROC curves compared. The dashed black curve at the top has the...main difference was the computation time. For example, on a 1275-node jazz musician network, the fast algorithm ran to completion in about one...königsberg bridges,” Scientific American , vol. 189, no. 1, pp. 66–70, 1953. [8] N. Biggs, E. K. Lloyd, and R. J. Wilson, Graph Theory, 1736-1936. Great

  14. Classifying medical relations in clinical text via convolutional neural networks.

    Science.gov (United States)

    He, Bin; Guan, Yi; Dai, Rui

    2018-05-16

    Deep learning research on relation classification has achieved solid performance in the general domain. This study proposes a convolutional neural network (CNN) architecture with a multi-pooling operation for medical relation classification on clinical records and explores a loss function with a category-level constraint matrix. Experiments using the 2010 i2b2/VA relation corpus demonstrate these models, which do not depend on any external features, outperform previous single-model methods and our best model is competitive with the existing ensemble-based method. Copyright © 2018. Published by Elsevier B.V.

  15. Feature selection for Bayesian network classifiers using the MDL-FS score

    NARCIS (Netherlands)

    Drugan, Madalina M.; Wiering, Marco A.

    When constructing a Bayesian network classifier from data, the more or less redundant features included in a dataset may bias the classifier and as a consequence may result in a relatively poor classification accuracy. In this paper, we study the problem of selecting appropriate subsets of features

  16. Transcriptional networks and chromatin remodeling controlling adipogenesis

    DEFF Research Database (Denmark)

    Siersbæk, Rasmus; Nielsen, Ronni; Mandrup, Susanne

    2012-01-01

    Adipocyte differentiation is tightly controlled by a transcriptional cascade, which directs the extensive reprogramming of gene expression required to convert fibroblast-like precursor cells into mature lipid-laden adipocytes. Recent global analyses of transcription factor binding and chromatin...... remodeling have revealed 'snapshots' of this cascade and the chromatin landscape at specific time-points of differentiation. These studies demonstrate that multiple adipogenic transcription factors co-occupy hotspots characterized by an open chromatin structure and specific epigenetic modifications....... Such transcription factor hotspots are likely to represent key signaling nodes which integrate multiple adipogenic signals at specific chromatin sites, thereby facilitating coordinated action on gene expression....

  17. A deep convolutional neural network model to classify heartbeats.

    Science.gov (United States)

    Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adam, Muhammad; Gertych, Arkadiusz; Tan, Ru San

    2017-10-01

    The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their correct classification into different diagnoses, based on ECG morphology. Heartbeats can be sub-divided into five categories namely non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is challenging and time-consuming to distinguish these heartbeats on ECG as these signals are typically corrupted by noise. We developed a 9-layer deep convolutional neural network (CNN) to automatically identify 5 different categories of heartbeats in ECG signals. Our experiment was conducted in original and noise attenuated sets of ECG signals derived from a publicly available database. This set was artificially augmented to even out the number of instances the 5 classes of heartbeats and filtered to remove high-frequency noise. The CNN was trained using the augmented data and achieved an accuracy of 94.03% and 93.47% in the diagnostic classification of heartbeats in original and noise free ECGs, respectively. When the CNN was trained with highly imbalanced data (original dataset), the accuracy of the CNN reduced to 89.07%% and 89.3% in noisy and noise-free ECGs. When properly trained, the proposed CNN model can serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmic heartbeats. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Trimming of mammalian transcriptional networks using network component analysis

    Directory of Open Access Journals (Sweden)

    Liao James C

    2010-10-01

    Full Text Available Abstract Background Network Component Analysis (NCA has been used to deduce the activities of transcription factors (TFs from gene expression data and the TF-gene binding relationship. However, the TF-gene interaction varies in different environmental conditions and tissues, but such information is rarely available and cannot be predicted simply by motif analysis. Thus, it is beneficial to identify key TF-gene interactions under the experimental condition based on transcriptome data. Such information would be useful in identifying key regulatory pathways and gene markers of TFs in further studies. Results We developed an algorithm to trim network connectivity such that the important regulatory interactions between the TFs and the genes were retained and the regulatory signals were deduced. Theoretical studies demonstrated that the regulatory signals were accurately reconstructed even in the case where only three independent transcriptome datasets were available. At least 80% of the main target genes were correctly predicted in the extreme condition of high noise level and small number of datasets. Our algorithm was tested with transcriptome data taken from mice under rapamycin treatment. The initial network topology from the literature contains 70 TFs, 778 genes, and 1423 edges between the TFs and genes. Our method retained 1074 edges (i.e. 75% of the original edge number and identified 17 TFs as being significantly perturbed under the experimental condition. Twelve of these TFs are involved in MAPK signaling or myeloid leukemia pathways defined in the KEGG database, or are known to physically interact with each other. Additionally, four of these TFs, which are Hif1a, Cebpb, Nfkb1, and Atf1, are known targets of rapamycin. Furthermore, the trimmed network was able to predict Eno1 as an important target of Hif1a; this key interaction could not be detected without trimming the regulatory network. Conclusions The advantage of our new algorithm

  19. A Constrained Multi-Objective Learning Algorithm for Feed-Forward Neural Network Classifiers

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    M. Njah

    2017-06-01

    Full Text Available This paper proposes a new approach to address the optimal design of a Feed-forward Neural Network (FNN based classifier. The originality of the proposed methodology, called CMOA, lie in the use of a new constraint handling technique based on a self-adaptive penalty procedure in order to direct the entire search effort towards finding only Pareto optimal solutions that are acceptable. Neurons and connections of the FNN Classifier are dynamically built during the learning process. The approach includes differential evolution to create new individuals and then keeps only the non-dominated ones as the basis for the next generation. The designed FNN Classifier is applied to six binary classification benchmark problems, obtained from the UCI repository, and results indicated the advantages of the proposed approach over other existing multi-objective evolutionary neural networks classifiers reported recently in the literature.

  20. Uncovering transcriptional regulation of metabolism by using metabolic network topology

    DEFF Research Database (Denmark)

    Patil, Kiran Raosaheb; Nielsen, Jens

    2005-01-01

    in the metabolic network that follow a common transcriptional response. Thus, the algorithm enables identification of so-called reporter metabolites (metabolites around which the most significant transcriptional changes occur) and a set of connected genes with significant and coordinated response to genetic......Cellular response to genetic and environmental perturbations is often reflected and/or mediated through changes in the metabolism, because the latter plays a key role in providing Gibbs free energy and precursors for biosynthesis. Such metabolic changes are often exerted through transcriptional...... therefore developed an algorithm that is based on hypothesis-driven data analysis to uncover the transcriptional regulatory architecture of metabolic networks. By using information on the metabolic network topology from genome-scale metabolic reconstruction, we show that it is possible to reveal patterns...

  1. Automatic construction of a recurrent neural network based classifier for vehicle passage detection

    Science.gov (United States)

    Burnaev, Evgeny; Koptelov, Ivan; Novikov, German; Khanipov, Timur

    2017-03-01

    Recurrent Neural Networks (RNNs) are extensively used for time-series modeling and prediction. We propose an approach for automatic construction of a binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for detection of a vehicle passage through a checkpoint. As an input to the classifier we use multidimensional signals of various sensors that are installed on the checkpoint. Obtained results demonstrate that the previous approach to handcrafting a classifier, consisting of a set of deterministic rules, can be successfully replaced by an automatic RNN training on an appropriately labelled data.

  2. On cycles in the transcription network of Saccharomyces cerevisiae

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    Berman Piotr

    2008-01-01

    Full Text Available Abstract Background We investigate the cycles in the transcription network of Saccharomyces cerevisiae. Unlike a similar network of Escherichia coli, it contains many cycles. We characterize properties of these cycles and their place in the regulatory mechanism of the cell. Results Almost all cycles in the transcription network of Saccharomyces cerevisiae are contained in a single strongly connected component, which we call LSCC (L for "largest", except for a single cycle of two transcription factors. The fact that LSCC includes almost all cycles is well explained by the properties of a random graph with the same in- and out-degrees of the nodes. Among different physiological conditions, cell cycle has the most significant relationship with LSCC, as the set of 64 transcription interactions that are active in all phases of the cell cycle has overlap of 27 with the interactions of LSCC (of which there are 49. Conversely, if we remove the interactions that are active in all phases of the cell cycle (25% of interactions to transcription factors, the LSCC would have only three nodes and 5 edges, many fewer than expected. This subgraph of the transcription network consists mostly of interactions that are active only in the stress response subnetwork. We also characterize the role of LSCC in the topology of the network. We show that LSCC can be used to define a natural hierarchy in the network and that in every physiological subnetwork LSCC plays a pivotal role. Conclusion Apart from those well-defined conditions, the transcription network of Saccharomyces cerevisiae is devoid of cycles. It was observed that two conditions that were studied and that have no cycles of their own are exogenous: diauxic shift and DNA repair, while cell cycle and sporulation are endogenous. We claim that in a certain sense (slow recovery stress response is endogenous as well.

  3. Elucidating MicroRNA Regulatory Networks Using Transcriptional, Post-transcriptional, and Histone Modification Measurements

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    Sara J.C. Gosline

    2016-01-01

    Full Text Available MicroRNAs (miRNAs regulate diverse biological processes by repressing mRNAs, but their modest effects on direct targets, together with their participation in larger regulatory networks, make it challenging to delineate miRNA-mediated effects. Here, we describe an approach to characterizing miRNA-regulatory networks by systematically profiling transcriptional, post-transcriptional and epigenetic activity in a pair of isogenic murine fibroblast cell lines with and without Dicer expression. By RNA sequencing (RNA-seq and CLIP (crosslinking followed by immunoprecipitation sequencing (CLIP-seq, we found that most of the changes induced by global miRNA loss occur at the level of transcription. We then introduced a network modeling approach that integrated these data with epigenetic data to identify specific miRNA-regulated transcription factors that explain the impact of miRNA perturbation on gene expression. In total, we demonstrate that combining multiple genome-wide datasets spanning diverse regulatory modes enables accurate delineation of the downstream miRNA-regulated transcriptional network and establishes a model for studying similar networks in other systems.

  4. A Critical Evaluation of Network and Pathway-Based Classifiers for Outcome Prediction in Breast Cancer

    NARCIS (Netherlands)

    C. Staiger (Christine); S. Cadot; R Kooter; M. Dittrich (Marcus); T. Müller (Tobias); G.W. Klau (Gunnar); L.F.A. Wessels (Lodewyk)

    2012-01-01

    htmlabstractRecently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically

  5. FERAL : Network-based classifier with application to breast cancer outcome prediction

    NARCIS (Netherlands)

    Allahyar, A.; De Ridder, J.

    2015-01-01

    Motivation: Breast cancer outcome prediction based on gene expression profiles is an important strategy for personalize patient care. To improve performance and consistency of discovered markers of the initial molecular classifiers, network-based outcome prediction methods (NOPs) have been proposed.

  6. Classifying the molecular functions of Rab GTPases in membrane trafficking using deep convolutional neural networks.

    Science.gov (United States)

    Le, Nguyen-Quoc-Khanh; Ho, Quang-Thai; Ou, Yu-Yen

    2018-06-13

    Deep learning has been increasingly used to solve a number of problems with state-of-the-art performance in a wide variety of fields. In biology, deep learning can be applied to reduce feature extraction time and achieve high levels of performance. In our present work, we apply deep learning via two-dimensional convolutional neural networks and position-specific scoring matrices to classify Rab protein molecules, which are main regulators in membrane trafficking for transferring proteins and other macromolecules throughout the cell. The functional loss of specific Rab molecular functions has been implicated in a variety of human diseases, e.g., choroideremia, intellectual disabilities, cancer. Therefore, creating a precise model for classifying Rabs is crucial in helping biologists understand the molecular functions of Rabs and design drug targets according to such specific human disease information. We constructed a robust deep neural network for classifying Rabs that achieved an accuracy of 99%, 99.5%, 96.3%, and 97.6% for each of four specific molecular functions. Our approach demonstrates superior performance to traditional artificial neural networks. Therefore, from our proposed study, we provide both an effective tool for classifying Rab proteins and a basis for further research that can improve the performance of biological modeling using deep neural networks. Copyright © 2018 Elsevier Inc. All rights reserved.

  7. Feature extraction using convolutional neural network for classifying breast density in mammographic images

    Science.gov (United States)

    Thomaz, Ricardo L.; Carneiro, Pedro C.; Patrocinio, Ana C.

    2017-03-01

    Breast cancer is the leading cause of death for women in most countries. The high levels of mortality relate mostly to late diagnosis and to the direct proportionally relationship between breast density and breast cancer development. Therefore, the correct assessment of breast density is important to provide better screening for higher risk patients. However, in modern digital mammography the discrimination among breast densities is highly complex due to increased contrast and visual information for all densities. Thus, a computational system for classifying breast density might be a useful tool for aiding medical staff. Several machine-learning algorithms are already capable of classifying small number of classes with good accuracy. However, machinelearning algorithms main constraint relates to the set of features extracted and used for classification. Although well-known feature extraction techniques might provide a good set of features, it is a complex task to select an initial set during design of a classifier. Thus, we propose feature extraction using a Convolutional Neural Network (CNN) for classifying breast density by a usual machine-learning classifier. We used 307 mammographic images downsampled to 260x200 pixels to train a CNN and extract features from a deep layer. After training, the activation of 8 neurons from a deep fully connected layer are extracted and used as features. Then, these features are feedforward to a single hidden layer neural network that is cross-validated using 10-folds to classify among four classes of breast density. The global accuracy of this method is 98.4%, presenting only 1.6% of misclassification. However, the small set of samples and memory constraints required the reuse of data in both CNN and MLP-NN, therefore overfitting might have influenced the results even though we cross-validated the network. Thus, although we presented a promising method for extracting features and classifying breast density, a greater database is

  8. Generating prior probabilities for classifiers of brain tumours using belief networks

    Directory of Open Access Journals (Sweden)

    Arvanitis Theodoros N

    2007-09-01

    Full Text Available Abstract Background Numerous methods for classifying brain tumours based on magnetic resonance spectra and imaging have been presented in the last 15 years. Generally, these methods use supervised machine learning to develop a classifier from a database of cases for which the diagnosis is already known. However, little has been published on developing classifiers based on mixed modalities, e.g. combining imaging information with spectroscopy. In this work a method of generating probabilities of tumour class from anatomical location is presented. Methods The method of "belief networks" is introduced as a means of generating probabilities that a tumour is any given type. The belief networks are constructed using a database of paediatric tumour cases consisting of data collected over five decades; the problems associated with using this data are discussed. To verify the usefulness of the networks, an application of the method is presented in which prior probabilities were generated and combined with a classification of tumours based solely on MRS data. Results Belief networks were constructed from a database of over 1300 cases. These can be used to generate a probability that a tumour is any given type. Networks are presented for astrocytoma grades I and II, astrocytoma grades III and IV, ependymoma, pineoblastoma, primitive neuroectodermal tumour (PNET, germinoma, medulloblastoma, craniopharyngioma and a group representing rare tumours, "other". Using the network to generate prior probabilities for classification improves the accuracy when compared with generating prior probabilities based on class prevalence. Conclusion Bayesian belief networks are a simple way of using discrete clinical information to generate probabilities usable in classification. The belief network method can be robust to incomplete datasets. Inclusion of a priori knowledge is an effective way of improving classification of brain tumours by non-invasive methods.

  9. Global Analysis of Photosynthesis Transcriptional Regulatory Networks

    Science.gov (United States)

    Imam, Saheed; Noguera, Daniel R.; Donohue, Timothy J.

    2014-01-01

    Photosynthesis is a crucial biological process that depends on the interplay of many components. This work analyzed the gene targets for 4 transcription factors: FnrL, PrrA, CrpK and MppG (RSP_2888), which are known or predicted to control photosynthesis in Rhodobacter sphaeroides. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) identified 52 operons under direct control of FnrL, illustrating its regulatory role in photosynthesis, iron homeostasis, nitrogen metabolism and regulation of sRNA synthesis. Using global gene expression analysis combined with ChIP-seq, we mapped the regulons of PrrA, CrpK and MppG. PrrA regulates ∼34 operons encoding mainly photosynthesis and electron transport functions, while CrpK, a previously uncharacterized Crp-family protein, regulates genes involved in photosynthesis and maintenance of iron homeostasis. Furthermore, CrpK and FnrL share similar DNA binding determinants, possibly explaining our observation of the ability of CrpK to partially compensate for the growth defects of a ΔFnrL mutant. We show that the Rrf2 family protein, MppG, plays an important role in photopigment biosynthesis, as part of an incoherent feed-forward loop with PrrA. Our results reveal a previously unrealized, high degree of combinatorial regulation of photosynthetic genes and significant cross-talk between their transcriptional regulators, while illustrating previously unidentified links between photosynthesis and the maintenance of iron homeostasis. PMID:25503406

  10. Global analysis of photosynthesis transcriptional regulatory networks.

    Directory of Open Access Journals (Sweden)

    Saheed Imam

    2014-12-01

    Full Text Available Photosynthesis is a crucial biological process that depends on the interplay of many components. This work analyzed the gene targets for 4 transcription factors: FnrL, PrrA, CrpK and MppG (RSP_2888, which are known or predicted to control photosynthesis in Rhodobacter sphaeroides. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq identified 52 operons under direct control of FnrL, illustrating its regulatory role in photosynthesis, iron homeostasis, nitrogen metabolism and regulation of sRNA synthesis. Using global gene expression analysis combined with ChIP-seq, we mapped the regulons of PrrA, CrpK and MppG. PrrA regulates ∼34 operons encoding mainly photosynthesis and electron transport functions, while CrpK, a previously uncharacterized Crp-family protein, regulates genes involved in photosynthesis and maintenance of iron homeostasis. Furthermore, CrpK and FnrL share similar DNA binding determinants, possibly explaining our observation of the ability of CrpK to partially compensate for the growth defects of a ΔFnrL mutant. We show that the Rrf2 family protein, MppG, plays an important role in photopigment biosynthesis, as part of an incoherent feed-forward loop with PrrA. Our results reveal a previously unrealized, high degree of combinatorial regulation of photosynthetic genes and significant cross-talk between their transcriptional regulators, while illustrating previously unidentified links between photosynthesis and the maintenance of iron homeostasis.

  11. Global transcriptional regulatory network for Escherichia coli robustly connects gene expression to transcription factor activities

    DEFF Research Database (Denmark)

    Fang, Xin; Sastry, Anand; Mih, Nathan

    2017-01-01

    Transcriptional regulatory networks (TRNs) have been studied intensely for >25 y. Yet, even for the Escherichia coli TRN-probably the best characterized TRN-several questions remain. Here, we address three questions: (i) How complete is our knowledge of the E. coli TRN; (ii) how well can we predi...

  12. Transcriptional network systems in cartilage development and disease.

    Science.gov (United States)

    Nishimura, Riko; Hata, Kenji; Nakamura, Eriko; Murakami, Tomohiko; Takahata, Yoshifumi

    2018-04-01

    Transcription factors play important roles in the regulation of cartilage development by controlling the expression of chondrogenic genes. Genetic studies have revealed that Sox9/Sox5/Sox6, Runx2/Runx3 and Osterix in particular are essential for the sequential steps of cartilage development. Importantly, these transcription factors form network systems that are also required for appropriate cartilage development. Molecular cloning approaches have largely contributed to the identification of several transcriptional partners for Sox9 and Runx2 during cartilage development. Although the importance of a negative-feedback loop between Indian hedgehog (Ihh) and parathyroid hormone-related protein (PTHrP) in chondrocyte hypertrophy has been well established, recent studies indicate that several transcription factors interact with the Ihh-PTHrP loop and demonstrated that Ihh has multiple functions in the regulation of cartilage development. The most common cartilage disorder, osteoarthritis, has been reported to result from the pathological action of several transcription factors, including Runx2, C/EBPβ and HIF-2α. On the other hand, NFAT family members appear to play roles in the protection of cartilage from osteoarthritis. It is also becoming important to understand the homeostasis and regulation of articular chondrocytes, because they have different cellular and molecular features from chondrocytes of the growth plate. This review summarizes the regulation and roles of transcriptional network systems in cartilage development and their pathological roles in osteoarthritis.

  13. Neural-network classifiers for automatic real-world aerial image recognition

    Science.gov (United States)

    Greenberg, Shlomo; Guterman, Hugo

    1996-08-01

    We describe the application of the multilayer perceptron (MLP) network and a version of the adaptive resonance theory version 2-A (ART 2-A) network to the problem of automatic aerial image recognition (AAIR). The classification of aerial images, independent of their positions and orientations, is required for automatic tracking and target recognition. Invariance is achieved by the use of different invariant feature spaces in combination with supervised and unsupervised neural networks. The performance of neural-network-based classifiers in conjunction with several types of invariant AAIR global features, such as the Fourier-transform space, Zernike moments, central moments, and polar transforms, are examined. The advantages of this approach are discussed. The performance of the MLP network is compared with that of a classical correlator. The MLP neural-network correlator outperformed the binary phase-only filter (BPOF) correlator. It was found that the ART 2-A distinguished itself with its speed and its low number of required training vectors. However, only the MLP classifier was able to deal with a combination of shift and rotation geometric distortions.

  14. Novel transcriptional networks regulated by CLOCK in human neurons.

    Science.gov (United States)

    Fontenot, Miles R; Berto, Stefano; Liu, Yuxiang; Werthmann, Gordon; Douglas, Connor; Usui, Noriyoshi; Gleason, Kelly; Tamminga, Carol A; Takahashi, Joseph S; Konopka, Genevieve

    2017-11-01

    The molecular mechanisms underlying human brain evolution are not fully understood; however, previous work suggested that expression of the transcription factor CLOCK in the human cortex might be relevant to human cognition and disease. In this study, we investigated this novel transcriptional role for CLOCK in human neurons by performing chromatin immunoprecipitation sequencing for endogenous CLOCK in adult neocortices and RNA sequencing following CLOCK knockdown in differentiated human neurons in vitro. These data suggested that CLOCK regulates the expression of genes involved in neuronal migration, and a functional assay showed that CLOCK knockdown increased neuronal migratory distance. Furthermore, dysregulation of CLOCK disrupts coexpressed networks of genes implicated in neuropsychiatric disorders, and the expression of these networks is driven by hub genes with human-specific patterns of expression. These data support a role for CLOCK-regulated transcriptional cascades involved in human brain evolution and function. © 2017 Fontenot et al.; Published by Cold Spring Harbor Laboratory Press.

  15. Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image.

    Science.gov (United States)

    Huan, Er-Yang; Wen, Gui-Hua; Zhang, Shi-Jun; Li, Dan-Yang; Hu, Yang; Chang, Tian-Yuan; Wang, Qing; Huang, Bing-Lin

    2017-01-01

    Body constitution classification is the basis and core content of traditional Chinese medicine constitution research. It is to extract the relevant laws from the complex constitution phenomenon and finally build the constitution classification system. Traditional identification methods have the disadvantages of inefficiency and low accuracy, for instance, questionnaires. This paper proposed a body constitution recognition algorithm based on deep convolutional neural network, which can classify individual constitution types according to face images. The proposed model first uses the convolutional neural network to extract the features of face image and then combines the extracted features with the color features. Finally, the fusion features are input to the Softmax classifier to get the classification result. Different comparison experiments show that the algorithm proposed in this paper can achieve the accuracy of 65.29% about the constitution classification. And its performance was accepted by Chinese medicine practitioners.

  16. Autoregressive Integrated Adaptive Neural Networks Classifier for EEG-P300 Classification

    Directory of Open Access Journals (Sweden)

    Demi Soetraprawata

    2013-06-01

    Full Text Available Brain Computer Interface has a potency to be applied in mechatronics apparatus and vehicles in the future. Compared to the other techniques, EEG is the most preferred for BCI designs. In this paper, a new adaptive neural network classifier of different mental activities from EEG-based P300 signals is proposed. To overcome the over-training that is caused by noisy and non-stationary data, the EEG signals are filtered and extracted using autoregressive models before passed to the adaptive neural networks classifier. To test the improvement in the EEG classification performance with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis. The experiment results show that the all subjects achieve a classification accuracy of 100%.

  17. ELHnet: a convolutional neural network for classifying cochlear endolymphatic hydrops imaged with optical coherence tomography.

    Science.gov (United States)

    Liu, George S; Zhu, Michael H; Kim, Jinkyung; Raphael, Patrick; Applegate, Brian E; Oghalai, John S

    2017-10-01

    Detection of endolymphatic hydrops is important for diagnosing Meniere's disease, and can be performed non-invasively using optical coherence tomography (OCT) in animal models as well as potentially in the clinic. Here, we developed ELHnet, a convolutional neural network to classify endolymphatic hydrops in a mouse model using learned features from OCT images of mice cochleae. We trained ELHnet on 2159 training and validation images from 17 mice, using only the image pixels and observer-determined labels of endolymphatic hydrops as the inputs. We tested ELHnet on 37 images from 37 mice that were previously not used, and found that the neural network correctly classified 34 of the 37 mice. This demonstrates an improvement in performance from previous work on computer-aided classification of endolymphatic hydrops. To the best of our knowledge, this is the first deep CNN designed for endolymphatic hydrops classification.

  18. Protein Secondary Structure Prediction Using AutoEncoder Network and Bayes Classifier

    Science.gov (United States)

    Wang, Leilei; Cheng, Jinyong

    2018-03-01

    Protein secondary structure prediction is belong to bioinformatics,and it's important in research area. In this paper, we propose a new prediction way of protein using bayes classifier and autoEncoder network. Our experiments show some algorithms including the construction of the model, the classification of parameters and so on. The data set is a typical CB513 data set for protein. In terms of accuracy, the method is the cross validation based on the 3-fold. Then we can get the Q3 accuracy. Paper results illustrate that the autoencoder network improved the prediction accuracy of protein secondary structure.

  19. Machine learning classifier using abnormal brain network topological metrics in major depressive disorder.

    Science.gov (United States)

    Guo, Hao; Cao, Xiaohua; Liu, Zhifen; Li, Haifang; Chen, Junjie; Zhang, Kerang

    2012-12-05

    Resting state functional brain networks have been widely studied in brain disease research. However, it is currently unclear whether abnormal resting state functional brain network metrics can be used with machine learning for the classification of brain diseases. Resting state functional brain networks were constructed for 28 healthy controls and 38 major depressive disorder patients by thresholding partial correlation matrices of 90 regions. Three nodal metrics were calculated using graph theory-based approaches. Nonparametric permutation tests were then used for group comparisons of topological metrics, which were used as classified features in six different algorithms. We used statistical significance as the threshold for selecting features and measured the accuracies of six classifiers with different number of features. A sensitivity analysis method was used to evaluate the importance of different features. The result indicated that some of the regions exhibited significantly abnormal nodal centralities, including the limbic system, basal ganglia, medial temporal, and prefrontal regions. Support vector machine with radial basis kernel function algorithm and neural network algorithm exhibited the highest average accuracy (79.27 and 78.22%, respectively) with 28 features (Pdisorder is associated with abnormal functional brain network topological metrics and statistically significant nodal metrics can be successfully used for feature selection in classification algorithms.

  20. Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach.

    Directory of Open Access Journals (Sweden)

    Christian L Barrett

    2006-05-01

    Full Text Available The number of complete, publicly available genome sequences is now greater than 200, and this number is expected to rapidly grow in the near future as metagenomic and environmental sequencing efforts escalate and the cost of sequencing drops. In order to make use of this data for understanding particular organisms and for discerning general principles about how organisms function, it will be necessary to reconstruct their various biochemical reaction networks. Principal among these will be transcriptional regulatory networks. Given the physical and logical complexity of these networks, the various sources of (often noisy data that can be utilized for their elucidation, the monetary costs involved, and the huge number of potential experiments approximately 10(12 that can be performed, experiment design algorithms will be necessary for synthesizing the various computational and experimental data to maximize the efficiency of regulatory network reconstruction. This paper presents an algorithm for experimental design to systematically and efficiently reconstruct transcriptional regulatory networks. It is meant to be applied iteratively in conjunction with an experimental laboratory component. The algorithm is presented here in the context of reconstructing transcriptional regulation for metabolism in Escherichia coli, and, through a retrospective analysis with previously performed experiments, we show that the produced experiment designs conform to how a human would design experiments. The algorithm is able to utilize probability estimates based on a wide range of computational and experimental sources to suggest experiments with the highest potential of discovering the greatest amount of new regulatory knowledge.

  1. Transcriptional control in the segmentation gene network of Drosophila.

    Directory of Open Access Journals (Sweden)

    Mark D Schroeder

    2004-09-01

    Full Text Available The segmentation gene network of Drosophila consists of maternal and zygotic factors that generate, by transcriptional (cross- regulation, expression patterns of increasing complexity along the anterior-posterior axis of the embryo. Using known binding site information for maternal and zygotic gap transcription factors, the computer algorithm Ahab recovers known segmentation control elements (modules with excellent success and predicts many novel modules within the network and genome-wide. We show that novel module predictions are highly enriched in the network and typically clustered proximal to the promoter, not only upstream, but also in intronic space and downstream. When placed upstream of a reporter gene, they consistently drive patterned blastoderm expression, in most cases faithfully producing one or more pattern elements of the endogenous gene. Moreover, we demonstrate for the entire set of known and newly validated modules that Ahab's prediction of binding sites correlates well with the expression patterns produced by the modules, revealing basic rules governing their composition. Specifically, we show that maternal factors consistently act as activators and that gap factors act as repressors, except for the bimodal factor Hunchback. Our data suggest a simple context-dependent rule for its switch from repressive to activating function. Overall, the composition of modules appears well fitted to the spatiotemporal distribution of their positive and negative input factors. Finally, by comparing Ahab predictions with different categories of transcription factor input, we confirm the global regulatory structure of the segmentation gene network, but find odd skipped behaving like a primary pair-rule gene. The study expands our knowledge of the segmentation gene network by increasing the number of experimentally tested modules by 50%. For the first time, the entire set of validated modules is analyzed for binding site composition under a

  2. Isolated guitar transcription using a deep belief network

    Directory of Open Access Journals (Sweden)

    Gregory Burlet

    2017-03-01

    Full Text Available Music transcription involves the transformation of an audio recording to common music notation, colloquially referred to as sheet music. Manually transcribing audio recordings is a difficult and time-consuming process, even for experienced musicians. In response, several algorithms have been proposed to automatically analyze and transcribe the notes sounding in an audio recording; however, these algorithms are often general-purpose, attempting to process any number of instruments producing any number of notes sounding simultaneously. This paper presents a polyphonic transcription algorithm that is constrained to processing the audio output of a single instrument, specifically an acoustic guitar. The transcription system consists of a novel note pitch estimation algorithm that uses a deep belief network and multi-label learning techniques to generate multiple pitch estimates for each analysis frame of the input audio signal. Using a compiled dataset of synthesized guitar recordings for evaluation, the algorithm described in this work results in an 11% increase in the f-measure of note transcriptions relative to Zhou et al.’s (2009 transcription algorithm in the literature. This paper demonstrates the effectiveness of deep, multi-label learning for the task of polyphonic transcription.

  3. Transcriptional regulation of the carbohydrate utilization network in Thermotoga maritima

    Directory of Open Access Journals (Sweden)

    Dmitry A Rodionov

    2013-08-01

    Full Text Available Hyperthermophilic bacteria from the Thermotogales lineage can produce hydrogen by fermenting a wide range of carbohydrates. Previous experimental studies identified a large fraction of genes committed to carbohydrate degradation and utilization in the model bacterium Thermotoga maritima. Knowledge of these genes enabled comprehensive reconstruction of biochemical pathways comprising the carbohydrate utilization network. However, transcriptional factors (TFs and regulatory mechanisms driving this network remained largely unknown. Here, we used an integrated approach based on comparative analysis of genomic and transcriptomic data for the reconstruction of the carbohydrate utilization regulatory networks in 11 Thermotogales genomes. We identified DNA-binding motifs and regulons for 19 orthologous TFs in the Thermotogales. The inferred regulatory network in T. maritima contains 181 genes encoding TFs, sugar catabolic enzymes and ABC-family transporters. In contrast to many previously described bacteria, a transcriptional regulation strategy of Thermotoga does not employ global regulatory factors. The reconstructed regulatory network in T. maritima was validated by gene expression profiling on a panel of mono- and disaccharides and by in vitro DNA-binding assays. The observed upregulation of genes involved in catabolism of pectin, trehalose, cellobiose, arabinose, rhamnose, xylose, glucose, galactose, and ribose showed a strong correlation with the UxaR, TreR, BglR, CelR, AraR, RhaR, XylR, GluR, GalR, and RbsR regulons. Ultimately, this study elucidated the transcriptional regulatory network and mechanisms controlling expression of carbohydrate utilization genes in T. maritima. In addition to improving the functional annotations of associated transporters and catabolic enzymes, this research provides novel insights into the evolution of regulatory networks in Thermotogales.

  4. Coevolution within a transcriptional network by compensatory trans and cis mutations

    KAUST Repository

    Kuo, D.; Licon, K.; Bandyopadhyay, S.; Chuang, R.; Luo, C.; Catalana, J.; Ravasi, Timothy; Tan, K.; Ideker, T.

    2010-01-01

    Transcriptional networks have been shown to evolve very rapidly, prompting questions as to how such changes arise and are tolerated. Recent comparisons of transcriptional networks across species have implicated variations in the cis-acting DNA

  5. Transcription Factor Networks Directing the Development, Function, and Evolution of Innate Lymphoid Effectors

    Science.gov (United States)

    Kang, Joonsoo; Malhotra, Nidhi

    2015-01-01

    Mammalian lymphoid immunity is mediated by fast and slow responders to pathogens. Fast innate lymphocytes are active within hours after infections in mucosal tissues. Slow adaptive lymphocytes are conventional T and B cells with clonal antigen receptors that function days after pathogen exposure. A transcription factor (TF) regulatory network guiding early T cell development is at the core of effector function diversification in all innate lymphocytes, and the kinetics of immune responses is set by developmental programming. Operational units within the innate lymphoid system are not classified by the types of pathogen-sensing machineries but rather by discrete effector functions programmed by regulatory TF networks. Based on the evolutionary history of TFs of the regulatory networks, fast effectors likely arose earlier in the evolution of animals to fortify body barriers, and in mammals they often develop in fetal ontogeny prior to the establishment of fully competent adaptive immunity. PMID:25650177

  6. Uncovering Transcriptional Regulatory Networks by Sparse Bayesian Factor Model

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    Qi Yuan(Alan

    2010-01-01

    Full Text Available Abstract The problem of uncovering transcriptional regulation by transcription factors (TFs based on microarray data is considered. A novel Bayesian sparse correlated rectified factor model (BSCRFM is proposed that models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF-regulated genes. The model admits prior knowledge from existing database regarding TF-regulated target genes based on a sparse prior and through a developed Gibbs sampling algorithm, a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can be obtained. The proposed model and the Gibbs sampling algorithm were evaluated on the simulated systems, and results demonstrated the validity and effectiveness of the proposed approach. The proposed model was then applied to the breast cancer microarray data of patients with Estrogen Receptor positive ( status and Estrogen Receptor negative ( status, respectively.

  7. Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree.

    Science.gov (United States)

    Özdemir, Merve Erkınay; Telatar, Ziya; Eroğul, Osman; Tunca, Yusuf

    2018-05-01

    Dysmorphic syndromes have different facial malformations. These malformations are significant to an early diagnosis of dysmorphic syndromes and contain distinctive information for face recognition. In this study we define the certain features of each syndrome by considering facial malformations and classify Fragile X, Hurler, Prader Willi, Down, Wolf Hirschhorn syndromes and healthy groups automatically. The reference points are marked on the face images and ratios between the points' distances are taken into consideration as features. We suggest a neural network based hierarchical decision tree structure in order to classify the syndrome types. We also implement k-nearest neighbor (k-NN) and artificial neural network (ANN) classifiers to compare classification accuracy with our hierarchical decision tree. The classification accuracy is 50, 73 and 86.7% with k-NN, ANN and hierarchical decision tree methods, respectively. Then, the same images are shown to a clinical expert who achieve a recognition rate of 46.7%. We develop an efficient system to recognize different syndrome types automatically in a simple, non-invasive imaging data, which is independent from the patient's age, sex and race at high accuracy. The promising results indicate that our method can be used for pre-diagnosis of the dysmorphic syndromes by clinical experts.

  8. Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis

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    Bach Phi Duong

    2018-04-01

    Full Text Available The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs. The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance.

  9. Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis.

    Science.gov (United States)

    Duong, Bach Phi; Kim, Jong-Myon

    2018-04-07

    The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance.

  10. Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis

    Science.gov (United States)

    Kim, Jong-Myon

    2018-01-01

    The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance. PMID:29642466

  11. Evolutionary Analysis of DELLA-Associated Transcriptional Networks

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    Miguel A. Blázquez

    2017-04-01

    Full Text Available DELLA proteins are transcriptional regulators present in all land plants which have been shown to modulate the activity of over 100 transcription factors in Arabidopsis, involved in multiple physiological and developmental processes. It has been proposed that DELLAs transduce environmental information to pre-wired transcriptional circuits because their stability is regulated by gibberellins (GAs, whose homeostasis largely depends on environmental signals. The ability of GAs to promote DELLA degradation coincides with the origin of vascular plants, but the presence of DELLAs in other land plants poses at least two questions: what regulatory properties have DELLAs provided to the behavior of transcriptional networks in land plants, and how has the recruitment of DELLAs by GA signaling affected this regulation. To address these issues, we have constructed gene co-expression networks of four different organisms within the green lineage with different properties regarding DELLAs: Arabidopsis thaliana and Solanum lycopersicum (both with GA-regulated DELLA proteins, Physcomitrella patens (with GA-independent DELLA proteins and Chlamydomonas reinhardtii (a green alga without DELLA, and we have examined the relative evolution of the subnetworks containing the potential DELLA-dependent transcriptomes. Network analysis indicates a relative increase in parameters associated with the degree of interconnectivity in the DELLA-associated subnetworks of land plants, with a stronger effect in species with GA-regulated DELLA proteins. These results suggest that DELLAs may have played a role in the coordination of multiple transcriptional programs along evolution, and the function of DELLAs as regulatory ‘hubs’ became further consolidated after their recruitment by GA signaling in higher plants.

  12. Inferring the role of transcription factors in regulatory networks

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    Le Borgne Michel

    2008-05-01

    Full Text Available Abstract Background Expression profiles obtained from multiple perturbation experiments are increasingly used to reconstruct transcriptional regulatory networks, from well studied, simple organisms up to higher eukaryotes. Admittedly, a key ingredient in developing a reconstruction method is its ability to integrate heterogeneous sources of information, as well as to comply with practical observability issues: measurements can be scarce or noisy. In this work, we show how to combine a network of genetic regulations with a set of expression profiles, in order to infer the functional effect of the regulations, as inducer or repressor. Our approach is based on a consistency rule between a network and the signs of variation given by expression arrays. Results We evaluate our approach in several settings of increasing complexity. First, we generate artificial expression data on a transcriptional network of E. coli extracted from the literature (1529 nodes and 3802 edges, and we estimate that 30% of the regulations can be annotated with about 30 profiles. We additionally prove that at most 40.8% of the network can be inferred using our approach. Second, we use this network in order to validate the predictions obtained with a compendium of real expression profiles. We describe a filtering algorithm that generates particularly reliable predictions. Finally, we apply our inference approach to S. cerevisiae transcriptional network (2419 nodes and 4344 interactions, by combining ChIP-chip data and 15 expression profiles. We are able to detect and isolate inconsistencies between the expression profiles and a significant portion of the model (15% of all the interactions. In addition, we report predictions for 14.5% of all interactions. Conclusion Our approach does not require accurate expression levels nor times series. Nevertheless, we show on both data, real and artificial, that a relatively small number of perturbation experiments are enough to determine

  13. Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes.

    Science.gov (United States)

    Luo, Yuan; Cheng, Yu; Uzuner, Özlem; Szolovits, Peter; Starren, Justin

    2018-01-01

    We propose Segment Convolutional Neural Networks (Seg-CNNs) for classifying relations from clinical notes. Seg-CNNs use only word-embedding features without manual feature engineering. Unlike typical CNN models, relations between 2 concepts are identified by simultaneously learning separate representations for text segments in a sentence: preceding, concept1, middle, concept2, and succeeding. We evaluate Seg-CNN on the i2b2/VA relation classification challenge dataset. We show that Seg-CNN achieves a state-of-the-art micro-average F-measure of 0.742 for overall evaluation, 0.686 for classifying medical problem-treatment relations, 0.820 for medical problem-test relations, and 0.702 for medical problem-medical problem relations. We demonstrate the benefits of learning segment-level representations. We show that medical domain word embeddings help improve relation classification. Seg-CNNs can be trained quickly for the i2b2/VA dataset on a graphics processing unit (GPU) platform. These results support the use of CNNs computed over segments of text for classifying medical relations, as they show state-of-the-art performance while requiring no manual feature engineering. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  14. Performance Analysis and Optimization for Cognitive Radio Networks with Classified Secondary Users and Impatient Packets

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    Yuan Zhao

    2017-01-01

    Full Text Available A cognitive radio network with classified Secondary Users (SUs is considered. There are two types of SU packets, namely, SU1 packets and SU2 packets, in the system. The SU1 packets have higher priority than the SU2 packets. Considering the diversity of the SU packets and the real-time need of the interrupted SU packets, a novel spectrum allocation strategy with classified SUs and impatient packets is proposed. Based on the number of PU packets, SU1 packets, and SU2 packets in the system, by modeling the queue dynamics of the networks users as a three-dimensional discrete-time Markov chain, the transition probability matrix of the Markov chain is given. Then with the steady-state analysis, some important performance measures of the SU2 packets are derived to show the system performance with numerical results. Specially, in order to optimize the system actions of the SU2 packets, the individually optimal strategy and the socially optimal strategy for the SU2 packets are demonstrated. Finally, a pricing mechanism is provided to oblige the SU2 packets to follow the socially optimal strategy.

  15. Use of artificial neural networks and geographic objects for classifying remote sensing imagery

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    Pedro Resende Silva

    2014-06-01

    Full Text Available The aim of this study was to develop a methodology for mapping land use and land cover in the northern region of Minas Gerais state, where, in addition to agricultural land, the landscape is dominated by native cerrado, deciduous forests, and extensive areas of vereda. Using forest inventory data, as well as RapidEye, Landsat TM and MODIS imagery, three specific objectives were defined: 1 to test use of image segmentation techniques for an object-based classification encompassing spectral, spatial and temporal information, 2 to test use of high spatial resolution RapidEye imagery combined with Landsat TM time series imagery for capturing the effects of seasonality, and 3 to classify data using Artificial Neural Networks. Using MODIS time series and forest inventory data, time signatures were extracted from the dominant vegetation formations, enabling selection of the best periods of the year to be represented in the classification process. Objects created with the segmentation of RapidEye images, along with the Landsat TM time series images, were classified by ten different Multilayer Perceptron network architectures. Results showed that the methodology in question meets both the purposes of this study and the characteristics of the local plant life. With excellent accuracy values for native classes, the study showed the importance of a well-structured database for classification and the importance of suitable image segmentation to meet specific purposes.

  16. Classifying Sources Influencing Indoor Air Quality (IAQ Using Artificial Neural Network (ANN

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    Shaharil Mad Saad

    2015-05-01

    Full Text Available Monitoring indoor air quality (IAQ is deemed important nowadays. A sophisticated IAQ monitoring system which could classify the source influencing the IAQ is definitely going to be very helpful to the users. Therefore, in this paper, an IAQ monitoring system has been proposed with a newly added feature which enables the system to identify the sources influencing the level of IAQ. In order to achieve this, the data collected has been trained with artificial neural network or ANN—a proven method for pattern recognition. Basically, the proposed system consists of sensor module cloud (SMC, base station and service-oriented client. The SMC contain collections of sensor modules that measure the air quality data and transmit the captured data to base station through wireless network. The IAQ monitoring system is also equipped with IAQ Index and thermal comfort index which could tell the users about the room’s conditions. The results showed that the system is able to measure the level of air quality and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, fragrance presence, foods and beverages and human activity.

  17. In silico transcriptional regulatory networks involved in tomato fruit ripening

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    Stilianos Arhondakis

    2016-08-01

    Full Text Available ABSTRACTTomato fruit ripening is a complex developmental programme partly mediated by transcriptional regulatory networks. Several transcription factors (TFs which are members of gene families such as MADS-box and ERF were shown to play a significant role in ripening through interconnections into an intricate network. The accumulation of large datasets of expression profiles corresponding to different stages of tomato fruit ripening and the availability of bioinformatics tools for their analysis provide an opportunity to identify TFs which might regulate gene clusters with similar co-expression patterns. We identified two TFs, a SlWRKY22-like and a SlER24 transcriptional activator which were shown to regulate modules by using the LeMoNe algorithm for the analysis of our microarray datasets representing four stages of fruit ripening, breaker, turning, pink and red ripe. The WRKY22-like module comprised a subgroup of six various calcium sensing transcripts with similar to the TF expression patterns according to real time PCR validation. A promoter motif search identified a cis acting element, the W-box, recognized by WRKY TFs that was present in the promoter region of all six calcium sensing genes. Moreover, publicly available microarray datasets of similar ripening stages were also analyzed with LeMoNe resulting in TFs such as SlERF.E1, SlERF.C1, SlERF.B2, SLERF.A2, SlWRKY24, SLWRKY37 and MADS-box/TM29 which might also play an important role in regulation of ripening. These results suggest that the SlWRKY22-like might be involved in the coordinated regulation of expression of the six calcium sensing genes. Conclusively the LeMoNe tool might lead to the identification of putative TF targets for further physiological analysis as regulators of tomato fruit ripening.

  18. Discrimination of panti p → tanti t events by a neural network classifier

    International Nuclear Information System (INIS)

    Cherubini, A.; Odorico, R.

    1992-01-01

    Neural network and conventional statistical techniques are compared in the problem of discriminating panti p→tanti t events, with top quarks decaying into anything, from the associated hadronic background at the energy of the Fermilab collider. The NN we develop for this sake is an improved version of Kohonen's learning vector quantization scheme. Performance of the NN as a tanti t event classifier is found to be less satisfactory than that achievable by statistical methods. We conclude that the probable reasons for that are: i) The NN approach presents advantages only when dealing with event distributions in the feature space which substantially differ from Gaussians; ii) NN's require much larger training sets of events than statistical discrimination in order to give comparable results. (orig.)

  19. Distributed Classification of Localization Attacks in Sensor Networks Using Exchange-Based Feature Extraction and Classifier

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    Su-Zhe Wang

    2016-01-01

    Full Text Available Secure localization under different forms of attack has become an essential task in wireless sensor networks. Despite the significant research efforts in detecting the malicious nodes, the problem of localization attack type recognition has not yet been well addressed. Motivated by this concern, we propose a novel exchange-based attack classification algorithm. This is achieved by a distributed expectation maximization extractor integrated with the PECPR-MKSVM classifier. First, the mixed distribution features based on the probabilistic modeling are extracted using a distributed expectation maximization algorithm. After feature extraction, by introducing the theory from support vector machine, an extensive contractive Peaceman-Rachford splitting method is derived to build the distributed classifier that diffuses the iteration calculation among neighbor sensors. To verify the efficiency of the distributed recognition scheme, four groups of experiments were carried out under various conditions. The average success rate of the proposed classification algorithm obtained in the presented experiments for external attacks is excellent and has achieved about 93.9% in some cases. These testing results demonstrate that the proposed algorithm can produce much greater recognition rate, and it can be also more robust and efficient even in the presence of excessive malicious scenario.

  20. Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles

    Science.gov (United States)

    Ha, Jin Gwan; Moon, Hyeonjoon; Kwak, Jin Tae; Hassan, Syed Ibrahim; Dang, Minh; Lee, O. New; Park, Han Yong

    2017-10-01

    Recently, unmanned aerial vehicles (UAVs) have gained much attention. In particular, there is a growing interest in utilizing UAVs for agricultural applications such as crop monitoring and management. We propose a computerized system that is capable of detecting Fusarium wilt of radish with high accuracy. The system adopts computer vision and machine learning techniques, including deep learning, to process the images captured by UAVs at low altitudes and to identify the infected radish. The whole radish field is first segmented into three distinctive regions (radish, bare ground, and mulching film) via a softmax classifier and K-means clustering. Then, the identified radish regions are further classified into healthy radish and Fusarium wilt of radish using a deep convolutional neural network (CNN). In identifying radish, bare ground, and mulching film from a radish field, we achieved an accuracy of ≥97.4%. In detecting Fusarium wilt of radish, the CNN obtained an accuracy of 93.3%. It also outperformed the standard machine learning algorithm, obtaining 82.9% accuracy. Therefore, UAVs equipped with computational techniques are promising tools for improving the quality and efficiency of agriculture today.

  1. Transcriptional Regulatory Network Analysis of MYB Transcription Factor Family Genes in Rice

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    Shuchi eSmita

    2015-12-01

    Full Text Available MYB transcription factor (TF is one of the largest TF families and regulates defense responses to various stresses, hormone signaling as well as many metabolic and developmental processes in plants. Understanding these regulatory hierarchies of gene expression networks in response to developmental and environmental cues is a major challenge due to the complex interactions between the genetic elements. Correlation analyses are useful to unravel co-regulated gene pairs governing biological process as well as identification of new candidate hub genes in response to these complex processes. High throughput expression profiling data are highly useful for construction of co-expression networks. In the present study, we utilized transcriptome data for comprehensive regulatory network studies of MYB TFs by top down and guide gene approaches. More than 50% of OsMYBs were strongly correlated under fifty experimental conditions with 51 hub genes via top down approach. Further, clusters were identified using Markov Clustering (MCL. To maximize the clustering performance, parameter evaluation of the MCL inflation score (I was performed in terms of enriched GO categories by measuring F-score. Comparison of co-expressed cluster and clads analyzed from phylogenetic analysis signifies their evolutionarily conserved co-regulatory role. We utilized compendium of known interaction and biological role with Gene Ontology enrichment analysis to hypothesize function of coexpressed OsMYBs. In the other part, the transcriptional regulatory network analysis by guide gene approach revealed 40 putative targets of 26 OsMYB TF hubs with high correlation value utilizing 815 microarray data. The putative targets with MYB-binding cis-elements enrichment in their promoter region, functional co-occurrence as well as nuclear localization supports our finding. Specially, enrichment of MYB binding regions involved in drought-inducibility implying their regulatory role in drought

  2. Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.

    Science.gov (United States)

    Chang, P; Grinband, J; Weinberg, B D; Bardis, M; Khy, M; Cadena, G; Su, M-Y; Cha, S; Filippi, C G; Bota, D; Baldi, P; Poisson, L M; Jain, R; Chow, D

    2018-05-10

    The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation. MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify isocitrate dehydrogenase 1 ( IDH1 ) mutation status, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase ( MGMT ) promotor methylation status. Principal component analysis of the final convolutional neural network layer was used to extract the key imaging features critical for successful classification. Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. Each genetic category was also associated with distinctive imaging features such as definition of tumor margins, T1 and FLAIR suppression, extent of edema, extent of necrosis, and textural features. Our results indicate that for The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas. We show that relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human-directed training. © 2018 by American Journal of Neuroradiology.

  3. Using network component analysis to dissect regulatory networks mediated by transcription factors in yeast.

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    Chun Ye

    2009-03-01

    Full Text Available Understanding the relationship between genetic variation and gene expression is a central question in genetics. With the availability of data from high-throughput technologies such as ChIP-Chip, expression, and genotyping arrays, we can begin to not only identify associations but to understand how genetic variations perturb the underlying transcription regulatory networks to induce differential gene expression. In this study, we describe a simple model of transcription regulation where the expression of a gene is completely characterized by two properties: the concentrations and promoter affinities of active transcription factors. We devise a method that extends Network Component Analysis (NCA to determine how genetic variations in the form of single nucleotide polymorphisms (SNPs perturb these two properties. Applying our method to a segregating population of Saccharomyces cerevisiae, we found statistically significant examples of trans-acting SNPs located in regulatory hotspots that perturb transcription factor concentrations and affinities for target promoters to cause global differential expression and cis-acting genetic variations that perturb the promoter affinities of transcription factors on a single gene to cause local differential expression. Although many genetic variations linked to gene expressions have been identified, it is not clear how they perturb the underlying regulatory networks that govern gene expression. Our work begins to fill this void by showing that many genetic variations affect the concentrations of active transcription factors in a cell and their affinities for target promoters. Understanding the effects of these perturbations can help us to paint a more complete picture of the complex landscape of transcription regulation. The software package implementing the algorithms discussed in this work is available as a MATLAB package upon request.

  4. Global transcriptional regulatory network for Escherichia coli robustly connects gene expression to transcription factor activities

    Science.gov (United States)

    Fang, Xin; Sastry, Anand; Mih, Nathan; Kim, Donghyuk; Tan, Justin; Lloyd, Colton J.; Gao, Ye; Yang, Laurence; Palsson, Bernhard O.

    2017-01-01

    Transcriptional regulatory networks (TRNs) have been studied intensely for >25 y. Yet, even for the Escherichia coli TRN—probably the best characterized TRN—several questions remain. Here, we address three questions: (i) How complete is our knowledge of the E. coli TRN; (ii) how well can we predict gene expression using this TRN; and (iii) how robust is our understanding of the TRN? First, we reconstructed a high-confidence TRN (hiTRN) consisting of 147 transcription factors (TFs) regulating 1,538 transcription units (TUs) encoding 1,764 genes. The 3,797 high-confidence regulatory interactions were collected from published, validated chromatin immunoprecipitation (ChIP) data and RegulonDB. For 21 different TF knockouts, up to 63% of the differentially expressed genes in the hiTRN were traced to the knocked-out TF through regulatory cascades. Second, we trained supervised machine learning algorithms to predict the expression of 1,364 TUs given TF activities using 441 samples. The algorithms accurately predicted condition-specific expression for 86% (1,174 of 1,364) of the TUs, while 193 TUs (14%) were predicted better than random TRNs. Third, we identified 10 regulatory modules whose definitions were robust against changes to the TRN or expression compendium. Using surrogate variable analysis, we also identified three unmodeled factors that systematically influenced gene expression. Our computational workflow comprehensively characterizes the predictive capabilities and systems-level functions of an organism’s TRN from disparate data types. PMID:28874552

  5. Transcription of Spanish Historical Handwritten Documents with Deep Neural Networks

    Directory of Open Access Journals (Sweden)

    Emilio Granell

    2018-01-01

    Full Text Available The digitization of historical handwritten document images is important for the preservation of cultural heritage. Moreover, the transcription of text images obtained from digitization is necessary to provide efficient information access to the content of these documents. Handwritten Text Recognition (HTR has become an important research topic in the areas of image and computational language processing that allows us to obtain transcriptions from text images. State-of-the-art HTR systems are, however, far from perfect. One difficulty is that they have to cope with image noise and handwriting variability. Another difficulty is the presence of a large amount of Out-Of-Vocabulary (OOV words in ancient historical texts. A solution to this problem is to use external lexical resources, but such resources might be scarce or unavailable given the nature and the age of such documents. This work proposes a solution to avoid this limitation. It consists of associating a powerful optical recognition system that will cope with image noise and variability, with a language model based on sub-lexical units that will model OOV words. Such a language modeling approach reduces the size of the lexicon while increasing the lexicon coverage. Experiments are first conducted on the publicly available Rodrigo dataset, which contains the digitization of an ancient Spanish manuscript, with a recognizer based on Hidden Markov Models (HMMs. They show that sub-lexical units outperform word units in terms of Word Error Rate (WER, Character Error Rate (CER and OOV word accuracy rate. This approach is then applied to deep net classifiers, namely Bi-directional Long-Short Term Memory (BLSTMs and Convolutional Recurrent Neural Nets (CRNNs. Results show that CRNNs outperform HMMs and BLSTMs, reaching the lowest WER and CER for this image dataset and significantly improving OOV recognition.

  6. WAVELET ANALYSIS AND NEURAL NETWORK CLASSIFIERS TO DETECT MID-SAGITTAL SECTIONS FOR NUCHAL TRANSLUCENCY MEASUREMENT

    Directory of Open Access Journals (Sweden)

    Giuseppa Sciortino

    2016-04-01

    Full Text Available We propose a methodology to support the physician in the automatic identification of mid-sagittal sections of the fetus in ultrasound videos acquired during the first trimester of pregnancy. A good mid-sagittal section is a key requirement to make the correct measurement of nuchal translucency which is one of the main marker for screening of chromosomal defects such as trisomy 13, 18 and 21. NT measurement is beyond the scope of this article. The proposed methodology is mainly based on wavelet analysis and neural network classifiers to detect the jawbone and on radial symmetry analysis to detect the choroid plexus. Those steps allow to identify the frames which represent correct mid-sagittal sections to be processed. The performance of the proposed methodology was analyzed on 3000 random frames uniformly extracted from 10 real clinical ultrasound videos. With respect to a ground-truth provided by an expert physician, we obtained a true positive, a true negative and a balanced accuracy equal to 87.26%, 94.98% and 91.12% respectively.

  7. An Unobtrusive Fall Detection and Alerting System Based on Kalman Filter and Bayes Network Classifier.

    Science.gov (United States)

    He, Jian; Bai, Shuang; Wang, Xiaoyi

    2017-06-16

    Falls are one of the main health risks among the elderly. A fall detection system based on inertial sensors can automatically detect fall event and alert a caregiver for immediate assistance, so as to reduce injuries causing by falls. Nevertheless, most inertial sensor-based fall detection technologies have focused on the accuracy of detection while neglecting quantization noise caused by inertial sensor. In this paper, an activity model based on tri-axial acceleration and gyroscope is proposed, and the difference between activities of daily living (ADLs) and falls is analyzed. Meanwhile, a Kalman filter is proposed to preprocess the raw data so as to reduce noise. A sliding window and Bayes network classifier are introduced to develop a wearable fall detection system, which is composed of a wearable motion sensor and a smart phone. The experiment shows that the proposed system distinguishes simulated falls from ADLs with a high accuracy of 95.67%, while sensitivity and specificity are 99.0% and 95.0%, respectively. Furthermore, the smart phone can issue an alarm to caregivers so as to provide timely and accurate help for the elderly, as soon as the system detects a fall.

  8. Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction.

    Science.gov (United States)

    Kumudha, P; Venkatesan, R

    Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities. The process of software development life cycle basically includes design, analysis, implementation, testing, and release phases. Generally, software testing is a critical task in the software development process wherein it is to save time and budget by detecting defects at the earliest and deliver a product without defects to the customers. This testing phase should be carefully operated in an effective manner to release a defect-free (bug-free) software product to the customers. In order to improve the software testing process, fault prediction methods identify the software parts that are more noted to be defect-prone. This paper proposes a prediction approach based on conventional radial basis function neural network (RBFNN) and the novel adaptive dimensional biogeography based optimization (ADBBO) model. The developed ADBBO based RBFNN model is tested with five publicly available datasets from the NASA data program repository. The computed results prove the effectiveness of the proposed ADBBO-RBFNN classifier approach with respect to the considered metrics in comparison with that of the early predictors available in the literature for the same datasets.

  9. Automatic Assessing of Tremor Severity Using Nonlinear Dynamics, Artificial Neural Networks and Neuro-Fuzzy Classifier

    Directory of Open Access Journals (Sweden)

    GEMAN, O.

    2014-02-01

    Full Text Available Neurological diseases like Alzheimer, epilepsy, Parkinson's disease, multiple sclerosis and other dementias influence the lives of patients, their families and society. Parkinson's disease (PD is a neurodegenerative disease that occurs due to loss of dopamine, a neurotransmitter and slow destruction of neurons. Brain area affected by progressive destruction of neurons is responsible for controlling movements, and patients with PD reveal rigid and uncontrollable gestures, postural instability, small handwriting and tremor. Commercial activity-promoting gaming systems such as the Nintendo Wii and Xbox Kinect can be used as tools for tremor, gait or other biomedical signals acquisitions. They also can aid for rehabilitation in clinical settings. This paper emphasizes the use of intelligent optical sensors or accelerometers in biomedical signal acquisition, and of the specific nonlinear dynamics parameters or fuzzy logic in Parkinson's disease tremor analysis. Nowadays, there is no screening test for early detection of PD. So, we investigated a method to predict PD, based on the image processing of the handwriting belonging to a candidate of PD. For classification and discrimination between healthy people and PD people we used Artificial Neural Networks (Radial Basis Function - RBF and Multilayer Perceptron - MLP and an Adaptive Neuro-Fuzzy Classifier (ANFC. In general, the results may be expressed as a prognostic (risk degree to contact PD.

  10. Convolutional Neural Networks with Batch Normalization for Classifying Hi-hat, Snare, and Bass Percussion Sound Samples

    DEFF Research Database (Denmark)

    Gajhede, Nicolai; Beck, Oliver; Purwins, Hendrik

    2016-01-01

    After having revolutionized image and speech processing, convolu- tional neural networks (CNN) are now starting to become more and more successful in music information retrieval as well. We compare four CNN types for classifying a dataset of more than 3000 acoustic and synthesized samples...

  11. Infrared dim moving target tracking via sparsity-based discriminative classifier and convolutional network

    Science.gov (United States)

    Qian, Kun; Zhou, Huixin; Wang, Bingjian; Song, Shangzhen; Zhao, Dong

    2017-11-01

    Infrared dim and small target tracking is a great challenging task. The main challenge for target tracking is to account for appearance change of an object, which submerges in the cluttered background. An efficient appearance model that exploits both the global template and local representation over infrared image sequences is constructed for dim moving target tracking. A Sparsity-based Discriminative Classifier (SDC) and a Convolutional Network-based Generative Model (CNGM) are combined with a prior model. In the SDC model, a sparse representation-based algorithm is adopted to calculate the confidence value that assigns more weights to target templates than negative background templates. In the CNGM model, simple cell feature maps are obtained by calculating the convolution between target templates and fixed filters, which are extracted from the target region at the first frame. These maps measure similarities between each filter and local intensity patterns across the target template, therefore encoding its local structural information. Then, all the maps form a representation, preserving the inner geometric layout of a candidate template. Furthermore, the fixed target template set is processed via an efficient prior model. The same operation is applied to candidate templates in the CNGM model. The online update scheme not only accounts for appearance variations but also alleviates the migration problem. At last, collaborative confidence values of particles are utilized to generate particles' importance weights. Experiments on various infrared sequences have validated the tracking capability of the presented algorithm. Experimental results show that this algorithm runs in real-time and provides a higher accuracy than state of the art algorithms.

  12. An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data

    Directory of Open Access Journals (Sweden)

    Evangelos Stromatias

    2017-06-01

    Full Text Available This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77% and Poker-DVS (100% real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.

  13. An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data.

    Science.gov (United States)

    Stromatias, Evangelos; Soto, Miguel; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé

    2017-01-01

    This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN) System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS) chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77%) and Poker-DVS (100%) real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.

  14. Classifying chemical mode of action using gene networks and machine learning: a case study with the herbicide linuron.

    Science.gov (United States)

    Ornostay, Anna; Cowie, Andrew M; Hindle, Matthew; Baker, Christopher J O; Martyniuk, Christopher J

    2013-12-01

    The herbicide linuron (LIN) is an endocrine disruptor with an anti-androgenic mode of action. The objectives of this study were to (1) improve knowledge of androgen and anti-androgen signaling in the teleostean ovary and to (2) assess the ability of gene networks and machine learning to classify LIN as an anti-androgen using transcriptomic data. Ovarian explants from vitellogenic fathead minnows (FHMs) were exposed to three concentrations of either 5α-dihydrotestosterone (DHT), flutamide (FLUT), or LIN for 12h. Ovaries exposed to DHT showed a significant increase in 17β-estradiol (E2) production while FLUT and LIN had no effect on E2. To improve understanding of androgen receptor signaling in the ovary, a reciprocal gene expression network was constructed for DHT and FLUT using pathway analysis and these data suggested that steroid metabolism, translation, and DNA replication are processes regulated through AR signaling in the ovary. Sub-network enrichment analysis revealed that FLUT and LIN shared more regulated gene networks in common compared to DHT. Using transcriptomic datasets from different fish species, machine learning algorithms classified LIN successfully with other anti-androgens. This study advances knowledge regarding molecular signaling cascades in the ovary that are responsive to androgens and anti-androgens and provides proof of concept that gene network analysis and machine learning can classify priority chemicals using experimental transcriptomic data collected from different fish species. © 2013.

  15. Negative autoregulation matches production and demand in synthetic transcriptional networks.

    Science.gov (United States)

    Franco, Elisa; Giordano, Giulia; Forsberg, Per-Ola; Murray, Richard M

    2014-08-15

    We propose a negative feedback architecture that regulates activity of artificial genes, or "genelets", to meet their output downstream demand, achieving robustness with respect to uncertain open-loop output production rates. In particular, we consider the case where the outputs of two genelets interact to form a single assembled product. We show with analysis and experiments that negative autoregulation matches the production and demand of the outputs: the magnitude of the regulatory signal is proportional to the "error" between the circuit output concentration and its actual demand. This two-device system is experimentally implemented using in vitro transcriptional networks, where reactions are systematically designed by optimizing nucleic acid sequences with publicly available software packages. We build a predictive ordinary differential equation (ODE) model that captures the dynamics of the system and can be used to numerically assess the scalability of this architecture to larger sets of interconnected genes. Finally, with numerical simulations we contrast our negative autoregulation scheme with a cross-activation architecture, which is less scalable and results in slower response times.

  16. Connectivity in the yeast cell cycle transcription network: inferences from neural networks.

    Directory of Open Access Journals (Sweden)

    Christopher E Hart

    2006-12-01

    Full Text Available A current challenge is to develop computational approaches to infer gene network regulatory relationships based on multiple types of large-scale functional genomic data. We find that single-layer feed-forward artificial neural network (ANN models can effectively discover gene network structure by integrating global in vivo protein:DNA interaction data (ChIP/Array with genome-wide microarray RNA data. We test this on the yeast cell cycle transcription network, which is composed of several hundred genes with phase-specific RNA outputs. These ANNs were robust to noise in data and to a variety of perturbations. They reliably identified and ranked 10 of 12 known major cell cycle factors at the top of a set of 204, based on a sum-of-squared weights metric. Comparative analysis of motif occurrences among multiple yeast species independently confirmed relationships inferred from ANN weights analysis. ANN models can capitalize on properties of biological gene networks that other kinds of models do not. ANNs naturally take advantage of patterns of absence, as well as presence, of factor binding associated with specific expression output; they are easily subjected to in silico "mutation" to uncover biological redundancies; and they can use the full range of factor binding values. A prominent feature of cell cycle ANNs suggested an analogous property might exist in the biological network. This postulated that "network-local discrimination" occurs when regulatory connections (here between MBF and target genes are explicitly disfavored in one network module (G2, relative to others and to the class of genes outside the mitotic network. If correct, this predicts that MBF motifs will be significantly depleted from the discriminated class and that the discrimination will persist through evolution. Analysis of distantly related Schizosaccharomyces pombe confirmed this, suggesting that network-local discrimination is real and complements well-known enrichment of

  17. Network based transcription factor analysis of regenerating axolotl limbs

    Directory of Open Access Journals (Sweden)

    Cameron Jo Ann

    2011-03-01

    Full Text Available Abstract Background Studies on amphibian limb regeneration began in the early 1700's but we still do not completely understand the cellular and molecular events of this unique process. Understanding a complex biological process such as limb regeneration is more complicated than the knowledge of the individual genes or proteins involved. Here we followed a systems biology approach in an effort to construct the networks and pathways of protein interactions involved in formation of the accumulation blastema in regenerating axolotl limbs. Results We used the human orthologs of proteins previously identified by our research team as bait to identify the transcription factor (TF pathways and networks that regulate blastema formation in amputated axolotl limbs. The five most connected factors, c-Myc, SP1, HNF4A, ESR1 and p53 regulate ~50% of the proteins in our data. Among these, c-Myc and SP1 regulate 36.2% of the proteins. c-Myc was the most highly connected TF (71 targets. Network analysis showed that TGF-β1 and fibronectin (FN lead to the activation of these TFs. We found that other TFs known to be involved in epigenetic reprogramming, such as Klf4, Oct4, and Lin28 are also connected to c-Myc and SP1. Conclusions Our study provides a systems biology approach to how different molecular entities inter-connect with each other during the formation of an accumulation blastema in regenerating axolotl limbs. This approach provides an in silico methodology to identify proteins that are not detected by experimental methods such as proteomics but are potentially important to blastema formation. We found that the TFs, c-Myc and SP1 and their target genes could potentially play a central role in limb regeneration. Systems biology has the potential to map out numerous other pathways that are crucial to blastema formation in regeneration-competent limbs, to compare these to the pathways that characterize regeneration-deficient limbs and finally, to identify stem

  18. Genetic networks of liver metabolism revealed by integration of metabolic and transcriptional profiling.

    Directory of Open Access Journals (Sweden)

    Christine T Ferrara

    2008-03-01

    Full Text Available Although numerous quantitative trait loci (QTL influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identification of the individual gene(s and molecular pathways leading to those phenotypes is often elusive. One way to improve understanding of genetic architecture is to classify phenotypes in greater depth by including transcriptional and metabolic profiling. In the current study, we have generated and analyzed mRNA expression and metabolic profiles in liver samples obtained in an F2 intercross between the diabetes-resistant C57BL/6 leptin(ob/ob and the diabetes-susceptible BTBR leptin(ob/ob mouse strains. This cross, which segregates for genotype and physiological traits, was previously used to identify several diabetes-related QTL. Our current investigation includes microarray analysis of over 40,000 probe sets, plus quantitative mass spectrometry-based measurements of sixty-seven intermediary metabolites in three different classes (amino acids, organic acids, and acyl-carnitines. We show that liver metabolites map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal networks for control of specific metabolic processes in liver. As a proof of principle of the practical significance of this integrative approach, we illustrate the construction of a specific causal network that links gene expression and metabolic changes in the context of glutamate metabolism, and demonstrate its validity by showing that genes in the network respond to changes in glutamine and glutamate availability. Thus, the methods described here have the potential to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes.

  19. Label-Driven Learning Framework: Towards More Accurate Bayesian Network Classifiers through Discrimination of High-Confidence Labels

    Directory of Open Access Journals (Sweden)

    Yi Sun

    2017-12-01

    Full Text Available Bayesian network classifiers (BNCs have demonstrated competitive classification accuracy in a variety of real-world applications. However, it is error-prone for BNCs to discriminate among high-confidence labels. To address this issue, we propose the label-driven learning framework, which incorporates instance-based learning and ensemble learning. For each testing instance, high-confidence labels are first selected by a generalist classifier, e.g., the tree-augmented naive Bayes (TAN classifier. Then, by focusing on these labels, conditional mutual information is redefined to more precisely measure mutual dependence between attributes, thus leading to a refined generalist with a more reasonable network structure. To enable finer discrimination, an expert classifier is tailored for each high-confidence label. Finally, the predictions of the refined generalist and the experts are aggregated. We extend TAN to LTAN (Label-driven TAN by applying the proposed framework. Extensive experimental results demonstrate that LTAN delivers superior classification accuracy to not only several state-of-the-art single-structure BNCs but also some established ensemble BNCs at the expense of reasonable computation overhead.

  20. Fault Diagnosis for Distribution Networks Using Enhanced Support Vector Machine Classifier with Classical Multidimensional Scaling

    Directory of Open Access Journals (Sweden)

    Ming-Yuan Cho

    2017-09-01

    Full Text Available In this paper, a new fault diagnosis techniques based on time domain reflectometry (TDR method with pseudo-random binary sequence (PRBS stimulus and support vector machine (SVM classifier has been investigated to recognize the different types of fault in the radial distribution feeders. This novel technique has considered the amplitude of reflected signals and the peaks of cross-correlation (CCR between the reflected and incident wave for generating fault current dataset for SVM. Furthermore, this multi-layer enhanced SVM classifier is combined with classical multidimensional scaling (CMDS feature extraction algorithm and kernel parameter optimization to increase training speed and improve overall classification accuracy. The proposed technique has been tested on a radial distribution feeder to identify ten different types of fault considering 12 input features generated by using Simulink software and MATLAB Toolbox. The success rate of SVM classifier is over 95% which demonstrates the effectiveness and the high accuracy of proposed method.

  1. Metabolic Network Topology Reveals Transcriptional Regulatory Signatures of Type 2 Diabetes

    DEFF Research Database (Denmark)

    Zelezniak, Aleksej; Pers, Tune Hannes; Pinho Soares, Simao Pedro

    2010-01-01

    mechanisms underlying these transcriptional changes and their impact on the cellular metabolic phenotype is a challenging task due to the complexity of transcriptional regulation and the highly interconnected nature of the metabolic network. In this study we integrate skeletal muscle gene expression datasets...... with human metabolic network reconstructions to identify key metabolic regulatory features of T2DM. These features include reporter metabolites—metabolites with significant collective transcriptional response in the associated enzyme-coding genes, and transcription factors with significant enrichment...... factor regulatory network connecting several parts of metabolism. The identified transcription factors include members of the CREB, NRF1 and PPAR family, among others, and represent regulatory targets for further experimental analysis. Overall, our results provide a holistic picture of key metabolic...

  2. Using Unsupervised Learning to Improve the Naive Bayes Classifier for Wireless Sensor Networks

    NARCIS (Netherlands)

    Zwartjes, G.J.; Havinga, Paul J.M.; Smit, Gerardus Johannes Maria; Hurink, Johann L.

    2012-01-01

    Online processing is essential for many sensor network applications. Sensor nodes can sample far more data than what can practically be transmitted using state of the art sensor network radios. Online processing, however, is complicated due to limited resources of individual nodes. The naive Bayes

  3. Deciphering Fur transcriptional regulatory network highlights its complex role beyond iron metabolism in Escherichia coli

    DEFF Research Database (Denmark)

    Seo, Sang Woo; Kim, Donghyuk; Latif, Haythem

    2014-01-01

    The ferric uptake regulator (Fur) plays a critical role in the transcriptional regulation of iron metabolism. However, the full regulatory potential of Fur remains undefined. Here we comprehensively reconstruct the Fur transcriptional regulatory network in Escherichia coli K-12 MG1655 in response...

  4. Current and emerging approaches to define intestinal epithelium-specific transcriptional networks

    DEFF Research Database (Denmark)

    Olsen, Anders Krüger; Boyd, Mette; Danielsen, Erik Thomas

    2012-01-01

    Upon developmental or environmental cues, the composition of transcription factors in a transcriptional regulatory network is deeply implicated in controlling the signature of the gene expression and thereby specifies the cell or tissue type. Novel methods including ChIP-chip and ChIP-Seq have been...

  5. Current and emerging approaches to define intestinal epithelium-specific transcriptional networks

    DEFF Research Database (Denmark)

    Olsen, Anders Krûger; Boyd, Mette; Danielsen, Erik Thomas

    2012-01-01

    Upon developmental or environmental cues, the composition of transcription factors in a transcriptional regulatory network is deeply implicated in controlling the signature of the gene expression and thereby specifies the cell- or tissue-type. Novel methods including ChIP-chip and ChIP-Seq have...

  6. Genetic variation shapes protein networks mainly through non-transcriptional mechanisms.

    Directory of Open Access Journals (Sweden)

    Eric J Foss

    2011-09-01

    Full Text Available Networks of co-regulated transcripts in genetically diverse populations have been studied extensively, but little is known about the degree to which these networks cause similar co-variation at the protein level. We quantified 354 proteins in a genetically diverse population of yeast segregants, which allowed for the first time construction of a coherent protein co-variation matrix. We identified tightly co-regulated groups of 36 and 93 proteins that were made up predominantly of genes involved in ribosome biogenesis and amino acid metabolism, respectively. Even though the ribosomal genes were tightly co-regulated at both the protein and transcript levels, genetic regulation of proteins was entirely distinct from that of transcripts, and almost no genes in this network showed a significant correlation between protein and transcript levels. This result calls into question the widely held belief that in yeast, as opposed to higher eukaryotes, ribosomal protein levels are regulated primarily by regulating transcript levels. Furthermore, although genetic regulation of the amino acid network was more similar for proteins and transcripts, regression analysis demonstrated that even here, proteins vary predominantly as a result of non-transcriptional variation. We also found that cis regulation, which is common in the transcriptome, is rare at the level of the proteome. We conclude that most inter-individual variation in levels of these particular high abundance proteins in this genetically diverse population is not caused by variation of their underlying transcripts.

  7. Classifying Sensors Depending on their IDs to Reduce Power Consumption in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Ayman Mohammd Brisha

    2010-05-01

    Full Text Available Wireless sensor networks produce a large amount of data that needs to be processed, delivered, and assessed according to the application objectives. Cluster-based is an effective architecture for data-gathering in wireless sensor networks. Clustering provides an effective way for prolonging the lifetime of a wireless sensor network. Current clustering algorithms usually utilize two techniques, selecting cluster heads with more residual energy and rotating cluster heads periodically, in order to distribute the energy consumption among nodes in each cluster and extend the network lifetime. Clustering sensors are divided into groups, so that sensors will communicate information only to cluster heads and then the cluster heads will communicate the aggregated information to the processing center, and this may save energy. In this paper we show Two Relay Sensor Algorithm (TRSA, which divide wireless Sensor Network (WSN into unequaled clusters, showing that it can effectively save power for maximizing the life time of the network. Simulation results show that the proposed unequal clustering mechanism (TRSA balances the energy consumption among all sensor nodes and achieves an obvious improvement on the network lifetime.

  8. Bayesian error analysis model for reconstructing transcriptional regulatory networks

    OpenAIRE

    Sun, Ning; Carroll, Raymond J.; Zhao, Hongyu

    2006-01-01

    Transcription regulation is a fundamental biological process, and extensive efforts have been made to dissect its mechanisms through direct biological experiments and regulation modeling based on physical–chemical principles and mathematical formulations. Despite these efforts, transcription regulation is yet not well understood because of its complexity and limitations in biological experiments. Recent advances in high throughput technologies have provided substantial amounts and diverse typ...

  9. Graphic Symbol Recognition using Graph Based Signature and Bayesian Network Classifier

    OpenAIRE

    Luqman, Muhammad Muzzamil; Brouard, Thierry; Ramel, Jean-Yves

    2010-01-01

    We present a new approach for recognition of complex graphic symbols in technical documents. Graphic symbol recognition is a well known challenge in the field of document image analysis and is at heart of most graphic recognition systems. Our method uses structural approach for symbol representation and statistical classifier for symbol recognition. In our system we represent symbols by their graph based signatures: a graphic symbol is vectorized and is converted to an attributed relational g...

  10. A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images.

    Science.gov (United States)

    Pang, Shuchao; Yu, Zhezhou; Orgun, Mehmet A

    2017-03-01

    Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset. In this study, we propose a highly reliable and accurate end-to-end classifier for all kinds of biomedical images via deep learning and transfer learning. We first apply domain transferred deep convolutional neural network for building a deep model; and then develop an overall deep learning architecture based on the raw pixels of original biomedical images using supervised training. In our model, we do not need the manual design of the feature space, seek an effective feature vector classifier or segment specific detection object and image patches, which are the main technological difficulties in the adoption of traditional image classification methods. Moreover, we do not need to be concerned with whether there are large training sets of annotated biomedical images, affordable parallel computing resources featuring GPUs or long times to wait for training a perfect deep model, which are the main problems to train deep neural networks for biomedical image classification as observed in recent works. With the utilization of a simple data augmentation method and fast convergence speed, our algorithm can achieve the best accuracy rate and outstanding classification ability for biomedical images. We have evaluated our classifier on several well-known public biomedical datasets and compared it with several state-of-the-art approaches. We propose a robust

  11. Carbon classified?

    DEFF Research Database (Denmark)

    Lippert, Ingmar

    2012-01-01

    . Using an actor- network theory (ANT) framework, the aim is to investigate the actors who bring together the elements needed to classify their carbon emission sources and unpack the heterogeneous relations drawn on. Based on an ethnographic study of corporate agents of ecological modernisation over...... a period of 13 months, this paper provides an exploration of three cases of enacting classification. Drawing on ANT, we problematise the silencing of a range of possible modalities of consumption facts and point to the ontological ethics involved in such performances. In a context of global warming...

  12. Aggregation of topological motifs in the Escherichia coli transcriptional regulatory network

    Directory of Open Access Journals (Sweden)

    Barabási Albert-László

    2004-01-01

    Full Text Available Abstract Background Transcriptional regulation of cellular functions is carried out through a complex network of interactions among transcription factors and the promoter regions of genes and operons regulated by them.To better understand the system-level function of such networks simplification of their architecture was previously achieved by identifying the motifs present in the network, which are small, overrepresented, topologically distinct regulatory interaction patterns (subgraphs. However, the interaction of such motifs with each other, and their form of integration into the full network has not been previously examined. Results By studying the transcriptional regulatory network of the bacterium, Escherichia coli, we demonstrate that the two previously identified motif types in the network (i.e., feed-forward loops and bi-fan motifs do not exist in isolation, but rather aggregate into homologous motif clusters that largely overlap with known biological functions. Moreover, these clusters further coalesce into a supercluster, thus establishing distinct topological hierarchies that show global statistical properties similar to the whole network. Targeted removal of motif links disintegrates the network into small, isolated clusters, while random disruptions of equal number of links do not cause such an effect. Conclusion Individual motifs aggregate into homologous motif clusters and a supercluster forming the backbone of the E. coli transcriptional regulatory network and play a central role in defining its global topological organization.

  13. Detecting Cyber-Attacks on Wireless Mobile Networks Using Multicriterion Fuzzy Classifier with Genetic Attribute Selection

    Directory of Open Access Journals (Sweden)

    El-Sayed M. El-Alfy

    2015-01-01

    Full Text Available With the proliferation of wireless and mobile network infrastructures and capabilities, a wide range of exploitable vulnerabilities emerges due to the use of multivendor and multidomain cross-network services for signaling and transport of Internet- and wireless-based data. Consequently, the rates and types of cyber-attacks have grown considerably and current security countermeasures for protecting information and communication may be no longer sufficient. In this paper, we investigate a novel methodology based on multicriterion decision making and fuzzy classification that can provide a viable second-line of defense for mitigating cyber-attacks. The proposed approach has the advantage of dealing with various types and sizes of attributes related to network traffic such as basic packet headers, content, and time. To increase the effectiveness and construct optimal models, we augmented the proposed approach with a genetic attribute selection strategy. This allows efficient and simpler models which can be replicated at various network components to cooperatively detect and report malicious behaviors. Using three datasets covering a variety of network attacks, the performance enhancements due to the proposed approach are manifested in terms of detection errors and model construction times.

  14. The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line

    DEFF Research Database (Denmark)

    Suzuki, Harukazu; Forrest, Alistair R R; van Nimwegen, Erik

    2009-01-01

    , we identified the key transcription regulators, their time-dependent activities and target genes. Systematic siRNA knockdown of 52 transcription factors confirmed the roles of individual factors in the regulatory network. Our results indicate that cellular states are constrained by complex networks......Using deep sequencing (deepCAGE), the FANTOM4 study measured the genome-wide dynamics of transcription-start-site usage in the human monocytic cell line THP-1 throughout a time course of growth arrest and differentiation. Modeling the expression dynamics in terms of predicted cis-regulatory sites...... involving both positive and negative regulatory interactions among substantial numbers of transcription factors and that no single transcription factor is both necessary and sufficient to drive the differentiation process....

  15. Model of hierarchical self-organizing neural networks for detecting and classifying diabetic retinopathy

    Directory of Open Access Journals (Sweden)

    Hossein Ghayoumi Zadeh

    2018-04-01

    Conclusion: These days, the cases of diabetes with hypertension are constantly increasing, and one of the main adverse effects of this disease is related to eyes. In this respect, the diagnosis of retinopathy, which is the same as identification of exudates, microanurysm and bleeding, is of particular importance. The results show that the proposed model is able to detect lesions in diabetic retinopathy images and classify them with an acceptable accuracy. In addition, the results suggest that this method has an acceptable performance compared to other methods.

  16. Network Intrusion Detection System (NIDS in Cloud Environment based on Hidden Naïve Bayes Multiclass Classifier

    Directory of Open Access Journals (Sweden)

    Hafza A. Mahmood

    2018-04-01

    Full Text Available Cloud Environment is next generation internet based computing system that supplies customiza-ble services to the end user to work or access to the various cloud applications. In order to provide security and decrease the damage of information system, network and computer system it is im-portant to provide intrusion detection system (IDS. Now Cloud environment are under threads from network intrusions, as one of most prevalent and offensive means Denial of Service (DoS attacks that cause dangerous impact on cloud computing systems. This paper propose Hidden naïve Bayes (HNB Classifier to handle DoS attacks which is a data mining (DM model used to relaxes the conditional independence assumption of Naïve Bayes classifier (NB, proposed sys-tem used HNB Classifier supported with discretization and feature selection where select the best feature enhance the performance of the system and reduce consuming time. To evaluate the per-formance of proposal system, KDD 99 CUP and NSL KDD Datasets has been used. The experi-mental results show that the HNB classifier improves the performance of NIDS in terms of accu-racy and detecting DoS attacks, where the accuracy of detect DoS is 100% in three test KDD cup 99 dataset by used only 12 feature that selected by use gain ratio while in NSL KDD Dataset the accuracy of detect DoS attack is 90 % in three Experimental NSL KDD dataset by select 10 fea-ture only.

  17. A Machine Learning Approach for Identifying and Classifying Faults in Wireless Sensor Networks

    NARCIS (Netherlands)

    Warriach, Ehsan Ullah; Aiello, Marco; Tei, Kenji

    2012-01-01

    Wireless Sensor Network (WSN) deployment experiences show that collected data is prone to be faulty. Faults are due to internal and external influences, such as calibration, low battery, environmental interference and sensor aging. However, only few solutions exist to deal with faulty sensory data

  18. On the complexity of neural network classifiers: a comparison between shallow and deep architectures.

    Science.gov (United States)

    Bianchini, Monica; Scarselli, Franco

    2014-08-01

    Recently, researchers in the artificial neural network field have focused their attention on connectionist models composed by several hidden layers. In fact, experimental results and heuristic considerations suggest that deep architectures are more suitable than shallow ones for modern applications, facing very complex problems, e.g., vision and human language understanding. However, the actual theoretical results supporting such a claim are still few and incomplete. In this paper, we propose a new approach to study how the depth of feedforward neural networks impacts on their ability in implementing high complexity functions. First, a new measure based on topological concepts is introduced, aimed at evaluating the complexity of the function implemented by a neural network, used for classification purposes. Then, deep and shallow neural architectures with common sigmoidal activation functions are compared, by deriving upper and lower bounds on their complexity, and studying how the complexity depends on the number of hidden units and the used activation function. The obtained results seem to support the idea that deep networks actually implements functions of higher complexity, so that they are able, with the same number of resources, to address more difficult problems.

  19. Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database.

    Science.gov (United States)

    Choi, Joon Yul; Yoo, Tae Keun; Seo, Jeong Gi; Kwak, Jiyong; Um, Terry Taewoong; Rim, Tyler Hyungtaek

    2017-01-01

    Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method. We applied deep learning convolutional neural network by using MatConvNet for an automated detection of multiple retinal diseases with fundus photographs involved in STructured Analysis of the REtina (STARE) database. Dataset was built by expanding data on 10 categories, including normal retina and nine retinal diseases. The optimal outcomes were acquired by using a random forest transfer learning based on VGG-19 architecture. The classification results depended greatly on the number of categories. As the number of categories increased, the performance of deep learning models was diminished. When all 10 categories were included, we obtained results with an accuracy of 30.5%, relative classifier information (RCI) of 0.052, and Cohen's kappa of 0.224. Considering three integrated normal, background diabetic retinopathy, and dry age-related macular degeneration, the multi-categorical classifier showed accuracy of 72.8%, 0.283 RCI, and 0.577 kappa. In addition, several ensemble classifiers enhanced the multi-categorical classification performance. The transfer learning incorporated with ensemble classifier of clustering and voting approach presented the best performance with accuracy of 36.7%, 0.053 RCI, and 0.225 kappa in the 10 retinal diseases classification problem. First, due to the small size of datasets, the deep learning techniques in this study were ineffective to be applied in clinics where numerous patients suffering from various types of retinal disorders visit for diagnosis and treatment. Second, we found that the transfer learning incorporated with ensemble classifiers can improve the classification performance in order to detect multi-categorical retinal diseases. Further studies should confirm the effectiveness of algorithms with large datasets obtained from hospitals.

  20. Crystal surface analysis using matrix textural features classified by a Probabilistic Neural Network

    International Nuclear Information System (INIS)

    Sawyer, C.R.; Quach, V.T.; Nason, D.; van den Berg, L.

    1991-01-01

    A system is under development in which surface quality of a growing bulk mercuric iodide crystal is monitored by video camera at regular intervals for early detection of growth irregularities. Mercuric iodide single crystals are employed in radiation detectors. A microcomputer system is used for image capture and processing. The digitized image is divided into multiple overlappings subimage and features are extracted from each subimage based on statistical measures of the gray tone distribution, according to the method of Haralick [1]. Twenty parameters are derived from each subimage and presented to a Probabilistic Neural Network (PNN) [2] for classification. This number of parameters was found to be optimal for the system. The PNN is a hierarchical, feed-forward network that can be rapidly reconfigured as additional training data become available. Training data is gathered by reviewing digital images of many crystals during their growth cycle and compiling two sets of images, those with and without irregularities. 6 refs., 4 figs

  1. Picasso: A Modular Framework for Visualizing the Learning Process of Neural Network Image Classifiers

    Directory of Open Access Journals (Sweden)

    Ryan Henderson

    2017-09-01

    Full Text Available Picasso is a free open-source (Eclipse Public License web application written in Python for rendering standard visualizations useful for analyzing convolutional neural networks. Picasso ships with occlusion maps and saliency maps, two visualizations which help reveal issues that evaluation metrics like loss and accuracy might hide: for example, learning a proxy classification task. Picasso works with the Tensorflow deep learning framework, and Keras (when the model can be loaded into the Tensorflow backend. Picasso can be used with minimal configuration by deep learning researchers and engineers alike across various neural network architectures. Adding new visualizations is simple: the user can specify their visualization code and HTML template separately from the application code.

  2. Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: a comparative study

    Science.gov (United States)

    Sheikhan, Mansour; Abbasnezhad Arabi, Mahdi; Gharavian, Davood

    2015-10-01

    Artificial neural networks are efficient models in pattern recognition applications, but their performance is dependent on employing suitable structure and connection weights. This study used a hybrid method for obtaining the optimal weight set and architecture of a recurrent neural emotion classifier based on gravitational search algorithm (GSA) and its binary version (BGSA), respectively. By considering the features of speech signal that were related to prosody, voice quality, and spectrum, a rich feature set was constructed. To select more efficient features, a fast feature selection method was employed. The performance of the proposed hybrid GSA-BGSA method was compared with similar hybrid methods based on particle swarm optimisation (PSO) algorithm and its binary version, PSO and discrete firefly algorithm, and hybrid of error back-propagation and genetic algorithm that were used for optimisation. Experimental tests on Berlin emotional database demonstrated the superior performance of the proposed method using a lighter network structure.

  3. Robust Template Decomposition without Weight Restriction for Cellular Neural Networks Implementing Arbitrary Boolean Functions Using Support Vector Classifiers

    Directory of Open Access Journals (Sweden)

    Yih-Lon Lin

    2013-01-01

    Full Text Available If the given Boolean function is linearly separable, a robust uncoupled cellular neural network can be designed as a maximal margin classifier. On the other hand, if the given Boolean function is linearly separable but has a small geometric margin or it is not linearly separable, a popular approach is to find a sequence of robust uncoupled cellular neural networks implementing the given Boolean function. In the past research works using this approach, the control template parameters and thresholds are restricted to assume only a given finite set of integers, and this is certainly unnecessary for the template design. In this study, we try to remove this restriction. Minterm- and maxterm-based decomposition algorithms utilizing the soft margin and maximal margin support vector classifiers are proposed to design a sequence of robust templates implementing an arbitrary Boolean function. Several illustrative examples are simulated to demonstrate the efficiency of the proposed method by comparing our results with those produced by other decomposition methods with restricted weights.

  4. Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology.

    Science.gov (United States)

    Bakhtiarizadeh, Mohammad Reza; Moradi-Shahrbabak, Mohammad; Ebrahimi, Mansour; Ebrahimie, Esmaeil

    2014-09-07

    Due to the central roles of lipid binding proteins (LBPs) in many biological processes, sequence based identification of LBPs is of great interest. The major challenge is that LBPs are diverse in sequence, structure, and function which results in low accuracy of sequence homology based methods. Therefore, there is a need for developing alternative functional prediction methods irrespective of sequence similarity. To identify LBPs from non-LBPs, the performances of support vector machine (SVM) and neural network were compared in this study. Comprehensive protein features and various techniques were employed to create datasets. Five-fold cross-validation (CV) and independent evaluation (IE) tests were used to assess the validity of the two methods. The results indicated that SVM outperforms neural network. SVM achieved 89.28% (CV) and 89.55% (IE) overall accuracy in identification of LBPs from non-LBPs and 92.06% (CV) and 92.90% (IE) (in average) for classification of different LBPs classes. Increasing the number and the range of extracted protein features as well as optimization of the SVM parameters significantly increased the efficiency of LBPs class prediction in comparison to the only previous report in this field. Altogether, the results showed that the SVM algorithm can be run on broad, computationally calculated protein features and offers a promising tool in detection of LBPs classes. The proposed approach has the potential to integrate and improve the common sequence alignment based methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

  5. A Neural Network Classifier Model for Forecasting Safety Behavior at Workplaces

    Directory of Open Access Journals (Sweden)

    Fakhradin Ghasemi

    2017-07-01

    Full Text Available The construction industry is notorious for having an unacceptable rate of fatal accidents. Unsafe behavior has been recognized as the main cause of most accidents occurring at workplaces, particularly construction sites. Having a predictive model of safety behavior can be helpful in preventing construction accidents. The aim of the present study was to build a predictive model of unsafe behavior using the Artificial Neural Network approach. A brief literature review was conducted on factors affecting safe behavior at workplaces and nine factors were selected to be included in the study. Data were gathered using a validated questionnaire from several construction sites. Multilayer perceptron approach was utilized for constructing the desired neural network. Several models with various architectures were tested to find the best one. Sensitivity analysis was conducted to find the most influential factors. The model with one hidden layer containing fourteen hidden neurons demonstrated the best performance (Sum of Squared Errors=6.73. The error rate of the model was approximately 21 percent. The results of sensitivity analysis showed that safety attitude, safety knowledge, supportive environment, and management commitment had the highest effects on safety behavior, while the effects from resource allocation and perceived work pressure were identified to be lower than those of others. The complex nature of human behavior at workplaces and the presence of many influential factors make it difficult to achieve a model with perfect performance.

  6. Fast, Simple and Accurate Handwritten Digit Classification by Training Shallow Neural Network Classifiers with the 'Extreme Learning Machine' Algorithm.

    Directory of Open Access Journals (Sweden)

    Mark D McDonnell

    Full Text Available Recent advances in training deep (multi-layer architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for difficult tasks on large datasets, such as image and speech recognition. However, here we show that error rates below 1% on the MNIST handwritten digit benchmark can be replicated with shallow non-convolutional neural networks. This is achieved by training such networks using the 'Extreme Learning Machine' (ELM approach, which also enables a very rapid training time (∼ 10 minutes. Adding distortions, as is common practise for MNIST, reduces error rates even further. Our methods are also shown to be capable of achieving less than 5.5% error rates on the NORB image database. To achieve these results, we introduce several enhancements to the standard ELM algorithm, which individually and in combination can significantly improve performance. The main innovation is to ensure each hidden-unit operates only on a randomly sized and positioned patch of each image. This form of random 'receptive field' sampling of the input ensures the input weight matrix is sparse, with about 90% of weights equal to zero. Furthermore, combining our methods with a small number of iterations of a single-batch backpropagation method can significantly reduce the number of hidden-units required to achieve a particular performance. Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems.

  7. Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data

    Directory of Open Access Journals (Sweden)

    de los Reyes Benildo G

    2008-04-01

    Full Text Available Abstract Background Integrating data from multiple global assays and curated databases is essential to understand the spatio-temporal interactions within cells. Different experiments measure cellular processes at various widths and depths, while databases contain biological information based on established facts or published data. Integrating these complementary datasets helps infer a mutually consistent transcriptional regulatory network (TRN with strong similarity to the structure of the underlying genetic regulatory modules. Decomposing the TRN into a small set of recurring regulatory patterns, called network motifs (NM, facilitates the inference. Identifying NMs defined by specific transcription factors (TF establishes the framework structure of a TRN and allows the inference of TF-target gene relationship. This paper introduces a computational framework for utilizing data from multiple sources to infer TF-target gene relationships on the basis of NMs. The data include time course gene expression profiles, genome-wide location analysis data, binding sequence data, and gene ontology (GO information. Results The proposed computational framework was tested using gene expression data associated with cell cycle progression in yeast. Among 800 cell cycle related genes, 85 were identified as candidate TFs and classified into four previously defined NMs. The NMs for a subset of TFs are obtained from literature. Support vector machine (SVM classifiers were used to estimate NMs for the remaining TFs. The potential downstream target genes for the TFs were clustered into 34 biologically significant groups. The relationships between TFs and potential target gene clusters were examined by training recurrent neural networks whose topologies mimic the NMs to which the TFs are classified. The identified relationships between TFs and gene clusters were evaluated using the following biological validation and statistical analyses: (1 Gene set enrichment

  8. Muon Neutrino Disappearance in NOvA with a Deep Convolutional Neural Network Classifier

    Energy Technology Data Exchange (ETDEWEB)

    Rocco, Dominick Rosario [Minnesota U.

    2016-03-01

    The NuMI Off-axis Neutrino Appearance Experiment (NOvA) is designed to study neutrino oscillation in the NuMI (Neutrinos at the Main Injector) beam. NOvA observes neutrino oscillation using two detectors separated by a baseline of 810 km; a 14 kt Far Detector in Ash River, MN and a functionally identical 0.3 kt Near Detector at Fermilab. The experiment aims to provide new measurements of Δm2 and θ23 and has potential to determine the neutrino mass hierarchy as well as observe CP violation in the neutrino sector. Essential to these analyses is the classification of neutrino interaction events in NOvA detectors. Raw detector output from NOvA is interpretable as a pair of images which provide orthogonal views of particle interactions. A recent advance in the field of computer vision is the advent of convolutional neural networks, which have delivered top results in the latest image recognition contests. This work presents an approach novel to particle physics analysis in which a convolutional neural network is used for classification of particle interactions. The approach has been demonstrated to improve the signal efficiency and purity of the event selection, and thus physics sensitivity. Early NOvA data has been analyzed (2.74×1020 POT, 14 kt equivalent) to provide new best- fit measurements of sin2(θ23) = 0.43 (with a statistically-degenerate compliment near 0.60) and |Δm2 | = 2.48 × 10-3 eV2.

  9. Integrated pathway-based transcription regulation network mining and visualization based on gene expression profiles.

    Science.gov (United States)

    Kibinge, Nelson; Ono, Naoaki; Horie, Masafumi; Sato, Tetsuo; Sugiura, Tadao; Altaf-Ul-Amin, Md; Saito, Akira; Kanaya, Shigehiko

    2016-06-01

    Conventionally, workflows examining transcription regulation networks from gene expression data involve distinct analytical steps. There is a need for pipelines that unify data mining and inference deduction into a singular framework to enhance interpretation and hypotheses generation. We propose a workflow that merges network construction with gene expression data mining focusing on regulation processes in the context of transcription factor driven gene regulation. The pipeline implements pathway-based modularization of expression profiles into functional units to improve biological interpretation. The integrated workflow was implemented as a web application software (TransReguloNet) with functions that enable pathway visualization and comparison of transcription factor activity between sample conditions defined in the experimental design. The pipeline merges differential expression, network construction, pathway-based abstraction, clustering and visualization. The framework was applied in analysis of actual expression datasets related to lung, breast and prostrate cancer. Copyright © 2016 Elsevier Inc. All rights reserved.

  10. Information-Theoretic Inference of Large Transcriptional Regulatory Networks

    Directory of Open Access Journals (Sweden)

    Meyer Patrick

    2007-01-01

    Full Text Available The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR, an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large (up to several thousands of genes network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.

  11. Information-Theoretic Inference of Large Transcriptional Regulatory Networks

    Directory of Open Access Journals (Sweden)

    Patrick E. Meyer

    2007-06-01

    Full Text Available The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR, an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large (up to several thousands of genes network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.

  12. Transcriptional regulation and steady-state modeling of metabolic networks

    DEFF Research Database (Denmark)

    Zelezniak, Aleksej

    Biological systems are characterized by a high degree of complexity wherein the individual components (e.g. proteins) are inter-linked in a way that leads to emergent behaviors that are difficult to decipher. Uncovering system complexity requires, at least, answers to the following three questions......: what are the components of the systems, how are the different components interconnected and how do these networks perform the functions that make the resulting system behavior? Modern analytical technologies allow us to unravel the constituents and interactions happening in a given system; however......, the third question is the ultimate challenge for systems biology. The work of this thesis systematically addresses this question in the context of metabolic networks, which are arguably the most well characterized cellular networks in terms of their constituting components and interactions among them...

  13. Comparative genomic reconstruction of transcriptional networks controlling central metabolism in the Shewanella genus

    Directory of Open Access Journals (Sweden)

    Kovaleva Galina

    2011-06-01

    Full Text Available Abstract Background Genome-scale prediction of gene regulation and reconstruction of transcriptional regulatory networks in bacteria is one of the critical tasks of modern genomics. The Shewanella genus is comprised of metabolically versatile gamma-proteobacteria, whose lifestyles and natural environments are substantially different from Escherichia coli and other model bacterial species. The comparative genomics approaches and computational identification of regulatory sites are useful for the in silico reconstruction of transcriptional regulatory networks in bacteria. Results To explore conservation and variations in the Shewanella transcriptional networks we analyzed the repertoire of transcription factors and performed genomics-based reconstruction and comparative analysis of regulons in 16 Shewanella genomes. The inferred regulatory network includes 82 transcription factors and their DNA binding sites, 8 riboswitches and 6 translational attenuators. Forty five regulons were newly inferred from the genome context analysis, whereas others were propagated from previously characterized regulons in the Enterobacteria and Pseudomonas spp.. Multiple variations in regulatory strategies between the Shewanella spp. and E. coli include regulon contraction and expansion (as in the case of PdhR, HexR, FadR, numerous cases of recruiting non-orthologous regulators to control equivalent pathways (e.g. PsrA for fatty acid degradation and, conversely, orthologous regulators to control distinct pathways (e.g. TyrR, ArgR, Crp. Conclusions We tentatively defined the first reference collection of ~100 transcriptional regulons in 16 Shewanella genomes. The resulting regulatory network contains ~600 regulated genes per genome that are mostly involved in metabolism of carbohydrates, amino acids, fatty acids, vitamins, metals, and stress responses. Several reconstructed regulons including NagR for N-acetylglucosamine catabolism were experimentally validated in S

  14. Classifying and profiling Social Networking Site users: a latent segmentation approach.

    Science.gov (United States)

    Alarcón-del-Amo, María-del-Carmen; Lorenzo-Romero, Carlota; Gómez-Borja, Miguel-Ángel

    2011-09-01

    Social Networking Sites (SNSs) have showed an exponential growth in the last years. The first step for an efficient use of SNSs stems from an understanding of the individuals' behaviors within these sites. In this research, we have obtained a typology of SNS users through a latent segmentation approach, based on the frequency by which users perform different activities within the SNSs, sociodemographic variables, experience in SNSs, and dimensions related to their interaction patterns. Four different segments have been obtained. The "introvert" and "novel" users are the more occasional. They utilize SNSs mainly to communicate with friends, although "introverts" are more passive users. The "versatile" user performs different activities, although occasionally. Finally, the "expert-communicator" performs a greater variety of activities with a higher frequency. They tend to perform some marketing-related activities such as commenting on ads or gathering information about products and brands as well as commenting ads. The companies can take advantage of these segmentation schemes in different ways: first, by tracking and monitoring information interchange between users regarding their products and brands. Second, they should match the SNS users' profiles with their market targets to use SNSs as marketing tools. Finally, for most business, the expert users could be interesting opinion leaders and potential brand influencers.

  15. Monitoring and classifying animal behavior using ZigBee-based mobile ad hoc wireless sensor networks and artificial neural networks

    DEFF Research Database (Denmark)

    S. Nadimi, Esmaeil; Nyholm Jørgensen, Rasmus; Blanes-Vidal, Victoria

    2012-01-01

    Animal welfare is an issue of great importance in modern food production systems. Because animal behavior provides reliable information about animal health and welfare, recent research has aimed at designing monitoring systems capable of measuring behavioral parameters and transforming them...... into their corresponding behavioral modes. However, network unreliability and high-energy consumption have limited the applicability of those systems. In this study, a 2.4-GHz ZigBee-based mobile ad hoc wireless sensor network (MANET) that is able to overcome those problems is presented. The designed MANET showed high...... communication reliability, low energy consumption and low packet loss rate (14.8%) due to the deployment of modern communication protocols (e.g. multi-hop communication and handshaking protocol). The measured behavioral parameters were transformed into the corresponding behavioral modes using a multilayer...

  16. Network analysis of inflammatory genes and their transcriptional regulators in coronary artery disease.

    Directory of Open Access Journals (Sweden)

    Jiny Nair

    Full Text Available Network analysis is a novel method to understand the complex pathogenesis of inflammation-driven atherosclerosis. Using this approach, we attempted to identify key inflammatory genes and their core transcriptional regulators in coronary artery disease (CAD. Initially, we obtained 124 candidate genes associated with inflammation and CAD using Polysearch and CADgene database for which protein-protein interaction network was generated using STRING 9.0 (Search Tool for the Retrieval of Interacting Genes and visualized using Cytoscape v 2.8.3. Based on betweenness centrality (BC and node degree as key topological parameters, we identified interleukin-6 (IL-6, vascular endothelial growth factor A (VEGFA, interleukin-1 beta (IL-1B, tumor necrosis factor (TNF and prostaglandin-endoperoxide synthase 2 (PTGS2 as hub nodes. The backbone network constructed with these five hub genes showed 111 nodes connected via 348 edges, with IL-6 having the largest degree and highest BC. Nuclear factor kappa B1 (NFKB1, signal transducer and activator of transcription 3 (STAT3 and JUN were identified as the three core transcription factors from the regulatory network derived using MatInspector. For the purpose of validation of the hub genes, 97 test networks were constructed, which revealed the accuracy of the backbone network to be 0.7763 while the frequency of the hub nodes remained largely unaltered. Pathway enrichment analysis with ClueGO, KEGG and REACTOME showed significant enrichment of six validated CAD pathways - smooth muscle cell proliferation, acute-phase response, calcidiol 1-monooxygenase activity, toll-like receptor signaling, NOD-like receptor signaling and adipocytokine signaling pathways. Experimental verification of the above findings in 64 cases and 64 controls showed increased expression of the five candidate genes and the three transcription factors in the cases relative to the controls (p<0.05. Thus, analysis of complex networks aid in the

  17. Localizing potentially active post-transcriptional regulations in the Ewing's sarcoma gene regulatory network

    Directory of Open Access Journals (Sweden)

    Delyon Bernard

    2010-11-01

    Full Text Available Abstract Background A wide range of techniques is now available for analyzing regulatory networks. Nonetheless, most of these techniques fail to interpret large-scale transcriptional data at the post-translational level. Results We address the question of using large-scale transcriptomic observation of a system perturbation to analyze a regulatory network which contained several types of interactions - transcriptional and post-translational. Our method consisted of post-processing the outputs of an open-source tool named BioQuali - an automatic constraint-based analysis mimicking biologist's local reasoning on a large scale. The post-processing relied on differences in the behavior of the transcriptional and post-translational levels in the network. As a case study, we analyzed a network representation of the genes and proteins controlled by an oncogene in the context of Ewing's sarcoma. The analysis allowed us to pinpoint active interactions specific to this cancer. We also identified the parts of the network which were incomplete and should be submitted for further investigation. Conclusions The proposed approach is effective for the qualitative analysis of cancer networks. It allows the integrative use of experimental data of various types in order to identify the specific information that should be considered a priority in the initial - and possibly very large - experimental dataset. Iteratively, new dataset can be introduced into the analysis to improve the network representation and make it more specific.

  18. Analysis of a Plant Transcriptional Regulatory Network Using Transient Expression Systems.

    Science.gov (United States)

    Díaz-Triviño, Sara; Long, Yuchen; Scheres, Ben; Blilou, Ikram

    2017-01-01

    In plant biology, transient expression systems have become valuable approaches used routinely to rapidly study protein expression, subcellular localization, protein-protein interactions, and transcriptional activity prior to in vivo studies. When studying transcriptional regulation, luciferase reporter assays offer a sensitive readout for assaying promoter behavior in response to different regulators or environmental contexts and to confirm and assess the functional relevance of predicted binding sites in target promoters. This chapter aims to provide detailed methods for using luciferase reporter system as a rapid, efficient, and versatile assay to analyze transcriptional regulation of target genes by transcriptional regulators. We describe a series of optimized transient expression systems consisting of Arabidopsis thaliana protoplasts, infiltrated Nicotiana benthamiana leaves, and human HeLa cells to study the transcriptional regulations of two well-characterized transcriptional regulators SCARECROW (SCR) and SHORT-ROOT (SHR) on one of their targets, CYCLIN D6 (CYCD6).Here, we illustrate similarities and differences in outcomes when using different systems. The plant-based systems revealed that the SCR-SHR complex enhances CYCD6 transcription, while analysis in HeLa cells showed that the complex is not sufficient to strongly induce CYCD6 transcription, suggesting that additional, plant-specific regulators are required for full activation. These results highlight the importance of the system and suggest that including heterologous systems, such as HeLa cells, can provide a more comprehensive analysis of a complex gene regulatory network.

  19. Forging T-Lymphocyte Identity: Intersecting Networks of Transcriptional Control.

    Science.gov (United States)

    Rothenberg, Ellen V; Ungerbäck, Jonas; Champhekar, Ameya

    2016-01-01

    T-lymphocyte development branches off from other lymphoid developmental programs through its requirement for sustained environmental signals through the Notch pathway. In the thymus, Notch signaling induces a succession of T-lineage regulatory factors that collectively create the T-cell identity through distinct steps. This process involves both the staged activation of T-cell identity genes and the staged repression of progenitor-cell-inherited regulatory genes once their roles in self-renewal and population expansion are no longer needed. With the recent characterization of innate lymphoid cells (ILCs) that share transcriptional regulation programs extensively with T-cell subsets, T-cell identity can increasingly be seen as defined in modular terms, as the processes selecting and actuating effector function are potentially detachable from the processes generating and selecting clonally unique T-cell receptor structures. The developmental pathways of different classes of T cells and ILCs are distinguished by the numbers of prerequisites of gene rearrangement, selection, and antigen contact before the cells gain access to nearly common regulatory mechanisms for choosing effector function. Here, the major classes of transcription factors that interact with Notch signals during T-lineage specification are discussed in terms of their roles in these programs, the evidence for their spectra of target genes at different stages, and their cross-regulatory and cooperative actions with each other. Specific topics include Notch modulation of PU.1 and GATA-3, PU.1-Notch competition, the relationship between PU.1 and GATA-3, and the roles of E proteins, Bcl11b, and GATA-3 in guiding acquisition of T-cell identity while avoiding redirection to an ILC fate. © 2016 Elsevier Inc. All rights reserved.

  20. Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets.

    Science.gov (United States)

    McAllister, Patrick; Zheng, Huiru; Bond, Raymond; Moorhead, Anne

    2018-04-01

    Obesity is increasing worldwide and can cause many chronic conditions such as type-2 diabetes, heart disease, sleep apnea, and some cancers. Monitoring dietary intake through food logging is a key method to maintain a healthy lifestyle to prevent and manage obesity. Computer vision methods have been applied to food logging to automate image classification for monitoring dietary intake. In this work we applied pretrained ResNet-152 and GoogleNet convolutional neural networks (CNNs), initially trained using ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset with MatConvNet package, to extract features from food image datasets; Food 5K, Food-11, RawFooT-DB, and Food-101. Deep features were extracted from CNNs and used to train machine learning classifiers including artificial neural network (ANN), support vector machine (SVM), Random Forest, and Naive Bayes. Results show that using ResNet-152 deep features with SVM with RBF kernel can accurately detect food items with 99.4% accuracy using Food-5K validation food image dataset and 98.8% with Food-5K evaluation dataset using ANN, SVM-RBF, and Random Forest classifiers. Trained with ResNet-152 features, ANN can achieve 91.34%, 99.28% when applied to Food-11 and RawFooT-DB food image datasets respectively and SVM with RBF kernel can achieve 64.98% with Food-101 image dataset. From this research it is clear that using deep CNN features can be used efficiently for diverse food item image classification. The work presented in this research shows that pretrained ResNet-152 features provide sufficient generalisation power when applied to a range of food image classification tasks. Copyright © 2018 Elsevier Ltd. All rights reserved.

  1. Coevolution within a transcriptional network by compensatory trans and cis mutations

    KAUST Repository

    Kuo, D.

    2010-10-26

    Transcriptional networks have been shown to evolve very rapidly, prompting questions as to how such changes arise and are tolerated. Recent comparisons of transcriptional networks across species have implicated variations in the cis-acting DNA sequences near genes as the main cause of divergence. What is less clear is how these changes interact with trans-acting changes occurring elsewhere in the genetic circuit. Here, we report the discovery of a system of compensatory trans and cis mutations in the yeast AP-1 transcriptional network that allows for conserved transcriptional regulation despite continued genetic change. We pinpoint a single species, the fungal pathogen Candida glabrata, in which a trans mutation has occurred very recently in a single AP-1 family member, distinguishing it from its Saccharomyces ortholog. Comparison of chromatin immunoprecipitation profiles between Candida and Saccharomyces shows that, despite their different DNA-binding domains, the AP-1 orthologs regulate a conserved block of genes. This conservation is enabled by concomitant changes in the cis-regulatory motifs upstream of each gene. Thus, both trans and cis mutations have perturbed the yeast AP-1 regulatory system in such a way as to compensate for one another. This demonstrates an example of “coevolution” between a DNA-binding transcription factor and its cis-regulatory site, reminiscent of the coevolution of protein binding partners.

  2. A network of paralogous stress response transcription factors in the human pathogen Candida glabrata.

    Directory of Open Access Journals (Sweden)

    Jawad eMerhej

    2016-05-01

    Full Text Available The yeast Candida glabrata has become the second cause of systemic candidemia in humans. However, relatively few genome-wide studies have been conducted in this organism and our knowledge of its transcriptional regulatory network is quite limited. In the present work, we combined genome-wide chromatin immunoprecipitation (ChIP-seq, transcriptome analyses and DNA binding motif predictions to describe the regulatory interactions of the seven Yap (Yeast AP1 transcription factors of C. glabrata. We described a transcriptional network containing 255 regulatory interactions and 309 potential target genes. We predicted with high confidence the preferred DNA binding sites for 5 of the 7 CgYaps and showed a strong conservation of the Yap DNA binding properties between S. cerevisiae and C. glabrata. We provided reliable functional annotation for 3 of the 7 Yaps and identified for Yap1 and Yap5 a core regulon which is conserved in S. cerevisiae, C. glabrata and C. albicans. We uncovered new roles for CgYap7 in the regulation of iron-sulfur cluster biogenesis, for CgYap1 in the regulation of heme biosynthesis and for CgYap5 in the repression of GRX4 in response to iron starvation. These transcription factors define an interconnected transcriptional network at the cross-roads between redox homeostasis, oxygen consumption and iron metabolism.

  3. Information processing in the transcriptional regulatory network of yeast: Functional robustness

    Directory of Open Access Journals (Sweden)

    Dehmer Matthias

    2009-03-01

    Full Text Available Abstract Background Gene networks are considered to represent various aspects of molecular biological systems meaningfully because they naturally provide a systems perspective of molecular interactions. In this respect, the functional understanding of the transcriptional regulatory network is considered as key to elucidate the functional organization of an organism. Results In this paper we study the functional robustness of the transcriptional regulatory network of S. cerevisiae. We model the information processing in the network as a first order Markov chain and study the influence of single gene perturbations on the global, asymptotic communication among genes. Modification in the communication is measured by an information theoretic measure allowing to predict genes that are 'fragile' with respect to single gene knockouts. Our results demonstrate that the predicted set of fragile genes contains a statistically significant enrichment of so called essential genes that are experimentally found to be necessary to ensure vital yeast. Further, a structural analysis of the transcriptional regulatory network reveals that there are significant differences between fragile genes, hub genes and genes with a high betweenness centrality value. Conclusion Our study does not only demonstrate that a combination of graph theoretical, information theoretical and statistical methods leads to meaningful biological results but also that such methods allow to study information processing in gene networks instead of just their structural properties.

  4. Circuit-wide Transcriptional Profiling Reveals Brain Region-Specific Gene Networks Regulating Depression Susceptibility.

    Science.gov (United States)

    Bagot, Rosemary C; Cates, Hannah M; Purushothaman, Immanuel; Lorsch, Zachary S; Walker, Deena M; Wang, Junshi; Huang, Xiaojie; Schlüter, Oliver M; Maze, Ian; Peña, Catherine J; Heller, Elizabeth A; Issler, Orna; Wang, Minghui; Song, Won-Min; Stein, Jason L; Liu, Xiaochuan; Doyle, Marie A; Scobie, Kimberly N; Sun, Hao Sheng; Neve, Rachael L; Geschwind, Daniel; Dong, Yan; Shen, Li; Zhang, Bin; Nestler, Eric J

    2016-06-01

    Depression is a complex, heterogeneous disorder and a leading contributor to the global burden of disease. Most previous research has focused on individual brain regions and genes contributing to depression. However, emerging evidence in humans and animal models suggests that dysregulated circuit function and gene expression across multiple brain regions drive depressive phenotypes. Here, we performed RNA sequencing on four brain regions from control animals and those susceptible or resilient to chronic social defeat stress at multiple time points. We employed an integrative network biology approach to identify transcriptional networks and key driver genes that regulate susceptibility to depressive-like symptoms. Further, we validated in vivo several key drivers and their associated transcriptional networks that regulate depression susceptibility and confirmed their functional significance at the levels of gene transcription, synaptic regulation, and behavior. Our study reveals novel transcriptional networks that control stress susceptibility and offers fundamentally new leads for antidepressant drug discovery. Copyright © 2016 Elsevier Inc. All rights reserved.

  5. Transcriptional dynamics of a conserved gene expression network associated with craniofacial divergence in Arctic charr.

    Science.gov (United States)

    Ahi, Ehsan Pashay; Kapralova, Kalina Hristova; Pálsson, Arnar; Maier, Valerie Helene; Gudbrandsson, Jóhannes; Snorrason, Sigurdur S; Jónsson, Zophonías O; Franzdóttir, Sigrídur Rut

    2014-01-01

    Understanding the molecular basis of craniofacial variation can provide insights into key developmental mechanisms of adaptive changes and their role in trophic divergence and speciation. Arctic charr (Salvelinus alpinus) is a polymorphic fish species, and, in Lake Thingvallavatn in Iceland, four sympatric morphs have evolved distinct craniofacial structures. We conducted a gene expression study on candidates from a conserved gene coexpression network, focusing on the development of craniofacial elements in embryos of two contrasting Arctic charr morphotypes (benthic and limnetic). Four Arctic charr morphs were studied: one limnetic and two benthic morphs from Lake Thingvallavatn and a limnetic reference aquaculture morph. The presence of morphological differences at developmental stages before the onset of feeding was verified by morphometric analysis. Following up on our previous findings that Mmp2 and Sparc were differentially expressed between morphotypes, we identified a network of genes with conserved coexpression across diverse vertebrate species. A comparative expression study of candidates from this network in developing heads of the four Arctic charr morphs verified the coexpression relationship of these genes and revealed distinct transcriptional dynamics strongly correlated with contrasting craniofacial morphologies (benthic versus limnetic). A literature review and Gene Ontology analysis indicated that a significant proportion of the network genes play a role in extracellular matrix organization and skeletogenesis, and motif enrichment analysis of conserved noncoding regions of network candidates predicted a handful of transcription factors, including Ap1 and Ets2, as potential regulators of the gene network. The expression of Ets2 itself was also found to associate with network gene expression. Genes linked to glucocorticoid signalling were also studied, as both Mmp2 and Sparc are responsive to this pathway. Among those, several transcriptional

  6. Architecture of transcriptional regulatory circuits is knitted over the topology of bio-molecular interaction networks

    DEFF Research Database (Denmark)

    Soberano de Oliveira, Ana Paula; Patil, Kiran Raosaheb; Nielsen, Jens

    2008-01-01

    is to use the topology of bio-molecular interaction networks in order to constrain the solution space. Such approaches systematically integrate the existing biological knowledge with the 'omics' data. Results: Here we introduce a hypothesis-driven method that integrates bio-molecular network topology......Background: Uncovering the operating principles underlying cellular processes by using 'omics' data is often a difficult task due to the high-dimensionality of the solution space that spans all interactions among the bio-molecules under consideration. A rational way to overcome this problem...... with transcriptome data, thereby allowing the identification of key biological features (Reporter Features) around which transcriptional changes are significantly concentrated. We have combined transcriptome data with different biological networks in order to identify Reporter Gene Ontologies, Reporter Transcription...

  7. Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier.

    Science.gov (United States)

    Raghu, S; Sriraam, N; Kumar, G Pradeep

    2017-02-01

    Electroencephalogram shortly termed as EEG is considered as the fundamental segment for the assessment of the neural activities in the brain. In cognitive neuroscience domain, EEG-based assessment method is found to be superior due to its non-invasive ability to detect deep brain structure while exhibiting superior spatial resolutions. Especially for studying the neurodynamic behavior of epileptic seizures, EEG recordings reflect the neuronal activity of the brain and thus provide required clinical diagnostic information for the neurologist. This specific proposed study makes use of wavelet packet based log and norm entropies with a recurrent Elman neural network (REN) for the automated detection of epileptic seizures. Three conditions, normal, pre-ictal and epileptic EEG recordings were considered for the proposed study. An adaptive Weiner filter was initially applied to remove the power line noise of 50 Hz from raw EEG recordings. Raw EEGs were segmented into 1 s patterns to ensure stationarity of the signal. Then wavelet packet using Haar wavelet with a five level decomposition was introduced and two entropies, log and norm were estimated and were applied to REN classifier to perform binary classification. The non-linear Wilcoxon statistical test was applied to observe the variation in the features under these conditions. The effect of log energy entropy (without wavelets) was also studied. It was found from the simulation results that the wavelet packet log entropy with REN classifier yielded a classification accuracy of 99.70 % for normal-pre-ictal, 99.70 % for normal-epileptic and 99.85 % for pre-ictal-epileptic.

  8. Integration and diversity of the regulatory network composed of Maf and CNC families of transcription factors.

    Science.gov (United States)

    Motohashi, Hozumi; O'Connor, Tania; Katsuoka, Fumiki; Engel, James Douglas; Yamamoto, Masayuki

    2002-07-10

    Recent progress in the analysis of transcriptional regulation has revealed the presence of an exquisite functional network comprising the Maf and Cap 'n' collar (CNC) families of regulatory proteins, many of which have been isolated. Among Maf factors, large Maf proteins are important in the regulation of embryonic development and cell differentiation, whereas small Maf proteins serve as obligatory heterodimeric partner molecules for members of the CNC family. Both Maf homodimers and CNC-small Maf heterodimers bind to the Maf recognition element (MARE). Since the MARE contains a consensus TRE sequence recognized by AP-1, Jun and Fos family members may act to compete or interfere with the function of CNC-small Maf heterodimers. Overall then, the quantitative balance of transcription factors interacting with the MARE determines its transcriptional activity. Many putative MARE-dependent target genes such as those induced by antioxidants and oxidative stress are under concerted regulation by the CNC family member Nrf2, as clearly proven by mouse germline mutagenesis. Since these genes represent a vital aspect of the cellular defense mechanism against oxidative stress, Nrf2-null mutant mice are highly sensitive to xenobiotic and oxidative insults. Deciphering the molecular basis of the regulatory network composed of Maf and CNC families of transcription factors will undoubtedly lead to a new paradigm for the cooperative function of transcription factors.

  9. TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks

    Directory of Open Access Journals (Sweden)

    Jensen Paul A

    2011-09-01

    Full Text Available Abstract Background Several methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation. Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate enzyme activity. Recently, mixed integer programming has been used to encode these gene-protein-reaction (GPR relationships into a single optimization problem, but these techniques are often of limited generality and lack a tool for automating the conversion of rules to a coupled regulatory/metabolic model. Results We present TIGER, a Toolbox for Integrating Genome-scale Metabolism, Expression, and Regulation. TIGER converts a series of generalized, Boolean or multilevel rules into a set of mixed integer inequalities. The package also includes implementations of existing algorithms to integrate high-throughput expression data with genome-scale models of metabolism and transcriptional regulation. We demonstrate how TIGER automates the coupling of a genome-scale metabolic model with GPR logic and models of transcriptional regulation, thereby serving as a platform for algorithm development and large-scale metabolic analysis. Additionally, we demonstrate how TIGER's algorithms can be used to identify inconsistencies and improve existing models of transcriptional regulation with examples from the reconstructed transcriptional regulatory network of Saccharomyces cerevisiae. Conclusion The TIGER package provides a consistent platform for algorithm development and extending existing genome-scale metabolic models with regulatory networks and high-throughput data.

  10. Co-expression networks reveal the tissue-specific regulation of transcription and splicing.

    Science.gov (United States)

    Saha, Ashis; Kim, Yungil; Gewirtz, Ariel D H; Jo, Brian; Gao, Chuan; McDowell, Ian C; Engelhardt, Barbara E; Battle, Alexis

    2017-11-01

    Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues. © 2017 Saha et al.; Published by Cold Spring Harbor Laboratory Press.

  11. A gene regulatory network controlling hhex transcription in the anterior endoderm of the organizer

    Science.gov (United States)

    Rankin, Scott A.; Kormish, Jay; Kofron, Matt; Jegga, Anil; Zorn, Aaron M.

    2011-01-01

    The homeobox gene hhex is one of the earliest markers of the anterior endoderm, which gives rise to foregut organs such as the liver, ventral pancreas, thyroid, and lungs. The regulatory networks controlling hhex transcription are poorly understood. In an extensive cis-regulatory analysis of the Xenopus hhex promoter we determined how the Nodal, Wnt, and BMP pathways and their downstream transcription factors regulate hhex expression in the gastrula organizer. We show that Nodal signaling, present throughout the endoderm, directly activates hhex transcription via FoxH1/Smad2 binding sites in the proximal −0.44 Kb promoter. This positive action of Nodal is suppressed in the ventral-posterior endoderm by Vent 1 and Vent2, homeodomain repressors that are induced by BMP signaling. Maternal Wnt/β-catenin on the dorsal side of the embryo cooperates with Nodal and indirectly activate hhex expression via the homeodomain activators Siamois and Twin. Siamois/Twin stimulate hhex transcription through two mechanisms: 1) They induce the expression of Otx2 and Lim1 and together Siamois, Twin, Otx2 and Lim1 appear to promote hhex transcription through homeobox sites in a Wnt-responsive element located between −0.65 to −0.55 Kb of the hhex promoter. 2) Siamois/Twin also induce the expression of the BMP-antagonists Chordin and Noggin, which are required to exclude Vents from the organizer allowing hhex transcription. This work reveals a complex network regulating anterior endoderm transcription in the early embryo. PMID:21215263

  12. SNRFCB: sub-network based random forest classifier for predicting chemotherapy benefit on survival for cancer treatment.

    Science.gov (United States)

    Shi, Mingguang; He, Jianmin

    2016-04-01

    Adjuvant chemotherapy (CTX) should be individualized to provide potential survival benefit and avoid potential harm to cancer patients. Our goal was to establish a computational approach for making personalized estimates of the survival benefit from adjuvant CTX. We developed Sub-Network based Random Forest classifier for predicting Chemotherapy Benefit (SNRFCB) based gene expression datasets of lung cancer. The SNRFCB approach was then validated in independent test cohorts for identifying chemotherapy responder cohorts and chemotherapy non-responder cohorts. SNRFCB involved the pre-selection of gene sub-network signatures based on the mutations and on protein-protein interaction data as well as the application of the random forest algorithm to gene expression datasets. Adjuvant CTX was significantly associated with the prolonged overall survival of lung cancer patients in the chemotherapy responder group (P = 0.008), but it was not beneficial to patients in the chemotherapy non-responder group (P = 0.657). Adjuvant CTX was significantly associated with the prolonged overall survival of lung cancer squamous cell carcinoma (SQCC) subtype patients in the chemotherapy responder cohorts (P = 0.024), but it was not beneficial to patients in the chemotherapy non-responder cohorts (P = 0.383). SNRFCB improved prediction performance as compared to the machine learning method, support vector machine (SVM). To test the general applicability of the predictive model, we further applied the SNRFCB approach to human breast cancer datasets and also observed superior performance. SNRFCB could provide recurrent probability for individual patients and identify which patients may benefit from adjuvant CTX in clinical trials.

  13. Uncovering transcription factor and microRNA risk regulatory pathways associated with osteoarthritis by network analysis.

    Science.gov (United States)

    Song, Zhenhua; Zhang, Chi; He, Lingxiao; Sui, Yanfang; Lin, Xiafei; Pan, Jingjing

    2018-05-01

    Osteoarthritis (OA) is the most common form of joint disease. The development of inflammation have been considered to play a key role during the progression of OA. Regulatory pathways are known to play crucial roles in many pathogenic processes. Thus, deciphering these risk regulatory pathways is critical for elucidating the mechanisms underlying OA. We constructed an OA-specific regulatory network by integrating comprehensive curated transcription and post-transcriptional resource involving transcription factor (TF) and microRNA (miRNA). To deepen our understanding of underlying molecular mechanisms of OA, we developed an integrated systems approach to identify OA-specific risk regulatory pathways. In this study, we identified 89 significantly differentially expressed genes between normal and inflamed areas of OA patients. We found the OA-specific regulatory network was a standard scale-free network with small-world properties. It significant enriched many immune response-related functions including leukocyte differentiation, myeloid differentiation and T cell activation. Finally, 141 risk regulatory pathways were identified based on OA-specific regulatory network, which contains some known regulator of OA. The risk regulatory pathways may provide clues for the etiology of OA and be a potential resource for the discovery of novel OA-associated disease genes. Copyright © 2018 Elsevier Inc. All rights reserved.

  14. Transcriptional interference networks coordinate the expression of functionally related genes clustered in the same genomic loci.

    Science.gov (United States)

    Boldogköi, Zsolt

    2012-01-01

    The regulation of gene expression is essential for normal functioning of biological systems in every form of life. Gene expression is primarily controlled at the level of transcription, especially at the phase of initiation. Non-coding RNAs are one of the major players at every level of genetic regulation, including the control of chromatin organization, transcription, various post-transcriptional processes, and translation. In this study, the Transcriptional Interference Network (TIN) hypothesis was put forward in an attempt to explain the global expression of antisense RNAs and the overall occurrence of tandem gene clusters in the genomes of various biological systems ranging from viruses to mammalian cells. The TIN hypothesis suggests the existence of a novel layer of genetic regulation, based on the interactions between the transcriptional machineries of neighboring genes at their overlapping regions, which are assumed to play a fundamental role in coordinating gene expression within a cluster of functionally linked genes. It is claimed that the transcriptional overlaps between adjacent genes are much more widespread in genomes than is thought today. The Waterfall model of the TIN hypothesis postulates a unidirectional effect of upstream genes on the transcription of downstream genes within a cluster of tandemly arrayed genes, while the Seesaw model proposes a mutual interdependence of gene expression between the oppositely oriented genes. The TIN represents an auto-regulatory system with an exquisitely timed and highly synchronized cascade of gene expression in functionally linked genes located in close physical proximity to each other. In this study, we focused on herpesviruses. The reason for this lies in the compressed nature of viral genes, which allows a tight regulation and an easier investigation of the transcriptional interactions between genes. However, I believe that the same or similar principles can be applied to cellular organisms too.

  15. A cross-sectional evaluation of meditation experience on electroencephalography data by artificial neural network and support vector machine classifiers.

    Science.gov (United States)

    Lee, Yu-Hao; Hsieh, Ya-Ju; Shiah, Yung-Jong; Lin, Yu-Huei; Chen, Chiao-Yun; Tyan, Yu-Chang; GengQiu, JiaCheng; Hsu, Chung-Yao; Chen, Sharon Chia-Ju

    2017-04-01

    To quantitate the meditation experience is a subjective and complex issue because it is confounded by many factors such as emotional state, method of meditation, and personal physical condition. In this study, we propose a strategy with a cross-sectional analysis to evaluate the meditation experience with 2 artificial intelligence techniques: artificial neural network and support vector machine. Within this analysis system, 3 features of the electroencephalography alpha spectrum and variant normalizing scaling are manipulated as the evaluating variables for the detection of accuracy. Thereafter, by modulating the sliding window (the period of the analyzed data) and shifting interval of the window (the time interval to shift the analyzed data), the effect of immediate analysis for the 2 methods is compared. This analysis system is performed on 3 meditation groups, categorizing their meditation experiences in 10-year intervals from novice to junior and to senior. After an exhausted calculation and cross-validation across all variables, the high accuracy rate >98% is achievable under the criterion of 0.5-minute sliding window and 2 seconds shifting interval for both methods. In a word, the minimum analyzable data length is 0.5 minute and the minimum recognizable temporal resolution is 2 seconds in the decision of meditative classification. Our proposed classifier of the meditation experience promotes a rapid evaluation system to distinguish meditation experience and a beneficial utilization of artificial techniques for the big-data analysis.

  16. Using SAR images to delineate ocean oil slicks with a texture-classifying neural network algorithm (TCNNA)

    Energy Technology Data Exchange (ETDEWEB)

    Garcia-Pineda, O.; MacDonald, I.R. [Florida State Univ., Tallahassee, FL (United States). Dept. of Oceanography; Zimmer, B. [Texas A and M Univ., Corpus Christi, TX (United States). Dept. of Mathematics and Statistics; Howard, M. [Texas A and M Univ., College Station, TX (United States). Dept. of Oceanography; Pichel, W. [National Oceanic and Atmospheric Administration, Camp Springs, MD (United States). Center for Satellite Applications and Research, National Environmental Satellite, Data and Information Service; Li, X. [National Oceanic and Atmospheric Administration, Camp Springs, MD (United States). Systems Group, National Environmental Satellite, Data and Information

    2009-10-15

    Synthetic aperture radar (SAR) is used to detect surfactant layers produced by floating oil on the ocean surface. This study presented details of a texture-classifying neural network algorithm (TCNNA) designed to process SAR data from a wide selection of beam modes. Patterns from SAR imagery were extracted in a semi-supervised procedure using a combination of edge-detection filters; texture descriptors; collection information; and environmental data. Various natural oil seeps in the Gulf of Mexico were used as case studies. An analysis of the case studies demonstrated that the TCNNA was able to extract targets and rapidly interpret images collected under a range of environmental conditions. Results presented by the TCNNA were used to evaluate the effects of different environmental conditions on the expressions of oil slicks detected by the data. Optimal incidence angle ranges and wind speed ranges for surfactant film detection were also presented. Results obtained by the TCNNA can be stored and manipulated in geographic information system (GIS) data layers. 26 refs., 1 tab., 7 figs.

  17. The future of genome-scale modeling of yeast through integration of a transcriptional regulatory network

    DEFF Research Database (Denmark)

    Liu, Guodong; Marras, Antonio; Nielsen, Jens

    2014-01-01

    regulatory information is necessary to improve the accuracy and predictive ability of metabolic models. Here we review the strategies for the reconstruction of a transcriptional regulatory network (TRN) for yeast and the integration of such a reconstruction into a flux balance analysis-based metabolic model......Metabolism is regulated at multiple levels in response to the changes of internal or external conditions. Transcriptional regulation plays an important role in regulating many metabolic reactions by altering the concentrations of metabolic enzymes. Thus, integration of the transcriptional....... While many large-scale TRN reconstructions have been reported for yeast, these reconstructions still need to be improved regarding the functionality and dynamic property of the regulatory interactions. In addition, mathematical modeling approaches need to be further developed to efficiently integrate...

  18. Regulation of a transcription factor network by Cdk1 coordinates late cell cycle gene expression.

    Science.gov (United States)

    Landry, Benjamin D; Mapa, Claudine E; Arsenault, Heather E; Poti, Kristin E; Benanti, Jennifer A

    2014-05-02

    To maintain genome stability, regulators of chromosome segregation must be expressed in coordination with mitotic events. Expression of these late cell cycle genes is regulated by cyclin-dependent kinase (Cdk1), which phosphorylates a network of conserved transcription factors (TFs). However, the effects of Cdk1 phosphorylation on many key TFs are not known. We find that elimination of Cdk1-mediated phosphorylation of four S-phase TFs decreases expression of many late cell cycle genes, delays mitotic progression, and reduces fitness in budding yeast. Blocking phosphorylation impairs degradation of all four TFs. Consequently, phosphorylation-deficient mutants of the repressors Yox1 and Yhp1 exhibit increased promoter occupancy and decreased expression of their target genes. Interestingly, although phosphorylation of the transcriptional activator Hcm1 on its N-terminus promotes its degradation, phosphorylation on its C-terminus is required for its activity, indicating that Cdk1 both activates and inhibits a single TF. We conclude that Cdk1 promotes gene expression by both activating transcriptional activators and inactivating transcriptional repressors. Furthermore, our data suggest that coordinated regulation of the TF network by Cdk1 is necessary for faithful cell division.

  19. Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network.

    Science.gov (United States)

    Guo, Hao; Qin, Mengna; Chen, Junjie; Xu, Yong; Xiang, Jie

    2017-01-01

    High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain regions. Accordingly, such networks are widely used to classify brain diseases. However, traditional methods for processing high-order functional connectivity networks generally include the clustering method, which reduces data dimensionality. As a result, such networks cannot be effectively interpreted in the context of neurology. Additionally, due to the large scale of high-order functional connectivity networks, it can be computationally very expensive to use complex network or graph theory to calculate certain topological properties. Here, we propose a novel method of generating a high-order minimum spanning tree functional connectivity network. This method increases the neurological significance of the high-order functional connectivity network, reduces network computing consumption, and produces a network scale that is conducive to subsequent network analysis. To ensure the quality of the topological information in the network structure, we used frequent subgraph mining technology to capture the discriminative subnetworks as features and combined this with quantifiable local network features. Then we applied a multikernel learning technique to the corresponding selected features to obtain the final classification results. We evaluated our proposed method using a data set containing 38 patients with major depressive disorder and 28 healthy controls. The experimental results showed a classification accuracy of up to 97.54%.

  20. Unraveling the WRKY transcription factors network in Arabidopsis Thaliana by integrative approach

    Directory of Open Access Journals (Sweden)

    Mouna Choura

    2015-06-01

    Full Text Available The WRKY transcription factors superfamily are involved in diverse biological processes in plants including response to biotic and abiotic stresses and plant immunity. Protein-protein interaction network is a useful approach for understanding these complex processes. The availability of Arabidopsis Thaliana interactome offers a good opportunity to do get a global view of protein network. In this work, we have constructed the WRKY transcription factor network by combining different sources of evidence and we characterized its topological features using computational tools. We found that WRKY network is a hub-based network involving multifunctional proteins denoted as hubs such as WRKY 70, WRKY40, WRKY 53, WRKY 60, WRKY 33 and WRKY 51. Functional annotation showed seven functional modules particularly involved in biotic stress and defense responses. Furthermore, the gene ontology and pathway enrichment analysis revealed that WRKY proteins are mainly involved in plant-pathogen interaction pathways and their functions are directly related to the stress response and immune system process.

  1. Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks

    Science.gov (United States)

    Marbach, Daniel; Roy, Sushmita; Ay, Ferhat; Meyer, Patrick E.; Candeias, Rogerio; Kahveci, Tamer; Bristow, Christopher A.; Kellis, Manolis

    2012-01-01

    Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein–protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level. PMID:22456606

  2. Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks.

    Science.gov (United States)

    Marbach, Daniel; Roy, Sushmita; Ay, Ferhat; Meyer, Patrick E; Candeias, Rogerio; Kahveci, Tamer; Bristow, Christopher A; Kellis, Manolis

    2012-07-01

    Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein-protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level.

  3. Transcriptional profiling uncovers a network of cholesterol-responsive atherosclerosis target genes.

    Directory of Open Access Journals (Sweden)

    Josefin Skogsberg

    2008-03-01

    Full Text Available Despite the well-documented effects of plasma lipid lowering regimes halting atherosclerosis lesion development and reducing morbidity and mortality of coronary artery disease and stroke, the transcriptional response in the atherosclerotic lesion mediating these beneficial effects has not yet been carefully investigated. We performed transcriptional profiling at 10-week intervals in atherosclerosis-prone mice with human-like hypercholesterolemia and a genetic switch to lower plasma lipoproteins (Ldlr(-/-Apo(100/100Mttp(flox/flox Mx1-Cre. Atherosclerotic lesions progressed slowly at first, then expanded rapidly, and plateaued after advanced lesions formed. Analysis of lesion expression profiles indicated that accumulation of lipid-poor macrophages reached a point that led to the rapid expansion phase with accelerated foam-cell formation and inflammation, an interpretation supported by lesion histology. Genetic lowering of plasma cholesterol (e.g., lipoproteins at this point all together prevented the formation of advanced plaques and parallel transcriptional profiling of the atherosclerotic arterial wall identified 37 cholesterol-responsive genes mediating this effect. Validation by siRNA-inhibition in macrophages incubated with acetylated-LDL revealed a network of eight cholesterol-responsive atherosclerosis genes regulating cholesterol-ester accumulation. Taken together, we have identified a network of atherosclerosis genes that in response to plasma cholesterol-lowering prevents the formation of advanced plaques. This network should be of interest for the development of novel atherosclerosis therapies.

  4. A Novel HMM Distributed Classifier for the Detection of Gait Phases by Means of a Wearable Inertial Sensor Network

    Directory of Open Access Journals (Sweden)

    Juri Taborri

    2014-09-01

    Full Text Available In this work, we decided to apply a hierarchical weighted decision, proposed and used in other research fields, for the recognition of gait phases. The developed and validated novel distributed classifier is based on hierarchical weighted decision from outputs of scalar Hidden Markov Models (HMM applied to angular velocities of foot, shank, and thigh. The angular velocities of ten healthy subjects were acquired via three uni-axial gyroscopes embedded in inertial measurement units (IMUs during one walking task, repeated three times, on a treadmill. After validating the novel distributed classifier and scalar and vectorial classifiers-already proposed in the literature, with a cross-validation, classifiers were compared for sensitivity, specificity, and computational load for all combinations of the three targeted anatomical segments. Moreover, the performance of the novel distributed classifier in the estimation of gait variability in terms of mean time and coefficient of variation was evaluated. The highest values of specificity and sensitivity (>0.98 for the three classifiers examined here were obtained when the angular velocity of the foot was processed. Distributed and vectorial classifiers reached acceptable values (>0.95 when the angular velocity of shank and thigh were analyzed. Distributed and scalar classifiers showed values of computational load about 100 times lower than the one obtained with the vectorial classifier. In addition, distributed classifiers showed an excellent reliability for the evaluation of mean time and a good/excellent reliability for the coefficient of variation. In conclusion, due to the better performance and the small value of computational load, the here proposed novel distributed classifier can be implemented in the real-time application of gait phases recognition, such as to evaluate gait variability in patients or to control active orthoses for the recovery of mobility of lower limb joints.

  5. Estimating the similarity of alternative Affymetrix probe sets using transcriptional networks

    Science.gov (United States)

    2013-01-01

    Background The usefulness of the data from Affymetrix microarray analysis depends largely on the reliability of the files describing the correspondence between probe sets, genes and transcripts. Particularly, when a gene is targeted by several probe sets, these files should give information about the similarity of each alternative probe set pair. Transcriptional networks integrate the multiple correlations that exist between all probe sets and supply much more information than a simple correlation coefficient calculated for two series of signals. In this study, we used the PSAWN (Probe Set Assignment With Networks) programme we developed to investigate whether similarity of alternative probe sets resulted in some specific properties. Findings PSAWNpy delivered a full textual description of each probe set and information on the number and properties of secondary targets. PSAWNml calculated the similarity of each alternative probe set pair and allowed finding relationships between similarity and localisation of probes in common transcripts or exons. Similar alternative probe sets had very low negative correlation, high positive correlation and similar neighbourhood overlap. Using these properties, we devised a test that allowed grouping similar probe sets in a given network. By considering several networks, additional information concerning the similarity reproducibility was obtained, which allowed defining the actual similarity of alternative probe set pairs. In particular, we calculated the common localisation of probes in exons and in known transcripts and we showed that similarity was correctly correlated with them. The information collected on all pairs of alternative probe sets in the most popular 3’ IVT Affymetrix chips is available in tabular form at http://bns.crbm.cnrs.fr/download.html. Conclusions These processed data can be used to obtain a finer interpretation when comparing microarray data between biological conditions. They are particularly well

  6. Estimating the similarity of alternative Affymetrix probe sets using transcriptional networks.

    Science.gov (United States)

    Bellis, Michel

    2013-03-21

    The usefulness of the data from Affymetrix microarray analysis depends largely on the reliability of the files describing the correspondence between probe sets, genes and transcripts. Particularly, when a gene is targeted by several probe sets, these files should give information about the similarity of each alternative probe set pair. Transcriptional networks integrate the multiple correlations that exist between all probe sets and supply much more information than a simple correlation coefficient calculated for two series of signals. In this study, we used the PSAWN (Probe Set Assignment With Networks) programme we developed to investigate whether similarity of alternative probe sets resulted in some specific properties. PSAWNpy delivered a full textual description of each probe set and information on the number and properties of secondary targets. PSAWNml calculated the similarity of each alternative probe set pair and allowed finding relationships between similarity and localisation of probes in common transcripts or exons. Similar alternative probe sets had very low negative correlation, high positive correlation and similar neighbourhood overlap. Using these properties, we devised a test that allowed grouping similar probe sets in a given network. By considering several networks, additional information concerning the similarity reproducibility was obtained, which allowed defining the actual similarity of alternative probe set pairs. In particular, we calculated the common localisation of probes in exons and in known transcripts and we showed that similarity was correctly correlated with them. The information collected on all pairs of alternative probe sets in the most popular 3' IVT Affymetrix chips is available in tabular form at http://bns.crbm.cnrs.fr/download.html. These processed data can be used to obtain a finer interpretation when comparing microarray data between biological conditions. They are particularly well adapted for searching 3' alternative

  7. Classifying Microorganisms

    DEFF Research Database (Denmark)

    Sommerlund, Julie

    2006-01-01

    This paper describes the coexistence of two systems for classifying organisms and species: a dominant genetic system and an older naturalist system. The former classifies species and traces their evolution on the basis of genetic characteristics, while the latter employs physiological characteris......This paper describes the coexistence of two systems for classifying organisms and species: a dominant genetic system and an older naturalist system. The former classifies species and traces their evolution on the basis of genetic characteristics, while the latter employs physiological...... characteristics. The coexistence of the classification systems does not lead to a conflict between them. Rather, the systems seem to co-exist in different configurations, through which they are complementary, contradictory and inclusive in different situations-sometimes simultaneously. The systems come...

  8. Exploring the bZIP transcription factor regulatory network in Neurospora crassa.

    Science.gov (United States)

    Tian, Chaoguang; Li, Jingyi; Glass, N Louise

    2011-03-01

    Transcription factors (TFs) are key nodes of regulatory networks in eukaryotic organisms, including filamentous fungi such as Neurospora crassa. The 178 predicted DNA-binding TFs in N. crassa are distributed primarily among six gene families, which represent an ancient expansion in filamentous ascomycete genomes; 98 TF genes show detectable expression levels during vegetative growth of N. crassa, including 35 that show a significant difference in expression level between hyphae at the periphery versus hyphae in the interior of a colony. Regulatory networks within a species genome include paralogous TFs and their respective target genes (TF regulon). To investigate TF network evolution in N. crassa, we focused on the basic leucine zipper (bZIP) TF family, which contains nine members. We performed baseline transcriptional profiling during vegetative growth of the wild-type and seven isogenic, viable bZIP deletion mutants. We further characterized the regulatory network of one member of the bZIP family, NCU03905. NCU03905 encodes an Ap1-like protein (NcAp-1), which is involved in resistance to multiple stress responses, including oxidative and heavy metal stress. Relocalization of NcAp-1 from the cytoplasm to the nucleus was associated with exposure to stress. A comparison of the NcAp-1 regulon with Ap1-like regulons in Saccharomyces cerevisiae, Schizosaccharomyces pombe, Candida albicans and Aspergillus fumigatus showed both conservation and divergence. These data indicate how N. crassa responds to stress and provide information on pathway evolution.

  9. Dissecting the expression relationships between RNA-binding proteins and their cognate targets in eukaryotic post-transcriptional regulatory networks

    Science.gov (United States)

    Nishtala, Sneha; Neelamraju, Yaseswini; Janga, Sarath Chandra

    2016-05-01

    RNA-binding proteins (RBPs) are pivotal in orchestrating several steps in the metabolism of RNA in eukaryotes thereby controlling an extensive network of RBP-RNA interactions. Here, we employed CLIP (cross-linking immunoprecipitation)-seq datasets for 60 human RBPs and RIP-ChIP (RNP immunoprecipitation-microarray) data for 69 yeast RBPs to construct a network of genome-wide RBP- target RNA interactions for each RBP. We show in humans that majority (~78%) of the RBPs are strongly associated with their target transcripts at transcript level while ~95% of the studied RBPs were also found to be strongly associated with expression levels of target transcripts when protein expression levels of RBPs were employed. At transcript level, RBP - RNA interaction data for the yeast genome, exhibited a strong association for 63% of the RBPs, confirming the association to be conserved across large phylogenetic distances. Analysis to uncover the features contributing to these associations revealed the number of target transcripts and length of the selected protein-coding transcript of an RBP at the transcript level while intensity of the CLIP signal, number of RNA-Binding domains, location of the binding site on the transcript, to be significant at the protein level. Our analysis will contribute to improved modelling and prediction of post-transcriptional networks.

  10. Embryonic maturation of epidermal Merkel cells is controlled by a redundant transcription factor network.

    Science.gov (United States)

    Perdigoto, Carolina N; Bardot, Evan S; Valdes, Victor J; Santoriello, Francis J; Ezhkova, Elena

    2014-12-01

    Merkel cell-neurite complexes are located in touch-sensitive areas of the mammalian skin and are involved in recognition of the texture and shape of objects. Merkel cells are essential for these tactile discriminations, as they generate action potentials in response to touch stimuli and induce the firing of innervating afferent nerves. It has been shown that Merkel cells originate from epidermal stem cells, but the cellular and molecular mechanisms of their development are largely unknown. In this study, we analyzed Merkel cell differentiation during development and found that it is a temporally regulated maturation process characterized by a sequential activation of Merkel cell-specific genes. We uncovered key transcription factors controlling this process and showed that the transcription factor Atoh1 is required for initial Merkel cell specification. The subsequent maturation steps of Merkel cell differentiation are controlled by cooperative function of the transcription factors Sox2 and Isl1, which physically interact and work to sustain Atoh1 expression. These findings reveal the presence of a robust transcriptional network required to produce functional Merkel cells that are required for tactile discrimination. © 2014. Published by The Company of Biologists Ltd.

  11. Identification of PEG-induced water stress responsive transcripts using co-expression network in Eucalyptus grandis.

    Science.gov (United States)

    Ghosh Dasgupta, Modhumita; Dharanishanthi, Veeramuthu

    2017-09-05

    Ecophysiological studies in Eucalyptus have shown that water is the principal factor limiting stem growth. Effect of water deficit conditions on physiological and biochemical parameters has been extensively reported in Eucalyptus. The present study was conducted to identify major polyethylene glycol induced water stress responsive transcripts in Eucalyptus grandis using gene co-expression network. A customized array representing 3359 water stress responsive genes was designed to document their expression in leaves of E. grandis cuttings subjected to -0.225MPa of PEG treatment. The differentially expressed transcripts were documented and significantly co-expressed transcripts were used for construction of network. The co-expression network was constructed with 915 nodes and 3454 edges with degree ranging from 2 to 45. Ninety four GO categories and 117 functional pathways were identified in the network. MCODE analysis generated 27 modules and module 6 with 479 nodes and 1005 edges was identified as the biologically relevant network. The major water responsive transcripts represented in the module included dehydrin, osmotin, LEA protein, expansin, arabinogalactans, heat shock proteins, major facilitator proteins, ARM repeat proteins, raffinose synthase, tonoplast intrinsic protein and transcription factors like DREB2A, ARF9, AGL24, UNE12, WLIM1 and MYB66, MYB70, MYB 55, MYB 16 and MYB 103. The coordinated analysis of gene expression patterns and coexpression networks developed in this study identified an array of transcripts that may regulate PEG induced water stress responses in E. grandis. Copyright © 2017 Elsevier B.V. All rights reserved.

  12. A genomic approach to identify regulatory nodes in the transcriptional network of systemic acquired resistance in plants.

    Directory of Open Access Journals (Sweden)

    Dong Wang

    2006-11-01

    Full Text Available Many biological processes are controlled by intricate networks of transcriptional regulators. With the development of microarray technology, transcriptional changes can be examined at the whole-genome level. However, such analysis often lacks information on the hierarchical relationship between components of a given system. Systemic acquired resistance (SAR is an inducible plant defense response involving a cascade of transcriptional events induced by salicylic acid through the transcription cofactor NPR1. To identify additional regulatory nodes in the SAR network, we performed microarray analysis on Arabidopsis plants expressing the NPR1-GR (glucocorticoid receptor fusion protein. Since nuclear translocation of NPR1-GR requires dexamethasone, we were able to control NPR1-dependent transcription and identify direct transcriptional targets of NPR1. We show that NPR1 directly upregulates the expression of eight WRKY transcription factor genes. This large family of 74 transcription factors has been implicated in various defense responses, but no specific WRKY factor has been placed in the SAR network. Identification of NPR1-regulated WRKY factors allowed us to perform in-depth genetic analysis on a small number of WRKY factors and test well-defined phenotypes of single and double mutants associated with NPR1. Among these WRKY factors we found both positive and negative regulators of SAR. This genomics-directed approach unambiguously positioned five WRKY factors in the complex transcriptional regulatory network of SAR. Our work not only discovered new transcription regulatory components in the signaling network of SAR but also demonstrated that functional studies of large gene families have to take into consideration sequence similarity as well as the expression patterns of the candidates.

  13. Using the TensorFlow Deep Neural Network to Classify Mainland China Visitor Behaviours in Hong Kong from Check-in Data

    Directory of Open Access Journals (Sweden)

    Shanshan Han

    2018-04-01

    Full Text Available Over the past decade, big data, including Global Positioning System (GPS data, mobile phone tracking data and social media check-in data, have been widely used to analyse human movements and behaviours. Tourism management researchers have noted the potential of applying these data to study tourist behaviours, and many studies have shown that social media check-in data can provide new opportunities for extracting tourism activities and tourist behaviours. However, traditional methods may not be suitable for extracting comprehensive tourist behaviours due to the complexity and diversity of human behaviours. Studies have shown that deep neural networks have outpaced the abilities of human beings in many fields and that deep neural networks can be explained in a psychological manner. Thus, deep neural network methods can potentially be used to understand human behaviours. In this paper, a deep learning neural network constructed in TensorFlow is applied to classify Mainland China visitor behaviours in Hong Kong, and the characteristics of these visitors are analysed to verify the classification results. For the social science classification problem investigated in this study, the deep neural network classifier in TensorFlow provides better accuracy and more lucid visualisation than do traditional neural network methods, even for erratic classification rules. Furthermore, the results of this study reveal that TensorFlow has considerable potential for application in the human geography field.

  14. Propagation of New Innovations: An Approach to Classify Human Behavior and Movement from Available Social Network Data

    Science.gov (United States)

    Mahmud, Faisal; Samiul, Hasan

    2010-01-01

    It is interesting to observe new innovations, products, or ideas propagating into the society. One important factor of this propagation is the role of individual's social network; while another factor is individual's activities. In this paper, an approach will be made to analyze the propagation of different ideas in a popular social network. Individuals' responses to different activities in the network will be analyzed. The properties of network will also be investigated for successful propagation of innovations.

  15. Hybrid modeling of the crosstalk between signaling and transcriptional networks using ordinary differential equations and multi-valued logic.

    Science.gov (United States)

    Khan, Faiz M; Schmitz, Ulf; Nikolov, Svetoslav; Engelmann, David; Pützer, Brigitte M; Wolkenhauer, Olaf; Vera, Julio

    2014-01-01

    A decade of successful results indicates that systems biology is the appropriate approach to investigate the regulation of complex biochemical networks involving transcriptional and post-transcriptional regulations. It becomes mandatory when dealing with highly interconnected biochemical networks, composed of hundreds of compounds, or when networks are enriched in non-linear motifs like feedback and feedforward loops. An emerging dilemma is to conciliate models of massive networks and the adequate description of non-linear dynamics in a suitable modeling framework. Boolean networks are an ideal representation of massive networks that are humble in terms of computational complexity and data demand. However, they are inappropriate when dealing with nested feedback/feedforward loops, structural motifs common in biochemical networks. On the other hand, models of ordinary differential equations (ODEs) cope well with these loops, but they require enormous amounts of quantitative data for a full characterization of the model. Here we propose hybrid models, composed of ODE and logical sub-modules, as a strategy to handle large scale, non-linear biochemical networks that include transcriptional and post-transcriptional regulations. We illustrate the construction of this kind of models using as example a regulatory network centered on E2F1, a transcription factor involved in cancer. The hybrid modeling approach proposed is a good compromise between quantitative/qualitative accuracy and scalability when considering large biochemical networks with a small highly interconnected core, and module of transcriptionally regulated genes that are not part of critical regulatory loops. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai. Copyright © 2013 Elsevier B.V. All rights reserved.

  16. Inferring transcriptional gene regulation network of starch metabolism in Arabidopsis thaliana leaves using graphical Gaussian model

    Directory of Open Access Journals (Sweden)

    Ingkasuwan Papapit

    2012-08-01

    Full Text Available Abstract Background Starch serves as a temporal storage of carbohydrates in plant leaves during day/night cycles. To study transcriptional regulatory modules of this dynamic metabolic process, we conducted gene regulation network analysis based on small-sample inference of graphical Gaussian model (GGM. Results Time-series significant analysis was applied for Arabidopsis leaf transcriptome data to obtain a set of genes that are highly regulated under a diurnal cycle. A total of 1,480 diurnally regulated genes included 21 starch metabolic enzymes, 6 clock-associated genes, and 106 transcription factors (TF. A starch-clock-TF gene regulation network comprising 117 nodes and 266 edges was constructed by GGM from these 133 significant genes that are potentially related to the diurnal control of starch metabolism. From this network, we found that β-amylase 3 (b-amy3: At4g17090, which participates in starch degradation in chloroplast, is the most frequently connected gene (a hub gene. The robustness of gene-to-gene regulatory network was further analyzed by TF binding site prediction and by evaluating global co-expression of TFs and target starch metabolic enzymes. As a result, two TFs, indeterminate domain 5 (AtIDD5: At2g02070 and constans-like (COL: At2g21320, were identified as positive regulators of starch synthase 4 (SS4: At4g18240. The inference model of AtIDD5-dependent positive regulation of SS4 gene expression was experimentally supported by decreased SS4 mRNA accumulation in Atidd5 mutant plants during the light period of both short and long day conditions. COL was also shown to positively control SS4 mRNA accumulation. Furthermore, the knockout of AtIDD5 and COL led to deformation of chloroplast and its contained starch granules. This deformity also affected the number of starch granules per chloroplast, which increased significantly in both knockout mutant lines. Conclusions In this study, we utilized a systematic approach of microarray

  17. Inference of Transcription Regulatory Network in Low Phytic Acid Soybean Seeds

    Directory of Open Access Journals (Sweden)

    Neelam Redekar

    2017-11-01

    Full Text Available A dominant loss of function mutation in myo-inositol phosphate synthase (MIPS gene and recessive loss of function mutations in two multidrug resistant protein type-ABC transporter genes not only reduce the seed phytic acid levels in soybean, but also affect the pathways associated with seed development, ultimately resulting in low emergence. To understand the regulatory mechanisms and identify key genes that intervene in the seed development process in low phytic acid crops, we performed computational inference of gene regulatory networks in low and normal phytic acid soybeans using a time course transcriptomic data and multiple network inference algorithms. We identified a set of putative candidate transcription factors and their regulatory interactions with genes that have functions in myo-inositol biosynthesis, auxin-ABA signaling, and seed dormancy. We evaluated the performance of our unsupervised network inference method by comparing the predicted regulatory network with published regulatory interactions in Arabidopsis. Some contrasting regulatory interactions were observed in low phytic acid mutants compared to non-mutant lines. These findings provide important hypotheses on expression regulation of myo-inositol metabolism and phytohormone signaling in developing low phytic acid soybeans. The computational pipeline used for unsupervised network learning in this study is provided as open source software and is freely available at https://lilabatvt.github.io/LPANetwork/.

  18. Opening up the blackbox: an interpretable deep neural network-based classifier for cell-type specific enhancer predictions.

    Science.gov (United States)

    Kim, Seong Gon; Theera-Ampornpunt, Nawanol; Fang, Chih-Hao; Harwani, Mrudul; Grama, Ananth; Chaterji, Somali

    2016-08-01

    Gene expression is mediated by specialized cis-regulatory modules (CRMs), the most prominent of which are called enhancers. Early experiments indicated that enhancers located far from the gene promoters are often responsible for mediating gene transcription. Knowing their properties, regulatory activity, and genomic targets is crucial to the functional understanding of cellular events, ranging from cellular homeostasis to differentiation. Recent genome-wide investigation of epigenomic marks has indicated that enhancer elements could be enriched for certain epigenomic marks, such as, combinatorial patterns of histone modifications. Our efforts in this paper are motivated by these recent advances in epigenomic profiling methods, which have uncovered enhancer-associated chromatin features in different cell types and organisms. Specifically, in this paper, we use recent state-of-the-art Deep Learning methods and develop a deep neural network (DNN)-based architecture, called EP-DNN, to predict the presence and types of enhancers in the human genome. It uses as features, the expression levels of the histone modifications at the peaks of the functional sites as well as in its adjacent regions. We apply EP-DNN to four different cell types: H1, IMR90, HepG2, and HeLa S3. We train EP-DNN using p300 binding sites as enhancers, and TSS and random non-DHS sites as non-enhancers. We perform EP-DNN predictions to quantify the validation rate for different levels of confidence in the predictions and also perform comparisons against two state-of-the-art computational models for enhancer predictions, DEEP-ENCODE and RFECS. We find that EP-DNN has superior accuracy and takes less time to make predictions. Next, we develop methods to make EP-DNN interpretable by computing the importance of each input feature in the classification task. This analysis indicates that the important histone modifications were distinct for different cell types, with some overlaps, e.g., H3K27ac was

  19. Inference of Transcriptional Network for Pluripotency in Mouse Embryonic Stem Cells

    International Nuclear Information System (INIS)

    Aburatani, S

    2015-01-01

    In embryonic stem cells, various transcription factors (TFs) maintain pluripotency. To gain insights into the regulatory system controlling pluripotency, I inferred the regulatory relationships between the TFs expressed in ES cells. In this study, I applied a method based on structural equation modeling (SEM), combined with factor analysis, to 649 expression profiles of 19 TF genes measured in mouse Embryonic Stem Cells (ESCs). The factor analysis identified 19 TF genes that were regulated by several unmeasured factors. Since the known cell reprogramming TF genes (Pou5f1, Sox2 and Nanog) are regulated by different factors, each estimated factor is considered to be an input for signal transduction to control pluripotency in mouse ESCs. In the inferred network model, TF proteins were also arranged as unmeasured factors that control other TFs. The interpretation of the inferred network model revealed the regulatory mechanism for controlling pluripotency in ES cells

  20. CoryneRegNet: an ontology-based data warehouse of corynebacterial transcription factors and regulatory networks.

    Science.gov (United States)

    Baumbach, Jan; Brinkrolf, Karina; Czaja, Lisa F; Rahmann, Sven; Tauch, Andreas

    2006-02-14

    The application of DNA microarray technology in post-genomic analysis of bacterial genome sequences has allowed the generation of huge amounts of data related to regulatory networks. This data along with literature-derived knowledge on regulation of gene expression has opened the way for genome-wide reconstruction of transcriptional regulatory networks. These large-scale reconstructions can be converted into in silico models of bacterial cells that allow a systematic analysis of network behavior in response to changing environmental conditions. CoryneRegNet was designed to facilitate the genome-wide reconstruction of transcriptional regulatory networks of corynebacteria relevant in biotechnology and human medicine. During the import and integration process of data derived from experimental studies or literature knowledge CoryneRegNet generates links to genome annotations, to identified transcription factors and to the corresponding cis-regulatory elements. CoryneRegNet is based on a multi-layered, hierarchical and modular concept of transcriptional regulation and was implemented by using the relational database management system MySQL and an ontology-based data structure. Reconstructed regulatory networks can be visualized by using the yFiles JAVA graph library. As an application example of CoryneRegNet, we have reconstructed the global transcriptional regulation of a cellular module involved in SOS and stress response of corynebacteria. CoryneRegNet is an ontology-based data warehouse that allows a pertinent data management of regulatory interactions along with the genome-scale reconstruction of transcriptional regulatory networks. These models can further be combined with metabolic networks to build integrated models of cellular function including both metabolism and its transcriptional regulation.

  1. CoryneRegNet: An ontology-based data warehouse of corynebacterial transcription factors and regulatory networks

    Directory of Open Access Journals (Sweden)

    Czaja Lisa F

    2006-02-01

    Full Text Available Abstract Background The application of DNA microarray technology in post-genomic analysis of bacterial genome sequences has allowed the generation of huge amounts of data related to regulatory networks. This data along with literature-derived knowledge on regulation of gene expression has opened the way for genome-wide reconstruction of transcriptional regulatory networks. These large-scale reconstructions can be converted into in silico models of bacterial cells that allow a systematic analysis of network behavior in response to changing environmental conditions. Description CoryneRegNet was designed to facilitate the genome-wide reconstruction of transcriptional regulatory networks of corynebacteria relevant in biotechnology and human medicine. During the import and integration process of data derived from experimental studies or literature knowledge CoryneRegNet generates links to genome annotations, to identified transcription factors and to the corresponding cis-regulatory elements. CoryneRegNet is based on a multi-layered, hierarchical and modular concept of transcriptional regulation and was implemented by using the relational database management system MySQL and an ontology-based data structure. Reconstructed regulatory networks can be visualized by using the yFiles JAVA graph library. As an application example of CoryneRegNet, we have reconstructed the global transcriptional regulation of a cellular module involved in SOS and stress response of corynebacteria. Conclusion CoryneRegNet is an ontology-based data warehouse that allows a pertinent data management of regulatory interactions along with the genome-scale reconstruction of transcriptional regulatory networks. These models can further be combined with metabolic networks to build integrated models of cellular function including both metabolism and its transcriptional regulation.

  2. Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain.

    Science.gov (United States)

    Krienen, Fenna M; Yeo, B T Thomas; Ge, Tian; Buckner, Randy L; Sherwood, Chet C

    2016-01-26

    The human brain is patterned with disproportionately large, distributed cerebral networks that connect multiple association zones in the frontal, temporal, and parietal lobes. The expansion of the cortical surface, along with the emergence of long-range connectivity networks, may be reflected in changes to the underlying molecular architecture. Using the Allen Institute's human brain transcriptional atlas, we demonstrate that genes particularly enriched in supragranular layers of the human cerebral cortex relative to mouse distinguish major cortical classes. The topography of transcriptional expression reflects large-scale brain network organization consistent with estimates from functional connectivity MRI and anatomical tracing in nonhuman primates. Microarray expression data for genes preferentially expressed in human upper layers (II/III), but enriched only in lower layers (V/VI) of mouse, were cross-correlated to identify molecular profiles across the cerebral cortex of postmortem human brains (n = 6). Unimodal sensory and motor zones have similar molecular profiles, despite being distributed across the cortical mantle. Sensory/motor profiles were anticorrelated with paralimbic and certain distributed association network profiles. Tests of alternative gene sets did not consistently distinguish sensory and motor regions from paralimbic and association regions: (i) genes enriched in supragranular layers in both humans and mice, (ii) genes cortically enriched in humans relative to nonhuman primates, (iii) genes related to connectivity in rodents, (iv) genes associated with human and mouse connectivity, and (v) 1,454 gene sets curated from known gene ontologies. Molecular innovations of upper cortical layers may be an important component in the evolution of long-range corticocortical projections.

  3. New neural network classifier of fall-risk based on the Mahalanobis distance and kinematic parameters assessed by a wearable device

    International Nuclear Information System (INIS)

    Giansanti, Daniele; Macellari, Velio; Maccioni, Giovanni

    2008-01-01

    Fall prevention lacks easy, quantitative and wearable methods for the classification of fall-risk (FR). Efforts must be thus devoted to the choice of an ad hoc classifier both to reduce the size of the sample used to train the classifier and to improve performances. A new methodology that uses a neural network (NN) and a wearable device are hereby proposed for this purpose. The NN uses kinematic parameters assessed by a wearable device with accelerometers and rate gyroscopes during a posturography protocol. The training of the NN was based on the Mahalanobis distance and was carried out on two groups of 30 elderly subjects with varying fall-risk Tinetti scores. The validation was done on two groups of 100 subjects with different fall-risk Tinetti scores and showed that, both in terms of specificity and sensitivity, the NN performed better than other classifiers (naive Bayes, Bayes net, multilayer perceptron, support vector machines, statistical classifiers). In particular, (i) the proposed NN methodology improved the specificity and sensitivity by a mean of 3% when compared to the statistical classifier based on the Mahalanobis distance (SCMD) described in Giansanti (2006 Physiol. Meas. 27 1081–90); (ii) the assessed specificity was 97%, the assessed sensitivity was 98% and the area under receiver operator characteristics was 0.965. (note)

  4. 76 FR 63811 - Structural Reforms To Improve the Security of Classified Networks and the Responsible Sharing and...

    Science.gov (United States)

    2011-10-13

    ... implementation of policies and minimum standards regarding information security, personnel security, and systems security; address both internal and external security threats and vulnerabilities; and provide policies and... policies and minimum standards will address all agencies that operate or access classified computer...

  5. SoxB1-driven transcriptional network underlies neural-specific interpretation of morphogen signals.

    Science.gov (United States)

    Oosterveen, Tony; Kurdija, Sanja; Ensterö, Mats; Uhde, Christopher W; Bergsland, Maria; Sandberg, Magnus; Sandberg, Rickard; Muhr, Jonas; Ericson, Johan

    2013-04-30

    The reiterative deployment of a small cadre of morphogen signals underlies patterning and growth of most tissues during embyogenesis, but how such inductive events result in tissue-specific responses remains poorly understood. By characterizing cis-regulatory modules (CRMs) associated with genes regulated by Sonic hedgehog (Shh), retinoids, or bone morphogenetic proteins in the CNS, we provide evidence that the neural-specific interpretation of morphogen signaling reflects a direct integration of these pathways with SoxB1 proteins at the CRM level. Moreover, expression of SoxB1 proteins in the limb bud confers on mesodermal cells the potential to activate neural-specific target genes upon Shh, retinoid, or bone morphogenetic protein signaling, and the collocation of binding sites for SoxB1 and morphogen-mediatory transcription factors in CRMs faithfully predicts neural-specific gene activity. Thus, an unexpectedly simple transcriptional paradigm appears to conceptually explain the neural-specific interpretation of pleiotropic signaling during vertebrate development. Importantly, genes induced in a SoxB1-dependent manner appear to constitute repressive gene regulatory networks that are directly interlinked at the CRM level to constrain the regional expression of patterning genes. Accordingly, not only does the topology of SoxB1-driven gene regulatory networks provide a tissue-specific mode of gene activation, but it also determines the spatial expression pattern of target genes within the developing neural tube.

  6. Medusa structure of the gene regulatory network: dominance of transcription factors in cancer subtype classification.

    Science.gov (United States)

    Guo, Yuchun; Feng, Ying; Trivedi, Niraj S; Huang, Sui

    2011-05-01

    Gene expression profiles consisting of ten thousands of transcripts are used for clustering of tissue, such as tumors, into subtypes, often without considering the underlying reason that the distinct patterns of expression arise because of constraints in the realization of gene expression profiles imposed by the gene regulatory network. The topology of this network has been suggested to consist of a regulatory core of genes represented most prominently by transcription factors (TFs) and microRNAs, that influence the expression of other genes, and of a periphery of 'enslaved' effector genes that are regulated but not regulating. This 'medusa' architecture implies that the core genes are much stronger determinants of the realized gene expression profiles. To test this hypothesis, we examined the clustering of gene expression profiles into known tumor types to quantitatively demonstrate that TFs, and even more pronounced, microRNAs, are much stronger discriminators of tumor type specific gene expression patterns than a same number of randomly selected or metabolic genes. These findings lend support to the hypothesis of a medusa architecture and of the canalizing nature of regulation by microRNAs. They also reveal the degree of freedom for the expression of peripheral genes that are less stringently associated with a tissue type specific global gene expression profile.

  7. Role of ALKBH1 in the Core Transcriptional Network of Embryonic Stem Cells

    Directory of Open Access Journals (Sweden)

    Rune Ougland

    2016-01-01

    Full Text Available Background/Aims: ALKBH1, an AlkB homologue in the 2-oxoglutarate and Fe2+ dependent hydroxylase family, is a histone dioxygenase that removes methyl groups from histone H2A. Studies of transgenic mice lacking Alkbh1 reveal that most Alkbh1-/- embryos die during embryonic development. Embryonic stem cells (ESCs derived from these mice have prolonged expression of pluripotency markers and delayed induction of genes involved in neural differentiation, indicating that ALKBH1 is involved in regulation of pluripotency and differentiation. The aim of this study was to further investigate the role ALKBH1 in early development. Methods: Double-filter methods for nitrocellulose-filter binding, dot blot, enzyme-linked immunosorbent assay (ELISA, immonocytochemistry, cell culture and differentiation of mouse ESCs, Co-IP and miRNA analysis. Results: We found that SOX2 and NANOG bind the ALKBH1 promoter, and we identified protein-protein interactions between ALKBH1 and these core transcription factors of the pluripotency network. Furthermore, lack of ALKBH1 affected the expression of developmentally important miRNAs, which are involved in the regulation of NANOG, SOX2 and neural differentiation. Conclusion: Our results suggest that ALKBH1 interacts with the core transcriptional pluripotency network of ESCs and is involved in regulation of pluripotency and differentiation.

  8. Transcriptional Network Analysis Reveals Drought Resistance Mechanisms of AP2/ERF Transgenic Rice

    Directory of Open Access Journals (Sweden)

    Hongryul Ahn

    2017-06-01

    Full Text Available This study was designed to investigate at the molecular level how a transgenic version of rice “Nipponbare” obtained a drought-resistant phenotype. Using multi-omics sequencing data, we compared wild-type rice (WT and a transgenic version (erf71 that had obtained a drought-resistant phenotype by overexpressing OsERF71, a member of the AP2/ERF transcription factor (TF family. A comprehensive bioinformatics analysis pipeline, including TF networks and a cascade tree, was developed for the analysis of multi-omics data. The results of the analysis showed that the presence of OsERF71 at the source of the network controlled global gene expression levels in a specific manner to make erf71 survive longer than WT. Our analysis of the time-series transcriptome data suggests that erf71 diverted more energy to survival-critical mechanisms related to translation, oxidative response, and DNA replication, while further suppressing energy-consuming mechanisms, such as photosynthesis. To support this hypothesis further, we measured the net photosynthesis level under physiological conditions, which confirmed the further suppression of photosynthesis in erf71. In summary, our work presents a comprehensive snapshot of transcriptional modification in transgenic rice and shows how this induced the plants to acquire a drought-resistant phenotype.

  9. A central integrator of transcription networks in plant stress and energy signalling.

    Science.gov (United States)

    Baena-González, Elena; Rolland, Filip; Thevelein, Johan M; Sheen, Jen

    2007-08-23

    Photosynthetic plants are the principal solar energy converter sustaining life on Earth. Despite its fundamental importance, little is known about how plants sense and adapt to darkness in the daily light-dark cycle, or how they adapt to unpredictable environmental stresses that compromise photosynthesis and respiration and deplete energy supplies. Current models emphasize diverse stress perception and signalling mechanisms. Using a combination of cellular and systems screens, we show here that the evolutionarily conserved Arabidopsis thaliana protein kinases, KIN10 and KIN11 (also known as AKIN10/At3g01090 and AKIN11/At3g29160, respectively), control convergent reprogramming of transcription in response to seemingly unrelated darkness, sugar and stress conditions. Sensing and signalling deprivation of sugar and energy, KIN10 targets a remarkably broad array of genes that orchestrate transcription networks, promote catabolism and suppress anabolism. Specific bZIP transcription factors partially mediate primary KIN10 signalling. Transgenic KIN10 overexpression confers enhanced starvation tolerance and lifespan extension, and alters architecture and developmental transitions. Significantly, double kin10 kin11 deficiency abrogates the transcriptional switch in darkness and stress signalling, and impairs starch mobilization at night and growth. These studies uncover surprisingly pivotal roles of KIN10/11 in linking stress, sugar and developmental signals to globally regulate plant metabolism, energy balance, growth and survival. In contrast to the prevailing view that sucrose activates plant SnRK1s (Snf1-related protein kinases), our functional analyses of Arabidopsis KIN10/11 provide compelling evidence that SnRK1s are inactivated by sugars and share central roles with the orthologous yeast Snf1 and mammalian AMPK in energy signalling.

  10. A Comparison of Spectral Angle Mapper and Artificial Neural Network Classifiers Combined with Landsat TM Imagery Analysis for Obtaining Burnt Area Mapping

    Directory of Open Access Journals (Sweden)

    Marko Scholze

    2010-03-01

    Full Text Available Satellite remote sensing, with its unique synoptic coverage capabilities, can provide accurate and immediately valuable information on fire analysis and post-fire assessment, including estimation of burnt areas. In this study the potential for burnt area mapping of the combined use of Artificial Neural Network (ANN and Spectral Angle Mapper (SAM classifiers with Landsat TM satellite imagery was evaluated in a Mediterranean setting. As a case study one of the most catastrophic forest fires, which occurred near the capital of Greece during the summer of 2007, was used. The accuracy of the two algorithms in delineating the burnt area from the Landsat TM imagery, acquired shortly after the fire suppression, was determined by the classification accuracy results of the produced thematic maps. In addition, the derived burnt area estimates from the two classifiers were compared with independent estimates available for the study region, obtained from the analysis of higher spatial resolution satellite data. In terms of the overall classification accuracy, ANN outperformed (overall accuracy 90.29%, Kappa coefficient 0.878 the SAM classifier (overall accuracy 83.82%, Kappa coefficient 0.795. Total burnt area estimates from the two classifiers were found also to be in close agreement with the other available estimates for the study region, with a mean absolute percentage difference of ~1% for ANN and ~6.5% for SAM. The study demonstrates the potential of the examined here algorithms in detecting burnt areas in a typical Mediterranean setting.

  11. Mondo/ChREBP-Mlx-Regulated Transcriptional Network Is Essential for Dietary Sugar Tolerance in Drosophila

    Science.gov (United States)

    Havula, Essi; Teesalu, Mari; Hyötyläinen, Tuulia; Seppälä, Heini; Hasygar, Kiran; Auvinen, Petri; Orešič, Matej; Sandmann, Thomas; Hietakangas, Ville

    2013-01-01

    Sugars are important nutrients for many animals, but are also proposed to contribute to overnutrition-derived metabolic diseases in humans. Understanding the genetic factors governing dietary sugar tolerance therefore has profound biological and medical significance. Paralogous Mondo transcription factors ChREBP and MondoA, with their common binding partner Mlx, are key sensors of intracellular glucose flux in mammals. Here we report analysis of the in vivo function of Drosophila melanogaster Mlx and its binding partner Mondo (ChREBP) in respect to tolerance to dietary sugars. Larvae lacking mlx or having reduced mondo expression show strikingly reduced survival on a diet with moderate or high levels of sucrose, glucose, and fructose. mlx null mutants display widespread changes in lipid and phospholipid profiles, signs of amino acid catabolism, as well as strongly elevated circulating glucose levels. Systematic loss-of-function analysis of Mlx target genes reveals that circulating glucose levels and dietary sugar tolerance can be genetically uncoupled: Krüppel-like transcription factor Cabut and carbonyl detoxifying enzyme Aldehyde dehydrogenase type III are essential for dietary sugar tolerance, but display no influence on circulating glucose levels. On the other hand, Phosphofructokinase 2, a regulator of the glycolysis pathway, is needed for both dietary sugar tolerance and maintenance of circulating glucose homeostasis. Furthermore, we show evidence that fatty acid synthesis, which is a highly conserved Mondo-Mlx-regulated process, does not promote dietary sugar tolerance. In contrast, survival of larvae with reduced fatty acid synthase expression is sugar-dependent. Our data demonstrate that the transcriptional network regulated by Mondo-Mlx is a critical determinant of the healthful dietary spectrum allowing Drosophila to exploit sugar-rich nutrient sources. PMID:23593032

  12. Structure, function and networks of transcription factors involved in abiotic stress responses

    DEFF Research Database (Denmark)

    Lindemose, Søren; O'Shea, Charlotte; Jensen, Michael Krogh

    2013-01-01

    Transcription factors (TFs) are master regulators of abiotic stress responses in plants. This review focuses on TFs from seven major TF families, known to play functional roles in response to abiotic stresses, including drought, high salinity, high osmolarity, temperature extremes...... and the phytohormone ABA. Although ectopic expression of several TFs has improved abiotic stress tolerance in plants, fine-tuning of TF expression and protein levels remains a challenge to avoid crop yield loss. To further our understanding of TFs in abiotic stress responses, emerging gene regulatory networks based...... on TFs and their direct targets genes are presented. These revealed components shared between ABA-dependent and independent signaling as well as abiotic and biotic stress signaling. Protein structure analysis suggested that TFs hubs of large interactomes have extended regions with protein intrinsic...

  13. Ethylene Control of Fruit Ripening: Revisiting the Complex Network of Transcriptional Regulation1

    Science.gov (United States)

    Chervin, Christian; Bouzayen, Mondher

    2015-01-01

    The plant hormone ethylene plays a key role in climacteric fruit ripening. Studies on components of ethylene signaling have revealed a linear transduction pathway leading to the activation of ethylene response factors. However, the means by which ethylene selects the ripening-related genes and interacts with other signaling pathways to regulate the ripening process are still to be elucidated. Using tomato (Solanum lycopersicum) as a reference species, the present review aims to revisit the mechanisms by which ethylene regulates fruit ripening by taking advantage of new tools available to perform in silico studies at the genome-wide scale, leading to a global view on the expression pattern of ethylene biosynthesis and response genes throughout ripening. Overall, it provides new insights on the transcriptional network by which this hormone coordinates the ripening process and emphasizes the interplay between ethylene and ripening-associated developmental factors and the link between epigenetic regulation and ethylene during fruit ripening. PMID:26511917

  14. Novel Strategy for Discrimination of Transcription Factor Binding Motifs Employing Mathematical Neural Network

    Science.gov (United States)

    Sugimoto, Asuka; Sumi, Takuya; Kang, Jiyoung; Tateno, Masaru

    2017-07-01

    Recognition in biological macromolecular systems, such as DNA-protein recognition, is one of the most crucial problems to solve toward understanding the fundamental mechanisms of various biological processes. Since specific base sequences of genome DNA are discriminated by proteins, such as transcription factors (TFs), finding TF binding motifs (TFBMs) in whole genome DNA sequences is currently a central issue in interdisciplinary biophysical and information sciences. In the present study, a novel strategy to create a discriminant function for discrimination of TFBMs by constituting mathematical neural networks (NNs) is proposed, together with a method to determine the boundary of signals (TFBMs) and noise in the NN-score (output) space. This analysis also leads to the mathematical limitation of discrimination in the recognition of features representing TFBMs, in an information geometrical manifold. Thus, the present strategy enables the identification of the whole space of TFBMs, right up to the noise boundary.

  15. Genome-wide analysis of a Wnt1-regulated transcriptional network implicates neurodegenerative pathways.

    Science.gov (United States)

    Wexler, Eric M; Rosen, Ezra; Lu, Daning; Osborn, Gregory E; Martin, Elizabeth; Raybould, Helen; Geschwind, Daniel H

    2011-10-04

    Wnt proteins are critical to mammalian brain development and function. The canonical Wnt signaling pathway involves the stabilization and nuclear translocation of β-catenin; however, Wnt also signals through alternative, noncanonical pathways. To gain a systems-level, genome-wide view of Wnt signaling, we analyzed Wnt1-stimulated changes in gene expression by transcriptional microarray analysis in cultured human neural progenitor (hNP) cells at multiple time points over a 72-hour time course. We observed a widespread oscillatory-like pattern of changes in gene expression, involving components of both the canonical and the noncanonical Wnt signaling pathways. A higher-order, systems-level analysis that combined independent component analysis, waveform analysis, and mutual information-based network construction revealed effects on pathways related to cell death and neurodegenerative disease. Wnt effectors were tightly clustered with presenilin1 (PSEN1) and granulin (GRN), which cause dominantly inherited forms of Alzheimer's disease and frontotemporal dementia (FTD), respectively. We further explored a potential link between Wnt1 and GRN and found that Wnt1 decreased GRN expression by hNPs. Conversely, GRN knockdown increased WNT1 expression, demonstrating that Wnt and GRN reciprocally regulate each other. Finally, we provided in vivo validation of the in vitro findings by analyzing gene expression data from individuals with FTD. These unbiased and genome-wide analyses provide evidence for a connection between Wnt signaling and the transcriptional regulation of neurodegenerative disease genes.

  16. Robust identification of transcriptional regulatory networks using a Gibbs sampler on outlier sum statistic.

    Science.gov (United States)

    Gu, Jinghua; Xuan, Jianhua; Riggins, Rebecca B; Chen, Li; Wang, Yue; Clarke, Robert

    2012-08-01

    Identification of transcriptional regulatory networks (TRNs) is of significant importance in computational biology for cancer research, providing a critical building block to unravel disease pathways. However, existing methods for TRN identification suffer from the inclusion of excessive 'noise' in microarray data and false-positives in binding data, especially when applied to human tumor-derived cell line studies. More robust methods that can counteract the imperfection of data sources are therefore needed for reliable identification of TRNs in this context. In this article, we propose to establish a link between the quality of one target gene to represent its regulator and the uncertainty of its expression to represent other target genes. Specifically, an outlier sum statistic was used to measure the aggregated evidence for regulation events between target genes and their corresponding transcription factors. A Gibbs sampling method was then developed to estimate the marginal distribution of the outlier sum statistic, hence, to uncover underlying regulatory relationships. To evaluate the effectiveness of our proposed method, we compared its performance with that of an existing sampling-based method using both simulation data and yeast cell cycle data. The experimental results show that our method consistently outperforms the competing method in different settings of signal-to-noise ratio and network topology, indicating its robustness for biological applications. Finally, we applied our method to breast cancer cell line data and demonstrated its ability to extract biologically meaningful regulatory modules related to estrogen signaling and action in breast cancer. The Gibbs sampler MATLAB package is freely available at http://www.cbil.ece.vt.edu/software.htm. xuan@vt.edu Supplementary data are available at Bioinformatics online.

  17. Single-cell Transcriptional Analysis Reveals Novel Neuronal Phenotypes and Interaction Networks involved In the Central Circadian Clock

    Directory of Open Access Journals (Sweden)

    James Park

    2016-10-01

    Full Text Available Single-cell heterogeneity confounds efforts to understand how a population of cells organizes into cellular networks that underlie tissue-level function. This complexity is prominent in the mammalian suprachiasmatic nucleus (SCN. Here, individual neurons exhibit a remarkable amount of asynchronous behavior and transcriptional heterogeneity. However, SCN neurons are able to generate precisely coordinated synaptic and molecular outputs that synchronize the body to a common circadian cycle by organizing into cellular networks. To understand this emergent cellular network property, it is important to reconcile single-neuron heterogeneity with network organization. In light of recent studies suggesting that transcriptionally heterogeneous cells organize into distinct cellular phenotypes, we characterized the transcriptional, spatial, and functional organization of 352 SCN neurons from mice experiencing phase-shifts in their circadian cycle. Using the community structure detection method and multivariate analytical techniques, we identified previously undescribed neuronal phenotypes that are likely to participate in regulatory networks with known SCN cell types. Based on the newly discovered neuronal phenotypes, we developed a data-driven neuronal network structure in which multiple cell types interact through known synaptic and paracrine signaling mechanisms. These results provide a basis from which to interpret the functional variability of SCN neurons and describe methodologies towards understanding how a population of heterogeneous single cells organizes into cellular networks that underlie tissue-level function.

  18. Reverse engineering of TLX oncogenic transcriptional networks identifies RUNX1 as tumor suppressor in T-ALL.

    Science.gov (United States)

    Della Gatta, Giusy; Palomero, Teresa; Perez-Garcia, Arianne; Ambesi-Impiombato, Alberto; Bansal, Mukesh; Carpenter, Zachary W; De Keersmaecker, Kim; Sole, Xavier; Xu, Luyao; Paietta, Elisabeth; Racevskis, Janis; Wiernik, Peter H; Rowe, Jacob M; Meijerink, Jules P; Califano, Andrea; Ferrando, Adolfo A

    2012-02-26

    The TLX1 and TLX3 transcription factor oncogenes have a key role in the pathogenesis of T cell acute lymphoblastic leukemia (T-ALL). Here we used reverse engineering of global transcriptional networks to decipher the oncogenic regulatory circuit controlled by TLX1 and TLX3. This systems biology analysis defined T cell leukemia homeobox 1 (TLX1) and TLX3 as master regulators of an oncogenic transcriptional circuit governing T-ALL. Notably, a network structure analysis of this hierarchical network identified RUNX1 as a key mediator of the T-ALL induced by TLX1 and TLX3 and predicted a tumor-suppressor role for RUNX1 in T cell transformation. Consistent with these results, we identified recurrent somatic loss-of-function mutations in RUNX1 in human T-ALL. Overall, these results place TLX1 and TLX3 at the top of an oncogenic transcriptional network controlling leukemia development, show the power of network analyses to identify key elements in the regulatory circuits governing human cancer and identify RUNX1 as a tumor-suppressor gene in T-ALL.

  19. Comparison of two neural network classifiers in the differential diagnosis of essential tremor and Parkinson's disease by {sup 123}I-FP-CIT brain SPECT

    Energy Technology Data Exchange (ETDEWEB)

    Palumbo, Barbara [University of Perugia, Nuclear Medicine Section, Department of Surgical, Radiological and Odontostomatological Sciences, Ospedale S. Maria della Misericordia, Perugia (Italy); Fravolini, Mario Luca [University of Perugia, Department of Electronic and Information Engineering, Perugia (Italy); Nuvoli, Susanna; Spanu, Angela; Madeddu, Giuseppe [University of Sassari, Department of Nuclear Medicine, Sassari (Italy); Paulus, Kai Stephan [University of Sassari, Department of Neurology, Sassari (Italy); Schillaci, Orazio [University Tor Vergata, Department of Biopathology and Diagnostic Imaging, Rome (Italy); IRCSS Neuromed, Pozzilli (Italy)

    2010-11-15

    To contribute to the differentiation of Parkinson's disease (PD) and essential tremor (ET), we compared two different artificial neural network classifiers using {sup 123}I-FP-CIT SPECT data, a probabilistic neural network (PNN) and a classification tree (ClT). {sup 123}I-FP-CIT brain SPECT with semiquantitative analysis was performed in 216 patients: 89 with ET, 64 with PD with a Hoehn and Yahr (H and Y) score of {<=}2 (early PD), and 63 with PD with a H and Y score of {>=}2.5 (advanced PD). For each of the 1,000 experiments carried out, 108 patients were randomly selected as the PNN training set, while the remaining 108 validated the trained PNN, and the percentage of the validation data correctly classified in the three groups of patients was computed. The expected performance of an ''average performance PNN'' was evaluated. In analogy, for ClT 1,000 classification trees with similar structures were generated. For PNN, the probability of correct classification in patients with early PD was 81.9{+-}8.1% (mean{+-}SD), in patients with advanced PD 78.9{+-}8.1%, and in ET patients 96.6{+-}2.6%. For ClT, the first decision rule gave a mean value for the putamen of 5.99, which resulted in a probability of correct classification of 93.5{+-}3.4%. This means that patients with putamen values >5.99 were classified as having ET, while patients with putamen values <5.99 were classified as having PD. Furthermore, if the caudate nucleus value was higher than 6.97 patients were classified as having early PD (probability 69.8{+-}5.3%), and if the value was <6.97 patients were classified as having advanced PD (probability 88.1%{+-}8.8%). These results confirm that PNN achieved valid classification results. Furthermore, ClT provided reliable cut-off values able to differentiate ET and PD of different severities. (orig.)

  20. Comparison of two neural network classifiers in the differential diagnosis of essential tremor and Parkinson's disease by 123I-FP-CIT brain SPECT

    International Nuclear Information System (INIS)

    Palumbo, Barbara; Fravolini, Mario Luca; Nuvoli, Susanna; Spanu, Angela; Madeddu, Giuseppe; Paulus, Kai Stephan; Schillaci, Orazio

    2010-01-01

    To contribute to the differentiation of Parkinson's disease (PD) and essential tremor (ET), we compared two different artificial neural network classifiers using 123 I-FP-CIT SPECT data, a probabilistic neural network (PNN) and a classification tree (ClT). 123 I-FP-CIT brain SPECT with semiquantitative analysis was performed in 216 patients: 89 with ET, 64 with PD with a Hoehn and Yahr (H and Y) score of ≤2 (early PD), and 63 with PD with a H and Y score of ≥2.5 (advanced PD). For each of the 1,000 experiments carried out, 108 patients were randomly selected as the PNN training set, while the remaining 108 validated the trained PNN, and the percentage of the validation data correctly classified in the three groups of patients was computed. The expected performance of an ''average performance PNN'' was evaluated. In analogy, for ClT 1,000 classification trees with similar structures were generated. For PNN, the probability of correct classification in patients with early PD was 81.9±8.1% (mean±SD), in patients with advanced PD 78.9±8.1%, and in ET patients 96.6±2.6%. For ClT, the first decision rule gave a mean value for the putamen of 5.99, which resulted in a probability of correct classification of 93.5±3.4%. This means that patients with putamen values >5.99 were classified as having ET, while patients with putamen values <5.99 were classified as having PD. Furthermore, if the caudate nucleus value was higher than 6.97 patients were classified as having early PD (probability 69.8±5.3%), and if the value was <6.97 patients were classified as having advanced PD (probability 88.1%±8.8%). These results confirm that PNN achieved valid classification results. Furthermore, ClT provided reliable cut-off values able to differentiate ET and PD of different severities. (orig.)

  1. Dissimilatory metabolism of nitrogen oxides in bacteria: comparative reconstruction of transcriptional networks.

    Directory of Open Access Journals (Sweden)

    2005-10-01

    denitrification genes. As the result, we demonstrate considerable interconnection between various nitrogen-oxides-responsive regulatory systems for the denitrification and NO detoxification genes and evolutionary plasticity of this transcriptional network.

  2. Dissimilatory Metabolism of Nitrogen Oxides in Bacteria:Comparative Reconstruction of Transcriptional Networks

    Energy Technology Data Exchange (ETDEWEB)

    Rodionov, Dmitry A.; Dubchak, Inna L.; Arkin, Adam P.; Alm, EricJ.; Gelfand, Mikhail S.

    2005-09-01

    result, we demonstrate considerable interconnection between various nitrogen-oxides-responsive regulatory systems for the denitrification and NO detoxification genes and evolutionary plasticity of this transcriptional network.

  3. Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier

    Directory of Open Access Journals (Sweden)

    Hesham El-Sayed

    2018-05-01

    Full Text Available Heterogeneous vehicular networks (HETVNETs evolve from vehicular ad hoc networks (VANETs, which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs. The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM kernels with a radial basis function (RBF. The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy.

  4. Patterns of subnet usage reveal distinct scales of regulation in the transcriptional regulatory network of Escherichia coli.

    Directory of Open Access Journals (Sweden)

    Carsten Marr

    Full Text Available The set of regulatory interactions between genes, mediated by transcription factors, forms a species' transcriptional regulatory network (TRN. By comparing this network with measured gene expression data, one can identify functional properties of the TRN and gain general insight into transcriptional control. We define the subnet of a node as the subgraph consisting of all nodes topologically downstream of the node, including itself. Using a large set of microarray expression data of the bacterium Escherichia coli, we find that the gene expression in different subnets exhibits a structured pattern in response to environmental changes and genotypic mutation. Subnets with fewer changes in their expression pattern have a higher fraction of feed-forward loop motifs and a lower fraction of small RNA targets within them. Our study implies that the TRN consists of several scales of regulatory organization: (1 subnets with more varying gene expression controlled by both transcription factors and post-transcriptional RNA regulation and (2 subnets with less varying gene expression having more feed-forward loops and less post-transcriptional RNA regulation.

  5. Pleiotropy constrains the evolution of protein but not regulatory sequences in a transcription regulatory network influencing complex social behaviours

    Directory of Open Access Journals (Sweden)

    Daria eMolodtsova

    2014-12-01

    Full Text Available It is increasingly apparent that genes and networks that influence complex behaviour are evolutionary conserved, which is paradoxical considering that behaviour is labile over evolutionary timescales. How does adaptive change in behaviour arise if behaviour is controlled by conserved, pleiotropic, and likely evolutionary constrained genes? Pleiotropy and connectedness are known to constrain the general rate of protein evolution, prompting some to suggest that the evolution of complex traits, including behaviour, is fuelled by regulatory sequence evolution. However, we seldom have data on the strength of selection on mutations in coding and regulatory sequences, and this hinders our ability to study how pleiotropy influences coding and regulatory sequence evolution. Here we use population genomics to estimate the strength of selection on coding and regulatory mutations for a transcriptional regulatory network that influences complex behaviour of honey bees. We found that replacement mutations in highly connected transcription factors and target genes experience significantly stronger negative selection relative to weakly connected transcription factors and targets. Adaptively evolving proteins were significantly more likely to reside at the periphery of the regulatory network, while proteins with signs of negative selection were near the core of the network. Interestingly, connectedness and network structure had minimal influence on the strength of selection on putative regulatory sequences for both transcription factors and their targets. Our study indicates that adaptive evolution of complex behaviour can arise because of positive selection on protein-coding mutations in peripheral genes, and on regulatory sequence mutations in both transcription factors and their targets throughout the network.

  6. Hierarchical structure and modules in the Escherichia coli transcriptional regulatory network revealed by a new top-down approach

    Directory of Open Access Journals (Sweden)

    Buer Jan

    2004-12-01

    Full Text Available Abstract Background Cellular functions are coordinately carried out by groups of genes forming functional modules. Identifying such modules in the transcriptional regulatory network (TRN of organisms is important for understanding the structure and function of these fundamental cellular networks and essential for the emerging modular biology. So far, the global connectivity structure of TRN has not been well studied and consequently not applied for the identification of functional modules. Moreover, network motifs such as feed forward loop are recently proposed to be basic building blocks of TRN. However, their relationship to functional modules is not clear. Results In this work we proposed a top-down approach to identify modules in the TRN of E. coli. By studying the global connectivity structure of the regulatory network, we first revealed a five-layer hierarchical structure in which all the regulatory relationships are downward. Based on this regulatory hierarchy, we developed a new method to decompose the regulatory network into functional modules and to identify global regulators governing multiple modules. As a result, 10 global regulators and 39 modules were identified and shown to have well defined functions. We then investigated the distribution and composition of the two basic network motifs (feed forward loop and bi-fan motif in the hierarchical structure of TRN. We found that most of these network motifs include global regulators, indicating that these motifs are not basic building blocks of modules since modules should not contain global regulators. Conclusion The transcriptional regulatory network of E. coli possesses a multi-layer hierarchical modular structure without feedback regulation at transcription level. This hierarchical structure builds the basis for a new and simple decomposition method which is suitable for the identification of functional modules and global regulators in the transcriptional regulatory network of E

  7. The transcriptional regulatory network mediated by banana (Musa acuminata) dehydration-responsive element binding (MaDREB) transcription factors in fruit ripening.

    Science.gov (United States)

    Kuang, Jian-Fei; Chen, Jian-Ye; Liu, Xun-Cheng; Han, Yan-Chao; Xiao, Yun-Yi; Shan, Wei; Tang, Yang; Wu, Ke-Qiang; He, Jun-Xian; Lu, Wang-Jin

    2017-04-01

    Fruit ripening is a complex, genetically programmed process involving the action of critical transcription factors (TFs). Despite the established significance of dehydration-responsive element binding (DREB) TFs in plant abiotic stress responses, the involvement of DREBs in fruit ripening is yet to be determined. Here, we identified four genes encoding ripening-regulated DREB TFs in banana (Musa acuminata), MaDREB1, MaDREB2, MaDREB3, and MaDREB4, and demonstrated that they play regulatory roles in fruit ripening. We showed that MaDREB1-MaDREB4 are nucleus-localized, induced by ethylene and encompass transcriptional activation activities. We performed a genome-wide chromatin immunoprecipitation and high-throughput sequencing (ChIP-Seq) experiment for MaDREB2 and identified 697 genomic regions as potential targets of MaDREB2. MaDREB2 binds to hundreds of loci with diverse functions and its binding sites are distributed in the promoter regions proximal to the transcriptional start site (TSS). Most of the MaDREB2-binding targets contain the conserved (A/G)CC(G/C)AC motif and MaDREB2 appears to directly regulate the expression of a number of genes involved in fruit ripening. In combination with transcriptome profiling (RNA sequencing) data, our results indicate that MaDREB2 may serve as both transcriptional activator and repressor during banana fruit ripening. In conclusion, our study suggests a hierarchical regulatory model of fruit ripening in banana and that the MaDREB TFs may act as transcriptional regulators in the regulatory network. © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.

  8. A DNA-binding-site landscape and regulatory network analysis for NAC transcription factors in Arabidopsis thaliana

    DEFF Research Database (Denmark)

    Lindemose, Søren; Jensen, Michael Krogh; de Velde, Jan Van

    2014-01-01

    regulatory networks of 12 NAC transcription factors. Our data offer specific single-base resolution fingerprints for most TFs studied and indicate that NAC DNA-binding specificities might be predicted from their DNA-binding domain's sequence. The developed methodology, including the application......Target gene identification for transcription factors is a prerequisite for the systems wide understanding of organismal behaviour. NAM-ATAF1/2-CUC2 (NAC) transcription factors are amongst the largest transcription factor families in plants, yet limited data exist from unbiased approaches to resolve...... the DNA-binding preferences of individual members. Here, we present a TF-target gene identification workflow based on the integration of novel protein binding microarray data with gene expression and multi-species promoter sequence conservation to identify the DNA-binding specificities and the gene...

  9. Dynamic transcription factor networks in epithelial-mesenchymal transition in breast cancer models.

    Science.gov (United States)

    Siletz, Anaar; Schnabel, Michael; Kniazeva, Ekaterina; Schumacher, Andrew J; Shin, Seungjin; Jeruss, Jacqueline S; Shea, Lonnie D

    2013-01-01

    The epithelial-mesenchymal transition (EMT) is a complex change in cell differentiation that allows breast carcinoma cells to acquire invasive properties. EMT involves a cascade of regulatory changes that destabilize the epithelial phenotype and allow mesenchymal features to manifest. As transcription factors (TFs) are upstream effectors of the genome-wide expression changes that result in phenotypic change, understanding the sequential changes in TF activity during EMT provides rich information on the mechanism of this process. Because molecular interactions will vary as cells progress from an epithelial to a mesenchymal differentiation program, dynamic networks are needed to capture the changing context of molecular processes. In this study we applied an emerging high-throughput, dynamic TF activity array to define TF activity network changes in three cell-based models of EMT in breast cancer based on HMLE Twist ER and MCF-7 mammary epithelial cells. The TF array distinguished conserved from model-specific TF activity changes in the three models. Time-dependent data was used to identify pairs of TF activities with significant positive or negative correlation, indicative of interdependent TF activity throughout the six-day study period. Dynamic TF activity patterns were clustered into groups of TFs that change along a time course of gene expression changes and acquisition of invasive capacity. Time-dependent TF activity data was combined with prior knowledge of TF interactions to construct dynamic models of TF activity networks as epithelial cells acquire invasive characteristics. These analyses show EMT from a unique and targetable vantage and may ultimately contribute to diagnosis and therapy.

  10. Dynamic transcription factor networks in epithelial-mesenchymal transition in breast cancer models.

    Directory of Open Access Journals (Sweden)

    Anaar Siletz

    Full Text Available The epithelial-mesenchymal transition (EMT is a complex change in cell differentiation that allows breast carcinoma cells to acquire invasive properties. EMT involves a cascade of regulatory changes that destabilize the epithelial phenotype and allow mesenchymal features to manifest. As transcription factors (TFs are upstream effectors of the genome-wide expression changes that result in phenotypic change, understanding the sequential changes in TF activity during EMT provides rich information on the mechanism of this process. Because molecular interactions will vary as cells progress from an epithelial to a mesenchymal differentiation program, dynamic networks are needed to capture the changing context of molecular processes. In this study we applied an emerging high-throughput, dynamic TF activity array to define TF activity network changes in three cell-based models of EMT in breast cancer based on HMLE Twist ER and MCF-7 mammary epithelial cells. The TF array distinguished conserved from model-specific TF activity changes in the three models. Time-dependent data was used to identify pairs of TF activities with significant positive or negative correlation, indicative of interdependent TF activity throughout the six-day study period. Dynamic TF activity patterns were clustered into groups of TFs that change along a time course of gene expression changes and acquisition of invasive capacity. Time-dependent TF activity data was combined with prior knowledge of TF interactions to construct dynamic models of TF activity networks as epithelial cells acquire invasive characteristics. These analyses show EMT from a unique and targetable vantage and may ultimately contribute to diagnosis and therapy.

  11. Classification of mass and normal breast tissue: A convolution neural network classifier with spatial domain and texture images

    International Nuclear Information System (INIS)

    Sahiner, B.; Chan, H.P.; Petrick, N.; Helvie, M.A.; Adler, D.D.; Goodsitt, M.M.; Wei, D.

    1996-01-01

    The authors investigated the classification of regions of interest (ROI's) on mammograms as either mass or normal tissue using a convolution neural network (CNN). A CNN is a back-propagation neural network with two-dimensional (2-D) weight kernels that operate on images. A generalized, fast and stable implementation of the CNN was developed. The input images to the CNN were obtained form the ROI's using two techniques. The first technique employed averaging and subsampling. The second technique employed texture feature extraction methods applied to small subregions inside the ROI. Features computed over different subregions were arranged as texture images, which were subsequently used as CNN inputs. The effects of CNN architecture and texture feature parameters on classification accuracy were studied. Receiver operating characteristic (ROC) methodology was used to evaluate the classification accuracy. A data set consisting of 168 ROI's containing biopsy-proven masses and 504 ROI's containing normal breast tissue was extracted from 168 mammograms by radiologists experienced in mammography. This data set was used for training and testing the CNN. With the best combination of CNN architecture and texture feature parameters, the area under the test ROC curve reached 0.87, which corresponded to a true-positive fraction of 90% at a false positive fraction of 31%. The results demonstrate the feasibility of using a CNN for classification of masses and normal tissue on mammograms

  12. Understanding large multiprotein complexes: applying a multiple allosteric networks model to explain the function of the Mediator transcription complex.

    Science.gov (United States)

    Lewis, Brian A

    2010-01-15

    The regulation of transcription and of many other cellular processes involves large multi-subunit protein complexes. In the context of transcription, it is known that these complexes serve as regulatory platforms that connect activator DNA-binding proteins to a target promoter. However, there is still a lack of understanding regarding the function of these complexes. Why do multi-subunit complexes exist? What is the molecular basis of the function of their constituent subunits, and how are these subunits organized within a complex? What is the reason for physical connections between certain subunits and not others? In this article, I address these issues through a model of network allostery and its application to the eukaryotic RNA polymerase II Mediator transcription complex. The multiple allosteric networks model (MANM) suggests that protein complexes such as Mediator exist not only as physical but also as functional networks of interconnected proteins through which information is transferred from subunit to subunit by the propagation of an allosteric state known as conformational spread. Additionally, there are multiple distinct sub-networks within the Mediator complex that can be defined by their connections to different subunits; these sub-networks have discrete functions that are activated when specific subunits interact with other activator proteins.

  13. Helicase-like transcription factor (Hltf regulates G2/M transition, Wt1/Gata4/Hif-1a cardiac transcription networks, and collagen biogenesis.

    Directory of Open Access Journals (Sweden)

    Rebecca A Helmer

    Full Text Available HLTF/Hltf regulates transcription, remodels chromatin, and coordinates DNA damage repair. Hltf is expressed in mouse brain and heart during embryonic and postnatal development. Silencing Hltf is semilethal. Seventy-four percent of congenic C57BL/6J Hltf knockout mice died, 75% within 12-24 hours of birth. Previous studies in neonatal (6-8 hour postpartum brain revealed silencing Hltf disrupted cell cycle progression, and attenuated DNA damage repair. An RNA-Seq snapshot of neonatal heart transcriptome showed 1,536 of 20,000 total transcripts were altered (p < 0.05 - 10 up- and 1,526 downregulated. Pathway enrichment analysis with MetaCore™ showed Hltf's regulation of the G2/M transition (p=9.726E(-15 of the cell cycle in heart is nearly identical to its role in brain. In addition, Brca1 and 12 members of the Brca1 associated genome surveillance complex are also downregulated. Activation of caspase 3 coincides with transcriptional repression of Bcl-2. Hltf loss caused downregulation of Wt1/Gata4/Hif-1a signaling cascades as well as Myh7b/miR499 transcription. Hltf-specific binding to promoters and/or regulatory regions of these genes was authenticated by ChIP-PCR. Hif-1a targets for prolyl (P4ha1, P4ha2 and lysyl (Plod2 collagen hydroxylation, PPIase enzymes (Ppid, Ppif, Ppil3 for collagen trimerization, and lysyl oxidase (Loxl2 for collagen-elastin crosslinking were downregulated. However, transcription of genes for collagens, fibronectin, Mmps and their inhibitors (Timps was unaffected. The collective downregulation of genes whose protein products control collagen biogenesis caused disorganization of the interstitial and perivascular myocardial collagen fibrillar network as viewed with picrosirius red-staining, and authenticated with spectral imaging. Wavy collagen bundles in control hearts contrasted with collagen fibers that were thin, short and disorganized in Hltf null hearts. Collagen bundles in Hltf null hearts were tangled and

  14. Fingerprint prediction using classifier ensembles

    CSIR Research Space (South Africa)

    Molale, P

    2011-11-01

    Full Text Available ); logistic discrimination (LgD), k-nearest neighbour (k-NN), artificial neural network (ANN), association rules (AR) decision tree (DT), naive Bayes classifier (NBC) and the support vector machine (SVM). The performance of several multiple classifier systems...

  15. A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

    Directory of Open Access Journals (Sweden)

    Chandra Nagasuma R

    2009-02-01

    Full Text Available Abstract Background A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN from transcript profiling data. Results The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting problem and solved finally by formulating a Linear Program (LP. A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known

  16. Transcriptional interference networks coordinate the expression of functionally-related genes clustered in the same genomic loci

    Directory of Open Access Journals (Sweden)

    Zsolt eBoldogkoi

    2012-07-01

    Full Text Available The regulation of gene expression is essential for normal functioning of biological systems in every form of life. Gene expression is primarily controlled at the level of transcription, especially at the phase of initiation. Non-coding RNAs are one of the major players at every level of genetic regulation, including the control of chromatin organisation, transcription, various post-transcriptional processes and translation. In this study, the Transcriptional Interference Network (TIN hypothesis was put forward in an attempt to explain the global expression of antisense RNAs and the overall occurrence of tandem gene clusters in the genomes of various biological systems ranging from viruses to mammalian cells. The TIN hypothesis suggests the existence of a novel layer of genetic regulation, based on the interactions between the transcriptional machineries of neighbouring genes at their overlapping regions, which are assumed to play a fundamental role in coordinating gene expression within a cluster of functionally-linked genes. It is claimed that the transcriptional overlaps between adjacent genes are much more widespread in genomes than is thought today. The Waterfall model of the TIN hypothesis postulates a unidirectional effect of upstream genes on the transcription of downstream genes within a cluster of tandemly-arrayed genes, while the Seesaw model proposes a mutual interdependence of gene expression between the oppositely-oriented genes. The TIN represents an auto-regulatory system with an exquisitely timed and highly synchronised cascade of gene expression in functionally-linked genes located in close physical proximity to each other. In this study, we focused on herpesviruses. The reason for this lies in the compressed nature of viral genes, which allows a tight regulation and an easier investigation of the transcriptional interactions between genes. However, I believe that the same or similar principles can be applied to cellular

  17. Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing

    Directory of Open Access Journals (Sweden)

    A. Moosavian

    2013-01-01

    Full Text Available Vibration analysis is an accepted method in condition monitoring of machines, since it can provide useful and reliable information about machine working condition. This paper surveys a new scheme for fault diagnosis of main journal-bearings of internal combustion (IC engine based on power spectral density (PSD technique and two classifiers, namely, K-nearest neighbor (KNN and artificial neural network (ANN. Vibration signals for three different conditions of journal-bearing; normal, with oil starvation condition and extreme wear fault were acquired from an IC engine. PSD was applied to process the vibration signals. Thirty features were extracted from the PSD values of signals as a feature source for fault diagnosis. KNN and ANN were trained by training data set and then used as diagnostic classifiers. Variable K value and hidden neuron count (N were used in the range of 1 to 20, with a step size of 1 for KNN and ANN to gain the best classification results. The roles of PSD, KNN and ANN techniques were studied. From the results, it is shown that the performance of ANN is better than KNN. The experimental results dèmonstrate that the proposed diagnostic method can reliably separate different fault conditions in main journal-bearings of IC engine.

  18. Transcriptional regulatory networks downstream of TAL1/SCL in T-cell acute lymphoblastic leukemia

    OpenAIRE

    Palomero, Teresa; Odom, Duncan T.; O'Neil, Jennifer; Ferrando, Adolfo A.; Margolin, Adam; Neuberg, Donna S.; Winter, Stuart S.; Larson, Richard S.; Li, Wei; Liu, X. Shirley; Young, Richard A.; Look, A. Thomas

    2006-01-01

    Aberrant expression of 1 or more transcription factor oncogenes is a critical component of the molecular pathogenesis of human T-cell acute lymphoblastic leukemia (T-ALL); however, oncogenic transcriptional programs downstream of T-ALL oncogenes are mostly unknown. TAL1/SCL is a basic helix-loop-helix (bHLH) transcription factor oncogene aberrantly expressed in 60% of human T-ALLs. We used chromatin immunoprecipitation (ChIP) on chip to identify 71 direct transcriptional targets of TAL1/SCL. ...

  19. Composite Classifiers for Automatic Target Recognition

    National Research Council Canada - National Science Library

    Wang, Lin-Cheng

    1998-01-01

    ...) using forward-looking infrared (FLIR) imagery. Two existing classifiers, one based on learning vector quantization and the other on modular neural networks, are used as the building blocks for our composite classifiers...

  20. Prospects of engineering thermotolerance in crops through modulation of heat stress transcription factor and heat shock protein networks.

    Science.gov (United States)

    Fragkostefanakis, Sotirios; Röth, Sascha; Schleiff, Enrico; Scharf, Klaus-Dieter

    2015-09-01

    Cell survival under high temperature conditions involves the activation of heat stress response (HSR), which in principle is highly conserved among different organisms, but shows remarkable complexity and unique features in plant systems. The transcriptional reprogramming at higher temperatures is controlled by the activity of the heat stress transcription factors (Hsfs). Hsfs allow the transcriptional activation of HSR genes, among which heat shock proteins (Hsps) are best characterized. Hsps belong to multigene families encoding for molecular chaperones involved in various processes including maintenance of protein homeostasis as a requisite for optimal development and survival under stress conditions. Hsfs form complex networks to activate downstream responses, but are concomitantly subjected to cell-type-dependent feedback regulation through factor-specific physical and functional interactions with chaperones belonging to Hsp90, Hsp70 and small Hsp families. There is increasing evidence that the originally assumed specialized function of Hsf/chaperone networks in the HSR turns out to be a complex central stress response system that is involved in the regulation of a broad variety of other stress responses and may also have substantial impact on various developmental processes. Understanding in detail the function of such regulatory networks is prerequisite for sustained improvement of thermotolerance in important agricultural crops. © 2014 John Wiley & Sons Ltd.

  1. Inferring the lithology of borehole rocks by applying neural network classifiers to downhole logs: an example from the Ocean Drilling Program

    Science.gov (United States)

    Benaouda, D.; Wadge, G.; Whitmarsh, R. B.; Rothwell, R. G.; MacLeod, C.

    1999-02-01

    In boreholes with partial or no core recovery, interpretations of lithology in the remainder of the hole are routinely attempted using data from downhole geophysical sensors. We present a practical neural net-based technique that greatly enhances lithological interpretation in holes with partial core recovery by using downhole data to train classifiers to give a global classification scheme for those parts of the borehole for which no core was retrieved. We describe the system and its underlying methods of data exploration, selection and classification, and present a typical example of the system in use. Although the technique is equally applicable to oil industry boreholes, we apply it here to an Ocean Drilling Program (ODP) borehole (Hole 792E, Izu-Bonin forearc, a mixture of volcaniclastic sandstones, conglomerates and claystones). The quantitative benefits of quality-control measures and different subsampling strategies are shown. Direct comparisons between a number of discriminant analysis methods and the use of neural networks with back-propagation of error are presented. The neural networks perform better than the discriminant analysis techniques both in terms of performance rates with test data sets (2-3 per cent better) and in qualitative correlation with non-depth-matched core. We illustrate with the Hole 792E data how vital it is to have a system that permits the number and membership of training classes to be changed as analysis proceeds. The initial classification for Hole 792E evolved from a five-class to a three-class and then to a four-class scheme with resultant classification performance rates for the back-propagation neural network method of 83, 84 and 93 per cent respectively.

  2. Non-equilibrium repressor binding kinetics link DNA damage dose to transcriptional timing within the SOS gene network.

    Science.gov (United States)

    Culyba, Matthew J; Kubiak, Jeffrey M; Mo, Charlie Y; Goulian, Mark; Kohli, Rahul M

    2018-06-01

    Biochemical pathways are often genetically encoded as simple transcription regulation networks, where one transcription factor regulates the expression of multiple genes in a pathway. The relative timing of each promoter's activation and shut-off within the network can impact physiology. In the DNA damage repair pathway (known as the SOS response) of Escherichia coli, approximately 40 genes are regulated by the LexA repressor. After a DNA damaging event, LexA degradation triggers SOS gene transcription, which is temporally separated into subsets of 'early', 'middle', and 'late' genes. Although this feature plays an important role in regulating the SOS response, both the range of this separation and its underlying mechanism are not experimentally defined. Here we show that, at low doses of DNA damage, the timing of promoter activities is not separated. Instead, timing differences only emerge at higher levels of DNA damage and increase as a function of DNA damage dose. To understand mechanism, we derived a series of synthetic SOS gene promoters which vary in LexA-operator binding kinetics, but are otherwise identical, and then studied their activity over a large dose-range of DNA damage. In distinction to established models based on rapid equilibrium assumptions, the data best fit a kinetic model of repressor occupancy at promoters, where the drop in cellular LexA levels associated with higher doses of DNA damage leads to non-equilibrium binding kinetics of LexA at operators. Operators with slow LexA binding kinetics achieve their minimal occupancy state at later times than operators with fast binding kinetics, resulting in a time separation of peak promoter activity between genes. These data provide insight into this remarkable feature of the SOS pathway by demonstrating how a single transcription factor can be employed to control the relative timing of each gene's transcription as a function of stimulus dose.

  3. Divergent Evolution of the Transcriptional Network Controlled by Snf1-Interacting Protein Sip4 in Budding Yeasts.

    Directory of Open Access Journals (Sweden)

    Constance Mehlgarten

    Full Text Available Cellular responses to starvation are of ancient origin since nutrient limitation has always been a common challenge to the stability of living systems. Hence, signaling molecules involved in sensing or transducing information about limiting metabolites are highly conserved, whereas transcription factors and the genes they regulate have diverged. In eukaryotes the AMP-activated protein kinase (AMPK functions as a central regulator of cellular energy homeostasis. The yeast AMPK ortholog SNF1 controls the transcriptional network that counteracts carbon starvation conditions by regulating a set of transcription factors. Among those Cat8 and Sip4 have overlapping DNA-binding specificity for so-called carbon source responsive elements and induce target genes upon SNF1 activation. To analyze the evolution of the Cat8-Sip4 controlled transcriptional network we have compared the response to carbon limitation of Saccharomyces cerevisiae to that of Kluyveromyces lactis. In high glucose, S. cerevisiae displays tumor cell-like aerobic fermentation and repression of respiration (Crabtree-positive while K. lactis has a respiratory-fermentative life-style, respiration being regulated by oxygen availability (Crabtree-negative, which is typical for many yeasts and for differentiated higher cells. We demonstrate divergent evolution of the Cat8-Sip4 network and present evidence that a role of Sip4 in controlling anabolic metabolism has been lost in the Saccharomyces lineage. We find that in K. lactis, but not in S. cerevisiae, the Sip4 protein plays an essential role in C2 carbon assimilation including induction of the glyoxylate cycle and the carnitine shuttle genes. Induction of KlSIP4 gene expression by KlCat8 is essential under these growth conditions and a primary function of KlCat8. Both KlCat8 and KlSip4 are involved in the regulation of lactose metabolism in K. lactis. In chromatin-immunoprecipitation experiments we demonstrate binding of both, KlSip4 and

  4. Melanoma cells revive an embryonic transcriptional network to dictate phenotypic heterogeneity.

    Science.gov (United States)

    Vandamme, Niels; Berx, Geert

    2014-01-01

    Compared to the overwhelming amount of literature describing how epithelial-to-mesenchymal transition (EMT)-inducing transcription factors orchestrate cellular plasticity in embryogenesis and epithelial cells, the functions of these factors in non-epithelial contexts, such as melanoma, are less clear. Melanoma is an aggressive tumor arising from melanocytes, endowed with unique features of cellular plasticity. The reversible phenotype-switching between differentiated and invasive phenotypes is increasingly appreciated as a mechanism accounting for heterogeneity in melanoma and is driven by oncogenic signaling and environmental cues. This phenotypic switch is coupled with an intriguing and somewhat counterintuitive signaling switch of EMT-inducing transcription factors. In contrast to carcinomas, different EMT-inducing transcription factors have antagonizing effects in melanoma. Balancing between these different EMT transcription factors is likely the key to successful metastatic spread of melanoma.

  5. Reverse engineering a mouse embryonic stem cell-specific transcriptional network reveals a new modulator of neuronal differentiation.

    Science.gov (United States)

    De Cegli, Rossella; Iacobacci, Simona; Flore, Gemma; Gambardella, Gennaro; Mao, Lei; Cutillo, Luisa; Lauria, Mario; Klose, Joachim; Illingworth, Elizabeth; Banfi, Sandro; di Bernardo, Diego

    2013-01-01

    Gene expression profiles can be used to infer previously unknown transcriptional regulatory interaction among thousands of genes, via systems biology 'reverse engineering' approaches. We 'reverse engineered' an embryonic stem (ES)-specific transcriptional network from 171 gene expression profiles, measured in ES cells, to identify master regulators of gene expression ('hubs'). We discovered that E130012A19Rik (E13), highly expressed in mouse ES cells as compared with differentiated cells, was a central 'hub' of the network. We demonstrated that E13 is a protein-coding gene implicated in regulating the commitment towards the different neuronal subtypes and glia cells. The overexpression and knock-down of E13 in ES cell lines, undergoing differentiation into neurons and glia cells, caused a strong up-regulation of the glutamatergic neurons marker Vglut2 and a strong down-regulation of the GABAergic neurons marker GAD65 and of the radial glia marker Blbp. We confirmed E13 expression in the cerebral cortex of adult mice and during development. By immuno-based affinity purification, we characterized protein partners of E13, involved in the Polycomb complex. Our results suggest a role of E13 in regulating the division between glutamatergic projection neurons and GABAergic interneurons and glia cells possibly by epigenetic-mediated transcriptional regulation.

  6. Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency

    Directory of Open Access Journals (Sweden)

    Yeh Cheng-Yu

    2009-12-01

    Full Text Available Abstract Background Prostate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer. Results To deal with missing values in microarray data, we used a K-nearest-neighbors (KNN algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2 regulated by RUNX1 and STAT3 is correlated to the pathological stage

  7. Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency.

    Science.gov (United States)

    Yeh, Hsiang-Yuan; Cheng, Shih-Wu; Lin, Yu-Chun; Yeh, Cheng-Yu; Lin, Shih-Fang; Soo, Von-Wun

    2009-12-21

    Prostate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer. To deal with missing values in microarray data, we used a K-nearest-neighbors (KNN) algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD) as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2) regulated by RUNX1 and STAT3 is correlated to the pathological stage. We provide a computational framework to reconstruct

  8. Inferring the transcriptional landscape of bovine skeletal muscle by integrating co-expression networks.

    Directory of Open Access Journals (Sweden)

    Nicholas J Hudson

    Full Text Available BACKGROUND: Despite modern technologies and novel computational approaches, decoding causal transcriptional regulation remains challenging. This is particularly true for less well studied organisms and when only gene expression data is available. In muscle a small number of well characterised transcription factors are proposed to regulate development. Therefore, muscle appears to be a tractable system for proposing new computational approaches. METHODOLOGY/PRINCIPAL FINDINGS: Here we report a simple algorithm that asks "which transcriptional regulator has the highest average absolute co-expression correlation to the genes in a co-expression module?" It correctly infers a number of known causal regulators of fundamental biological processes, including cell cycle activity (E2F1, glycolysis (HLF, mitochondrial transcription (TFB2M, adipogenesis (PIAS1, neuronal development (TLX3, immune function (IRF1 and vasculogenesis (SOX17, within a skeletal muscle context. However, none of the canonical pro-myogenic transcription factors (MYOD1, MYOG, MYF5, MYF6 and MEF2C were linked to muscle structural gene expression modules. Co-expression values were computed using developing bovine muscle from 60 days post conception (early foetal to 30 months post natal (adulthood for two breeds of cattle, in addition to a nutritional comparison with a third breed. A number of transcriptional landscapes were constructed and integrated into an always correlated landscape. One notable feature was a 'metabolic axis' formed from glycolysis genes at one end, nuclear-encoded mitochondrial protein genes at the other, and centrally tethered by mitochondrially-encoded mitochondrial protein genes. CONCLUSIONS/SIGNIFICANCE: The new module-to-regulator algorithm complements our recently described Regulatory Impact Factor analysis. Together with a simple examination of a co-expression module's contents, these three gene expression approaches are starting to illuminate the in vivo

  9. Altered Gradients of Glutamate and Gamma-Aminobutyric Acid Transcripts in the Cortical Visuospatial Working Memory Network in Schizophrenia.

    Science.gov (United States)

    Hoftman, Gil D; Dienel, Samuel J; Bazmi, Holly H; Zhang, Yun; Chen, Kehui; Lewis, David A

    2018-04-15

    Visuospatial working memory (vsWM), which is impaired in schizophrenia, requires information transfer across multiple nodes in the cerebral cortex, including visual, posterior parietal, and dorsolateral prefrontal regions. Information is conveyed across these regions via the excitatory projections of glutamatergic pyramidal neurons located in layer 3, whose activity is modulated by local inhibitory gamma-aminobutyric acidergic (GABAergic) neurons. Key properties of these neurons differ across these cortical regions. Consequently, in schizophrenia, alterations in the expression of gene products regulating these properties could disrupt vsWM function in different ways, depending on the region(s) affected. Here, we quantified the expression of markers of glutamate and GABA neurotransmission selectively in layer 3 of four cortical regions in the vsWM network from 20 matched pairs of schizophrenia and unaffected comparison subjects. In comparison subjects, levels of glutamate transcripts tended to increase, whereas GABA transcript levels tended to decrease, from caudal to rostral, across cortical regions of the vsWM network. Composite measures across all transcripts revealed a significant effect of region, with the glutamate measure lowest in the primary visual cortex and highest in the dorsolateral prefrontal cortex, whereas the GABA measure showed the opposite pattern. In schizophrenia subjects, the expression levels of many of these transcripts were altered. However, this disease effect differed across regions, such that the caudal-to-rostral increase in the glutamate measure was blunted and the caudal-to-rostral decline in the GABA measure was enhanced in the illness. Differential alterations in layer 3 glutamate and GABA neurotransmission across cortical regions may contribute to vsWM deficits in schizophrenia. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  10. Network analysis of the transcriptional pattern of young and old cells of Escherichia coli during lag phase

    Directory of Open Access Journals (Sweden)

    Hinton Jay CD

    2009-11-01

    Full Text Available Abstract Background The aging process of bacteria in stationary phase is halted if cells are subcultured and enter lag phase and it is then followed by cellular division. Network science has been applied to analyse the transcriptional response, during lag phase, of bacterial cells starved previously in stationary phase for 1 day (young cells and 16 days (old cells. Results A genome scale network was constructed for E. coli K-12 by connecting genes with operons, transcription and sigma factors, metabolic pathways and cell functional categories. Most of the transcriptional changes were detected immediately upon entering lag phase and were maintained throughout this period. The lag period was longer for older cells and the analysis of the transcriptome revealed different intracellular activity in young and old cells. The number of genes differentially expressed was smaller in old cells (186 than in young cells (467. Relatively, few genes (62 were up- or down-regulated in both cultures. Transcription of genes related to osmotolerance, acid resistance, oxidative stress and adaptation to other stresses was down-regulated in both young and old cells. Regarding carbohydrate metabolism, genes related to the citrate cycle were up-regulated in young cells while old cells up-regulated the Entner Doudoroff and gluconate pathways and down-regulated the pentose phosphate pathway. In both old and young cells, anaerobic respiration and fermentation pathways were down-regulated, but only young cells up-regulated aerobic respiration while there was no evidence of aerobic respiration in old cells. Numerous genes related to DNA maintenance and replication, translation, ribosomal biosynthesis and RNA processing as well as biosynthesis of the cell envelope and flagellum and several components of the chemotaxis signal transduction complex were up-regulated only in young cells. The genes for several transport proteins for iron compounds were up-regulated in both young

  11. Network analysis of the transcriptional pattern of young and old cells of Escherichia coli during lag phase

    LENUS (Irish Health Repository)

    Pin, Carmen

    2009-11-16

    Abstract Background The aging process of bacteria in stationary phase is halted if cells are subcultured and enter lag phase and it is then followed by cellular division. Network science has been applied to analyse the transcriptional response, during lag phase, of bacterial cells starved previously in stationary phase for 1 day (young cells) and 16 days (old cells). Results A genome scale network was constructed for E. coli K-12 by connecting genes with operons, transcription and sigma factors, metabolic pathways and cell functional categories. Most of the transcriptional changes were detected immediately upon entering lag phase and were maintained throughout this period. The lag period was longer for older cells and the analysis of the transcriptome revealed different intracellular activity in young and old cells. The number of genes differentially expressed was smaller in old cells (186) than in young cells (467). Relatively, few genes (62) were up- or down-regulated in both cultures. Transcription of genes related to osmotolerance, acid resistance, oxidative stress and adaptation to other stresses was down-regulated in both young and old cells. Regarding carbohydrate metabolism, genes related to the citrate cycle were up-regulated in young cells while old cells up-regulated the Entner Doudoroff and gluconate pathways and down-regulated the pentose phosphate pathway. In both old and young cells, anaerobic respiration and fermentation pathways were down-regulated, but only young cells up-regulated aerobic respiration while there was no evidence of aerobic respiration in old cells. Numerous genes related to DNA maintenance and replication, translation, ribosomal biosynthesis and RNA processing as well as biosynthesis of the cell envelope and flagellum and several components of the chemotaxis signal transduction complex were up-regulated only in young cells. The genes for several transport proteins for iron compounds were up-regulated in both young and old cells

  12. c-Myc Antagonises the Transcriptional Activity of the Androgen Receptor in Prostate Cancer Affecting Key Gene Networks.

    Science.gov (United States)

    Barfeld, Stefan J; Urbanucci, Alfonso; Itkonen, Harri M; Fazli, Ladan; Hicks, Jessica L; Thiede, Bernd; Rennie, Paul S; Yegnasubramanian, Srinivasan; DeMarzo, Angelo M; Mills, Ian G

    2017-04-01

    Prostate cancer (PCa) is the most common non-cutaneous cancer in men. The androgen receptor (AR), a ligand-activated transcription factor, constitutes the main drug target for advanced cases of the disease. However, a variety of other transcription factors and signaling networks have been shown to be altered in patients and to influence AR activity. Amongst these, the oncogenic transcription factor c-Myc has been studied extensively in multiple malignancies and elevated protein levels of c-Myc are commonly observed in PCa. Its impact on AR activity, however, remains elusive. In this study, we assessed the impact of c-Myc overexpression on AR activity and transcriptional output in a PCa cell line model and validated the antagonistic effect of c-MYC on AR-targets in patient samples. We found that c-Myc overexpression partially reprogrammed AR chromatin occupancy and was associated with altered histone marks distribution, most notably H3K4me1 and H3K27me3. We found c-Myc and the AR co-occupy a substantial number of binding sites and these exhibited enhancer-like characteristics. Interestingly, c-Myc overexpression antagonised clinically relevant AR target genes. Therefore, as an example, we validated the antagonistic relationship between c-Myc and two AR target genes, KLK3 (alias PSA, prostate specific antigen), and Glycine N-Methyltransferase (GNMT), in patient samples. Our findings provide unbiased evidence that MYC overexpression deregulates the AR transcriptional program, which is thought to be a driving force in PCa. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

  13. c-Myc Antagonises the Transcriptional Activity of the Androgen Receptor in Prostate Cancer Affecting Key Gene Networks

    Directory of Open Access Journals (Sweden)

    Stefan J. Barfeld

    2017-04-01

    Full Text Available Prostate cancer (PCa is the most common non-cutaneous cancer in men. The androgen receptor (AR, a ligand-activated transcription factor, constitutes the main drug target for advanced cases of the disease. However, a variety of other transcription factors and signaling networks have been shown to be altered in patients and to influence AR activity. Amongst these, the oncogenic transcription factor c-Myc has been studied extensively in multiple malignancies and elevated protein levels of c-Myc are commonly observed in PCa. Its impact on AR activity, however, remains elusive. In this study, we assessed the impact of c-Myc overexpression on AR activity and transcriptional output in a PCa cell line model and validated the antagonistic effect of c-MYC on AR-targets in patient samples. We found that c-Myc overexpression partially reprogrammed AR chromatin occupancy and was associated with altered histone marks distribution, most notably H3K4me1 and H3K27me3. We found c-Myc and the AR co-occupy a substantial number of binding sites and these exhibited enhancer-like characteristics. Interestingly, c-Myc overexpression antagonised clinically relevant AR target genes. Therefore, as an example, we validated the antagonistic relationship between c-Myc and two AR target genes, KLK3 (alias PSA, prostate specific antigen, and Glycine N-Methyltransferase (GNMT, in patient samples. Our findings provide unbiased evidence that MYC overexpression deregulates the AR transcriptional program, which is thought to be a driving force in PCa.

  14. MIR@NT@N: a framework integrating transcription factors, microRNAs and their targets to identify sub-network motifs in a meta-regulation network model

    Directory of Open Access Journals (Sweden)

    Wasserman Wyeth W

    2011-03-01

    Full Text Available Abstract Background To understand biological processes and diseases, it is crucial to unravel the concerted interplay of transcription factors (TFs, microRNAs (miRNAs and their targets within regulatory networks and fundamental sub-networks. An integrative computational resource generating a comprehensive view of these regulatory molecular interactions at a genome-wide scale would be of great interest to biologists, but is not available to date. Results To identify and analyze molecular interaction networks, we developed MIR@NT@N, an integrative approach based on a meta-regulation network model and a large-scale database. MIR@NT@N uses a graph-based approach to predict novel molecular actors across multiple regulatory processes (i.e. TFs acting on protein-coding or miRNA genes, or miRNAs acting on messenger RNAs. Exploiting these predictions, the user can generate networks and further analyze them to identify sub-networks, including motifs such as feedback and feedforward loops (FBL and FFL. In addition, networks can be built from lists of molecular actors with an a priori role in a given biological process to predict novel and unanticipated interactions. Analyses can be contextualized and filtered by integrating additional information such as microarray expression data. All results, including generated graphs, can be visualized, saved and exported into various formats. MIR@NT@N performances have been evaluated using published data and then applied to the regulatory program underlying epithelium to mesenchyme transition (EMT, an evolutionary-conserved process which is implicated in embryonic development and disease. Conclusions MIR@NT@N is an effective computational approach to identify novel molecular regulations and to predict gene regulatory networks and sub-networks including conserved motifs within a given biological context. Taking advantage of the M@IA environment, MIR@NT@N is a user-friendly web resource freely available at http

  15. Uncovering packaging features of co-regulated modules based on human protein interaction and transcriptional regulatory networks

    Directory of Open Access Journals (Sweden)

    He Weiming

    2010-07-01

    Full Text Available Abstract Background Network co-regulated modules are believed to have the functionality of packaging multiple biological entities, and can thus be assumed to coordinate many biological functions in their network neighbouring regions. Results Here, we weighted edges of a human protein interaction network and a transcriptional regulatory network to construct an integrated network, and introduce a probabilistic model and a bipartite graph framework to exploit human co-regulated modules and uncover their specific features in packaging different biological entities (genes, protein complexes or metabolic pathways. Finally, we identified 96 human co-regulated modules based on this method, and evaluate its effectiveness by comparing it with four other methods. Conclusions Dysfunctions in co-regulated interactions often occur in the development of cancer. Therefore, we focussed on an example co-regulated module and found that it could integrate a number of cancer-related genes. This was extended to causal dysfunctions of some complexes maintained by several physically interacting proteins, thus coordinating several metabolic pathways that directly underlie cancer.

  16. Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells

    OpenAIRE

    Zhou Qing; Plath Kathrin; Fan Guoping; Mason Mike J; Horvath Steve

    2009-01-01

    Abstract Background Recent work has revealed that a core group of transcription factors (TFs) regulates the key characteristics of embryonic stem (ES) cells: pluripotency and self-renewal. Current efforts focus on identifying genes that play important roles in maintaining pluripotency and self-renewal in ES cells and aim to understand the interactions among these genes. To that end, we...

  17. Transcription Factor Networks derived from Breast Cancer Stem Cells control the immune response in the Basal subtype

    DEFF Research Database (Denmark)

    da Silveira, W A; Palma, P V B; Sicchieri, R D

    2017-01-01

    Breast cancer is the most common cancer in women worldwide and metastatic dissemination is the principal factor related to death by this disease. Breast cancer stem cells (bCSC) are thought to be responsible for metastasis and chemoresistance. In this study, based on whole transcriptome analysis...... of these networks in patient tumours is predictive of engraftment success. Our findings point out a potential molecular mechanism underlying the balance between immune surveillance and EMT activation in breast cancer. This molecular mechanism may be useful to the development of new target therapies....... and IKZF3 transcription factors which correspond to immune response modulators. Immune response network expression is correlated with pathological response to chemotherapy, and in the Basal subtype is related to better recurrence-free survival. In patient-derived xenografts, the expression...

  18. Automatic diagnosis of abnormal macula in retinal optical coherence tomography images using wavelet-based convolutional neural network features and random forests classifier

    Science.gov (United States)

    Rasti, Reza; Mehridehnavi, Alireza; Rabbani, Hossein; Hajizadeh, Fedra

    2018-03-01

    The present research intends to propose a fully automatic algorithm for the classification of three-dimensional (3-D) optical coherence tomography (OCT) scans of patients suffering from abnormal macula from normal candidates. The method proposed does not require any denoising, segmentation, retinal alignment processes to assess the intraretinal layers, as well as abnormalities or lesion structures. To classify abnormal cases from the control group, a two-stage scheme was utilized, which consists of automatic subsystems for adaptive feature learning and diagnostic scoring. In the first stage, a wavelet-based convolutional neural network (CNN) model was introduced and exploited to generate B-scan representative CNN codes in the spatial-frequency domain, and the cumulative features of 3-D volumes were extracted. In the second stage, the presence of abnormalities in 3-D OCTs was scored over the extracted features. Two different retinal SD-OCT datasets are used for evaluation of the algorithm based on the unbiased fivefold cross-validation (CV) approach. The first set constitutes 3-D OCT images of 30 normal subjects and 30 diabetic macular edema (DME) patients captured from the Topcon device. The second publicly available set consists of 45 subjects with a distribution of 15 patients in age-related macular degeneration, DME, and normal classes from the Heidelberg device. With the application of the algorithm on overall OCT volumes and 10 repetitions of the fivefold CV, the proposed scheme obtained an average precision of 99.33% on dataset1 as a two-class classification problem and 98.67% on dataset2 as a three-class classification task.

  19. Automatic diagnosis of abnormal macula in retinal optical coherence tomography images using wavelet-based convolutional neural network features and random forests classifier.

    Science.gov (United States)

    Rasti, Reza; Mehridehnavi, Alireza; Rabbani, Hossein; Hajizadeh, Fedra

    2018-03-01

    The present research intends to propose a fully automatic algorithm for the classification of three-dimensional (3-D) optical coherence tomography (OCT) scans of patients suffering from abnormal macula from normal candidates. The method proposed does not require any denoising, segmentation, retinal alignment processes to assess the intraretinal layers, as well as abnormalities or lesion structures. To classify abnormal cases from the control group, a two-stage scheme was utilized, which consists of automatic subsystems for adaptive feature learning and diagnostic scoring. In the first stage, a wavelet-based convolutional neural network (CNN) model was introduced and exploited to generate B-scan representative CNN codes in the spatial-frequency domain, and the cumulative features of 3-D volumes were extracted. In the second stage, the presence of abnormalities in 3-D OCTs was scored over the extracted features. Two different retinal SD-OCT datasets are used for evaluation of the algorithm based on the unbiased fivefold cross-validation (CV) approach. The first set constitutes 3-D OCT images of 30 normal subjects and 30 diabetic macular edema (DME) patients captured from the Topcon device. The second publicly available set consists of 45 subjects with a distribution of 15 patients in age-related macular degeneration, DME, and normal classes from the Heidelberg device. With the application of the algorithm on overall OCT volumes and 10 repetitions of the fivefold CV, the proposed scheme obtained an average precision of 99.33% on dataset1 as a two-class classification problem and 98.67% on dataset2 as a three-class classification task. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

  20. Genome-Wide Mapping of Collier In Vivo Binding Sites Highlights Its Hierarchical Position in Different Transcription Regulatory Networks.

    Directory of Open Access Journals (Sweden)

    Mathilde de Taffin

    Full Text Available Collier, the single Drosophila COE (Collier/EBF/Olf-1 transcription factor, is required in several developmental processes, including head patterning and specification of muscle and neuron identity during embryogenesis. To identify direct Collier (Col targets in different cell types, we used ChIP-seq to map Col binding sites throughout the genome, at mid-embryogenesis. In vivo Col binding peaks were associated to 415 potential direct target genes. Gene Ontology analysis revealed a strong enrichment in proteins with DNA binding and/or transcription-regulatory properties. Characterization of a selection of candidates, using transgenic CRM-reporter assays, identified direct Col targets in dorso-lateral somatic muscles and specific neuron types in the central nervous system. These data brought new evidence that Col direct control of the expression of the transcription regulators apterous and eyes-absent (eya is critical to specifying neuronal identities. They also showed that cross-regulation between col and eya in muscle progenitor cells is required for specification of muscle identity, revealing a new parallel between the myogenic regulatory networks operating in Drosophila and vertebrates. Col regulation of eya, both in specific muscle and neuronal lineages, may illustrate one mechanism behind the evolutionary diversification of Col biological roles.

  1. Genome-Wide Mapping of Collier In Vivo Binding Sites Highlights Its Hierarchical Position in Different Transcription Regulatory Networks

    Science.gov (United States)

    Dubois, Laurence; Bataillé, Laetitia; Painset, Anaïs; Le Gras, Stéphanie; Jost, Bernard; Crozatier, Michèle; Vincent, Alain

    2015-01-01

    Collier, the single Drosophila COE (Collier/EBF/Olf-1) transcription factor, is required in several developmental processes, including head patterning and specification of muscle and neuron identity during embryogenesis. To identify direct Collier (Col) targets in different cell types, we used ChIP-seq to map Col binding sites throughout the genome, at mid-embryogenesis. In vivo Col binding peaks were associated to 415 potential direct target genes. Gene Ontology analysis revealed a strong enrichment in proteins with DNA binding and/or transcription-regulatory properties. Characterization of a selection of candidates, using transgenic CRM-reporter assays, identified direct Col targets in dorso-lateral somatic muscles and specific neuron types in the central nervous system. These data brought new evidence that Col direct control of the expression of the transcription regulators apterous and eyes-absent (eya) is critical to specifying neuronal identities. They also showed that cross-regulation between col and eya in muscle progenitor cells is required for specification of muscle identity, revealing a new parallel between the myogenic regulatory networks operating in Drosophila and vertebrates. Col regulation of eya, both in specific muscle and neuronal lineages, may illustrate one mechanism behind the evolutionary diversification of Col biological roles. PMID:26204530

  2. The transcriptional and gene regulatory network of Lactococcus lactis MG1363 during growth in milk.

    Directory of Open Access Journals (Sweden)

    Anne de Jong

    Full Text Available In the present study we examine the changes in the expression of genes of Lactococcus lactis subspecies cremoris MG1363 during growth in milk. To reveal which specific classes of genes (pathways, operons, regulons, COGs are important, we performed a transcriptome time series experiment. Global analysis of gene expression over time showed that L. lactis adapted quickly to the environmental changes. Using upstream sequences of genes with correlated gene expression profiles, we uncovered a substantial number of putative DNA binding motifs that may be relevant for L. lactis fermentative growth in milk. All available novel and literature-derived data were integrated into network reconstruction building blocks, which were used to reconstruct and visualize the L. lactis gene regulatory network. This network enables easy mining in the chrono-transcriptomics data. A freely available website at http://milkts.molgenrug.nl gives full access to all transcriptome data, to the reconstructed network and to the individual network building blocks.

  3. Phylogeny, Functional Annotation, and Protein Interaction Network Analyses of the Xenopus tropicalis Basic Helix-Loop-Helix Transcription Factors

    Directory of Open Access Journals (Sweden)

    Wuyi Liu

    2013-01-01

    Full Text Available The previous survey identified 70 basic helix-loop-helix (bHLH proteins, but it was proved to be incomplete, and the functional information and regulatory networks of frog bHLH transcription factors were not fully known. Therefore, we conducted an updated genome-wide survey in the Xenopus tropicalis genome project databases and identified 105 bHLH sequences. Among the retrieved 105 sequences, phylogenetic analyses revealed that 103 bHLH proteins belonged to 43 families or subfamilies with 46, 26, 11, 3, 15, and 4 members in the corresponding supergroups. Next, gene ontology (GO enrichment analyses showed 65 significant GO annotations of biological processes and molecular functions and KEGG pathways counted in frequency. To explore the functional pathways, regulatory gene networks, and/or related gene groups coding for Xenopus tropicalis bHLH proteins, the identified bHLH genes were put into the databases KOBAS and STRING to get the signaling information of pathways and protein interaction networks according to available public databases and known protein interactions. From the genome annotation and pathway analysis using KOBAS, we identified 16 pathways in the Xenopus tropicalis genome. From the STRING interaction analysis, 68 hub proteins were identified, and many hub proteins created a tight network or a functional module within the protein families.

  4. Unexpected complexity of the reef-building coral Acropora millepora transcription factor network.

    KAUST Repository

    Ryu, Tae Woo

    2011-04-28

    Coral reefs are disturbed on a global scale by environmental changes including rising sea surface temperatures and ocean acidification. Little is known about how corals respond or adapt to these environmental changes especially at the molecular level. This is mostly because of the paucity of genome-wide studies on corals and the application of systems approaches that incorporate the latter. Like in any other organism, the response of corals to stress is tightly controlled by the coordinated interplay of many transcription factors.

  5. Unexpected complexity of the reef-building coral Acropora millepora transcription factor network.

    KAUST Repository

    Ryu, Tae Woo; Mavromatis, Charalampos Harris; Bayer, Till; Voolstra, Christian R.; Ravasi, Timothy

    2011-01-01

    Coral reefs are disturbed on a global scale by environmental changes including rising sea surface temperatures and ocean acidification. Little is known about how corals respond or adapt to these environmental changes especially at the molecular level. This is mostly because of the paucity of genome-wide studies on corals and the application of systems approaches that incorporate the latter. Like in any other organism, the response of corals to stress is tightly controlled by the coordinated interplay of many transcription factors.

  6. Comparative study of discretization methods of microarray data for inferring transcriptional regulatory networks

    Directory of Open Access Journals (Sweden)

    Ji Wei

    2010-10-01

    Full Text Available Abstract Background Microarray data discretization is a basic preprocess for many algorithms of gene regulatory network inference. Some common discretization methods in informatics are used to discretize microarray data. Selection of the discretization method is often arbitrary and no systematic comparison of different discretization has been conducted, in the context of gene regulatory network inference from time series gene expression data. Results In this study, we propose a new discretization method "bikmeans", and compare its performance with four other widely-used discretization methods using different datasets, modeling algorithms and number of intervals. Sensitivities, specificities and total accuracies were calculated and statistical analysis was carried out. Bikmeans method always gave high total accuracies. Conclusions Our results indicate that proper discretization methods can consistently improve gene regulatory network inference independent of network modeling algorithms and datasets. Our new method, bikmeans, resulted in significant better total accuracies than other methods.

  7. Conservation of lipid metabolic gene transcriptional regulatory networks in fish and mammals.

    Science.gov (United States)

    Carmona-Antoñanzas, Greta; Tocher, Douglas R; Martinez-Rubio, Laura; Leaver, Michael J

    2014-01-15

    Lipid content and composition in aquafeeds have changed rapidly as a result of the recent drive to replace ecologically limited marine ingredients, fishmeal and fish oil (FO). Terrestrial plant products are the most economic and sustainable alternative; however, plant meals and oils are devoid of physiologically important cholesterol and long-chain polyunsaturated fatty acids (LC-PUFA), eicosapentaenoic (EPA), docosahexaenoic (DHA) and arachidonic (ARA) acids. Although replacement of dietary FO with vegetable oil (VO) has little effect on growth in Atlantic salmon (Salmo salar), several studies have shown major effects on the activity and expression of genes involved in lipid homeostasis. In vertebrates, sterols and LC-PUFA play crucial roles in lipid metabolism by direct interaction with lipid-sensing transcription factors (TFs) and consequent regulation of target genes. The primary aim of the present study was to elucidate the role of key TFs in the transcriptional regulation of lipid metabolism in fish by transfection and overexpression of TFs. The results show that the expression of genes of LC-PUFA biosynthesis (elovl and fads2) and cholesterol metabolism (abca1) are regulated by Lxr and Srebp TFs in salmon, indicating highly conserved regulatory mechanism across vertebrates. In addition, srebp1 and srebp2 mRNA respond to replacement of dietary FO with VO. Thus, Atlantic salmon adjust lipid metabolism in response to dietary lipid composition through the transcriptional regulation of gene expression. It may be possible to further increase efficient and effective use of sustainable alternatives to marine products in aquaculture by considering these important molecular interactions when formulating diets. © 2013.

  8. Reconstructing Generalized Logical Networks of Transcriptional Regulation in Mouse Brain from Temporal Gene Expression Data

    Energy Technology Data Exchange (ETDEWEB)

    Song, Mingzhou (Joe) [New Mexico State University, Las Cruces; Lewis, Chris K. [New Mexico State University, Las Cruces; Lance, Eric [New Mexico State University, Las Cruces; Chesler, Elissa J [ORNL; Kirova, Roumyana [Bristol-Myers Squibb Pharmaceutical Research & Development, NJ; Langston, Michael A [University of Tennessee, Knoxville (UTK); Bergeson, Susan [Texas Tech University, Lubbock

    2009-01-01

    The problem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from high-throughput transcriptomic data is addressed. A network reconstruction algorithm was developed that uses the statistical significance as a criterion for network selection to avoid false-positive interactions arising from pure chance. Using temporal gene expression data collected from the brains of alcohol-treated mice in an analysis of the molecular response to alcohol, this algorithm identified genes from a major neuronal pathway as putative components of the alcohol response mechanism. Three of these genes have known associations with alcohol in the literature. Several other potentially relevant genes, highlighted and agreeing with independent results from literature mining, may play a role in the response to alcohol. Additional, previously-unknown gene interactions were discovered that, subject to biological verification, may offer new clues in the search for the elusive molecular mechanisms of alcoholism.

  9. A Multiparameter Network Reveals Extensive Divergence between C. elegans bHLH Transcription Factors

    DEFF Research Database (Denmark)

    Grove, C.; De Masi, Federico; Newburger, Daniel

    2009-01-01

    parameters remain undetermined. We comprehensively identify dimerization partners, spatiotemporal expression patterns, and DNA-binding specificities for the C. elegans bHLH family of TFs, and model these data into an integrated network. This network displays both specificity and promiscuity, as some b......HLH proteins, DNA sequences, and tissues are highly connected, whereas others are not. By comparing all bHLH TFs, we find extensive divergence and that all three parameters contribute equally to bHLH divergence. Our approach provides a framework for examining divergence for other protein families in C. elegans...

  10. A model for genetic and epigenetic regulatory networks identifies rare pathways for transcription factor induced pluripotency

    Science.gov (United States)

    Artyomov, Maxim; Meissner, Alex; Chakraborty, Arup

    2010-03-01

    Most cells in an organism have the same DNA. Yet, different cell types express different proteins and carry out different functions. This is because of epigenetic differences; i.e., DNA in different cell types is packaged distinctly, making it hard to express certain genes while facilitating the expression of others. During development, upon receipt of appropriate cues, pluripotent embryonic stem cells differentiate into diverse cell types that make up the organism (e.g., a human). There has long been an effort to make this process go backward -- i.e., reprogram a differentiated cell (e.g., a skin cell) to pluripotent status. Recently, this has been achieved by transfecting certain transcription factors into differentiated cells. This method does not use embryonic material and promises the development of patient-specific regenerative medicine, but it is inefficient. The mechanisms that make reprogramming rare, or even possible, are poorly understood. We have developed the first computational model of transcription factor-induced reprogramming. Results obtained from the model are consistent with diverse observations, and identify the rare pathways that allow reprogramming to occur. If validated, our model could be further developed to design optimal strategies for reprogramming and shed light on basic questions in biology.

  11. Hybrid classifiers methods of data, knowledge, and classifier combination

    CERN Document Server

    Wozniak, Michal

    2014-01-01

    This book delivers a definite and compact knowledge on how hybridization can help improving the quality of computer classification systems. In order to make readers clearly realize the knowledge of hybridization, this book primarily focuses on introducing the different levels of hybridization and illuminating what problems we will face with as dealing with such projects. In the first instance the data and knowledge incorporated in hybridization were the action points, and then a still growing up area of classifier systems known as combined classifiers was considered. This book comprises the aforementioned state-of-the-art topics and the latest research results of the author and his team from Department of Systems and Computer Networks, Wroclaw University of Technology, including as classifier based on feature space splitting, one-class classification, imbalance data, and data stream classification.

  12. A Knockout Screen of ApiAP2 Genes Reveals Networks of Interacting Transcriptional Regulators Controlling the Plasmodium Life Cycle.

    Science.gov (United States)

    Modrzynska, Katarzyna; Pfander, Claudia; Chappell, Lia; Yu, Lu; Suarez, Catherine; Dundas, Kirsten; Gomes, Ana Rita; Goulding, David; Rayner, Julian C; Choudhary, Jyoti; Billker, Oliver

    2017-01-11

    A family of apicomplexa-specific proteins containing AP2 DNA-binding domains (ApiAP2s) was identified in malaria parasites. This family includes sequence-specific transcription factors that are key regulators of development. However, functions for the majority of ApiAP2 genes remain unknown. Here, a systematic knockout screen in Plasmodium berghei identified ten ApiAP2 genes that were essential for mosquito transmission: four were critical for the formation of infectious ookinetes, and three were required for sporogony. We describe non-essential functions for AP2-O and AP2-SP proteins in blood stages, and identify AP2-G2 as a repressor active in both asexual and sexual stages. Comparative transcriptomics across mutants and developmental stages revealed clusters of co-regulated genes with shared cis promoter elements, whose expression can be controlled positively or negatively by different ApiAP2 factors. We propose that stage-specific interactions between ApiAP2 proteins on partly overlapping sets of target genes generate the complex transcriptional network that controls the Plasmodium life cycle. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

  13. Distinct transcriptional networks in quiescent myoblasts: a role for Wnt signaling in reversible vs. irreversible arrest.

    Directory of Open Access Journals (Sweden)

    Sindhu Subramaniam

    Full Text Available Most cells in adult mammals are non-dividing: differentiated cells exit the cell cycle permanently, but stem cells exist in a state of reversible arrest called quiescence. In damaged skeletal muscle, quiescent satellite stem cells re-enter the cell cycle, proliferate and subsequently execute divergent programs to regenerate both post-mitotic myofibers and quiescent stem cells. The molecular basis for these alternative programs of arrest is poorly understood. In this study, we used an established myogenic culture model (C2C12 myoblasts to generate cells in alternative states of arrest and investigate their global transcriptional profiles. Using cDNA microarrays, we compared G0 myoblasts with post-mitotic myotubes. Our findings define the transcriptional program of quiescent myoblasts in culture and establish that distinct gene expression profiles, especially of tumour suppressor genes and inhibitors of differentiation characterize reversible arrest, distinguishing this state from irreversibly arrested myotubes. We also reveal the existence of a tissue-specific quiescence program by comparing G0 C2C12 myoblasts to isogenic G0 fibroblasts (10T1/2. Intriguingly, in myoblasts but not fibroblasts, quiescence is associated with a signature of Wnt pathway genes. We provide evidence that different levels of signaling via the canonical Wnt pathway characterize distinct cellular states (proliferation vs. quiescence vs. differentiation. Moderate induction of Wnt signaling in quiescence is associated with critical properties such as clonogenic self-renewal. Exogenous Wnt treatment subverts the quiescence program and negatively affects clonogenicity. Finally, we identify two new quiescence-induced regulators of canonical Wnt signaling, Rgs2 and Dkk3, whose induction in G0 is required for clonogenic self-renewal. These results support the concept that active signal-mediated regulation of quiescence contributes to stem cell properties, and have implications for

  14. Distinct transcriptional networks in quiescent myoblasts: a role for Wnt signaling in reversible vs. irreversible arrest.

    Science.gov (United States)

    Subramaniam, Sindhu; Sreenivas, Prethish; Cheedipudi, Sirisha; Reddy, Vatrapu Rami; Shashidhara, Lingadahalli Subrahmanya; Chilukoti, Ravi Kumar; Mylavarapu, Madhavi; Dhawan, Jyotsna

    2014-01-01

    Most cells in adult mammals are non-dividing: differentiated cells exit the cell cycle permanently, but stem cells exist in a state of reversible arrest called quiescence. In damaged skeletal muscle, quiescent satellite stem cells re-enter the cell cycle, proliferate and subsequently execute divergent programs to regenerate both post-mitotic myofibers and quiescent stem cells. The molecular basis for these alternative programs of arrest is poorly understood. In this study, we used an established myogenic culture model (C2C12 myoblasts) to generate cells in alternative states of arrest and investigate their global transcriptional profiles. Using cDNA microarrays, we compared G0 myoblasts with post-mitotic myotubes. Our findings define the transcriptional program of quiescent myoblasts in culture and establish that distinct gene expression profiles, especially of tumour suppressor genes and inhibitors of differentiation characterize reversible arrest, distinguishing this state from irreversibly arrested myotubes. We also reveal the existence of a tissue-specific quiescence program by comparing G0 C2C12 myoblasts to isogenic G0 fibroblasts (10T1/2). Intriguingly, in myoblasts but not fibroblasts, quiescence is associated with a signature of Wnt pathway genes. We provide evidence that different levels of signaling via the canonical Wnt pathway characterize distinct cellular states (proliferation vs. quiescence vs. differentiation). Moderate induction of Wnt signaling in quiescence is associated with critical properties such as clonogenic self-renewal. Exogenous Wnt treatment subverts the quiescence program and negatively affects clonogenicity. Finally, we identify two new quiescence-induced regulators of canonical Wnt signaling, Rgs2 and Dkk3, whose induction in G0 is required for clonogenic self-renewal. These results support the concept that active signal-mediated regulation of quiescence contributes to stem cell properties, and have implications for pathological

  15. A novel SALL4/OCT4 transcriptional feedback network for pluripotency of embryonic stem cells.

    Directory of Open Access Journals (Sweden)

    Jianchang Yang

    Full Text Available BACKGROUND: SALL4 is a member of the SALL gene family that encodes a group of putative developmental transcription factors. Murine Sall4 plays a critical role in maintaining embryonic stem cell (ES cell pluripotency and self-renewal. We have shown that Sall4 activates Oct4 and is a master regulator in murine ES cells. Other SALL gene members, especially Sall1 and Sall3 are expressed in both murine and human ES cells, and deletions of these two genes in mice lead to perinatal death due to developmental defects. To date, little is known about the molecular mechanisms controlling the regulation of expressions of SALL4 or other SALL gene family members. METHODOLOGY/PRINCIPAL FINDINGS: This report describes a novel SALL4/OCT4 regulator feedback loop in ES cells in balancing the proper expression dosage of SALL4 and OCT4 for the maintenance of ESC stem cell properties. While we have observed that a positive feedback relationship is present between SALL4 and OCT4, the strong self-repression of SALL4 seems to be the "break" for this loop. In addition, we have shown that SALL4 can repress the promoters of other SALL family members, such as SALL1 and SALL3, which competes with the activation of these two genes by OCT4. CONCLUSIONS/SIGNIFICANCE: Our findings, when taken together, indicate that SALL4 is a master regulator that controls its own expression and the expression of OCT4. SALL4 and OCT4 work antagonistically to balance the expressions of other SALL gene family members. This novel SALL4/OCT4 transcription regulation feedback loop should provide more insight into the mechanism of governing the "stemness" of ES cells.

  16. Transcriptional regulatory network triggered by oxidative signals configures the early response mechanisms of japonica rice to chilling stress

    Directory of Open Access Journals (Sweden)

    Wijaya Edward

    2010-01-01

    Full Text Available Abstract Background The transcriptional regulatory network involved in low temperature response leading to acclimation has been established in Arabidopsis. In japonica rice, which can only withstand transient exposure to milder cold stress (10°C, an oxidative-mediated network has been proposed to play a key role in configuring early responses and short-term defenses. The components, hierarchical organization and physiological consequences of this network were further dissected by a systems-level approach. Results Regulatory clusters responding directly to oxidative signals were prominent during the initial 6 to 12 hours at 10°C. Early events mirrored a typical oxidative response based on striking similarities of the transcriptome to disease, elicitor and wounding induced processes. Targets of oxidative-mediated mechanisms are likely regulated by several classes of bZIP factors acting on as1/ocs/TGA-like element enriched clusters, ERF factors acting on GCC-box/JAre-like element enriched clusters and R2R3-MYB factors acting on MYB2-like element enriched clusters. Temporal induction of several H2O2-induced bZIP, ERF and MYB genes coincided with the transient H2O2 spikes within the initial 6 to 12 hours. Oxidative-independent responses involve DREB/CBF, RAP2 and RAV1 factors acting on DRE/CRT/rav1-like enriched clusters and bZIP factors acting on ABRE-like enriched clusters. Oxidative-mediated clusters were activated earlier than ABA-mediated clusters. Conclusion Genome-wide, physiological and whole-plant level analyses established a holistic view of chilling stress response mechanism of japonica rice. Early response regulatory network triggered by oxidative signals is critical for prolonged survival under sub-optimal temperature. Integration of stress and developmental responses leads to modulated growth and vigor maintenance contributing to a delay of plastic injuries.

  17. Transcriptional regulatory network triggered by oxidative signals configures the early response mechanisms of japonica rice to chilling stress

    KAUST Repository

    Yun, Kil-Young

    2010-01-25

    Background: The transcriptional regulatory network involved in low temperature response leading to acclimation has been established in Arabidopsis. In japonica rice, which can only withstand transient exposure to milder cold stress (10C), an oxidative-mediated network has been proposed to play a key role in configuring early responses and short-term defenses. The components, hierarchical organization and physiological consequences of this network were further dissected by a systems-level approach.Results: Regulatory clusters responding directly to oxidative signals were prominent during the initial 6 to 12 hours at 10C. Early events mirrored a typical oxidative response based on striking similarities of the transcriptome to disease, elicitor and wounding induced processes. Targets of oxidative-mediated mechanisms are likely regulated by several classes of bZIP factors acting on as1/ocs/TGA-like element enriched clusters, ERF factors acting on GCC-box/JAre-like element enriched clusters and R2R3-MYB factors acting on MYB2-like element enriched clusters.Temporal induction of several H2O2-induced bZIP, ERF and MYB genes coincided with the transient H2O2spikes within the initial 6 to 12 hours. Oxidative-independent responses involve DREB/CBF, RAP2 and RAV1 factors acting on DRE/CRT/rav1-like enriched clusters and bZIP factors acting on ABRE-like enriched clusters. Oxidative-mediated clusters were activated earlier than ABA-mediated clusters.Conclusion: Genome-wide, physiological and whole-plant level analyses established a holistic view of chilling stress response mechanism of japonica rice. Early response regulatory network triggered by oxidative signals is critical for prolonged survival under sub-optimal temperature. Integration of stress and developmental responses leads to modulated growth and vigor maintenance contributing to a delay of plastic injuries. 2010 Yun et al; licensee BioMed Central Ltd.

  18. Natural and Unnatural Oil Layers on the Surface of the Gulf of Mexico Detected and Quantified in Synthetic Aperture RADAR Images with Texture Classifying Neural Network Algorithms

    Science.gov (United States)

    MacDonald, I. R.; Garcia-Pineda, O. G.; Morey, S. L.; Huffer, F.

    2011-12-01

    Effervescent hydrocarbons rise naturally from hydrocarbon seeps in the Gulf of Mexico and reach the ocean surface. This oil forms thin (~0.1 μm) layers that enhance specular reflectivity and have been widely used to quantify the abundance and distribution of natural seeps using synthetic aperture radar (SAR). An analogous process occurred at a vastly greater scale for oil and gas discharged from BP's Macondo well blowout. SAR data allow direct comparison of the areas of the ocean surface covered by oil from natural sources and the discharge. We used a texture classifying neural network algorithm to quantify the areas of naturally occurring oil-covered water in 176 SAR image collections from the Gulf of Mexico obtained between May 1997 and November 2007, prior to the blowout. Separately we also analyzed 36 SAR images collections obtained between 26 April and 30 July, 2010 while the discharged oil was visible in the Gulf of Mexico. For the naturally occurring oil, we removed pollution events and transient oceanographic effects by including only the reflectance anomalies that that recurred in the same locality over multiple images. We measured the area of oil layers in a grid of 10x10 km cells covering the entire Gulf of Mexico. Floating oil layers were observed in only a fraction of the total Gulf area amounting to 1.22x10^5 km^2. In a bootstrap sample of 2000 replications, the combined average area of these layers was 7.80x10^2 km^2 (sd 86.03). For a regional comparison, we divided the Gulf of Mexico into four quadrates along 90° W longitude, and 25° N latitude. The NE quadrate, where the BP discharge occurred, received on average 7.0% of the total natural seepage in the Gulf of Mexico (5.24 x10^2 km^2, sd 21.99); the NW quadrate received on average 68.0% of this total (5.30 x10^2 km^2, sd 69.67). The BP blowout occurred in the NE quadrate of the Gulf of Mexico; discharged oil that reached the surface drifted over a large area north of 25° N. Performing a

  19. Identification of activated enhancers and linked transcription factors in breast, prostate, and kidney tumors by tracing enhancer networks using epigenetic traits.

    Science.gov (United States)

    Rhie, Suhn Kyong; Guo, Yu; Tak, Yu Gyoung; Yao, Lijing; Shen, Hui; Coetzee, Gerhard A; Laird, Peter W; Farnham, Peggy J

    2016-01-01

    Although technological advances now allow increased tumor profiling, a detailed understanding of the mechanisms leading to the development of different cancers remains elusive. Our approach toward understanding the molecular events that lead to cancer is to characterize changes in transcriptional regulatory networks between normal and tumor tissue. Because enhancer activity is thought to be critical in regulating cell fate decisions, we have focused our studies on distal regulatory elements and transcription factors that bind to these elements. Using DNA methylation data, we identified more than 25,000 enhancers that are differentially activated in breast, prostate, and kidney tumor tissues, as compared to normal tissues. We then developed an analytical approach called Tracing Enhancer Networks using Epigenetic Traits that correlates DNA methylation levels at enhancers with gene expression to identify more than 800,000 genome-wide links from enhancers to genes and from genes to enhancers. We found more than 1200 transcription factors to be involved in these tumor-specific enhancer networks. We further characterized several transcription factors linked to a large number of enhancers in each tumor type, including GATA3 in non-basal breast tumors, HOXC6 and DLX1 in prostate tumors, and ZNF395 in kidney tumors. We showed that HOXC6 and DLX1 are associated with different clusters of prostate tumor-specific enhancers and confer distinct transcriptomic changes upon knockdown in C42B prostate cancer cells. We also discovered de novo motifs enriched in enhancers linked to ZNF395 in kidney tumors. Our studies characterized tumor-specific enhancers and revealed key transcription factors involved in enhancer networks for specific tumor types and subgroups. Our findings, which include a large set of identified enhancers and transcription factors linked to those enhancers in breast, prostate, and kidney cancers, will facilitate understanding of enhancer networks and mechanisms

  20. Problem-Solving Skills among Precollege Students in Clinical Immunology and Microbiology: Classifying Strategies with a Rubric and Artificial Neural Network Technology.

    Science.gov (United States)

    Kanowith-Klein, Susan; Stave, Mel; Stevens, Ron; Casillas, Adrian M.

    2001-01-01

    Investigates methods for classifying problem solving strategies of high school students who studied infectious and non-infectious diseases by using a software system that can generate a picture of students' strategies in solving problems. (Contains 24 references.) (Author/YDS)

  1. Identification of a Dynamic Core Transcriptional Network in t(8;21 AML that Regulates Differentiation Block and Self-Renewal

    Directory of Open Access Journals (Sweden)

    Anetta Ptasinska

    2014-09-01

    Full Text Available Oncogenic transcription factors such as RUNX1/ETO, which is generated by the chromosomal translocation t(8;21, subvert normal blood cell development by impairing differentiation and driving malignant self-renewal. Here, we use digital footprinting and chromatin immunoprecipitation sequencing (ChIP-seq to identify the core RUNX1/ETO-responsive transcriptional network of t(8;21 cells. We show that the transcriptional program underlying leukemic propagation is regulated by a dynamic equilibrium between RUNX1/ETO and RUNX1 complexes, which bind to identical DNA sites in a mutually exclusive fashion. Perturbation of this equilibrium in t(8;21 cells by RUNX1/ETO depletion leads to a global redistribution of transcription factor complexes within preexisting open chromatin, resulting in the formation of a transcriptional network that drives myeloid differentiation. Our work demonstrates on a genome-wide level that the extent of impaired myeloid differentiation in t(8;21 is controlled by the dynamic balance between RUNX1/ETO and RUNX1 activities through the repression of transcription factors that drive differentiation.

  2. Loss of variation of state detected in soybean metabolic and human myelomonocytic leukaemia cell transcriptional networks under external stimuli

    KAUST Repository

    Sakata, Katsumi

    2016-10-24

    Soybean (Glycine max) is sensitive to flooding stress, and flood damage at the seedling stage is a barrier to growth. We constructed two mathematical models of the soybean metabolic network, a control model and a flooded model, from metabolic profiles in soybean plants. We simulated the metabolic profiles with perturbations before and after the flooding stimulus using the two models. We measured the variation of state that the system could maintain from a state–space description of the simulated profiles. The results showed a loss of variation of state during the flooding response in the soybean plants. Loss of variation of state was also observed in a human myelomonocytic leukaemia cell transcriptional network in response to a phorbol-ester stimulus. Thus, we detected a loss of variation of state under external stimuli in two biological systems, regardless of the regulation and stimulus types. Our results suggest that a loss of robustness may occur concurrently with the loss of variation of state in biological systems. We describe the possible applications of the quantity of variation of state in plant genetic engineering and cell biology. Finally, we present a hypothetical “external stimulus-induced information loss” model of biological systems.

  3. Loss of variation of state detected in soybean metabolic and human myelomonocytic leukaemia cell transcriptional networks under external stimuli

    KAUST Repository

    Sakata, Katsumi; Saito, Toshiyuki; Ohyanagi, Hajime; Okumura, Jun; Ishige, Kentaro; Suzuki, Harukazu; Nakamura, Takuji; Komatsu, Setsuko

    2016-01-01

    Soybean (Glycine max) is sensitive to flooding stress, and flood damage at the seedling stage is a barrier to growth. We constructed two mathematical models of the soybean metabolic network, a control model and a flooded model, from metabolic profiles in soybean plants. We simulated the metabolic profiles with perturbations before and after the flooding stimulus using the two models. We measured the variation of state that the system could maintain from a state–space description of the simulated profiles. The results showed a loss of variation of state during the flooding response in the soybean plants. Loss of variation of state was also observed in a human myelomonocytic leukaemia cell transcriptional network in response to a phorbol-ester stimulus. Thus, we detected a loss of variation of state under external stimuli in two biological systems, regardless of the regulation and stimulus types. Our results suggest that a loss of robustness may occur concurrently with the loss of variation of state in biological systems. We describe the possible applications of the quantity of variation of state in plant genetic engineering and cell biology. Finally, we present a hypothetical “external stimulus-induced information loss” model of biological systems.

  4. Evolution of Transcriptional Regulatory Networks in Pseudomonas aeruginosa During Long Time Growth in Human Hosts

    DEFF Research Database (Denmark)

    Andresen, Eva Kammer

    extent these observations relate to natural microbial populations. The focus of this thesis has been to study how regulatory networks evolve in natural systems. By using a particular infectious disease scenario (human associated persistent airway infections caused by the bacterium Pseudomonas aeruginosa...... in global regulator genes facilitate the generation of novel phenotypes which again facilitate the shift in life-style of the bacterium from an environmental opportunistic pathogen to a human airway specific pathogen. These findings are not only applicable to P. aeruginosa specific studies, but suggest that...

  5. Supra-optimal expression of the cold-regulated OsMyb4 transcription factor in transgenic rice changes the complexity of transcriptional network with major effects on stress tolerance and panicle development

    KAUST Repository

    Park, Myoungryoul

    2010-09-28

    The R2R3-type OsMyb4 transcription factor of rice has been shown to play a role in the regulation of osmotic adjustment in heterologous overexpression studies. However, the exact composition and organization of its underlying transcriptional network has not been established to be a robust tool for stress tolerance enhancement by regulon engineering. OsMyb4 network was dissected based on commonalities between the global chilling stress transcriptome and the transcriptome configured by OsMyb4 overexpression. OsMyb4 controls a hierarchical network comprised of several regulatory sub-clusters associated with cellular defense and rescue, metabolism and development. It regulates target genes either directly or indirectly through intermediary MYB, ERF, bZIP, NAC, ARF and CCAAT-HAP transcription factors. Regulatory sub-clusters have different combinations of MYB-like, GCC-box-like, ERD1-box-like, ABRE-like, G-box-like, as1/ocs/TGA-like, AuxRE-like, gibberellic acid response element (GARE)-like and JAre-like cis-elements. Cold-dependent network activity enhanced cellular antioxidant capacity through radical scavenging mechanisms and increased activities of phenylpropanoid and isoprenoid metabolic processes involving various abscisic acid (ABA), jasmonic acid (JA), salicylic acid (SA), ethylene and reactive oxygen species (ROS) responsive genes. OsMyb4 network is independent of drought response element binding protein/C-repeat binding factor (DREB/CBF) and its sub-regulons operate with possible co-regulators including nuclear factor-Y. Because of its upstream position in the network hierarchy, OsMyb4 functions quantitatively and pleiotrophically. Supra-optimal expression causes misexpression of alternative targets with costly trade-offs to panicle development. © 2010 Blackwell Publishing Ltd.

  6. Supra-optimal expression of the cold-regulated OsMyb4 transcription factor in transgenic rice changes the complexity of transcriptional network with major effects on stress tolerance and panicle development.

    Science.gov (United States)

    Park, Myoung-Ryoul; Yun, Kil-Young; Mohanty, Bijayalaxmi; Herath, Venura; Xu, Fuyu; Wijaya, Edward; Bajic, Vladimir B; Yun, Song-Joong; De Los Reyes, Benildo G

    2010-12-01

    The R2R3-type OsMyb4 transcription factor of rice has been shown to play a role in the regulation of osmotic adjustment in heterologous overexpression studies. However, the exact composition and organization of its underlying transcriptional network has not been established to be a robust tool for stress tolerance enhancement by regulon engineering. OsMyb4 network was dissected based on commonalities between the global chilling stress transcriptome and the transcriptome configured by OsMyb4 overexpression. OsMyb4 controls a hierarchical network comprised of several regulatory sub-clusters associated with cellular defense and rescue, metabolism and development. It regulates target genes either directly or indirectly through intermediary MYB, ERF, bZIP, NAC, ARF and CCAAT-HAP transcription factors. Regulatory sub-clusters have different combinations of MYB-like, GCC-box-like, ERD1-box-like, ABRE-like, G-box-like, as1/ocs/TGA-like, AuxRE-like, gibberellic acid response element (GARE)-like and JAre-like cis-elements. Cold-dependent network activity enhanced cellular antioxidant capacity through radical scavenging mechanisms and increased activities of phenylpropanoid and isoprenoid metabolic processes involving various abscisic acid (ABA), jasmonic acid (JA), salicylic acid (SA), ethylene and reactive oxygen species (ROS) responsive genes. OsMyb4 network is independent of drought response element binding protein/C-repeat binding factor (DREB/CBF) and its sub-regulons operate with possible co-regulators including nuclear factor-Y. Because of its upstream position in the network hierarchy, OsMyb4 functions quantitatively and pleiotrophically. Supra-optimal expression causes misexpression of alternative targets with costly trade-offs to panicle development. © 2010 Blackwell Publishing Ltd.

  7. A Gata2-Dependent Transcription Network Regulates Uterine Progesterone Responsiveness and Endometrial Function

    Directory of Open Access Journals (Sweden)

    Cory A. Rubel

    2016-10-01

    Full Text Available Altered progesterone responsiveness leads to female infertility and cancer, but underlying mechanisms remain unclear. Mice with uterine-specific ablation of GATA binding protein 2 (Gata2 are infertile, showing failures in embryo implantation, endometrial decidualization, and uninhibited estrogen signaling. Gata2 deficiency results in reduced progesterone receptor (PGR expression and attenuated progesterone signaling, as evidenced by genome-wide expression profiling and chromatin immunoprecipitation. GATA2 not only occupies at and promotes expression of the Pgr gene but also regulates downstream progesterone responsive genes in conjunction with the PGR. Additionally, Gata2 knockout uteri exhibit abnormal luminal epithelia with ectopic TRP63 expressing squamous cells and a cancer-related molecular profile in a progesterone-independent manner. Lastly, we found a conserved GATA2-PGR regulatory network in both human and mice based on gene signature and path analyses using gene expression profiles of human endometrial tissues. In conclusion, uterine Gata2 regulates a key regulatory network of gene expression for progesterone signaling at the early pregnancy stage.

  8. Untangling the transcription regulatory network of the bacitracin synthase operon in Bacillus licheniformis DW2.

    Science.gov (United States)

    Wang, Dong; Wang, Qin; Qiu, Yimin; Nomura, Christopher T; Li, Junhui; Chen, Shouwen

    The bacitracin synthetase gene cluster in Bacillus licheniformis DW2 is composed of the bacABC operon encoding a non-ribosomal peptide synthetase and bacT encoding a thioesterase. Although the bacitracin gene cluster has been well studied, little is known about how this gene cluster is regulated. This study provides insight into how the transcription factors Spo0A and AbrB regulate bacitracin biosynthesis. Deletion of spo0A resulted in drastically reduced expression of bacA and bacT, and subsequently bacitracin production. On the other hand, the expression of bacA and bacT increased significantly in B. licheniformis DW2ΔabrB and DW2Δ0AΔabrB compared to the wild-type strain DW2. The bacitracin yields on cell numbers (U/CFU) in DW2ΔabrB and DW2Δ0A/pHY300-0A-sad67 were 17.5% and 14.9% higher than that of the wild-type strain. An electrophoretic mobility shift assay (EMSA) further confirmed that AbrB could directly bind to the promoter regions of bacA and bacT. These results indicate that AbrB acts as a repressor of bacitracin biosynthesis by inhibiting bacA and bacT expression, while Spo0A indirectly promotes bacitracin biosynthesis by repressing abrB expression. Copyright © 2017 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

  9. Positioning the expanded akirin gene family of Atlantic salmon within the transcriptional networks of myogenesis

    International Nuclear Information System (INIS)

    Macqueen, Daniel J.; Bower, Neil I.; Johnston, Ian A.

    2010-01-01

    Research highlights: → The expanded akirin gene family of Atlantic salmon was characterised. → akirin paralogues are regulated between mono- and multi-nucleated muscle cells. → akirin paralogues positioned within known genetic networks controlling myogenesis. → Co-expression of akirin paralogues is evident across cell types/during myogenesis. → Selection has likely maintained common regulatory elements among akirin paralogues. -- Abstract: Vertebrate akirin genes usually form a family with one-to-three members that regulate gene expression during the innate immune response, carcinogenesis and myogenesis. We recently established that an expanded family of eight akirin genes is conserved across salmonid fish. Here, we measured mRNA levels of the akirin family of Atlantic salmon (Salmo salar L.) during the differentiation of primary myoblasts cultured from fast-skeletal muscle. Using hierarchical clustering and correlation, the data was positioned into a network of expression profiles including twenty further genes that regulate myogenesis. akirin1(2b) was not significantly regulated during the maturation of the cell culture. akirin2(1a) and 2(1b), along with IGF-II and several igfbps, were most highly expressed in mononuclear cells, then significantly and constitutively downregulated as differentiation proceeded and myotubes formed/matured. Conversely, akirin1(1a), 1(1b), 1(2a), 2(2a) and 2(2b) were expressed at lowest levels when mononuclear cells dominated the culture and highest levels when confluent layers of myotubes were evident. However, akirin1(2a) and 2(2a) were first upregulated earlier than akirin1(1a), 1(1b) and 2(2b), when rates of myoblast proliferation were highest. Interestingly, akirin1(1b), 1(2a), 2(2a) and 2(2b) formed part of a module of co-expressed genes involved in muscle differentiation, including myod1a, myog, mef2a, 14-3-3β and 14-3-3γ. All akirin paralogues were expressed ubiquitously across ten tissues, although mRNA levels

  10. Positioning the expanded akirin gene family of Atlantic salmon within the transcriptional networks of myogenesis

    Energy Technology Data Exchange (ETDEWEB)

    Macqueen, Daniel J., E-mail: djm59@st-andrews.ac.uk [Laboratory of Physiological and Evolutionary Genomics, Scottish Oceans Institute, University of St Andrews, St Andrews, Fife KY16 8LB (United Kingdom); Bower, Neil I., E-mail: nib@st-andrews.ac.uk [Laboratory of Physiological and Evolutionary Genomics, Scottish Oceans Institute, University of St Andrews, St Andrews, Fife KY16 8LB (United Kingdom); Johnston, Ian A., E-mail: iaj@st-andrews.ac.uk [Laboratory of Physiological and Evolutionary Genomics, Scottish Oceans Institute, University of St Andrews, St Andrews, Fife KY16 8LB (United Kingdom)

    2010-10-01

    Research highlights: {yields} The expanded akirin gene family of Atlantic salmon was characterised. {yields} akirin paralogues are regulated between mono- and multi-nucleated muscle cells. {yields} akirin paralogues positioned within known genetic networks controlling myogenesis. {yields} Co-expression of akirin paralogues is evident across cell types/during myogenesis. {yields} Selection has likely maintained common regulatory elements among akirin paralogues. -- Abstract: Vertebrate akirin genes usually form a family with one-to-three members that regulate gene expression during the innate immune response, carcinogenesis and myogenesis. We recently established that an expanded family of eight akirin genes is conserved across salmonid fish. Here, we measured mRNA levels of the akirin family of Atlantic salmon (Salmo salar L.) during the differentiation of primary myoblasts cultured from fast-skeletal muscle. Using hierarchical clustering and correlation, the data was positioned into a network of expression profiles including twenty further genes that regulate myogenesis. akirin1(2b) was not significantly regulated during the maturation of the cell culture. akirin2(1a) and 2(1b), along with IGF-II and several igfbps, were most highly expressed in mononuclear cells, then significantly and constitutively downregulated as differentiation proceeded and myotubes formed/matured. Conversely, akirin1(1a), 1(1b), 1(2a), 2(2a) and 2(2b) were expressed at lowest levels when mononuclear cells dominated the culture and highest levels when confluent layers of myotubes were evident. However, akirin1(2a) and 2(2a) were first upregulated earlier than akirin1(1a), 1(1b) and 2(2b), when rates of myoblast proliferation were highest. Interestingly, akirin1(1b), 1(2a), 2(2a) and 2(2b) formed part of a module of co-expressed genes involved in muscle differentiation, including myod1a, myog, mef2a, 14-3-3{beta} and 14-3-3{gamma}. All akirin paralogues were expressed ubiquitously across ten

  11. FoxP2 isoforms delineate spatiotemporal transcriptional networks for vocal learning in the zebra finch

    Science.gov (United States)

    Day, Nancy F; Kimball, Todd Haswell; Aamodt, Caitlin M; Heston, Jonathan B; Hilliard, Austin T; Xiao, Xinshu; White, Stephanie A

    2018-01-01

    Human speech is one of the few examples of vocal learning among mammals yet ~half of avian species exhibit this ability. Its neurogenetic basis is largely unknown beyond a shared requirement for FoxP2 in both humans and zebra finches. We manipulated FoxP2 isoforms in Area X, a song-specific region of the avian striatopallidum analogous to human anterior striatum, during a critical period for song development. We delineate, for the first time, unique contributions of each isoform to vocal learning. Weighted gene coexpression network analysis of RNA-seq data revealed gene modules correlated to singing, learning, or vocal variability. Coexpression related to singing was found in juvenile and adult Area X whereas coexpression correlated to learning was unique to juveniles. The confluence of learning and singing coexpression in juvenile Area X may underscore molecular processes that drive vocal learning in young zebra finches and, by analogy, humans. PMID:29360038

  12. Large-scale analysis of Arabidopsis transcription reveals a basal co-regulation network

    Directory of Open Access Journals (Sweden)

    Chamovitz Daniel A

    2009-09-01

    Full Text Available Abstract Background Analyses of gene expression data from microarray experiments has become a central tool for identifying co-regulated, functional gene modules. A crucial aspect of such analysis is the integration of data from different experiments and different laboratories. How to weigh the contribution of different experiments is an important point influencing the final outcomes. We have developed a novel method for this integration, and applied it to genome-wide data from multiple Arabidopsis microarray experiments performed under a variety of experimental conditions. The goal of this study is to identify functional globally co-regulated gene modules in the Arabidopsis genome. Results Following the analysis of 21,000 Arabidopsis genes in 43 datasets and about 2 × 108 gene pairs, we identified a globally co-expressed gene network. We found clusters of globally co-expressed Arabidopsis genes that are enriched for known Gene Ontology annotations. Two types of modules were identified in the regulatory network that differed in their sensitivity to the node-scoring parameter; we further showed these two pertain to general and specialized modules. Some of these modules were further investigated using the Genevestigator compendium of microarray experiments. Analyses of smaller subsets of data lead to the identification of condition-specific modules. Conclusion Our method for identification of gene clusters allows the integration of diverse microarray experiments from many sources. The analysis reveals that part of the Arabidopsis transcriptome is globally co-expressed, and can be further divided into known as well as novel functional gene modules. Our methodology is general enough to apply to any set of microarray experiments, using any scoring function.

  13. Identification of a nutrient-sensing transcriptional network in monocytes by using inbred rat models on a cafeteria diet

    Directory of Open Access Journals (Sweden)

    Neus Martínez-Micaelo

    2016-10-01

    Full Text Available Obesity has reached pandemic levels worldwide. The current models of diet-induced obesity in rodents use predominantly high-fat based diets that do not take into account the consumption of variety of highly palatable, energy-dense foods that are prevalent in Western society. We and others have shown that the cafeteria (CAF diet is a robust and reproducible model of human metabolic syndrome with tissue inflammation in the rat. We have previously shown that inbred rat strains such as Wistar Kyoto (WKY and Lewis (LEW show different susceptibilities to CAF diets with distinct metabolic and morphometric profiles. Here, we show a difference in plasma MCP-1 levels and investigate the effect of the CAF diet on peripheral blood monocyte transcriptome, as powerful stress-sensing immune cells, in WKY and LEW rats. We found that 75.5% of the differentially expressed transcripts under the CAF diet were upregulated in WKY rats and were functionally related to the activation of the immune response. Using a gene co-expression network constructed from the genes differentially expressed between CAF diet-fed LEW and WKY rats, we identified acyl-CoA synthetase short-chain family member 2 (Acss2 as a hub gene for a nutrient-sensing cluster of transcripts in monocytes. The Acss2 genomic region is significantly enriched for previously established metabolism quantitative trait loci in the rat. Notably, monocyte expression levels of Acss2 significantly correlated with plasma glucose, triglyceride, leptin and non-esterified fatty acid (NEFA levels as well as morphometric measurements such as body weight and the total fat following feeding with the CAF diet in the rat. These results show the importance of the genetic background in nutritional genomics and identify inbred rat strains as potential models for CAF-diet-induced obesity.

  14. Characterization of transcription factor networks involved in umbilical cord blood CD34+ stem cells-derived erythropoiesis.

    Directory of Open Access Journals (Sweden)

    Biaoru Li

    Full Text Available Fetal stem cells isolated from umbilical cord blood (UCB possess a great capacity for proliferation and differentiation and serve as a valuable model system to study gene regulation. Expanded knowledge of the molecular control of hemoglobin synthesis will provide a basis for rational design of therapies for β-hemoglobinopathies. Transcriptome data are available for erythroid progenitors derived from adult stem cells, however studies to define molecular mechanisms controlling globin gene regulation during fetal erythropoiesis are limited. Here, we utilize UCB-CD34+ stem cells induced to undergo erythroid differentiation to characterize the transcriptome and transcription factor networks (TFNs associated with the γ/β-globin switch during fetal erythropoiesis. UCB-CD34+ stem cells grown in the one-phase liquid culture system displayed a higher proliferative capacity than adult CD34+ stem cells. The γ/β-globin switch was observed after day 42 during fetal erythropoiesis in contrast to adult progenitors where the switch occurred around day 21. To gain insights into transcription factors involved in globin gene regulation, microarray analysis was performed on RNA isolated from UCB-CD34+ cell-derived erythroid progenitors harvested on day 21, 42, 49 and 56 using the HumanHT-12 Expression BeadChip. After data normalization, Gene Set Enrichment Analysis identified transcription factors (TFs with significant changes in expression during the γ/β-globin switch. Forty-five TFs were silenced by day 56 (Profile-1 and 30 TFs were activated by day 56 (Profile-2. Both GSEA datasets were analyzed using the MIMI Cytoscape platform, which discovered TFNs centered on KLF4 and GATA2 (Profile-1 and KLF1 and GATA1 for Profile-2 genes. Subsequent shRNA studies in KU812 leukemia cells and human erythroid progenitors generated from UCB-CD34+ cells supported a negative role of MAFB in γ-globin regulation. The characteristics of erythroblasts derived from UCB-CD34

  15. AP2/EREBP transcription factors are part of gene regulatory networks and integrate metabolic, hormonal and environmental signals in stress acclimation and retrograde signalling.

    Science.gov (United States)

    Dietz, Karl-Josef; Vogel, Marc Oliver; Viehhauser, Andrea

    2010-09-01

    To optimize acclimation responses to environmental growth conditions, plants integrate and weigh a diversity of input signals. Signal integration within the signalling networks occurs at different sites including the level of transcription factor activation. Accumulating evidence assigns a major and diversified role in environmental signal integration to the family of APETALA 2/ethylene response element binding protein (AP2/EREBP) transcription factors. Presently, the Plant Transcription Factor Database 3.0 assigns 147 gene loci to this family in Arabidopsis thaliana, 200 in Populus trichocarpa and 163 in Oryza sativa subsp. japonica as compared to 13 to 14 in unicellular algae ( http://plntfdb.bio.uni-potsdam.de/v3.0/ ). AP2/EREBP transcription factors have been implicated in hormone, sugar and redox signalling in context of abiotic stresses such as cold and drought. This review exemplarily addresses present-day knowledge of selected AP2/EREBP with focus on a function in stress signal integration and retrograde signalling and defines AP2/EREBP-linked gene networks from transcriptional profiling-based graphical Gaussian models. The latter approach suggests highly interlinked functions of AP2/EREBPs in retrograde and stress signalling.

  16. The B-MYB transcriptional network guides cell cycle progression and fate decisions to sustain self-renewal and the identity of pluripotent stem cells.

    Science.gov (United States)

    Zhan, Ming; Riordon, Daniel R; Yan, Bin; Tarasova, Yelena S; Bruweleit, Sarah; Tarasov, Kirill V; Li, Ronald A; Wersto, Robert P; Boheler, Kenneth R

    2012-01-01

    Embryonic stem cells (ESCs) are pluripotent and have unlimited self-renewal capacity. Although pluripotency and differentiation have been examined extensively, the mechanisms responsible for self-renewal are poorly understood and are believed to involve an unusual cell cycle, epigenetic regulators and pluripotency-promoting transcription factors. Here we show that B-MYB, a cell cycle regulated phosphoprotein and transcription factor critical to the formation of inner cell mass, is central to the transcriptional and co-regulatory networks that sustain normal cell cycle progression and self-renewal properties of ESCs. Phenotypically, B-MYB is robustly expressed in ESCs and induced pluripotent stem cells (iPSCs), and it is present predominantly in a hypo-phosphorylated state. Knockdown of B-MYB results in functional cell cycle abnormalities that involve S, G2 and M phases, and reduced expression of critical cell cycle regulators like ccnb1 and plk1. By conducting gene expression profiling on control and B-MYB deficient cells, ChIP-chip experiments, and integrative computational analyses, we unraveled a highly complex B-MYB-mediated transcriptional network that guides ESC self-renewal. The network encompasses critical regulators of all cell cycle phases and epigenetic regulators, pluripotency transcription factors, and differentiation determinants. B-MYB along with E2F1 and c-MYC preferentially co-regulate cell cycle target genes. B-MYB also co-targets genes regulated by OCT4, SOX2 and NANOG that are significantly associated with stem cell differentiation, embryonic development, and epigenetic control. Moreover, loss of B-MYB leads to a breakdown of the transcriptional hierarchy present in ESCs. These results coupled with functional studies demonstrate that B-MYB not only controls and accelerates cell cycle progression in ESCs it contributes to fate decisions and maintenance of pluripotent stem cell identity.

  17. Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field

    Directory of Open Access Journals (Sweden)

    Xiu Jin

    2018-03-01

    Full Text Available Classification of healthy and diseased wheat heads in a rapid and non-destructive manner for the early diagnosis of Fusarium head blight disease research is difficult. Our work applies a deep neural network classification algorithm to the pixels of hyperspectral image to accurately discern the disease area. The spectra of hyperspectral image pixels in a manually selected region of interest are preprocessed via mean removal to eliminate interference, due to the time interval and the environment. The generalization of the classification model is considered, and two improvements are made to the model framework. First, the pixel spectra data are reshaped into a two-dimensional data structure for the input layer of a Convolutional Neural Network (CNN. After training two types of CNNs, the assessment shows that a two-dimensional CNN model is more efficient than a one-dimensional CNN. Second, a hybrid neural network with a convolutional layer and bidirectional recurrent layer is reconstructed to improve the generalization of the model. When considering the characteristics of the dataset and models, the confusion matrices that are based on the testing dataset indicate that the classification model is effective for background and disease classification of hyperspectral image pixels. The results of the model show that the two-dimensional convolutional bidirectional gated recurrent unit neural network (2D-CNN-BidGRU has an F1 score and accuracy of 0.75 and 0.743, respectively, for the total testing dataset. A comparison of all the models shows that the hybrid neural network of 2D-CNN-BidGRU is the best at preventing over-fitting and optimize the generalization. Our results illustrate that the hybrid structure deep neural network is an excellent classification algorithm for healthy and Fusarium head blight diseased classification in the field of hyperspectral imagery.

  18. The transcriptional regulatory network of Corynebacterium jeikeium K411 and its interaction with metabolic routes contributing to human body odor formation.

    Science.gov (United States)

    Barzantny, Helena; Schröder, Jasmin; Strotmeier, Jasmin; Fredrich, Eugenie; Brune, Iris; Tauch, Andreas

    2012-06-15

    Lipophilic corynebacteria are involved in the generation of volatile odorous products in the process of human body odor formation by degrading skin lipids and specific odor precursors. Therefore, these bacteria represent appropriate model systems for the cosmetic industry to examine axillary malodor formation on the molecular level. To understand the transcriptional control of metabolic pathways involved in this process, the transcriptional regulatory network of the lipophilic axilla isolate Corynebacterium jeikeium K411 was reconstructed from the complete genome sequence. This bioinformatic approach detected a gene-regulatory repertoire of 83 candidate proteins, including 56 DNA-binding transcriptional regulators, nine two-component systems, nine sigma factors, and nine regulators with diverse physiological functions. Furthermore, a cross-genome comparison among selected corynebacterial species of the taxonomic cluster 3 revealed a common gene-regulatory repertoire of 44 transcriptional regulators, including the MarR-like regulator Jk0257, which is exclusively encoded in the genomes of this taxonomical subline. The current network reconstruction comprises 48 transcriptional regulators and 674 gene-regulatory interactions that were assigned to five interconnected functional modules. Most genes involved in lipid degradation are under the combined control of the global cAMP-sensing transcriptional regulator GlxR and the LuxR-family regulator RamA, probably reflecting the essential role of lipid degradation in C. jeikeium. This study provides the first genome-scale in silico analysis of the transcriptional regulation of metabolism in a lipophilic bacterium involved in the formation of human body odor. Copyright © 2012 Elsevier B.V. All rights reserved.

  19. Promotion of cooperation in the form C0C1D classified by 'degree grads' in a scale-free network

    International Nuclear Information System (INIS)

    Zhao, Li; Ye, Xiang-Jun; Huang, Zi-Gang; Sun, Jin-Tu; Yang, Lei; Wang, Ying-Hai; Do, Younghae

    2010-01-01

    In this paper, we revisit the issue of the public goods game (PGG) on a heterogeneous graph. By introducing a new effective topology parameter, 'degree grads' ψ, we clearly classify the agents into three kinds, namely, C 0 , C 1 , and D. The mechanism for the heterogeneous topology promoting cooperation is discussed in detail from the perspective of C 0 C 1 D, which reflects the fact that the unreasoning imitation behaviour of C 1 agents, who are 'cheated' by the well-paid C 0 agents inhabiting special positions, stabilizes the formation of the cooperation community. The analytical and simulation results for certain parameters are found to coincide well with each other. The C 0 C 1 D case provides a picture of the actual behaviours in real society and thus is potentially of interest

  20. Relation of Transcriptional Factors to the Expression and Activity of Cytochrome P450 and UDP-Glucuronosyltransferases 1A in Human Liver: Co-Expression Network Analysis.

    Science.gov (United States)

    Zhong, Shilong; Han, Weichao; Hou, Chuqi; Liu, Junjin; Wu, Lili; Liu, Menghua; Liang, Zhi; Lin, Haoming; Zhou, Lili; Liu, Shuwen; Tang, Lan

    2017-01-01

    Cytochrome P450 (CYPs) and UDP-glucuronosyltransferases (UGTs) play important roles in the metabolism of exogenous and endogenous compounds. The gene transcription of CYPs and UGTs can be enhanced or reduced by transcription factors (TFs). This study aims to explore novel TFs involved in the regulatory network of human hepatic UGTs/CYPs. Correlations between the transcription levels of 683 key TFs and CYPs/UGTs in three different human liver expression profiles (n = 640) were calculated first. Supervised weighted correlation network analysis (sWGCNA) was employed to define hub genes among the selected TFs. The relationship among 17 defined TFs, CYPs/UGTs expression, and activity were evaluated in 30 liver samples from Chinese patients. The positive controls (e.g., PPARA, NR1I2, NR1I3) and hub TFs (NFIA, NR3C2, and AR) in the Grey sWGCNA Module were significantly and positively associated with CYPs/UGTs expression. And the cancer- or inflammation-related TFs (TEAD4, NFKB2, and NFKB1) were negatively associated with mRNA expression of CYP2C9/CYP2E1/UGT1A9. Furthermore, the effect of NR1I2, NR1I3, AR, TEAD4, and NFKB2 on CYP450/UGT1A gene transcription translated into moderate influences on enzyme activities. To our knowledge, this is the first study to integrate Gene Expression Omnibus (GEO) datasets and supervised weighted correlation network analysis (sWGCNA) for defining TFs potentially related to CYPs/UGTs. We detected several novel TFs involved in the regulatory network of hepatic CYPs and UGTs in humans. Further validation and investigation may reveal their exact mechanism of CYPs/UGTs regulation.

  1. Dynamic Response Genes in CD4+ T Cells Reveal a Network of Interactive Proteins that Classifies Disease Activity in Multiple Sclerosis

    Directory of Open Access Journals (Sweden)

    Sandra Hellberg

    2016-09-01

    Full Text Available Multiple sclerosis (MS is a chronic inflammatory disease of the CNS and has a varying disease course as well as variable response to treatment. Biomarkers may therefore aid personalized treatment. We tested whether in vitro activation of MS patient-derived CD4+ T cells could reveal potential biomarkers. The dynamic gene expression response to activation was dysregulated in patient-derived CD4+ T cells. By integrating our findings with genome-wide association studies, we constructed a highly connected MS gene module, disclosing cell activation and chemotaxis as central components. Changes in several module genes were associated with differences in protein levels, which were measurable in cerebrospinal fluid and were used to classify patients from control individuals. In addition, these measurements could predict disease activity after 2 years and distinguish low and high responders to treatment in two additional, independent cohorts. While further validation is needed in larger cohorts prior to clinical implementation, we have uncovered a set of potentially promising biomarkers.

  2. Hybrid Neuro-Fuzzy Classifier Based On Nefclass Model

    Directory of Open Access Journals (Sweden)

    Bogdan Gliwa

    2011-01-01

    Full Text Available The paper presents hybrid neuro-fuzzy classifier, based on NEFCLASS model, which wasmodified. The presented classifier was compared to popular classifiers – neural networks andk-nearest neighbours. Efficiency of modifications in classifier was compared with methodsused in original model NEFCLASS (learning methods. Accuracy of classifier was testedusing 3 datasets from UCI Machine Learning Repository: iris, wine and breast cancer wisconsin.Moreover, influence of ensemble classification methods on classification accuracy waspresented.

  3. Classifying features in CT imagery: accuracy for some single- and multiple-species classifiers

    Science.gov (United States)

    Daniel L. Schmoldt; Jing He; A. Lynn Abbott

    1998-01-01

    Our current approach to automatically label features in CT images of hardwood logs classifies each pixel of an image individually. These feature classifiers use a back-propagation artificial neural network (ANN) and feature vectors that include a small, local neighborhood of pixels and the distance of the target pixel to the center of the log. Initially, this type of...

  4. Functional and gene network analyses of transcriptional signatures characterizing pre-weaned bovine mammary parenchyma or fat pad uncovered novel inter-tissue signaling networks during development

    Directory of Open Access Journals (Sweden)

    Lewin Harris A

    2010-05-01

    Full Text Available Abstract Background The neonatal bovine mammary fat pad (MFP surrounding the mammary parenchyma (PAR is thought to exert proliferative effects on the PAR through secretion of local modulators of growth induced by systemic hormones. We used bioinformatics to characterize transcriptomics differences between PAR and MFP from ~65 d old Holstein heifers. Data were mined to uncover potential crosstalk through the analyses of signaling molecules preferentially expressed in one tissue relative to the other. Results Over 9,000 differentially expressed genes (DEG; False discovery rate ≤ 0.05 were found of which 1,478 had a ≥1.5-fold difference between PAR and MFP. Within the DEG highly-expressed in PAR vs. MFP (n = 736 we noted significant enrichment of functions related to cell cycle, structural organization, signaling, and DNA/RNA metabolism. Only actin cytoskeletal signaling was significant among canonical pathways. DEG more highly-expressed in MFP vs. PAR (n = 742 belong to lipid metabolism, signaling, cell movement, and immune-related functions. Canonical pathways associated with metabolism and signaling, particularly immune- and metabolism-related were significantly-enriched. Network analysis uncovered a central role of MYC, TP53, and CTNNB1 in controlling expression of DEG highly-expressed in PAR vs. MFP. Similar analysis suggested a central role for PPARG, KLF2, EGR2, and EPAS1 in regulating expression of more highly-expressed DEG in MFP vs. PAR. Gene network analyses revealed putative inter-tissue crosstalk between cytokines and growth factors preferentially expressed in one tissue (e.g., ANGPTL1, SPP1, IL1B in PAR vs. MFP; ADIPOQ, IL13, FGF2, LEP in MFP vs. PAR with DEG preferentially expressed in the other tissue, particularly transcription factors or pathways (e.g., MYC, TP53, and actin cytoskeletal signaling in PAR vs. MFP; PPARG and LXR/RXR Signaling in MFP vs. PAR. Conclusions Functional analyses underscored a reciprocal influence in

  5. Classifying Returns as Extreme

    DEFF Research Database (Denmark)

    Christiansen, Charlotte

    2014-01-01

    I consider extreme returns for the stock and bond markets of 14 EU countries using two classification schemes: One, the univariate classification scheme from the previous literature that classifies extreme returns for each market separately, and two, a novel multivariate classification scheme tha...

  6. Seed maturation associated transcriptional programs and regulatory networks underlying genotypic difference in seed dormancy and size/weight in wheat (Triticum aestivum L.).

    Science.gov (United States)

    Yamasaki, Yuji; Gao, Feng; Jordan, Mark C; Ayele, Belay T

    2017-09-16

    Maturation forms one of the critical seed developmental phases and it is characterized mainly by programmed cell death, dormancy and desiccation, however, the transcriptional programs and regulatory networks underlying acquisition of dormancy and deposition of storage reserves during the maturation phase of seed development are poorly understood in wheat. The present study performed comparative spatiotemporal transcriptomic analysis of seed maturation in two wheat genotypes with contrasting seed weight/size and dormancy phenotype. The embryo and endosperm tissues of maturing seeds appeared to exhibit genotype-specific temporal shifts in gene expression profile that might contribute to the seed phenotypic variations. Functional annotations of gene clusters suggest that the two tissues exhibit distinct but genotypically overlapping molecular functions. Motif enrichment predicts genotypically distinct abscisic acid (ABA) and gibberellin (GA) regulated transcriptional networks contribute to the contrasting seed weight/size and dormancy phenotypes between the two genotypes. While other ABA responsive element (ABRE) motifs are enriched in both genotypes, the prevalence of G-box-like motif specifically in tissues of the dormant genotype suggests distinct ABA mediated transcriptional mechanisms control the establishment of dormancy during seed maturation. In agreement with this, the bZIP transcription factors that co-express with ABRE enriched embryonic genes differ with genotype. The enrichment of SITEIIATCYTC motif specifically in embryo clusters of maturing seeds irrespective of genotype predicts a tissue specific role for the respective TCP transcription factors with no or minimal contribution to the variations in seed dormancy. The results of this study advance our understanding of the seed maturation associated molecular mechanisms underlying variation in dormancy and weight/size in wheat seeds, which is a critical step towards the designing of molecular strategies

  7. An Integrative Analysis of Preeclampsia Based on the Construction of an Extended Composite Network Featuring Protein-Protein Physical Interactions and Transcriptional Relationships.

    Directory of Open Access Journals (Sweden)

    Daniel Vaiman

    Full Text Available Preeclampsia (PE is a pregnancy disorder defined by hypertension and proteinuria. This disease remains a major cause of maternal and fetal morbidity and mortality. Defective placentation is generally described as being at the root of the disease. The characterization of the transcriptome signature of the preeclamptic placenta has allowed to identify differentially expressed genes (DEGs. However, we still lack a detailed knowledge on how these DEGs impact the function of the placenta. The tools of network biology offer a methodology to explore complex diseases at a systems level. In this study we performed a cross-platform meta-analysis of seven publically available gene expression datasets comparing non-pathological and preeclamptic placentas. Using the rank product algorithm we identified a total of 369 DEGs consistently modified in PE. The DEGs were used as seeds to build both an extended physical protein-protein interactions network and a transcription factors regulatory network. Topological and clustering analysis was conducted to analyze the connectivity properties of the networks. Finally both networks were merged into a composite network which presents an integrated view of the regulatory pathways involved in preeclampsia and the crosstalk between them. This network is a useful tool to explore the relationship between the DEGs and enable hypothesis generation for functional experimentation.

  8. LCC: Light Curves Classifier

    Science.gov (United States)

    Vo, Martin

    2017-08-01

    Light Curves Classifier uses data mining and machine learning to obtain and classify desired objects. This task can be accomplished by attributes of light curves or any time series, including shapes, histograms, or variograms, or by other available information about the inspected objects, such as color indices, temperatures, and abundances. After specifying features which describe the objects to be searched, the software trains on a given training sample, and can then be used for unsupervised clustering for visualizing the natural separation of the sample. The package can be also used for automatic tuning parameters of used methods (for example, number of hidden neurons or binning ratio). Trained classifiers can be used for filtering outputs from astronomical databases or data stored locally. The Light Curve Classifier can also be used for simple downloading of light curves and all available information of queried stars. It natively can connect to OgleII, OgleIII, ASAS, CoRoT, Kepler, Catalina and MACHO, and new connectors or descriptors can be implemented. In addition to direct usage of the package and command line UI, the program can be used through a web interface. Users can create jobs for ”training” methods on given objects, querying databases and filtering outputs by trained filters. Preimplemented descriptors, classifier and connectors can be picked by simple clicks and their parameters can be tuned by giving ranges of these values. All combinations are then calculated and the best one is used for creating the filter. Natural separation of the data can be visualized by unsupervised clustering.

  9. ChIPBase v2.0: decoding transcriptional regulatory networks of non-coding RNAs and protein-coding genes from ChIP-seq data.

    Science.gov (United States)

    Zhou, Ke-Ren; Liu, Shun; Sun, Wen-Ju; Zheng, Ling-Ling; Zhou, Hui; Yang, Jian-Hua; Qu, Liang-Hu

    2017-01-04

    The abnormal transcriptional regulation of non-coding RNAs (ncRNAs) and protein-coding genes (PCGs) is contributed to various biological processes and linked with human diseases, but the underlying mechanisms remain elusive. In this study, we developed ChIPBase v2.0 (http://rna.sysu.edu.cn/chipbase/) to explore the transcriptional regulatory networks of ncRNAs and PCGs. ChIPBase v2.0 has been expanded with ∼10 200 curated ChIP-seq datasets, which represent about 20 times expansion when comparing to the previous released version. We identified thousands of binding motif matrices and their binding sites from ChIP-seq data of DNA-binding proteins and predicted millions of transcriptional regulatory relationships between transcription factors (TFs) and genes. We constructed 'Regulator' module to predict hundreds of TFs and histone modifications that were involved in or affected transcription of ncRNAs and PCGs. Moreover, we built a web-based tool, Co-Expression, to explore the co-expression patterns between DNA-binding proteins and various types of genes by integrating the gene expression profiles of ∼10 000 tumor samples and ∼9100 normal tissues and cell lines. ChIPBase also provides a ChIP-Function tool and a genome browser to predict functions of diverse genes and visualize various ChIP-seq data. This study will greatly expand our understanding of the transcriptional regulations of ncRNAs and PCGs. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  10. Use of Bayesian networks classifiers for long-term mean wind turbine energy output estimation at a potential wind energy conversion site

    Energy Technology Data Exchange (ETDEWEB)

    Carta, Jose A. [Department of Mechanical Engineering, University of Las Palmas de Gran Canaria, Campus de Tafira s/n, 35017 Las Palmas de Gran Canaria, Canary Islands (Spain); Velazquez, Sergio [Department of Electronics and Automatics Engineering, University of Las Palmas de Gran Canaria, Campus de Tafira s/n, 35017 Las Palmas de Gran Canaria, Canary Islands (Spain); Matias, J.M. [Department of Statistics, University of Vigo, Lagoas Marcosende, 36200 Vigo (Spain)

    2011-02-15

    Due to the interannual variability of wind speed a feasibility analysis for the installation of a Wind Energy Conversion System at a particular site requires estimation of the long-term mean wind turbine energy output. A method is proposed in this paper which, based on probabilistic Bayesian networks (BNs), enables estimation of the long-term mean wind speed histogram for a site where few measurements of the wind resource are available. For this purpose, the proposed method allows the use of multiple reference stations with a long history of wind speed and wind direction measurements. That is to say, the model that is proposed in this paper is able to involve and make use of regional information about the wind resource. With the estimated long-term wind speed histogram and the power curve of a wind turbine it is possible to use the method of bins to determine the long-term mean energy output for that wind turbine. The intelligent system employed, the knowledgebase of which is a joint probability function of all the model variables, uses efficient calculation techniques for conditional probabilities to perform the reasoning. This enables automatic model learning and inference to be performed efficiently based on the available evidence. The proposed model is applied in this paper to wind speeds and wind directions recorded at four weather stations located in the Canary Islands (Spain). Ten years of mean hourly wind speed and direction data are available for these stations. One of the conclusions reached is that the BN with three reference stations gave fewer errors between the real and estimated long-term mean wind turbine energy output than when using two measure-correlate-predict algorithms which were evaluated and which use a linear regression between the candidate station and one reference station. (author)

  11. Use of Bayesian networks classifiers for long-term mean wind turbine energy output estimation at a potential wind energy conversion site

    International Nuclear Information System (INIS)

    Carta, Jose A.; Velazquez, Sergio; Matias, J.M.

    2011-01-01

    Due to the interannual variability of wind speed a feasibility analysis for the installation of a Wind Energy Conversion System at a particular site requires estimation of the long-term mean wind turbine energy output. A method is proposed in this paper which, based on probabilistic Bayesian networks (BNs), enables estimation of the long-term mean wind speed histogram for a site where few measurements of the wind resource are available. For this purpose, the proposed method allows the use of multiple reference stations with a long history of wind speed and wind direction measurements. That is to say, the model that is proposed in this paper is able to involve and make use of regional information about the wind resource. With the estimated long-term wind speed histogram and the power curve of a wind turbine it is possible to use the method of bins to determine the long-term mean energy output for that wind turbine. The intelligent system employed, the knowledgebase of which is a joint probability function of all the model variables, uses efficient calculation techniques for conditional probabilities to perform the reasoning. This enables automatic model learning and inference to be performed efficiently based on the available evidence. The proposed model is applied in this paper to wind speeds and wind directions recorded at four weather stations located in the Canary Islands (Spain). Ten years of mean hourly wind speed and direction data are available for these stations. One of the conclusions reached is that the BN with three reference stations gave fewer errors between the real and estimated long-term mean wind turbine energy output than when using two measure-correlate-predict algorithms which were evaluated and which use a linear regression between the candidate station and one reference station.

  12. Combined genome-wide expression profiling and targeted RNA interference in primary mouse macrophages reveals perturbation of transcriptional networks associated with interferon signalling

    Directory of Open Access Journals (Sweden)

    Craigon Marie

    2009-08-01

    Full Text Available Abstract Background Interferons (IFNs are potent antiviral cytokines capable of reprogramming the macrophage phenotype through the induction of interferon-stimulated genes (ISGs. Here we have used targeted RNA interference to suppress the expression of a number of key genes associated with IFN signalling in murine macrophages prior to stimulation with interferon-gamma. Genome-wide changes in transcript abundance caused by siRNA activity were measured using exon-level microarrays in the presence or absence of IFNγ. Results Transfection of murine bone-marrow derived macrophages (BMDMs with a non-targeting (control siRNA and 11 sequence-specific siRNAs was performed using a cationic lipid transfection reagent (Lipofectamine2000 prior to stimulation with IFNγ. Total RNA was harvested from cells and gene expression measured on Affymetrix GeneChip Mouse Exon 1.0 ST Arrays. Network-based analysis of these data revealed six siRNAs to cause a marked shift in the macrophage transcriptome in the presence or absence IFNγ. These six siRNAs targeted the Ifnb1, Irf3, Irf5, Stat1, Stat2 and Nfkb2 transcripts. The perturbation of the transcriptome by the six siRNAs was highly similar in each case and affected the expression of over 600 downstream transcripts. Regulated transcripts were clustered based on co-expression into five major groups corresponding to transcriptional networks associated with the type I and II IFN response, cell cycle regulation, and NF-KB signalling. In addition we have observed a significant non-specific immune stimulation of cells transfected with siRNA using Lipofectamine2000, suggesting use of this reagent in BMDMs, even at low concentrations, is enough to induce a type I IFN response. Conclusion Our results provide evidence that the type I IFN response in murine BMDMs is dependent on Ifnb1, Irf3, Irf5, Stat1, Stat2 and Nfkb2, and that siRNAs targeted to these genes results in perturbation of key transcriptional networks associated

  13. A Transcriptional Regulatory Network Containing Nuclear Receptors and Long Noncoding RNAs Controls Basal and Drug-Induced Expression of Cytochrome P450s in HepaRG Cells.

    Science.gov (United States)

    Chen, Liming; Bao, Yifan; Piekos, Stephanie C; Zhu, Kexin; Zhang, Lirong; Zhong, Xiao-Bo

    2018-07-01

    Cytochrome P450 (P450) enzymes are responsible for metabolizing drugs. Expression of P450s can directly affect drug metabolism, resulting in various outcomes in therapeutic efficacy and adverse effects. Several nuclear receptors are transcription factors that can regulate expression of P450s at both basal and drug-induced levels. Some long noncoding RNAs (lncRNAs) near a transcription factor are found to participate in the regulatory functions of the transcription factors. The aim of this study is to determine whether there is a transcriptional regulatory network containing nuclear receptors and lncRNAs controlling both basal and drug-induced expression of P450s in HepaRG cells. Small interfering RNAs or small hairpin RNAs were applied to knock down four nuclear receptors [hepatocyte nuclear factor 1 α (HNF1 α ), hepatocyte nuclear factor 4 α (HNF4 α ), pregnane X receptor (PXR), and constitutive androstane receptor (CAR)] as well as two lncRNAs [HNF1 α antisense RNA 1 (HNF1 α -AS1) and HNF4 α antisense RNA 1 (HNF4 α -AS1)] in HepaRG cells with or without treatment of phenobarbital or rifampicin. Expression of eight P450 enzymes was examined in both basal and drug-induced levels. CAR and PXR mainly regulated expression of specific P450s. HNF1 α and HNF4 α affected expression of a wide range of P450s as well as other transcription factors. HNF1 α and HNF4 α controlled the expression of their neighborhood lncRNAs, HNF1 α -AS1 and HNF4 α -AS1, respectively. HNF1 α -AS1 and HNF4 α -AS1 was also involved in the regulation of P450s and transcription factors in diverse manners. Altogether, our study concludes that a transcription regulatory network containing the nuclear receptors and lncRNAs controls both basal and drug-induced expression of P450s in HepaRG cells. Copyright © 2018 by The American Society for Pharmacology and Experimental Therapeutics.

  14. Intelligent Garbage Classifier

    Directory of Open Access Journals (Sweden)

    Ignacio Rodríguez Novelle

    2008-12-01

    Full Text Available IGC (Intelligent Garbage Classifier is a system for visual classification and separation of solid waste products. Currently, an important part of the separation effort is based on manual work, from household separation to industrial waste management. Taking advantage of the technologies currently available, a system has been built that can analyze images from a camera and control a robot arm and conveyor belt to automatically separate different kinds of waste.

  15. Classifying Linear Canonical Relations

    OpenAIRE

    Lorand, Jonathan

    2015-01-01

    In this Master's thesis, we consider the problem of classifying, up to conjugation by linear symplectomorphisms, linear canonical relations (lagrangian correspondences) from a finite-dimensional symplectic vector space to itself. We give an elementary introduction to the theory of linear canonical relations and present partial results toward the classification problem. This exposition should be accessible to undergraduate students with a basic familiarity with linear algebra.

  16. The testosterone-dependent and independent transcriptional networks in the hypothalamus of Gpr54 and Kiss1 knockout male mice are not fully equivalent

    Directory of Open Access Journals (Sweden)

    Sutcliffe Margaret

    2011-04-01

    Full Text Available Abstract Background Humans and mice with loss of function mutations in GPR54 (KISS1R or kisspeptin do not progress through puberty, caused by a failure to release GnRH. The transcriptional networks regulated by these proteins in the hypothalamus have yet to be explored by genome-wide methods. Results We show here, using 1 million exon mouse arrays (Exon 1.0 Affymetrix and quantitative polymerase chain reaction (QPCR validation to analyse microdissected hypothalamic tissue from Gpr54 and Kiss1 knockout mice, the extent of transcriptional regulation in the hypothalamus. The sensitivity to detect important transcript differences in microdissected RNA was confirmed by the observation of counter-regulation of Kiss1 expression in Gpr54 knockouts and confirmed by immunohistochemistry (IHC. Since Gpr54 and Kiss1 knockout animals are effectively pre-pubertal with low testosterone (T levels, we also determined which of the validated transcripts were T-responsive and which varied according to genotype alone. We observed four types of transcriptional regulation (i genotype only dependent regulation, (ii T only dependent regulation, (iii genotype and T-dependent regulation with interaction between these variables, (iv genotype and T-dependent regulation with no interaction between these variables. The results implicate for the first time several transcription factors (e.g. Npas4, Esr2, proteases (Klk1b22, and the orphan 10-transmembrane transporter TMEM144 in the biology of GPR54/kisspeptin function in the hypothalamus. We show for the neuronal activity regulated transcription factor NPAS4, that distinct protein over-expression is seen in the hypothalamus and hippocampus in Gpr54 knockout mice. This links for the first time the hypothalamic-gonadal axis with this important regulator of inhibitory synapse formation. Similarly we confirm TMEM144 up-regulation in the hypothalamus by RNA in situ hybridization and western blot. Conclusions Taken together, global

  17. Application of a fuzzy neural network model in predicting polycyclic aromatic hydrocarbon-mediated perturbations of the Cyp1b1 transcriptional regulatory network in mouse skin

    Energy Technology Data Exchange (ETDEWEB)

    Larkin, Andrew [Department of Environmental and Molecular Toxicology, Oregon State University (United States); Department of Statistics, Oregon State University (United States); Superfund Research Center, Oregon State University (United States); Siddens, Lisbeth K. [Department of Environmental and Molecular Toxicology, Oregon State University (United States); Superfund Research Center, Oregon State University (United States); Krueger, Sharon K. [Superfund Research Center, Oregon State University (United States); Linus Pauling Institute, Oregon State University (United States); Tilton, Susan C.; Waters, Katrina M. [Superfund Research Center, Oregon State University (United States); Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory, Richland, WA 99352 (United States); Williams, David E., E-mail: david.williams@oregonstate.edu [Department of Environmental and Molecular Toxicology, Oregon State University (United States); Superfund Research Center, Oregon State University (United States); Linus Pauling Institute, Oregon State University (United States); Environmental Health Sciences Center, Oregon State University, Corvallis, OR 97331 (United States); Baird, William M. [Department of Environmental and Molecular Toxicology, Oregon State University (United States); Superfund Research Center, Oregon State University (United States); Environmental Health Sciences Center, Oregon State University, Corvallis, OR 97331 (United States)

    2013-03-01

    Polycyclic aromatic hydrocarbons (PAHs) are present in the environment as complex mixtures with components that have diverse carcinogenic potencies and mostly unknown interactive effects. Non-additive PAH interactions have been observed in regulation of cytochrome P450 (CYP) gene expression in the CYP1 family. To better understand and predict biological effects of complex mixtures, such as environmental PAHs, an 11 gene input-1 gene output fuzzy neural network (FNN) was developed for predicting PAH-mediated perturbations of dermal Cyp1b1 transcription in mice. Input values were generalized using fuzzy logic into low, medium, and high fuzzy subsets, and sorted using k-means clustering to create Mamdani logic functions for predicting Cyp1b1 mRNA expression. Model testing was performed with data from microarray analysis of skin samples from FVB/N mice treated with toluene (vehicle control), dibenzo[def,p]chrysene (DBC), benzo[a]pyrene (BaP), or 1 of 3 combinations of diesel particulate extract (DPE), coal tar extract (CTE) and cigarette smoke condensate (CSC) using leave-one-out cross-validation. Predictions were within 1 log{sub 2} fold change unit of microarray data, with the exception of the DBC treatment group, where the unexpected down-regulation of Cyp1b1 expression was predicted but did not reach statistical significance on the microarrays. Adding CTE to DPE was predicted to increase Cyp1b1 expression, whereas adding CSC to CTE and DPE was predicted to have no effect, in agreement with microarray results. The aryl hydrocarbon receptor repressor (Ahrr) was determined to be the most significant input variable for model predictions using back-propagation and normalization of FNN weights. - Highlights: ► Tested a model to predict PAH mixture-mediated changes in Cyp1b1 expression ► Quantitative predictions in agreement with microarrays for Cyp1b1 induction ► Unexpected difference in expression between DBC and other treatments predicted ► Model predictions

  18. Construction and analysis of the transcription factor-microRNA co-regulatory network response to Mycobacterium tuberculosis: a view from the blood.

    Science.gov (United States)

    Lin, Yan; Duan, Zipeng; Xu, Feng; Zhang, Jiayuan; Shulgina, Marina V; Li, Fan

    2017-01-01

    Mycobacterium tuberculosis ( Mtb ) infection has been regional outbreak, recently. The traditional focus on the patterns of "reductionism" which was associated with single molecular changes has been unable to meet the demand of early diagnosis and clinical application when current tuberculosis infection happened. In this study, we employed a systems biology approach to collect large microarray data sets including mRNAs and microRNAs (miRNAs) to identify the differentially expressed mRNAs and miRNAs in the whole blood of TB patients. The aim was to identify key genes associated with the immune response in the pathogenic process of tuberculosis by analyzing the co-regulatory network that was consisted of transcription factors and miRNAs as well as their target genes. The network along with their co-regulatory genes was analyzed utilizing Transcriptional Regulatory Element Database (TRED) and Database for Annotation, Visualization and Integrated Discovery (DAVID). We got 21 (19 up-regulated and 2 down-regulated) differentially expressed genes that were co-regulated by transcription factors and miRNAs. KEGG pathway enrichment analysis showed that the 21 differentially expressed genes were predominantly involved in Tuberculosis signaling pathway, which may play a major role in tuberculosis biological process. Quantitative real-time PCR was performed to verify the over expression of co-regulatory genes ( FCGR1A and CEBPB ). The genetic expression was correlated with clinicopathological characteristics in TB patients and inferences drawn. Our results suggest the TF-miRNA gene co-regulatory network may help us further understand the molecular mechanism of immune response to tuberculosis and provide us a new angle of future biomarker and therapeutic targets.

  19. Right putamen and age are the most discriminant features to diagnose Parkinson's disease by using 123I-FP-CIT brain SPET data by using an artificial neural network classifier, a classification tree (ClT).

    Science.gov (United States)

    Cascianelli, S; Tranfaglia, C; Fravolini, M L; Bianconi, F; Minestrini, M; Nuvoli, S; Tambasco, N; Dottorini, M E; Palumbo, B

    2017-01-01

    The differential diagnosis of Parkinson's disease (PD) and other conditions, such as essential tremor and drug-induced parkinsonian syndrome or normal aging brain, represents a diagnostic challenge. 123 I-FP-CIT brain SPET is able to contribute to the differential diagnosis. Semiquantitative analysis of radiopharmaceutical uptake in basal ganglia (caudate nuclei and putamina) is very useful to support the diagnostic process. An artificial neural network classifier using 123 I-FP-CIT brain SPET data, a classification tree (CIT), was applied. CIT is an automatic classifier composed of a set of logical rules, organized as a decision tree to produce an optimised threshold based classification of data to provide discriminative cut-off values. We applied a CIT to 123 I-FP-CIT brain SPET semiquantitave data, to obtain cut-off values of radiopharmaceutical uptake ratios in caudate nuclei and putamina with the aim to diagnose PD versus other conditions. We retrospectively investigated 187 patients undergoing 123 I-FP-CIT brain SPET (Millenium VG, G.E.M.S.) with semiquantitative analysis performed with Basal Ganglia (BasGan) V2 software according to EANM guidelines; among them 113 resulted affected by PD (PD group) and 74 (N group) by other non parkinsonian conditions, such as Essential Tremor and drug-induced PD. PD group included 113 subjects (60M and 53F of age: 60-81yrs) having Hoehn and Yahr score (HY): 0.5-1.5; Unified Parkinson Disease Rating Scale (UPDRS) score: 6-38; N group included 74 subjects (36M and 38 F range of age 60-80 yrs). All subjects were clinically followed for at least 6-18 months to confirm the diagnosis. To examinate data obtained by using CIT, for each of the 1,000 experiments carried out, 10% of patients were randomly selected as the CIT training set, while the remaining 90% validated the trained CIT, and the percentage of the validation data correctly classified in the two groups of patients was computed. The expected performance of an "average

  20. Mapping Mammalian Cell-type-specific Transcriptional Regulatory Networks Using KD-CAGE and ChIP-seq Data in the TC-YIK Cell Line

    Science.gov (United States)

    Lizio, Marina; Ishizu, Yuri; Itoh, Masayoshi; Lassmann, Timo; Hasegawa, Akira; Kubosaki, Atsutaka; Severin, Jessica; Kawaji, Hideya; Nakamura, Yukio; Suzuki, Harukazu; Hayashizaki, Yoshihide; Carninci, Piero; Forrest, Alistair R. R.

    2015-01-01

    Mammals are composed of hundreds of different cell types with specialized functions. Each of these cellular phenotypes are controlled by different combinations of transcription factors. Using a human non islet cell insulinoma cell line (TC-YIK) which expresses insulin and the majority of known pancreatic beta cell specific genes as an example, we describe a general approach to identify key cell-type-specific transcription factors (TFs) and their direct and indirect targets. By ranking all human TFs by their level of enriched expression in TC-YIK relative to a broad collection of samples (FANTOM5), we confirmed known key regulators of pancreatic function and development. Systematic siRNA mediated perturbation of these TFs followed by qRT-PCR revealed their interconnections with NEUROD1 at the top of the regulation hierarchy and its depletion drastically reducing insulin levels. For 15 of the TF knock-downs (KD), we then used Cap Analysis of Gene Expression (CAGE) to identify thousands of their targets genome-wide (KD-CAGE). The data confirm NEUROD1 as a key positive regulator in the transcriptional regulatory network (TRN), and ISL1, and PROX1 as antagonists. As a complimentary approach we used ChIP-seq on four of these factors to identify NEUROD1, LMX1A, PAX6, and RFX6 binding sites in the human genome. Examining the overlap between genes perturbed in the KD-CAGE experiments and genes with a ChIP-seq peak within 50 kb of their promoter, we identified direct transcriptional targets of these TFs. Integration of KD-CAGE and ChIP-seq data shows that both NEUROD1 and LMX1A work as the main transcriptional activators. In the core TRN (i.e., TF-TF only), NEUROD1 directly transcriptionally activates the pancreatic TFs HSF4, INSM1, MLXIPL, MYT1, NKX6-3, ONECUT2, PAX4, PROX1, RFX6, ST18, DACH1, and SHOX2, while LMX1A directly transcriptionally activates DACH1, SHOX2, PAX6, and PDX1. Analysis of these complementary datasets suggests the need for caution in interpreting Ch

  1. Classifying network attack scenarios using an ontology

    CSIR Research Space (South Africa)

    Van Heerden, RP

    2012-03-01

    Full Text Available ) or to the target?s reputation. The Residue sub-phase refers to damage or artefacts of the attack that occur after the attack goal has been achieved, and occurs because the attacker loses control of some systems. For example after the launch of a DDOS..., A. (1995). Hacking theft of $10 million from citibank revealed. Retrieved 10/10, 2011, from http://articles.latimes.com/1995-08-19/business/fi-36656_1_citibank-system Hurley, E. (2004). SCO site succumbs to DDoS attack. Retrieved 10/10, 2011, from...

  2. A Simple Neural Network Contextual Classifier

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Tidemann, J.

    1997-01-01

    I. Kanellopoulos, G.G. Wilkinson, F. Roli and J. Austin (Eds.)Proceedings of European Union Environment and Climate Programme Concerted Action COMPARES (COnnectionist Methods in Pre-processing and Analysis of REmote Sensing data)....

  3. Stack filter classifiers

    Energy Technology Data Exchange (ETDEWEB)

    Porter, Reid B [Los Alamos National Laboratory; Hush, Don [Los Alamos National Laboratory

    2009-01-01

    Just as linear models generalize the sample mean and weighted average, weighted order statistic models generalize the sample median and weighted median. This analogy can be continued informally to generalized additive modeels in the case of the mean, and Stack Filters in the case of the median. Both of these model classes have been extensively studied for signal and image processing but it is surprising to find that for pattern classification, their treatment has been significantly one sided. Generalized additive models are now a major tool in pattern classification and many different learning algorithms have been developed to fit model parameters to finite data. However Stack Filters remain largely confined to signal and image processing and learning algorithms for classification are yet to be seen. This paper is a step towards Stack Filter Classifiers and it shows that the approach is interesting from both a theoretical and a practical perspective.

  4. Identification of HDA15-PIF1 as a key repression module directing the transcriptional network of seed germination in the dark.

    Science.gov (United States)

    Gu, Dachuan; Chen, Chia-Yang; Zhao, Minglei; Zhao, Linmao; Duan, Xuewu; Duan, Jun; Wu, Keqiang; Liu, Xuncheng

    2017-07-07

    Light is a major external factor in regulating seed germination. Photoreceptor phytochrome B (PHYB) plays a predominant role in promoting seed germination in the initial phase after imbibition, partially by repressing phytochrome-interacting factor1 (PIF1). However, the mechanism underlying the PHYB-PIF1-mediated transcription regulation remains largely unclear. Here, we identified that histone deacetylase15 (HDA15) is a negative component of PHYB-dependent seed germination. Overexpression of HDA15 in Arabidopsis inhibits PHYB-dependent seed germination, whereas loss of function of HDA15 increases PHYB-dependent seed germination. Genetic evidence indicated that HDA15 acts downstream of PHYB and represses seed germination dependent on PIF1. Furthermore, HDA15 interacts with PIF1 both in vitro and in vivo. Genome-wide transcriptome analysis revealed that HDA15 and PIF1 co-regulate the transcription of the light-responsive genes involved in multiple hormonal signaling pathways and cellular processes in germinating seeds in the dark. In addition, PIF1 recruits HDA15 to the promoter regions of target genes and represses their expression by decreasing the histone H3 acetylation levels in the dark. Taken together, our analysis uncovered the role of histone deacetylation in the light-regulated seed germination process and identified that HDA15-PIF1 acts as a key repression module directing the transcription network of seed germination. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  5. Flux coupling and transcriptional regulation within the metabolic network of the photosynthetic bacterium Synechocystis sp. PCC6803

    DEFF Research Database (Denmark)

    Montagud, Arnau; Zelezniak, Aleksej; Navarro, Emilio

    2011-01-01

    networks, surrounded by a stable core of pathways leading to biomass building blocks. This analysis identified potential bottlenecks for hydrogen and ethanol production. Integration of transcriptomic data with the Synechocystis flux coupling networks lead to identification of reporter flux coupling pairs...

  6. Early transcriptome analyses of Z-3-Hexenol-treated zea mays revealed distinct transcriptional networks and anti-herbivore defense potential of green leaf volatiles.

    Directory of Open Access Journals (Sweden)

    Jurgen Engelberth

    Full Text Available Green leaf volatiles (GLV, which are rapidly emitted by plants in response to insect herbivore damage, are now established as volatile defense signals. Receiving plants utilize these molecules to prime their defenses and respond faster and stronger when actually attacked. To further characterize the biological activity of these compounds we performed a microarray analysis of global gene expression. The focus of this project was to identify early transcriptional events elicited by Z-3-hexenol (Z-3-HOL as our model GLV in maize (Zea mays seedlings. The microarray results confirmed previous studies on Z-3-HOL -induced gene expression but also provided novel information about the complexity of Z-3-HOL -induced transcriptional networks. Besides identifying a distinct set of genes involved in direct and indirect defenses we also found significant expression of genes involved in transcriptional regulation, Ca(2+-and lipid-related signaling, and cell wall reinforcement. By comparing these results with those obtained by treatment of maize seedlings with insect elicitors we found a high degree of correlation between the two expression profiles at this early time point, in particular for those genes related to defense. We further analyzed defense gene expression induced by other volatile defense signals and found Z-3-HOL to be significantly more active than methyl jasmonate, methyl salicylate, and ethylene. The data presented herein provides important information on early genetic networks that are activated by Z-3-HOL and demonstrates the effectiveness of this compound in the regulation of typical plant defenses against insect herbivores in maize.

  7. Transcriptional regulatory network triggered by oxidative signals configures the early response mechanisms of japonica rice to chilling stress

    KAUST Repository

    Yun, Kil-Young; Park, Myoung Ryoul; Mohanty, Bijayalaxmi; Herath, Venura; Xu, Fuyu; Mauleon, Ramil; Wijaya, Edward; Bajic, Vladimir B.; Bruskiewich, Richard; de los Reyes, Benildo G

    2010-01-01

    -plant level analyses established a holistic view of chilling stress response mechanism of japonica rice. Early response regulatory network triggered by oxidative signals is critical for prolonged survival under sub-optimal temperature. Integration of stress

  8. Directed network modules

    International Nuclear Information System (INIS)

    Palla, Gergely; Farkas, Illes J; Pollner, Peter; Derenyi, Imre; Vicsek, Tamas

    2007-01-01

    A search technique locating network modules, i.e. internally densely connected groups of nodes in directed networks is introduced by extending the clique percolation method originally proposed for undirected networks. After giving a suitable definition for directed modules we investigate their percolation transition in the Erdos-Renyi graph both analytically and numerically. We also analyse four real-world directed networks, including Google's own web-pages, an email network, a word association graph and the transcriptional regulatory network of the yeast Saccharomyces cerevisiae. The obtained directed modules are validated by additional information available for the nodes. We find that directed modules of real-world graphs inherently overlap and the investigated networks can be classified into two major groups in terms of the overlaps between the modules. Accordingly, in the word-association network and Google's web-pages, overlaps are likely to contain in-hubs, whereas the modules in the email and transcriptional regulatory network tend to overlap via out-hubs

  9. Integration analysis of microRNA and mRNA paired expression profiling identifies deregulated microRNA-transcription factor-gene regulatory networks in ovarian endometriosis.

    Science.gov (United States)

    Zhao, Luyang; Gu, Chenglei; Ye, Mingxia; Zhang, Zhe; Li, Li'an; Fan, Wensheng; Meng, Yuanguang

    2018-01-22

    The etiology and pathophysiology of endometriosis remain unclear. Accumulating evidence suggests that aberrant microRNA (miRNA) and transcription factor (TF) expression may be involved in the pathogenesis and development of endometriosis. This study therefore aims to survey the key miRNAs, TFs and genes and further understand the mechanism of endometriosis. Paired expression profiling of miRNA and mRNA in ectopic endometria compared with eutopic endometria were determined by high-throughput sequencing techniques in eight patients with ovarian endometriosis. Binary interactions and circuits among the miRNAs, TFs, and corresponding genes were identified by the Pearson correlation coefficients. miRNA-TF-gene regulatory networks were constructed using bioinformatic methods. Eleven selected miRNAs and TFs were validated by quantitative reverse transcription-polymerase chain reaction in 22 patients. Overall, 107 differentially expressed miRNAs and 6112 differentially expressed mRNAs were identified by comparing the sequencing of the ectopic endometrium group and the eutopic endometrium group. The miRNA-TF-gene regulatory network consists of 22 miRNAs, 12 TFs and 430 corresponding genes. Specifically, some key regulators from the miR-449 and miR-34b/c cluster, miR-200 family, miR-106a-363 cluster, miR-182/183, FOX family, GATA family, and E2F family as well as CEBPA, SOX9 and HNF4A were suggested to play vital regulatory roles in the pathogenesis of endometriosis. Integration analysis of the miRNA and mRNA expression profiles presents a unique insight into the regulatory network of this enigmatic disorder and possibly provides clues regarding replacement therapy for endometriosis.

  10. Transcriptional regulation by competing transcription factor modules.

    Directory of Open Access Journals (Sweden)

    Rutger Hermsen

    2006-12-01

    Full Text Available Gene regulatory networks lie at the heart of cellular computation. In these networks, intracellular and extracellular signals are integrated by transcription factors, which control the expression of transcription units by binding to cis-regulatory regions on the DNA. The designs of both eukaryotic and prokaryotic cis-regulatory regions are usually highly complex. They frequently consist of both repetitive and overlapping transcription factor binding sites. To unravel the design principles of these promoter architectures, we have designed in silico prokaryotic transcriptional logic gates with predefined input-output relations using an evolutionary algorithm. The resulting cis-regulatory designs are often composed of modules that consist of tandem arrays of binding sites to which the transcription factors bind cooperatively. Moreover, these modules often overlap with each other, leading to competition between them. Our analysis thus identifies a new signal integration motif that is based upon the interplay between intramodular cooperativity and intermodular competition. We show that this signal integration mechanism drastically enhances the capacity of cis-regulatory domains to integrate signals. Our results provide a possible explanation for the complexity of promoter architectures and could be used for the rational design of synthetic gene circuits.

  11. Multi-tissue analysis of co-expression networks by higher-order generalized singular value decomposition identifies functionally coherent transcriptional modules.

    Science.gov (United States)

    Xiao, Xiaolin; Moreno-Moral, Aida; Rotival, Maxime; Bottolo, Leonardo; Petretto, Enrico

    2014-01-01

    Recent high-throughput efforts such as ENCODE have generated a large body of genome-scale transcriptional data in multiple conditions (e.g., cell-types and disease states). Leveraging these data is especially important for network-based approaches to human disease, for instance to identify coherent transcriptional modules (subnetworks) that can inform functional disease mechanisms and pathological pathways. Yet, genome-scale network analysis across conditions is significantly hampered by the paucity of robust and computationally-efficient methods. Building on the Higher-Order Generalized Singular Value Decomposition, we introduce a new algorithmic approach for efficient, parameter-free and reproducible identification of network-modules simultaneously across multiple conditions. Our method can accommodate weighted (and unweighted) networks of any size and can similarly use co-expression or raw gene expression input data, without hinging upon the definition and stability of the correlation used to assess gene co-expression. In simulation studies, we demonstrated distinctive advantages of our method over existing methods, which was able to recover accurately both common and condition-specific network-modules without entailing ad-hoc input parameters as required by other approaches. We applied our method to genome-scale and multi-tissue transcriptomic datasets from rats (microarray-based) and humans (mRNA-sequencing-based) and identified several common and tissue-specific subnetworks with functional significance, which were not detected by other methods. In humans we recapitulated the crosstalk between cell-cycle progression and cell-extracellular matrix interactions processes in ventricular zones during neocortex expansion and further, we uncovered pathways related to development of later cognitive functions in the cortical plate of the developing brain which were previously unappreciated. Analyses of seven rat tissues identified a multi-tissue subnetwork of co

  12. Multi-tissue analysis of co-expression networks by higher-order generalized singular value decomposition identifies functionally coherent transcriptional modules.

    Directory of Open Access Journals (Sweden)

    Xiaolin Xiao

    2014-01-01

    Full Text Available Recent high-throughput efforts such as ENCODE have generated a large body of genome-scale transcriptional data in multiple conditions (e.g., cell-types and disease states. Leveraging these data is especially important for network-based approaches to human disease, for instance to identify coherent transcriptional modules (subnetworks that can inform functional disease mechanisms and pathological pathways. Yet, genome-scale network analysis across conditions is significantly hampered by the paucity of robust and computationally-efficient methods. Building on the Higher-Order Generalized Singular Value Decomposition, we introduce a new algorithmic approach for efficient, parameter-free and reproducible identification of network-modules simultaneously across multiple conditions. Our method can accommodate weighted (and unweighted networks of any size and can similarly use co-expression or raw gene expression input data, without hinging upon the definition and stability of the correlation used to assess gene co-expression. In simulation studies, we demonstrated distinctive advantages of our method over existing methods, which was able to recover accurately both common and condition-specific network-modules without entailing ad-hoc input parameters as required by other approaches. We applied our method to genome-scale and multi-tissue transcriptomic datasets from rats (microarray-based and humans (mRNA-sequencing-based and identified several common and tissue-specific subnetworks with functional significance, which were not detected by other methods. In humans we recapitulated the crosstalk between cell-cycle progression and cell-extracellular matrix interactions processes in ventricular zones during neocortex expansion and further, we uncovered pathways related to development of later cognitive functions in the cortical plate of the developing brain which were previously unappreciated. Analyses of seven rat tissues identified a multi

  13. The response of the prostate to circulating cholesterol: activating transcription factor 3 (ATF3 as a prominent node in a cholesterol-sensing network.

    Directory of Open Access Journals (Sweden)

    Jayoung Kim

    Full Text Available Elevated circulating cholesterol is a systemic risk factor for cardiovascular disease and metabolic syndrome, however the manner in which the normal prostate responds to variations in cholesterol levels is poorly understood. In this study we addressed the molecular and cellular effects of elevated and suppressed levels of circulating cholesterol on the normal prostate. Integrated bioinformatic analysis was performed using DNA microarray data from two experimental formats: (1 ventral prostate from male mice with chronically elevated circulating cholesterol and (2 human prostate cells exposed acutely to cholesterol depletion. A cholesterol-sensitive gene expression network was constructed from these data and the transcription factor ATF3 was identified as a prominent node in the network. Validation experiments confirmed that elevated cholesterol reduced ATF3 expression and enhanced proliferation of prostate cells, while cholesterol depletion increased ATF3 levels and inhibited proliferation. Cholesterol reduction in vivo alleviated dense lymphomononuclear infiltrates in the periprostatic adipose tissue, which were closely associated with nerve tracts and blood vessels. These findings open new perspectives on the role of cholesterol in prostate health, and provide a novel role for ATF3, and associated proteins within a large signaling network, as a cholesterol-sensing mechanism.

  14. The transcriptional regulatory network in the drought response and its crosstalk in abiotic stress responses including drought, cold and heat

    Directory of Open Access Journals (Sweden)

    Kazuo eNakashima

    2014-05-01

    Full Text Available Drought negatively impacts plant growth and the productivity of crops around the world. Understanding the molecular mechanisms in the drought response is important for improvement of drought tolerance using molecular techniques. In plants, abscisic acid (ABA is accumulated under osmotic stress conditions caused by drought, and has a key role in stress responses and tolerance. Comprehensive molecular analyses have shown that ABA regulates the expression of many genes under osmotic stress conditions, and the ABA-responsive element (ABRE is the major cis-element for ABA-responsive gene expression. Transcription factors (TFs are master regulators of gene expression. ABRE-binding protein (AREB and ABRE-binding factor (ABF TFs control gene expression in an ABA-dependent manner. SNF1-related protein kinases 2, group A 2C-type protein phosphatases, and ABA receptors were shown to control the ABA signaling pathway. ABA-independent signaling pathways such as dehydration-responsive element-binding protein (DREB TFs and NAC TFs are also involved in stress responses including drought, heat and cold. Recent studies have suggested that there are interactions between the major ABA signaling pathway and other signaling factors in stress responses. The important roles of these transcription factors in crosstalk among abiotic stress responses will be discussed. Control of ABA or stress signaling factor expression can improve tolerance to environmental stresses. Recent studies using crops have shown that stress-specific overexpression of TFs improves drought tolerance and grain yield compared with controls in the field.

  15. Investigations of Escherichia coli promoter sequences with artificial neural networks: new signals discovered upstream of the transcriptional startpoint

    DEFF Research Database (Denmark)

    Pedersen, Anders Gorm; Engelbrecht, Jacob

    1995-01-01

    We present a novel method for using the learning ability of a neural network as a measure of information in local regions of input data. Using the method to analyze Escherichia coli promoters, we discover all previously described signals, and furthermore find new signals that are regularly spaced...

  16. Gearbox Condition Monitoring Using Advanced Classifiers

    Directory of Open Access Journals (Sweden)

    P. Večeř

    2010-01-01

    Full Text Available New efficient and reliable methods for gearbox diagnostics are needed in automotive industry because of growing demand for production quality. This paper presents the application of two different classifiers for gearbox diagnostics – Kohonen Neural Networks and the Adaptive-Network-based Fuzzy Interface System (ANFIS. Two different practical applications are presented. In the first application, the tested gearboxes are separated into two classes according to their condition indicators. In the second example, ANFIS is applied to label the tested gearboxes with a Quality Index according to the condition indicators. In both applications, the condition indicators were computed from the vibration of the gearbox housing. 

  17. A Bayesian classifier for symbol recognition

    OpenAIRE

    Barrat , Sabine; Tabbone , Salvatore; Nourrissier , Patrick

    2007-01-01

    URL : http://www.buyans.com/POL/UploadedFile/134_9977.pdf; International audience; We present in this paper an original adaptation of Bayesian networks to symbol recognition problem. More precisely, a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor, is presented. In this perspective, we use a simple Bayesian classifier, called naive Bayes. In fact, probabilistic graphical models, more spec...

  18. HFR1 Sequesters PIF1 to Govern the Transcriptional Network Underlying Light-Initiated Seed Germination in Arabidopsis[C][W][OPEN

    Science.gov (United States)

    Shi, Hui; Zhong, Shangwei; Mo, Xiaorong; Liu, Na; Nezames, Cynthia D.; Deng, Xing Wang

    2013-01-01

    Seed germination is the first step for seed plants to initiate a new life cycle. Light plays a predominant role in promoting seed germination, where the initial phase is mediated by photoreceptor phytochrome B (phyB). Previous studies showed that PHYTOCHROME-INTERACTING FACTOR1 (PIF1) represses seed germination downstream of phyB. Here, we identify a positive regulator of phyB-dependent seed germination, LONG HYPOCOTYL IN FAR-RED1 (HFR1). HFR1 blocks PIF1 transcriptional activity by forming a heterodimer with PIF1 that prevents PIF1 from binding to DNA. Our whole-genomic analysis shows that HFR1 and PIF1 oppositely mediate the light-regulated transcriptome in imbibed seeds. Through the HFR1–PIF1 module, light regulates expression of numerous genes involved in cell wall loosening, cell division, and hormone pathways to initiate seed germination. The functionally antagonistic HFR1–PIF1 pair constructs a fail-safe mechanism for fine-tuning seed germination during low-level illumination, ensuring a rapid response to favorable environmental changes. This study identifies the HFR1–PIF1 pair as a central module directing the whole genomic transcriptional network to rapidly initiate light-induced seed germination. PMID:24179122

  19. Detecting and classifying faults on transmission systems using a backpropagation neural network; Deteccion y clasificacion de fallas en sistemas de transmision empleando una red neuronal con retropropagacion del error

    Energy Technology Data Exchange (ETDEWEB)

    Rosas Ortiz, German

    2000-01-01

    Fault detection and diagnosis on transmission systems is an interesting area of investigation to Artificial Intelligence (AI) based systems. Neurocomputing is one of fastest growing areas of research in the fields of AI and pattern recognition. This work explores the possible suitability of pattern recognition approach of neural networks for fault detection and classification on power systems. The conventional detection techniques in modern relays are based in digital processing of signals and it need some time (around 1 cycle) to send a tripping signal, also they are likely to make incorrect decisions if the signals are noisy. It's desirable to develop a fast, accurate and robust approach that perform accurately for changing system conditions (like load variations and fault resistance). The aim of this work is to develop a novel technique based on Artificial Neural Networks (ANN), which explores the suitability of a pattern classification approach for fault detection and diagnosis. The suggested approach is based in the fact that when a fault occurs, a change in the system impedance take place and, as a consequence changes in amplitude and phase of line voltage and current signals take place. The ANN-based fault discriminator is trained to detect this changes as indicators of the instant of fault inception. This detector uses instantaneous values of these signals to make decisions. Suitability of using neural network as pattern classifiers for transmission systems fault diagnosis is described in detail a neural network design and simulation environment for real-time is presented. Results showing the performance of this approach are presented and indicate that it is fast, secure and exact enough, and it can be used in high speed fault detection and classification schemes. [Spanish] El diagnostico y la deteccion de fallas en sistemas de transmision es una area de interes en investigacion para sistemas basados en Inteligencia Artificial (IA). El calculo neuronal

  20. The transcriptional regulatory network in the drought response and its crosstalk in abiotic stress responses including drought, cold, and heat.

    Science.gov (United States)

    Nakashima, Kazuo; Yamaguchi-Shinozaki, Kazuko; Shinozaki, Kazuo

    2014-01-01

    Drought negatively impacts plant growth and the productivity of crops around the world. Understanding the molecular mechanisms in the drought response is important for improvement of drought tolerance using molecular techniques. In plants, abscisic acid (ABA) is accumulated under osmotic stress conditions caused by drought, and has a key role in stress responses and tolerance. Comprehensive molecular analyses have shown that ABA regulates the expression of many genes under osmotic stress conditions, and the ABA-responsive element (ABRE) is the major cis-element for ABA-responsive gene expression. Transcription factors (TFs) are master regulators of gene expression. ABRE-binding protein and ABRE-binding factor TFs control gene expression in an ABA-dependent manner. SNF1-related protein kinases 2, group A 2C-type protein phosphatases, and ABA receptors were shown to control the ABA signaling pathway. ABA-independent signaling pathways such as dehydration-responsive element-binding protein TFs and NAC TFs are also involved in stress responses including drought, heat, and cold. Recent studies have suggested that there are interactions between the major ABA signaling pathway and other signaling factors in stress responses. The important roles of these TFs in crosstalk among abiotic stress responses will be discussed. Control of ABA or stress signaling factor expression can improve tolerance to environmental stresses. Recent studies using crops have shown that stress-specific overexpression of TFs improves drought tolerance and grain yield compared with controls in the field.

  1. Occupancy classification of position weight matrix-inferred transcription factor binding sites.

    Directory of Open Access Journals (Sweden)

    Hollis Wright

    Full Text Available BACKGROUND: Computational prediction of Transcription Factor Binding Sites (TFBS from sequence data alone is difficult and error-prone. Machine learning techniques utilizing additional environmental information about a predicted binding site (such as distances from the site to particular chromatin features to determine its occupancy/functionality class show promise as methods to achieve more accurate prediction of true TFBS in silico. We evaluate the Bayesian Network (BN and Support Vector Machine (SVM machine learning techniques on four distinct TFBS data sets and analyze their performance. We describe the features that are most useful for classification and contrast and compare these feature sets between the factors. RESULTS: Our results demonstrate good performance of classifiers both on TFBS for transcription factors used for initial training and for TFBS for other factors in cross-classification experiments. We find that distances to chromatin modifications (specifically, histone modification islands as well as distances between such modifications to be effective predictors of TFBS occupancy, though the impact of individual predictors is largely TF specific. In our experiments, Bayesian network classifiers outperform SVM classifiers. CONCLUSIONS: Our results demonstrate good performance of machine learning techniques on the problem of occupancy classification, and demonstrate that effective classification can be achieved using distances to chromatin features. We additionally demonstrate that cross-classification of TFBS is possible, suggesting the possibility of constructing a generalizable occupancy classifier capable of handling TFBS for many different transcription factors.

  2. Community Structure Analysis of Transcriptional Networks Reveals Distinct Molecular Pathways for Early- and Late-Onset Temporal Lobe Epilepsy with Childhood Febrile Seizures

    Science.gov (United States)

    Moreira-Filho, Carlos Alberto; Bando, Silvia Yumi; Bertonha, Fernanda Bernardi; Iamashita, Priscila; Silva, Filipi Nascimento; Costa, Luciano da Fontoura; Silva, Alexandre Valotta; Castro, Luiz Henrique Martins; Wen, Hung-Tzu

    2015-01-01

    Age at epilepsy onset has a broad impact on brain plasticity and epilepsy pathomechanisms. Prolonged febrile seizures in early childhood (FS) constitute an initial precipitating insult (IPI) commonly associated with mesial temporal lobe epilepsy (MTLE). FS-MTLE patients may have early disease onset, i.e. just after the IPI, in early childhood, or late-onset, ranging from mid-adolescence to early adult life. The mechanisms governing early (E) or late (L) disease onset are largely unknown. In order to unveil the molecular pathways underlying E and L subtypes of FS-MTLE we investigated global gene expression in hippocampal CA3 explants of FS-MTLE patients submitted to hippocampectomy. Gene coexpression networks (GCNs) were obtained for the E and L patient groups. A network-based approach for GCN analysis was employed allowing: i) the visualization and analysis of differentially expressed (DE) and complete (CO) - all valid GO annotated transcripts - GCNs for the E and L groups; ii) the study of interactions between all the system’s constituents based on community detection and coarse-grained community structure methods. We found that the E-DE communities with strongest connection weights harbor highly connected genes mainly related to neural excitability and febrile seizures, whereas in L-DE communities these genes are not only involved in network excitability but also playing roles in other epilepsy-related processes. Inversely, in E-CO the strongly connected communities are related to compensatory pathways (seizure inhibition, neuronal survival and responses to stress conditions) while in L-CO these communities harbor several genes related to pro-epileptic effects, seizure-related mechanisms and vulnerability to epilepsy. These results fit the concept, based on fMRI and behavioral studies, that early onset epilepsies, although impacting more severely the hippocampus, are associated to compensatory mechanisms, while in late MTLE development the brain is less able to

  3. Aberrant transcriptional networks in step-wise neurogenesis of paroxysmal kinesigenic dyskinesia-induced pluripotent stem cells.

    Science.gov (United States)

    Li, Chun; Ma, Yu; Zhang, Kunshan; Gu, Junjie; Tang, Fan; Chen, Shengdi; Cao, Li; Li, Siguang; Jin, Ying

    2016-08-16

    Paroxysmal kinesigenic dyskinesia (PKD) is an episodic movement disorder with autosomal-dominant inheritance and marked variability in clinical manifestations.Proline-rich transmembrane protein 2 (PRRT2) has been identified as a causative gene of PKD, but the molecular mechanism underlying the pathogenesis of PKD still remains a mystery. The phenotypes and transcriptional patterns of the PKD disease need further clarification. Here, we report the generation and neural differentiation of iPSC lines from two familial PKD patients with c.487C>T (p. Gln163X) and c.573dupT (p. Gly192Trpfs*8) PRRT2 mutations, respectively. Notably, an extremely lower efficiency in neural conversion from PKD-iPSCs than control-iPSCs is observed by a step-wise neural differentiation method of dual inhibition of SMAD signaling. Moreover, we show the high expression level of PRRT2 throughout the human brain and the expression pattern of PRRT2 in other human tissues for the first time. To gain molecular insight into the development of the disease, we conduct global gene expression profiling of PKD cells at four different stages of neural induction and identify altered gene expression patterns, which peculiarly reflect dysregulated neural transcriptome signatures and a differentiation tendency to mesodermal development, in comparison to control-iPSCs. Additionally, functional and signaling pathway analyses indicate significantly different cell fate determination between PKD-iPSCs and control-iPSCs. Together, the establishment of PKD-specific in vitro models and the illustration of transcriptome features in PKD cells would certainly help us with better understanding of the defects in neural conversion as well as further investigations in the pathogenesis of the PKD disease.

  4. Comparative transcriptional profiling of 3 murine models of SLE nephritis reveals both unique and shared regulatory networks.

    Directory of Open Access Journals (Sweden)

    Ramalingam Bethunaickan

    Full Text Available To define shared and unique features of SLE nephritis in mouse models of proliferative and glomerulosclerotic renal disease.Perfused kidneys from NZB/W F1, NZW/BXSB and NZM2410 mice were harvested before and after nephritis onset. Affymetrix based gene expression profiles of kidney RNA were analyzed using Genomatix Pathway Systems and Ingenuity Pathway Analysis software. Gene expression patterns were confirmed using real-time PCR.955, 1168 and 755 genes were regulated in the kidneys of nephritic NZB/W F1, NZM2410 and NZW/BXSB mice respectively. 263 genes were regulated concordantly in all three strains reflecting immune cell infiltration, endothelial cell activation, complement activation, cytokine signaling, tissue remodeling and hypoxia. STAT3 was the top associated transcription factor, having a binding site in the gene promoter of 60/263 regulated genes. The two strains with proliferative nephritis shared a macrophage/DC infiltration and activation signature. NZB/W and NZM2410 mice shared a mitochondrial dysfunction signature. Dominant T cell and plasma cell signatures in NZB/W mice reflected lymphoid aggregates; this was the only strain with regulatory T cell infiltrates. NZW/BXSB mice manifested tubular regeneration and NZM2410 mice had the most metabolic stress and manifested loss of nephrin, indicating podocyte loss.These findings identify shared inflammatory mechanisms of SLE nephritis that can be therapeutically targeted. Nevertheless, the heterogeneity of effector mechanisms suggests that individualized therapy might need to be based on biopsy findings. Some common mechanisms are shared with non-immune-mediated renal diseases, suggesting that strategies to prevent tissue hypoxia and remodeling may be useful in SLE nephritis.

  5. Familial or Sporadic Idiopathic Scoliosis – classification based on artificial neural network and GAPDH and ACTB transcription profile

    Science.gov (United States)

    2013-01-01

    Background Importance of hereditary factors in the etiology of Idiopathic Scoliosis is widely accepted. In clinical practice some of the IS patients present with positive familial history of the deformity and some do not. Traditionally about 90% of patients have been considered as sporadic cases without familial recurrence. However the exact proportion of Familial and Sporadic Idiopathic Scoliosis is still unknown. Housekeeping genes encode proteins that are usually essential for the maintenance of basic cellular functions. ACTB and GAPDH are two housekeeping genes encoding respectively a cytoskeletal protein β-actin, and glyceraldehyde-3-phosphate dehydrogenase, an enzyme of glycolysis. Although their expression levels can fluctuate between different tissues and persons, human housekeeping genes seem to exhibit a preserved tissue-wide expression ranking order. It was hypothesized that expression ranking order of two representative housekeeping genes ACTB and GAPDH might be disturbed in the tissues of patients with Familial Idiopathic Scoliosis (with positive family history of idiopathic scoliosis) opposed to the patients with no family members affected (Sporadic Idiopathic Scoliosis). An artificial neural network (ANN) was developed that could serve to differentiate between familial and sporadic cases of idiopathic scoliosis based on the expression levels of ACTB and GAPDH in different tissues of scoliotic patients. The aim of the study was to investigate whether the expression levels of ACTB and GAPDH in different tissues of idiopathic scoliosis patients could be used as a source of data for specially developed artificial neural network in order to predict the positive family history of index patient. Results The comparison of developed models showed, that the most satisfactory classification accuracy was achieved for ANN model with 18 nodes in the first hidden layer and 16 nodes in the second hidden layer. The classification accuracy for positive Idiopathic

  6. Uncovering transcriptional interactions via an adaptive fuzzy logic approach

    Directory of Open Access Journals (Sweden)

    Chen Chung-Ming

    2009-12-01

    Full Text Available Abstract Background To date, only a limited number of transcriptional regulatory interactions have been uncovered. In a pilot study integrating sequence data with microarray data, a position weight matrix (PWM performed poorly in inferring transcriptional interactions (TIs, which represent physical interactions between transcription factors (TF and upstream sequences of target genes. Inferring a TI means that the promoter sequence of a target is inferred to match the consensus sequence motifs of a potential TF, and their interaction type such as AT or RT is also predicted. Thus, a robust PWM (rPWM was developed to search for consensus sequence motifs. In addition to rPWM, one feature extracted from ChIP-chip data was incorporated to identify potential TIs under specific conditions. An interaction type classifier was assembled to predict activation/repression of potential TIs using microarray data. This approach, combining an adaptive (learning fuzzy inference system and an interaction type classifier to predict transcriptional regulatory networks, was named AdaFuzzy. Results AdaFuzzy was applied to predict TIs using real genomics data from Saccharomyces cerevisiae. Following one of the latest advances in predicting TIs, constrained probabilistic sparse matrix factorization (cPSMF, and using 19 transcription factors (TFs, we compared AdaFuzzy to four well-known approaches using over-representation analysis and gene set enrichment analysis. AdaFuzzy outperformed these four algorithms. Furthermore, AdaFuzzy was shown to perform comparably to 'ChIP-experimental method' in inferring TIs identified by two sets of large scale ChIP-chip data, respectively. AdaFuzzy was also able to classify all predicted TIs into one or more of the four promoter architectures. The results coincided with known promoter architectures in yeast and provided insights into transcriptional regulatory mechanisms. Conclusion AdaFuzzy successfully integrates multiple types of

  7. Classified

    CERN Multimedia

    Computer Security Team

    2011-01-01

    In the last issue of the Bulletin, we have discussed recent implications for privacy on the Internet. But privacy of personal data is just one facet of data protection. Confidentiality is another one. However, confidentiality and data protection are often perceived as not relevant in the academic environment of CERN.   But think twice! At CERN, your personal data, e-mails, medical records, financial and contractual documents, MARS forms, group meeting minutes (and of course your password!) are all considered to be sensitive, restricted or even confidential. And this is not all. Physics results, in particular when being preliminary and pending scrutiny, are sensitive, too. Just recently, an ATLAS collaborator copy/pasted the abstract of an ATLAS note onto an external public blog, despite the fact that this document was clearly marked as an "Internal Note". Such an act was not only embarrassing to the ATLAS collaboration, and had negative impact on CERN’s reputation --- i...

  8. Transcriptional networks associated with the immune system are disrupted by organochlorine pesticides in largemouth bass (Micropterus salmoides) ovary.

    Science.gov (United States)

    Martyniuk, Christopher J; Doperalski, Nicholas J; Feswick, April; Prucha, Melinda S; Kroll, Kevin J; Barber, David S; Denslow, Nancy D

    2016-08-01

    Largemouth bass (Micropterus salmoides) inhabiting Lake Apopka, Florida are exposed to high levels of persistent organochlorine pesticides (OCPs) and dietary uptake is a significant route of exposure for these apex predators. The objectives of this study were to determine the dietary effects of two organochlorine pesticides (p, p'-dichlorodiphenyldichloroethylene; p, p' DDE and methoxychlor; MXC) on the reproductive axis of largemouth bass. Reproductive bass (late vitellogenesis) were fed one of the following diets: control pellets, 125ppm p, p'-DDE, or 10ppm MXC (mg/kg) for 84days. Due to the fact that both p,p' DDE and MXC have anti-androgenic properties, the anti-androgenic pharmaceutical flutamide was fed to a fourth group of largemouth bass (750ppm). Following a 3 month exposure, fish incorporated p,p' DDE and MXC into both muscle and ovary tissue, with the ovary incorporating 3 times more organochlorine pesticides compared to muscle. Endpoints assessed were those related to reproduction due to previous studies demonstrating that these pesticides impact the reproductive axis and we hypothesized that a dietary exposure would result in impaired reproduction. However, oocyte distribution, gonadosomatic index, plasma vitellogenin, and plasma sex steroids (17β-estradiol, E2 and testosterone, T) were not different between control animals and contaminant-fed largemouth bass. Moreover, neither p, p' DDE nor MXC affected E2 or T production in ex vivo oocyte cultures from chemical-fed largemouth bass. However, both pesticides did interfere with the normal upregulation of androgen receptor that is observed in response to human chorionic gonadotropin in ex vivo cultures, an observation that may be related to their anti-androgenic properties. Transcriptomics profiling in the ovary revealed that gene networks related to cell processes such as leukocyte cell adhesion, ossification, platelet function and inhibition, xenobiotic metabolism, fibrinolysis, and thermoregulation

  9. Classifying Sluice Occurrences in Dialogue

    DEFF Research Database (Denmark)

    Baird, Austin; Hamza, Anissa; Hardt, Daniel

    2018-01-01

    perform manual annotation with acceptable inter-coder agreement. We build classifier models with Decision Trees and Naive Bayes, with accuracy of 67%. We deploy a classifier to automatically classify sluice occurrences in OpenSubtitles, resulting in a corpus with 1.7 million occurrences. This will support....... Despite this, the corpus can be of great use in research on sluicing and development of systems, and we are making the corpus freely available on request. Furthermore, we are in the process of improving the accuracy of sluice identification and annotation for the purpose of created a subsequent version...

  10. A module of human peripheral blood mononuclear cell transcriptional network containing primitive and differentiation markers is related to specific cardiovascular health variables.

    Directory of Open Access Journals (Sweden)

    Leni Moldovan

    Full Text Available Peripheral blood mononuclear cells (PBMCs, including rare circulating stem and progenitor cells (CSPCs, have important yet poorly understood roles in the maintenance and repair of blood vessels and perfused organs. Our hypothesis was that the identities and functions of CSPCs in cardiovascular health could be ascertained by analyzing the patterns of their co-expressed markers in unselected PBMC samples. Because gene microarrays had failed to detect many stem cell-associated genes, we performed quantitative real-time PCR to measure the expression of 45 primitive and tissue differentiation markers in PBMCs from healthy and hypertensive human subjects. We compared these expression levels to the subjects' demographic and cardiovascular risk factors, including vascular stiffness. The tested marker genes were expressed in all of samples and organized in hierarchical transcriptional network modules, constructed by a bottom-up approach. An index of gene expression in one of these modules (metagene, defined as the average standardized relative copy numbers of 15 pluripotency and cardiovascular differentiation markers, was negatively correlated (all p<0.03 with age (R2 = -0.23, vascular stiffness (R2 = -0.24, and central aortic pressure (R2 = -0.19 and positively correlated with body mass index (R2 = 0.72, in women. The co-expression of three neovascular markers was validated at the single-cell level using mRNA in situ hybridization and immunocytochemistry. The overall gene expression in this cardiovascular module was reduced by 72±22% in the patients compared with controls. However, the compactness of both modules was increased in the patients' samples, which was reflected in reduced dispersion of their nodes' degrees of connectivity, suggesting a more primitive character of the patients' CSPCs. In conclusion, our results show that the relationship between CSPCs and vascular function is encoded in modules of the PBMCs transcriptional

  11. Classifying objects in LWIR imagery via CNNs

    Science.gov (United States)

    Rodger, Iain; Connor, Barry; Robertson, Neil M.

    2016-10-01

    The aim of the presented work is to demonstrate enhanced target recognition and improved false alarm rates for a mid to long range detection system, utilising a Long Wave Infrared (LWIR) sensor. By exploiting high quality thermal image data and recent techniques in machine learning, the system can provide automatic target recognition capabilities. A Convolutional Neural Network (CNN) is trained and the classifier achieves an overall accuracy of > 95% for 6 object classes related to land defence. While the highly accurate CNN struggles to recognise long range target classes, due to low signal quality, robust target discrimination is achieved for challenging candidates. The overall performance of the methodology presented is assessed using human ground truth information, generating classifier evaluation metrics for thermal image sequences.

  12. Bayes classifiers for imbalanced traffic accidents datasets.

    Science.gov (United States)

    Mujalli, Randa Oqab; López, Griselda; Garach, Laura

    2016-03-01

    Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class (majority). This, however, supposes a challenging problem for classification algorithms and may cause obtaining a model that well cover the slight injuries instances whereas the killed or severe injuries instances are misclassified frequently. Based on traffic accidents data collected on urban and suburban roads in Jordan for three years (2009-2011); three different data balancing techniques were used: under-sampling which removes some instances of the majority class, oversampling which creates new instances of the minority class and a mix technique that combines both. In addition, different Bayes classifiers were compared for the different imbalanced and balanced data sets: Averaged One-Dependence Estimators, Weightily Average One-Dependence Estimators, and Bayesian networks in order to identify factors that affect the severity of an accident. The results indicated that using the balanced data sets, especially those created using oversampling techniques, with Bayesian networks improved classifying a traffic accident according to its severity and reduced the misclassification of killed and severe injuries instances. On the other hand, the following variables were found to contribute to the occurrence of a killed causality or a severe injury in a traffic accident: number of vehicles involved, accident pattern, number of directions, accident type, lighting, surface condition, and speed limit. This work, to the knowledge of the authors, is the first that aims at analyzing historical data records for traffic accidents occurring in Jordan and the first to apply balancing techniques to analyze injury severity of traffic accidents. Copyright © 2015 Elsevier Ltd. All rights reserved.

  13. Quantum ensembles of quantum classifiers.

    Science.gov (United States)

    Schuld, Maria; Petruccione, Francesco

    2018-02-09

    Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis. Many of those algorithms are implementations of quantum classifiers, or models for the classification of data inputs with a quantum computer. Following the success of collective decision making with ensembles in classical machine learning, this paper introduces the concept of quantum ensembles of quantum classifiers. Creating the ensemble corresponds to a state preparation routine, after which the quantum classifiers are evaluated in parallel and their combined decision is accessed by a single-qubit measurement. This framework naturally allows for exponentially large ensembles in which - similar to Bayesian learning - the individual classifiers do not have to be trained. As an example, we analyse an exponentially large quantum ensemble in which each classifier is weighed according to its performance in classifying the training data, leading to new results for quantum as well as classical machine learning.

  14. IAEA safeguards and classified materials

    International Nuclear Information System (INIS)

    Pilat, J.F.; Eccleston, G.W.; Fearey, B.L.; Nicholas, N.J.; Tape, J.W.; Kratzer, M.

    1997-01-01

    The international community in the post-Cold War period has suggested that the International Atomic Energy Agency (IAEA) utilize its expertise in support of the arms control and disarmament process in unprecedented ways. The pledges of the US and Russian presidents to place excess defense materials, some of which are classified, under some type of international inspections raises the prospect of using IAEA safeguards approaches for monitoring classified materials. A traditional safeguards approach, based on nuclear material accountancy, would seem unavoidably to reveal classified information. However, further analysis of the IAEA's safeguards approaches is warranted in order to understand fully the scope and nature of any problems. The issues are complex and difficult, and it is expected that common technical understandings will be essential for their resolution. Accordingly, this paper examines and compares traditional safeguards item accounting of fuel at a nuclear power station (especially spent fuel) with the challenges presented by inspections of classified materials. This analysis is intended to delineate more clearly the problems as well as reveal possible approaches, techniques, and technologies that could allow the adaptation of safeguards to the unprecedented task of inspecting classified materials. It is also hoped that a discussion of these issues can advance ongoing political-technical debates on international inspections of excess classified materials

  15. 3D Bayesian contextual classifiers

    DEFF Research Database (Denmark)

    Larsen, Rasmus

    2000-01-01

    We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours.......We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours....

  16. The Journey of a Transcription Factor

    DEFF Research Database (Denmark)

    Pireyre, Marie

    Plants have developed astonishing networks regulating their metabolism to adapt to their environment. The complexity of these networks is illustrated by the expansion of families of regulators such as transcription factors in the plant kingdom. Transcription factors specifically impact...... transcriptional networks by integrating exogenous and endogenous stimuli and regulating gene expression accordingly. Regulation of transcription factors and their activation is thus highly important to modulate the transcriptional programs and increase fitness of the plant in a given environment. Plant metabolism....... The biosynthetic machinery of GLS is governed by interplay of six MYB and three bHLH transcription factors. MYB28, MYB29 and MYB76 regulate methionine-derived GLS, and MYB51, MYB34 and MYB122 regulate tryptophan-derived GLS. The three bHLH transcription factors MYC2, MYC3 and MYC4 physically interact with all six...

  17. Comparison of artificial intelligence classifiers for SIP attack data

    Science.gov (United States)

    Safarik, Jakub; Slachta, Jiri

    2016-05-01

    Honeypot application is a source of valuable data about attacks on the network. We run several SIP honeypots in various computer networks, which are separated geographically and logically. Each honeypot runs on public IP address and uses standard SIP PBX ports. All information gathered via honeypot is periodically sent to the centralized server. This server classifies all attack data by neural network algorithm. The paper describes optimizations of a neural network classifier, which lower the classification error. The article contains the comparison of two neural network algorithm used for the classification of validation data. The first is the original implementation of the neural network described in recent work; the second neural network uses further optimizations like input normalization or cross-entropy cost function. We also use other implementations of neural networks and machine learning classification algorithms. The comparison test their capabilities on validation data to find the optimal classifier. The article result shows promise for further development of an accurate SIP attack classification engine.

  18. Knowledge Uncertainty and Composed Classifier

    Czech Academy of Sciences Publication Activity Database

    Klimešová, Dana; Ocelíková, E.

    2007-01-01

    Roč. 1, č. 2 (2007), s. 101-105 ISSN 1998-0140 Institutional research plan: CEZ:AV0Z10750506 Keywords : Boosting architecture * contextual modelling * composed classifier * knowledge management, * knowledge * uncertainty Subject RIV: IN - Informatics, Computer Science

  19. Correlation Dimension-Based Classifier

    Czech Academy of Sciences Publication Activity Database

    Jiřina, Marcel; Jiřina jr., M.

    2014-01-01

    Roč. 44, č. 12 (2014), s. 2253-2263 ISSN 2168-2267 R&D Projects: GA MŠk(CZ) LG12020 Institutional support: RVO:67985807 Keywords : classifier * multidimensional data * correlation dimension * scaling exponent * polynomial expansion Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 3.469, year: 2014

  20. Ship localization in Santa Barbara Channel using machine learning classifiers.

    Science.gov (United States)

    Niu, Haiqiang; Ozanich, Emma; Gerstoft, Peter

    2017-11-01

    Machine learning classifiers are shown to outperform conventional matched field processing for a deep water (600 m depth) ocean acoustic-based ship range estimation problem in the Santa Barbara Channel Experiment when limited environmental information is known. Recordings of three different ships of opportunity on a vertical array were used as training and test data for the feed-forward neural network and support vector machine classifiers, demonstrating the feasibility of machine learning methods to locate unseen sources. The classifiers perform well up to 10 km range whereas the conventional matched field processing fails at about 4 km range without accurate environmental information.

  1. Implications of physical symmetries in adaptive image classifiers

    DEFF Research Database (Denmark)

    Sams, Thomas; Hansen, Jonas Lundbek

    2000-01-01

    It is demonstrated that rotational invariance and reflection symmetry of image classifiers lead to a reduction in the number of free parameters in the classifier. When used in adaptive detectors, e.g. neural networks, this may be used to decrease the number of training samples necessary to learn...... a given classification task, or to improve generalization of the neural network. Notably, the symmetrization of the detector does not compromise the ability to distinguish objects that break the symmetry. (C) 2000 Elsevier Science Ltd. All rights reserved....

  2. Classified facilities for environmental protection

    International Nuclear Information System (INIS)

    Anon.

    1993-02-01

    The legislation of the classified facilities governs most of the dangerous or polluting industries or fixed activities. It rests on the law of 9 July 1976 concerning facilities classified for environmental protection and its application decree of 21 September 1977. This legislation, the general texts of which appear in this volume 1, aims to prevent all the risks and the harmful effects coming from an installation (air, water or soil pollutions, wastes, even aesthetic breaches). The polluting or dangerous activities are defined in a list called nomenclature which subjects the facilities to a declaration or an authorization procedure. The authorization is delivered by the prefect at the end of an open and contradictory procedure after a public survey. In addition, the facilities can be subjected to technical regulations fixed by the Environment Minister (volume 2) or by the prefect for facilities subjected to declaration (volume 3). (A.B.)

  3. Energy-Efficient Neuromorphic Classifiers.

    Science.gov (United States)

    Martí, Daniel; Rigotti, Mattia; Seok, Mingoo; Fusi, Stefano

    2016-10-01

    Neuromorphic engineering combines the architectural and computational principles of systems neuroscience with semiconductor electronics, with the aim of building efficient and compact devices that mimic the synaptic and neural machinery of the brain. The energy consumptions promised by neuromorphic engineering are extremely low, comparable to those of the nervous system. Until now, however, the neuromorphic approach has been restricted to relatively simple circuits and specialized functions, thereby obfuscating a direct comparison of their energy consumption to that used by conventional von Neumann digital machines solving real-world tasks. Here we show that a recent technology developed by IBM can be leveraged to realize neuromorphic circuits that operate as classifiers of complex real-world stimuli. Specifically, we provide a set of general prescriptions to enable the practical implementation of neural architectures that compete with state-of-the-art classifiers. We also show that the energy consumption of these architectures, realized on the IBM chip, is typically two or more orders of magnitude lower than that of conventional digital machines implementing classifiers with comparable performance. Moreover, the spike-based dynamics display a trade-off between integration time and accuracy, which naturally translates into algorithms that can be flexibly deployed for either fast and approximate classifications, or more accurate classifications at the mere expense of longer running times and higher energy costs. This work finally proves that the neuromorphic approach can be efficiently used in real-world applications and has significant advantages over conventional digital devices when energy consumption is considered.

  4. Transcriptional profiling in human HaCaT keratinocytes in response to kaempferol and identification of potential transcription factors for regulating differential gene expression

    Science.gov (United States)

    Kang, Byung Young; Lee, Ki-Hwan; Lee, Yong Sung; Hong, Il; Lee, Mi-Ock; Min, Daejin; Chang, Ihseop; Hwang, Jae Sung; Park, Jun Seong; Kim, Duck Hee

    2008-01-01

    Kaempferol is the major flavonol in green tea and exhibits many biomedically useful properties such as antioxidative, cytoprotective and anti-apoptotic activities. To elucidate its effects on the skin, we investigated the transcriptional profiles of kaempferol-treated HaCaT cells using cDNA microarray analysis and identified 147 transcripts that exhibited significant changes in expression. Of these, 18 were up-regulated and 129 were down-regulated. These transcripts were then classified into 12 categories according to their functional roles: cell adhesion/cytoskeleton, cell cycle, redox homeostasis, immune/defense responses, metabolism, protein biosynthesis/modification, intracellular transport, RNA processing, DNA modification/ replication, regulation of transcription, signal transduction and transport. We then analyzed the promoter sequences of differentially-regulated genes and identified over-represented regulatory sites and candidate transcription factors (TFs) for gene regulation by kaempferol. These included c-REL, SAP-1, Ahr-ARNT, Nrf-2, Elk-1, SPI-B, NF-κB and p65. In addition, we validated the microarray results and promoter analyses using conventional methods such as real-time PCR and ELISA-based transcription factor assay. Our microarray analysis has provided useful information for determining the genetic regulatory network affected by kaempferol, and this approach will be useful for elucidating gene-phytochemical interactions. PMID:18446059

  5. 76 FR 34761 - Classified National Security Information

    Science.gov (United States)

    2011-06-14

    ... MARINE MAMMAL COMMISSION Classified National Security Information [Directive 11-01] AGENCY: Marine... Commission's (MMC) policy on classified information, as directed by Information Security Oversight Office... of Executive Order 13526, ``Classified National Security Information,'' and 32 CFR part 2001...

  6. Two channel EEG thought pattern classifier.

    Science.gov (United States)

    Craig, D A; Nguyen, H T; Burchey, H A

    2006-01-01

    This paper presents a real-time electro-encephalogram (EEG) identification system with the goal of achieving hands free control. With two EEG electrodes placed on the scalp of the user, EEG signals are amplified and digitised directly using a ProComp+ encoder and transferred to the host computer through the RS232 interface. Using a real-time multilayer neural network, the actual classification for the control of a powered wheelchair has a very fast response. It can detect changes in the user's thought pattern in 1 second. Using only two EEG electrodes at positions O(1) and C(4) the system can classify three mental commands (forward, left and right) with an accuracy of more than 79 %

  7. STATISTICAL TOOLS FOR CLASSIFYING GALAXY GROUP DYNAMICS

    International Nuclear Information System (INIS)

    Hou, Annie; Parker, Laura C.; Harris, William E.; Wilman, David J.

    2009-01-01

    The dynamical state of galaxy groups at intermediate redshifts can provide information about the growth of structure in the universe. We examine three goodness-of-fit tests, the Anderson-Darling (A-D), Kolmogorov, and χ 2 tests, in order to determine which statistical tool is best able to distinguish between groups that are relaxed and those that are dynamically complex. We perform Monte Carlo simulations of these three tests and show that the χ 2 test is profoundly unreliable for groups with fewer than 30 members. Power studies of the Kolmogorov and A-D tests are conducted to test their robustness for various sample sizes. We then apply these tests to a sample of the second Canadian Network for Observational Cosmology Redshift Survey (CNOC2) galaxy groups and find that the A-D test is far more reliable and powerful at detecting real departures from an underlying Gaussian distribution than the more commonly used χ 2 and Kolmogorov tests. We use this statistic to classify a sample of the CNOC2 groups and find that 34 of 106 groups are inconsistent with an underlying Gaussian velocity distribution, and thus do not appear relaxed. In addition, we compute velocity dispersion profiles (VDPs) for all groups with more than 20 members and compare the overall features of the Gaussian and non-Gaussian groups, finding that the VDPs of the non-Gaussian groups are distinct from those classified as Gaussian.

  8. Classification of wheat varieties: Use of two-dimensional gel electrophoresis for varieties that can not be classified by matrix assisted laser desorption/ionization-time of flight-mass spectrometry and an artificial neural network

    DEFF Research Database (Denmark)

    Jacobsen, Susanne; Nesic, Ljiljana; Petersen, Marianne Kjerstine

    2001-01-01

    Analyzing a gliadin extract by matrix assisted laser desorption/ionization-time of flight-mass spectrometry (MALDI- TOF-MS) combined with an artificial neural network (ANN) is a suitable method for identification of wheat varieties. However, the ANN can not distinguish between all different wheat...

  9. Diagnosis of Broiler Livers by Classifying Image Patches

    DEFF Research Database (Denmark)

    Jørgensen, Anders; Fagertun, Jens; Moeslund, Thomas B.

    2017-01-01

    The manual health inspection are becoming the bottleneck at poultry processing plants. We present a computer vision method for automatic diagnosis of broiler livers. The non-rigid livers, of varying shape and sizes, are classified in patches by a convolutional neural network, outputting maps...

  10. The Closing of the Classified Catalog at Boston University

    Science.gov (United States)

    Hazen, Margaret Hindle

    1974-01-01

    Although the classified catalog at Boston University libraries has been a useful research tool, it has proven too expensive to keep current. The library has converted to a traditional alphabetic subject catalog and will recieve catalog cards from the Ohio College Library Center through the New England Library Network. (Author/LS)

  11. A systems biology-based classifier for hepatocellular carcinoma diagnosis.

    Directory of Open Access Journals (Sweden)

    Yanqiong Zhang

    Full Text Available AIM: The diagnosis of hepatocellular carcinoma (HCC in the early stage is crucial to the application of curative treatments which are the only hope for increasing the life expectancy of patients. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with HCC progression. However, those marker sets shared few genes in common and were poorly validated using independent data. Therefore, we developed a systems biology based classifier by combining the differential gene expression with topological features of human protein interaction networks to enhance the ability of HCC diagnosis. METHODS AND RESULTS: In the Oncomine platform, genes differentially expressed in HCC tissues relative to their corresponding normal tissues were filtered by a corrected Q value cut-off and Concept filters. The identified genes that are common to different microarray datasets were chosen as the candidate markers. Then, their networks were analyzed by GeneGO Meta-Core software and the hub genes were chosen. After that, an HCC diagnostic classifier was constructed by Partial Least Squares modeling based on the microarray gene expression data of the hub genes. Validations of diagnostic performance showed that this classifier had high predictive accuracy (85.88∼92.71% and area under ROC curve (approximating 1.0, and that the network topological features integrated into this classifier contribute greatly to improving the predictive performance. Furthermore, it has been demonstrated that this modeling strategy is not only applicable to HCC, but also to other cancers. CONCLUSION: Our analysis suggests that the systems biology-based classifier that combines the differential gene expression and topological features of human protein interaction network may enhance the diagnostic performance of HCC classifier.

  12. Feature genes in metastatic breast cancer identified by MetaDE and SVM classifier methods.

    Science.gov (United States)

    Tuo, Youlin; An, Ning; Zhang, Ming

    2018-03-01

    The aim of the present study was to investigate the feature genes in metastatic breast cancer samples. A total of 5 expression profiles of metastatic breast cancer samples were downloaded from the Gene Expression Omnibus database, which were then analyzed using the MetaQC and MetaDE packages in R language. The feature genes between metastasis and non‑metastasis samples were screened under the threshold of PSVM) classifier training and verification. The accuracy of the SVM classifier was then evaluated using another independent dataset from The Cancer Genome Atlas database. Finally, function and pathway enrichment analyses for genes in the SVM classifier were performed. A total of 541 feature genes were identified between metastatic and non‑metastatic samples. The top 10 genes with the highest betweenness centrality values in the PPI network of feature genes were Nuclear RNA Export Factor 1, cyclin‑dependent kinase 2 (CDK2), myelocytomatosis proto‑oncogene protein (MYC), Cullin 5, SHC Adaptor Protein 1, Clathrin heavy chain, Nucleolin, WD repeat domain 1, proteasome 26S subunit non‑ATPase 2 and telomeric repeat binding factor 2. The cyclin‑dependent kinase inhibitor 1A (CDKN1A), E2F transcription factor 1 (E2F1), and MYC interacted with CDK2. The SVM classifier constructed by the top 30 feature genes was able to distinguish metastatic samples from non‑metastatic samples [correct rate, specificity, positive predictive value and negative predictive value >0.89; sensitivity >0.84; area under the receiver operating characteristic curve (AUROC) >0.96]. The verification of the SVM classifier in an independent dataset (35 metastatic samples and 143 non‑metastatic samples) revealed an accuracy of 94.38% and AUROC of 0.958. Cell cycle associated functions and pathways were the most significant terms of the 30 feature genes. A SVM classifier was constructed to assess the possibility of breast cancer metastasis, which presented high accuracy in several

  13. Waste classifying and separation device

    International Nuclear Information System (INIS)

    Kakiuchi, Hiroki.

    1997-01-01

    A flexible plastic bags containing solid wastes of indefinite shape is broken and the wastes are classified. The bag cutting-portion of the device has an ultrasonic-type or a heater-type cutting means, and the cutting means moves in parallel with the transferring direction of the plastic bags. A classification portion separates and discriminates the plastic bag from the contents and conducts classification while rotating a classification table. Accordingly, the plastic bag containing solids of indefinite shape can be broken and classification can be conducted efficiently and reliably. The device of the present invention has a simple structure which requires small installation space and enables easy maintenance. (T.M.)

  14. Defining and Classifying Interest Groups

    DEFF Research Database (Denmark)

    Baroni, Laura; Carroll, Brendan; Chalmers, Adam

    2014-01-01

    The interest group concept is defined in many different ways in the existing literature and a range of different classification schemes are employed. This complicates comparisons between different studies and their findings. One of the important tasks faced by interest group scholars engaged...... in large-N studies is therefore to define the concept of an interest group and to determine which classification scheme to use for different group types. After reviewing the existing literature, this article sets out to compare different approaches to defining and classifying interest groups with a sample...... in the organizational attributes of specific interest group types. As expected, our comparison of coding schemes reveals a closer link between group attributes and group type in narrower classification schemes based on group organizational characteristics than those based on a behavioral definition of lobbying....

  15. Supra-optimal expression of the cold-regulated OsMyb4 transcription factor in transgenic rice changes the complexity of transcriptional network with major effects on stress tolerance and panicle development

    KAUST Repository

    Park, Myoungryoul; Yun, Kilyoung; Mohanty, Bijayalaxmi; Herath, Venura; Xu, Fuyu; Wijaya, Edward; Bajic, Vladimir B.; Yun, Songjoong; De Los Reyes, Benildo G.

    2010-01-01

    acid (JA), salicylic acid (SA), ethylene and reactive oxygen species (ROS) responsive genes. OsMyb4 network is independent of drought response element binding protein/C-repeat binding factor (DREB/CBF) and its sub-regulons operate with possible co

  16. Adaptive Regularization of Neural Classifiers

    DEFF Research Database (Denmark)

    Andersen, Lars Nonboe; Larsen, Jan; Hansen, Lars Kai

    1997-01-01

    We present a regularization scheme which iteratively adapts the regularization parameters by minimizing the validation error. It is suggested to use the adaptive regularization scheme in conjunction with optimal brain damage pruning to optimize the architecture and to avoid overfitting. Furthermo......, we propose an improved neural classification architecture eliminating an inherent redundancy in the widely used SoftMax classification network. Numerical results demonstrate the viability of the method...

  17. Small-Molecule Inhibition of Rho/MKL/SRF Transcription in Prostate Cancer Cells: Modulation of Cell Cycle, ER Stress, and Metastasis Gene Networks

    Directory of Open Access Journals (Sweden)

    Chris R. Evelyn

    2016-05-01

    Full Text Available Metastasis is the major cause of cancer deaths and control of gene transcription has emerged as a critical contributing factor. RhoA- and RhoC-induced gene transcription via the actin-regulated transcriptional co-activator megakaryocytic leukemia (MKL and serum response factor (SRF drive metastasis in breast cancer and melanoma. We recently identified a compound, CCG-1423, which blocks Rho/MKL/SRF-mediated transcription and inhibits PC-3 prostate cancer cell invasion. Here, we undertook a genome-wide expression study in PC-3 cells to explore the mechanism and function of this compound. There was significant overlap in the genes modulated by CCG-1423 and Latrunculin B (Lat B, which blocks the Rho/MKL/SRF pathway by preventing actin polymerization. In contrast, the general transcription inhibitor 5,6-dichloro-1-β-d-ribofuranosyl-1H-benzimidazole (DRB showed a markedly different pattern. Effects of CCG-1423 and Lat B on gene expression correlated with literature studies of MKL knock-down. Gene sets involved in DNA synthesis and repair, G1/S transition, and apoptosis were modulated by CCG-1423. It also upregulated genes involved in endoplasmic reticulum stress. Targets of the known Rho target transcription factor family E2F and genes related to melanoma progression and metastasis were strongly suppressed by CCG-1423. These results confirm the ability of our compound to inhibit expression of numerous Rho/MKL-dependent genes and show effects on stress pathways as well. This suggests a novel approach to targeting aggressive cancers and metastasis.

  18. Decoding genome-wide GadEWX-transcriptional regulatory networks reveals multifaceted cellular responses to acid stress in Escherichia coli

    DEFF Research Database (Denmark)

    Seo, Sang Woo; Kim, Donghyuk; O'Brien, Edward J.

    2015-01-01

    The regulators GadE, GadW and GadX (which we refer to as GadEWX) play a critical role in the transcriptional regulation of the glutamate-dependent acid resistance (GDAR) system in Escherichia coli K-12 MG1655. However, the genome-wide regulatory role of GadEWX is still unknown. Here we comprehens...

  19. The identification of transcription factors expressed in the notochord of Ciona intestinalis adds new potential players to the brachyury gene regulatory network.

    Science.gov (United States)

    José-Edwards, Diana S; Kerner, Pierre; Kugler, Jamie E; Deng, Wei; Jiang, Di; Di Gregorio, Anna

    2011-07-01

    The notochord is the distinctive characteristic of chordates; however, the knowledge of the complement of transcription factors governing the development of this structure is still incomplete. Here we present the expression patterns of seven transcription factor genes detected in the notochord of the ascidian Ciona intestinalis at various stages of embryonic development. Four of these transcription factors, Fos-a, NFAT5, AFF and Klf15, have not been directly associated with the notochord in previous studies, while the others, including Spalt-like-a, Lmx-like, and STAT5/6-b, display evolutionarily conserved expression in this structure as well as in other domains. We examined the hierarchical relationships between these genes and the transcription factor Brachyury, which is necessary for notochord development in all chordates. We found that Ciona Brachyury regulates the expression of most, although not all, of these genes. These results shed light on the genetic regulatory program underlying notochord formation in Ciona and possibly other chordates. Copyright © 2011 Wiley-Liss, Inc.

  20. A pathway-specific microarray analysis highlights the complex and co-ordinated transcriptional networks of the developing grain of field-grown barley

    DEFF Research Database (Denmark)

    Hansen, Michael; Friis, Carsten; Bowra, Steve

    2009-01-01

    The aim of the study was to describe the molecular and biochemical interactions associated with amino acid biosynthesis and storage protein accumulation in the developing grains of field-grown barley. Our strategy was to analyse the transcription of genes associated with the biosynthesis of stora...

  1. Classifying Transition Behaviour in Postural Activity Monitoring

    Directory of Open Access Journals (Sweden)

    James BRUSEY

    2009-10-01

    Full Text Available A few accelerometers positioned on different parts of the body can be used to accurately classify steady state behaviour, such as walking, running, or sitting. Such systems are usually built using supervised learning approaches. Transitions between postures are, however, difficult to deal with using posture classification systems proposed to date, since there is no label set for intermediary postures and also the exact point at which the transition occurs can sometimes be hard to pinpoint. The usual bypass when using supervised learning to train such systems is to discard a section of the dataset around each transition. This leads to poorer classification performance when the systems are deployed out of the laboratory and used on-line, particularly if the regimes monitored involve fast paced activity changes. Time-based filtering that takes advantage of sequential patterns is a potential mechanism to improve posture classification accuracy in such real-life applications. Also, such filtering should reduce the number of event messages needed to be sent across a wireless network to track posture remotely, hence extending the system’s life. To support time-based filtering, understanding transitions, which are the major event generators in a classification system, is a key. This work examines three approaches to post-process the output of a posture classifier using time-based filtering: a naïve voting scheme, an exponentially weighted voting scheme, and a Bayes filter. Best performance is obtained from the exponentially weighted voting scheme although it is suspected that a more sophisticated treatment of the Bayes filter might yield better results.

  2. Aggregation Operator Based Fuzzy Pattern Classifier Design

    DEFF Research Database (Denmark)

    Mönks, Uwe; Larsen, Henrik Legind; Lohweg, Volker

    2009-01-01

    This paper presents a novel modular fuzzy pattern classifier design framework for intelligent automation systems, developed on the base of the established Modified Fuzzy Pattern Classifier (MFPC) and allows designing novel classifier models which are hardware-efficiently implementable....... The performances of novel classifiers using substitutes of MFPC's geometric mean aggregator are benchmarked in the scope of an image processing application against the MFPC to reveal classification improvement potentials for obtaining higher classification rates....

  3. Transcriptional Waves in the Yeast Cell Cycle

    OpenAIRE

    Oliva, Anna; Rosebrock, Adam; Ferrezuelo, Francisco; Pyne, Saumyadipta; Chen, Haiying; Skiena, Steve; Futcher, Bruce; Leatherwood, Janet

    2005-01-01

    Many genes are regulated as an innate part of the eukaryotic cell cycle, and a complex transcriptional network helps enable the cyclic behavior of dividing cells. This transcriptional network has been studied in Saccharomyces cerevisiae (budding yeast) and elsewhere. To provide more perspective on these regulatory mechanisms, we have used microarrays to measure gene expression through the cell cycle of Schizosaccharomyces pombe (fission yeast). The 750 genes with the most significant oscillat...

  4. 15 CFR 4.8 - Classified Information.

    Science.gov (United States)

    2010-01-01

    ... 15 Commerce and Foreign Trade 1 2010-01-01 2010-01-01 false Classified Information. 4.8 Section 4... INFORMATION Freedom of Information Act § 4.8 Classified Information. In processing a request for information..., the information shall be reviewed to determine whether it should remain classified. Ordinarily the...

  5. Genome-wide Reconstruction of OxyR and SoxRS Transcriptional Regulatory Networks under Oxidative Stress in Escherichia coli K-12 MG1655

    Directory of Open Access Journals (Sweden)

    Sang Woo Seo

    2015-08-01

    Full Text Available Three transcription factors (TFs, OxyR, SoxR, and SoxS, play a critical role in transcriptional regulation of the defense system for oxidative stress in bacteria. However, their full genome-wide regulatory potential is unknown. Here, we perform a genome-scale reconstruction of the OxyR, SoxR, and SoxS regulons in Escherichia coli K-12 MG1655. Integrative data analysis reveals that a total of 68 genes in 51 transcription units (TUs belong to these regulons. Among them, 48 genes showed more than 2-fold changes in expression level under single-TF-knockout conditions. This reconstruction expands the genome-wide roles of these factors to include direct activation of genes related to amino acid biosynthesis (methionine and aromatic amino acids, cell wall synthesis (lipid A biosynthesis and peptidoglycan growth, and divalent metal ion transport (Mn2+, Zn2+, and Mg2+. Investigating the co-regulation of these genes with other stress-response TFs reveals that they are independently regulated by stress-specific TFs.

  6. In Silico Analysis of Gene Expression Network Components Underlying Pigmentation Phenotypes in the Python Identified Evolutionarily Conserved Clusters of Transcription Factor Binding Sites

    Directory of Open Access Journals (Sweden)

    Kristopher J. L. Irizarry

    2016-01-01

    Full Text Available Color variation provides the opportunity to investigate the genetic basis of evolution and selection. Reptiles are less studied than mammals. Comparative genomics approaches allow for knowledge gained in one species to be leveraged for use in another species. We describe a comparative vertebrate analysis of conserved regulatory modules in pythons aimed at assessing bioinformatics evidence that transcription factors important in mammalian pigmentation phenotypes may also be important in python pigmentation phenotypes. We identified 23 python orthologs of mammalian genes associated with variation in coat color phenotypes for which we assessed the extent of pairwise protein sequence identity between pythons and mouse, dog, horse, cow, chicken, anole lizard, and garter snake. We next identified a set of melanocyte/pigment associated transcription factors (CREB, FOXD3, LEF-1, MITF, POU3F2, and USF-1 that exhibit relatively conserved sequence similarity within their DNA binding regions across species based on orthologous alignments across multiple species. Finally, we identified 27 evolutionarily conserved clusters of transcription factor binding sites within ~200-nucleotide intervals of the 1500-nucleotide upstream regions of AIM1, DCT, MC1R, MITF, MLANA, OA1, PMEL, RAB27A, and TYR from Python bivittatus. Our results provide insight into pigment phenotypes in pythons.

  7. Classifier fusion for VoIP attacks classification

    Science.gov (United States)

    Safarik, Jakub; Rezac, Filip

    2017-05-01

    SIP is one of the most successful protocols in the field of IP telephony communication. It establishes and manages VoIP calls. As the number of SIP implementation rises, we can expect a higher number of attacks on the communication system in the near future. This work aims at malicious SIP traffic classification. A number of various machine learning algorithms have been developed for attack classification. The paper presents a comparison of current research and the use of classifier fusion method leading to a potential decrease in classification error rate. Use of classifier combination makes a more robust solution without difficulties that may affect single algorithms. Different voting schemes, combination rules, and classifiers are discussed to improve the overall performance. All classifiers have been trained on real malicious traffic. The concept of traffic monitoring depends on the network of honeypot nodes. These honeypots run in several networks spread in different locations. Separation of honeypots allows us to gain an independent and trustworthy attack information.

  8. Networking

    OpenAIRE

    Rauno Lindholm, Daniel; Boisen Devantier, Lykke; Nyborg, Karoline Lykke; Høgsbro, Andreas; Fries, de; Skovlund, Louise

    2016-01-01

    The purpose of this project was to examine what influencing factor that has had an impact on the presumed increasement of the use of networking among academics on the labour market and how it is expressed. On the basis of the influence from globalization on the labour market it can be concluded that the globalization has transformed the labour market into a market based on the organization of networks. In this new organization there is a greater emphasis on employees having social qualificati...

  9. Wavelet classifier used for diagnosing shock absorbers in cars

    Directory of Open Access Journals (Sweden)

    Janusz GARDULSKI

    2007-01-01

    Full Text Available The paper discusses some commonly used methods of hydraulic absorbertesting. Disadvantages of the methods are described. A vibro-acoustic method is presented and recommended for practical use on existing test rigs. The method is based on continuous wavelet analysis combined with neural classifier and 25-neuron, one-way, three-layer back propagation network. The analysis satisfies the intended aim.

  10. Current Directional Protection of Series Compensated Line Using Intelligent Classifier

    Directory of Open Access Journals (Sweden)

    M. Mollanezhad Heydarabadi

    2016-12-01

    Full Text Available Current inversion condition leads to incorrect operation of current based directional relay in power system with series compensated device. Application of the intelligent system for fault direction classification has been suggested in this paper. A new current directional protection scheme based on intelligent classifier is proposed for the series compensated line. The proposed classifier uses only half cycle of pre-fault and post fault current samples at relay location to feed the classifier. A lot of forward and backward fault simulations under different system conditions upon a transmission line with a fixed series capacitor are carried out using PSCAD/EMTDC software. The applicability of decision tree (DT, probabilistic neural network (PNN and support vector machine (SVM are investigated using simulated data under different system conditions. The performance comparison of the classifiers indicates that the SVM is a best suitable classifier for fault direction discriminating. The backward faults can be accurately distinguished from forward faults even under current inversion without require to detect of the current inversion condition.

  11. Dynamic cluster generation for a fuzzy classifier with ellipsoidal regions.

    Science.gov (United States)

    Abe, S

    1998-01-01

    In this paper, we discuss a fuzzy classifier with ellipsoidal regions that dynamically generates clusters. First, for the data belonging to a class we define a fuzzy rule with an ellipsoidal region. Namely, using the training data for each class, we calculate the center and the covariance matrix of the ellipsoidal region for the class. Then we tune the fuzzy rules, i.e., the slopes of the membership functions, successively until there is no improvement in the recognition rate of the training data. Then if the number of the data belonging to a class that are misclassified into another class exceeds a prescribed number, we define a new cluster to which those data belong and the associated fuzzy rule. Then we tune the newly defined fuzzy rules in the similar way as stated above, fixing the already obtained fuzzy rules. We iterate generation of clusters and tuning of the newly generated fuzzy rules until the number of the data belonging to a class that are misclassified into another class does not exceed the prescribed number. We evaluate our method using thyroid data, Japanese Hiragana data of vehicle license plates, and blood cell data. By dynamic cluster generation, the generalization ability of the classifier is improved and the recognition rate of the fuzzy classifier for the test data is the best among the neural network classifiers and other fuzzy classifiers if there are no discrete input variables.

  12. Identification of flooded area from satellite images using Hybrid Kohonen Fuzzy C-Means sigma classifier

    Directory of Open Access Journals (Sweden)

    Krishna Kant Singh

    2017-06-01

    Full Text Available A novel neuro fuzzy classifier Hybrid Kohonen Fuzzy C-Means-σ (HKFCM-σ is proposed in this paper. The proposed classifier is a hybridization of Kohonen Clustering Network (KCN with FCM-σ clustering algorithm. The network architecture of HKFCM-σ is similar to simple KCN network having only two layers, i.e., input and output layer. However, the selection of winner neuron is done based on FCM-σ algorithm. Thus, embedding the features of both, a neural network and a fuzzy clustering algorithm in the classifier. This hybridization results in a more efficient, less complex and faster classifier for classifying satellite images. HKFCM-σ is used to identify the flooding that occurred in Kashmir area in September 2014. The HKFCM-σ classifier is applied on pre and post flooding Landsat 8 OLI images of Kashmir to detect the areas that were flooded due to the heavy rainfalls of September, 2014. The classifier is trained using the mean values of the various spectral indices like NDVI, NDWI, NDBI and first component of Principal Component Analysis. The error matrix was computed to test the performance of the method. The method yields high producer’s accuracy, consumer’s accuracy and kappa coefficient value indicating that the proposed classifier is highly effective and efficient.

  13. Error minimizing algorithms for nearest eighbor classifiers

    Energy Technology Data Exchange (ETDEWEB)

    Porter, Reid B [Los Alamos National Laboratory; Hush, Don [Los Alamos National Laboratory; Zimmer, G. Beate [TEXAS A& M

    2011-01-03

    Stack Filters define a large class of discrete nonlinear filter first introd uced in image and signal processing for noise removal. In recent years we have suggested their application to classification problems, and investigated their relationship to other types of discrete classifiers such as Decision Trees. In this paper we focus on a continuous domain version of Stack Filter Classifiers which we call Ordered Hypothesis Machines (OHM), and investigate their relationship to Nearest Neighbor classifiers. We show that OHM classifiers provide a novel framework in which to train Nearest Neighbor type classifiers by minimizing empirical error based loss functions. We use the framework to investigate a new cost sensitive loss function that allows us to train a Nearest Neighbor type classifier for low false alarm rate applications. We report results on both synthetic data and real-world image data.

  14. Multi-Omics and Integrated Network Analyses Reveal New Insights into the Systems Relationships between Metabolites, Structural Genes, and Transcriptional Regulators in Developing Grape Berries (Vitis vinifera L. Exposed to Water Deficit

    Directory of Open Access Journals (Sweden)

    Stefania Savoi

    2017-07-01

    Full Text Available Grapes are one of the major fruit crops and they are cultivated in many dry environments. This study comprehensively characterizes the metabolic response of grape berries exposed to water deficit at different developmental stages. Increases of proline, branched-chain amino acids, phenylpropanoids, anthocyanins, and free volatile organic compounds have been previously observed in grape berries exposed to water deficit. Integrating RNA-sequencing analysis of the transcriptome with large-scale analysis of central and specialized metabolites, we reveal that these increases occur via a coordinated regulation of key structural pathway genes. Water deficit-induced up-regulation of flavonoid genes is also coordinated with the down-regulation of many stilbene synthases and a consistent decrease in stilbenoid concentration. Water deficit activated both ABA-dependent and ABA-independent signal transduction pathways by modulating the expression of several transcription factors. Gene-gene and gene-metabolite network analyses showed that water deficit-responsive transcription factors such as bZIPs, AP2/ERFs, MYBs, and NACs are implicated in the regulation of stress-responsive metabolites. Enrichment of known and novel cis-regulatory elements in the promoters of several ripening-specific/water deficit-induced modules further affirms the involvement of a transcription factor cross-talk in the berry response to water deficit. Together, our integrated approaches show that water deficit-regulated gene modules are strongly linked to key fruit-quality metabolites and multiple signal transduction pathways may be critical to achieve a balance between the regulation of the stress-response and the berry ripening program. This study constitutes an invaluable resource for future discoveries and comparative studies, in grapes and other fruits, centered on reproductive tissue metabolism under abiotic stress.

  15. Multi-Omics and Integrated Network Analyses Reveal New Insights into the Systems Relationships between Metabolites, Structural Genes, and Transcriptional Regulators in Developing Grape Berries (Vitis vinifera L.) Exposed to Water Deficit.

    Science.gov (United States)

    Savoi, Stefania; Wong, Darren C J; Degu, Asfaw; Herrera, Jose C; Bucchetti, Barbara; Peterlunger, Enrico; Fait, Aaron; Mattivi, Fulvio; Castellarin, Simone D

    2017-01-01

    Grapes are one of the major fruit crops and they are cultivated in many dry environments. This study comprehensively characterizes the metabolic response of grape berries exposed to water deficit at different developmental stages. Increases of proline, branched-chain amino acids, phenylpropanoids, anthocyanins, and free volatile organic compounds have been previously observed in grape berries exposed to water deficit. Integrating RNA-sequencing analysis of the transcriptome with large-scale analysis of central and specialized metabolites, we reveal that these increases occur via a coordinated regulation of key structural pathway genes. Water deficit-induced up-regulation of flavonoid genes is also coordinated with the down-regulation of many stilbene synthases and a consistent decrease in stilbenoid concentration. Water deficit activated both ABA-dependent and ABA-independent signal transduction pathways by modulating the expression of several transcription factors. Gene-gene and gene-metabolite network analyses showed that water deficit-responsive transcription factors such as bZIPs, AP2/ERFs, MYBs, and NACs are implicated in the regulation of stress-responsive metabolites. Enrichment of known and novel cis -regulatory elements in the promoters of several ripening-specific/water deficit-induced modules further affirms the involvement of a transcription factor cross-talk in the berry response to water deficit. Together, our integrated approaches show that water deficit-regulated gene modules are strongly linked to key fruit-quality metabolites and multiple signal transduction pathways may be critical to achieve a balance between the regulation of the stress-response and the berry ripening program. This study constitutes an invaluable resource for future discoveries and comparative studies, in grapes and other fruits, centered on reproductive tissue metabolism under abiotic stress.

  16. The Streptococcus pyogenes Serotype M49 Nra-Ralp3 Transcriptional Regulatory Network and Its Control of Virulence Factor Expression from the Novel eno ralp3 epf sagA Pathogenicity Region▿ †

    Science.gov (United States)

    Kreikemeyer, Bernd; Nakata, Masanobu; Köller, Thomas; Hildisch, Hendrikje; Kourakos, Vassilios; Standar, Kerstin; Kawabata, Shigetada; Glocker, Michael O.; Podbielski, Andreas

    2007-01-01

    Many Streptococcus pyogenes (group A streptococcus [GAS]) virulence factor- and transcriptional regulator-encoding genes cluster together in discrete genomic regions. Nra is a central regulator of the FCT region. Previous studies exclusively described Nra as a transcriptional repressor of adhesin and toxin genes. Here transcriptome and proteome analysis of a serotype M49 GAS strain and an isogenic Nra mutant of this strain revealed the complete Nra regulon profile. Nra is active in all growth phases tested, with the largest regulon in the transition phase. Almost exclusively, virulence factor-encoding genes are repressed by Nra; these genes include the GAS pilus operon, the capsule synthesis operon, the cytolysin-mediated translocation system genes, all Mga region core virulence genes, and genes encoding other regulators, like the Ihk/Irr system, Rgg, and two additional RofA-like protein family regulators. Surprisingly, our experiments revealed that Nra additionally acts as a positive regulator, mostly for genes encoding proteins and enzymes with metabolic functions. Epidemiological investigations revealed strong genetic linkage of one particular Nra-repressed regulator, Ralp3 (SPy0735), with a gene encoding Epf (extracellular protein factor from Streptococcus suis). In a serotype-specific fashion, this ralp3 epf gene block is integrated, most likely via transposition, into the eno sagA virulence gene block, which is present in all GAS serotypes. In GAS serotypes M1, M4, M12, M28, and M49 this novel discrete genetic region is therefore designated the eno ralp3 epf sagA (ERES) pathogenicity region. Functional experiments showed that Epf is a novel GAS plasminogen-binding protein and revealed that Ralp3 activity counteracts Nra and MsmR regulatory activity. In addition to the Mga and FCT regions, the ERES region is the third discrete chromosomal pathogenicity region. All of these regions are transcriptionally linked, adding another level of complexity to the known

  17. The Streptococcus pyogenes serotype M49 Nra-Ralp3 transcriptional regulatory network and its control of virulence factor expression from the novel eno ralp3 epf sagA pathogenicity region.

    Science.gov (United States)

    Kreikemeyer, Bernd; Nakata, Masanobu; Köller, Thomas; Hildisch, Hendrikje; Kourakos, Vassilios; Standar, Kerstin; Kawabata, Shigetada; Glocker, Michael O; Podbielski, Andreas

    2007-12-01

    Many Streptococcus pyogenes (group A streptococcus [GAS]) virulence factor- and transcriptional regulator-encoding genes cluster together in discrete genomic regions. Nra is a central regulator of the FCT region. Previous studies exclusively described Nra as a transcriptional repressor of adhesin and toxin genes. Here transcriptome and proteome analysis of a serotype M49 GAS strain and an isogenic Nra mutant of this strain revealed the complete Nra regulon profile. Nra is active in all growth phases tested, with the largest regulon in the transition phase. Almost exclusively, virulence factor-encoding genes are repressed by Nra; these genes include the GAS pilus operon, the capsule synthesis operon, the cytolysin-mediated translocation system genes, all Mga region core virulence genes, and genes encoding other regulators, like the Ihk/Irr system, Rgg, and two additional RofA-like protein family regulators. Surprisingly, our experiments revealed that Nra additionally acts as a positive regulator, mostly for genes encoding proteins and enzymes with metabolic functions. Epidemiological investigations revealed strong genetic linkage of one particular Nra-repressed regulator, Ralp3 (SPy0735), with a gene encoding Epf (extracellular protein factor from Streptococcus suis). In a serotype-specific fashion, this ralp3 epf gene block is integrated, most likely via transposition, into the eno sagA virulence gene block, which is present in all GAS serotypes. In GAS serotypes M1, M4, M12, M28, and M49 this novel discrete genetic region is therefore designated the eno ralp3 epf sagA (ERES) pathogenicity region. Functional experiments showed that Epf is a novel GAS plasminogen-binding protein and revealed that Ralp3 activity counteracts Nra and MsmR regulatory activity. In addition to the Mga and FCT regions, the ERES region is the third discrete chromosomal pathogenicity region. All of these regions are transcriptionally linked, adding another level of complexity to the known

  18. Hierarchical mixtures of naive Bayes classifiers

    NARCIS (Netherlands)

    Wiering, M.A.

    2002-01-01

    Naive Bayes classifiers tend to perform very well on a large number of problem domains, although their representation power is quite limited compared to more sophisticated machine learning algorithms. In this pa- per we study combining multiple naive Bayes classifiers by using the hierar- chical

  19. Comparing classifiers for pronunciation error detection

    NARCIS (Netherlands)

    Strik, H.; Truong, K.; Wet, F. de; Cucchiarini, C.

    2007-01-01

    Providing feedback on pronunciation errors in computer assisted language learning systems requires that pronunciation errors be detected automatically. In the present study we compare four types of classifiers that can be used for this purpose: two acoustic-phonetic classifiers (one of which employs

  20. Feature extraction for dynamic integration of classifiers

    NARCIS (Netherlands)

    Pechenizkiy, M.; Tsymbal, A.; Puuronen, S.; Patterson, D.W.

    2007-01-01

    Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. In this paper, we present an algorithm for the dynamic integration of classifiers in the space of extracted features (FEDIC). It is based on the technique

  1. Speaker emotion recognition: from classical classifiers to deep neural networks

    Science.gov (United States)

    Mezghani, Eya; Charfeddine, Maha; Nicolas, Henri; Ben Amar, Chokri

    2018-04-01

    Speaker emotion recognition is considered among the most challenging tasks in recent years. In fact, automatic systems for security, medicine or education can be improved when considering the speech affective state. In this paper, a twofold approach for speech emotion classification is proposed. At the first side, a relevant set of features is adopted, and then at the second one, numerous supervised training techniques, involving classic methods as well as deep learning, are experimented. Experimental results indicate that deep architecture can improve classification performance on two affective databases, the Berlin Dataset of Emotional Speech and the SAVEE Dataset Surrey Audio-Visual Expressed Emotion.

  2. Evaluating Machine Learning Classifiers for Hybrid Network Intrusion Detection Systems

    Science.gov (United States)

    2015-03-26

    and the value-focused method. Comparing results from the two evaluation methods, fallacies are revealed with 2 of the 5 notional weighting schemes...for them, because of their relentless support, love , and encouragement. I give a sincere thank you to my research advisor, Dr. Robert Mills, for his...though Ad- aBoost.BayesNet dominated the traditional PR space using a single curve approach. This evaluation fallacy has not been demonstrated prior to

  3. Using Conjugate Gradient Network to Classify Stress Level of Patients.

    Directory of Open Access Journals (Sweden)

    Er. S. Pawar

    2013-02-01

    Full Text Available Diagnosis of stress is important because it can cause many diseases e.g., heart disease, headache, migraine, sleep problems, irritability etc. Diagnosis of stress in patients often involves acquisition of biological signals for example heart rate, electrocardiogram (ECG, electromyography signals (EMG etc. Stress diagnosis using biomedical signals is difficult and since the biomedical signals are too complex to generate any rule an experienced person or expert is needed to determine stress levels. Also, it is not feasible to use all the features that are available or possible to extract from the signal. So, relevant features should be chosen from the extracted features that are capable to diagnose stress. Electronics devices are increasingly being seen in the field of medicine for diagnosis, therapy, checking of stress levels etc. The research and development work of medical electronics engineers leads to the manufacturing of sophisticated diagnostic medical equipment needed to ensure good health care. Biomedical engineering combines the design and problem solving skills of engineering with medical and biological sciences to improve health care diagnosis and treatment.

  4. Voltage-gated Na+ channel SCN5A is a key regulator of a gene transcriptional network that controls colon cancer invasion

    Science.gov (United States)

    House, Carrie D.; Vaske, Charles J.; Schwartz, Arnold M.; Obias, Vincent; Frank, Bryan; Luu, Truong; Sarvazyan, Narine; Irby, Rosalyn; Strausberg, Robert L.; Hales, Tim G.; Stuart, Joshua M.; Lee, Norman H.

    2010-01-01

    Voltage-gated Na+ channels (VGSCs) have been implicated in the metastatic potential of human breast, prostate and lung cancer cells. Specifically, the SCN5A gene encoding the VGSC isotype Nav1.5 has been defined as a key driver of human cancer cell invasion. In this study, we examined the expression and function of VGSCs in a panel of colon cancer cell lines by electrophysiological recordings. Na+ channel activity and invasive potential were inhibited pharmacologically by tetrodotoxin or genetically by siRNAs specifically targeting SCN5A. Clinical relevance was established by immunohistochemistry of patient biopsies, where there was strong Nav1.5 protein staining in colon cancer specimens but little to no staining in matched-paired normal colon tissues. We explored the mechanism of VGSC-mediated invasive potential on the basis of reported links between VGSC activity and gene expression in excitable cells. Probabilistic modeling of loss-of-function screens and microarray data established an unequivocal role of VGSC SCN5A as a high level regulator of a colon cancer invasion network, involving genes that encompass Wnt signaling, cell migration, ectoderm development, response to biotic stimulus, steroid metabolic process and cell cycle control. siRNA-mediated knockdown of predicted downstream network components caused a loss of invasive behavior, demonstrating network connectivity and its function in driving colon cancer invasion. PMID:20651255

  5. Deconvolution When Classifying Noisy Data Involving Transformations

    KAUST Repository

    Carroll, Raymond

    2012-09-01

    In the present study, we consider the problem of classifying spatial data distorted by a linear transformation or convolution and contaminated by additive random noise. In this setting, we show that classifier performance can be improved if we carefully invert the data before the classifier is applied. However, the inverse transformation is not constructed so as to recover the original signal, and in fact, we show that taking the latter approach is generally inadvisable. We introduce a fully data-driven procedure based on cross-validation, and use several classifiers to illustrate numerical properties of our approach. Theoretical arguments are given in support of our claims. Our procedure is applied to data generated by light detection and ranging (Lidar) technology, where we improve on earlier approaches to classifying aerosols. This article has supplementary materials online.

  6. Deconvolution When Classifying Noisy Data Involving Transformations.

    Science.gov (United States)

    Carroll, Raymond; Delaigle, Aurore; Hall, Peter

    2012-09-01

    In the present study, we consider the problem of classifying spatial data distorted by a linear transformation or convolution and contaminated by additive random noise. In this setting, we show that classifier performance can be improved if we carefully invert the data before the classifier is applied. However, the inverse transformation is not constructed so as to recover the original signal, and in fact, we show that taking the latter approach is generally inadvisable. We introduce a fully data-driven procedure based on cross-validation, and use several classifiers to illustrate numerical properties of our approach. Theoretical arguments are given in support of our claims. Our procedure is applied to data generated by light detection and ranging (Lidar) technology, where we improve on earlier approaches to classifying aerosols. This article has supplementary materials online.

  7. Deconvolution When Classifying Noisy Data Involving Transformations

    KAUST Repository

    Carroll, Raymond; Delaigle, Aurore; Hall, Peter

    2012-01-01

    In the present study, we consider the problem of classifying spatial data distorted by a linear transformation or convolution and contaminated by additive random noise. In this setting, we show that classifier performance can be improved if we carefully invert the data before the classifier is applied. However, the inverse transformation is not constructed so as to recover the original signal, and in fact, we show that taking the latter approach is generally inadvisable. We introduce a fully data-driven procedure based on cross-validation, and use several classifiers to illustrate numerical properties of our approach. Theoretical arguments are given in support of our claims. Our procedure is applied to data generated by light detection and ranging (Lidar) technology, where we improve on earlier approaches to classifying aerosols. This article has supplementary materials online.

  8. The transcriptional landscape

    DEFF Research Database (Denmark)

    Nielsen, Henrik

    2011-01-01

    The application of new and less biased methods to study the transcriptional output from genomes, such as tiling arrays and deep sequencing, has revealed that most of the genome is transcribed and that there is substantial overlap of transcripts derived from the two strands of DNA. In protein coding...... regions, the map of transcripts is very complex due to small transcripts from the flanking ends of the transcription unit, the use of multiple start and stop sites for the main transcript, production of multiple functional RNA molecules from the same primary transcript, and RNA molecules made...... by independent transcription from within the unit. In genomic regions separating those that encode proteins or highly abundant RNA molecules with known function, transcripts are generally of low abundance and short-lived. In most of these cases, it is unclear to what extent a function is related to transcription...

  9. Comparison of Classifier Architectures for Online Neural Spike Sorting.

    Science.gov (United States)

    Saeed, Maryam; Khan, Amir Ali; Kamboh, Awais Mehmood

    2017-04-01

    High-density, intracranial recordings from micro-electrode arrays need to undergo Spike Sorting in order to associate the recorded neuronal spikes to particular neurons. This involves spike detection, feature extraction, and classification. To reduce the data transmission and power requirements, on-chip real-time processing is becoming very popular. However, high computational resources are required for classifiers in on-chip spike-sorters, making scalability a great challenge. In this review paper, we analyze several popular classifiers to propose five new hardware architectures using the off-chip training with on-chip classification approach. These include support vector classification, fuzzy C-means classification, self-organizing maps classification, moving-centroid K-means classification, and Cosine distance classification. The performance of these architectures is analyzed in terms of accuracy and resource requirement. We establish that the neural networks based Self-Organizing Maps classifier offers the most viable solution. A spike sorter based on the Self-Organizing Maps classifier, requires only 7.83% of computational resources of the best-reported spike sorter, hierarchical adaptive means, while offering a 3% better accuracy at 7 dB SNR.

  10. A CLASSIFIER SYSTEM USING SMOOTH GRAPH COLORING

    Directory of Open Access Journals (Sweden)

    JORGE FLORES CRUZ

    2017-01-01

    Full Text Available Unsupervised classifiers allow clustering methods with less or no human intervention. Therefore it is desirable to group the set of items with less data processing. This paper proposes an unsupervised classifier system using the model of soft graph coloring. This method was tested with some classic instances in the literature and the results obtained were compared with classifications made with human intervention, yielding as good or better results than supervised classifiers, sometimes providing alternative classifications that considers additional information that humans did not considered.

  11. High dimensional classifiers in the imbalanced case

    DEFF Research Database (Denmark)

    Bak, Britta Anker; Jensen, Jens Ledet

    We consider the binary classification problem in the imbalanced case where the number of samples from the two groups differ. The classification problem is considered in the high dimensional case where the number of variables is much larger than the number of samples, and where the imbalance leads...... to a bias in the classification. A theoretical analysis of the independence classifier reveals the origin of the bias and based on this we suggest two new classifiers that can handle any imbalance ratio. The analytical results are supplemented by a simulation study, where the suggested classifiers in some...

  12. Deep Feature Learning and Cascaded Classifier for Large Scale Data

    DEFF Research Database (Denmark)

    Prasoon, Adhish

    from data rather than having a predefined feature set. We explore deep learning approach of convolutional neural network (CNN) for segmenting three dimensional medical images. We propose a novel system integrating three 2D CNNs, which have a one-to-one association with the xy, yz and zx planes of 3D......This thesis focuses on voxel/pixel classification based approaches for image segmentation. The main application is segmentation of articular cartilage in knee MRIs. The first major contribution of the thesis deals with large scale machine learning problems. Many medical imaging problems need huge...... amount of training data to cover sufficient biological variability. Learning methods scaling badly with number of training data points cannot be used in such scenarios. This may restrict the usage of many powerful classifiers having excellent generalization ability. We propose a cascaded classifier which...

  13. Classifiers based on optimal decision rules

    KAUST Repository

    Amin, Talha

    2013-11-25

    Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).

  14. Classifiers based on optimal decision rules

    KAUST Repository

    Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

    2013-01-01

    Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).

  15. Combining multiple classifiers for age classification

    CSIR Research Space (South Africa)

    Van Heerden, C

    2009-11-01

    Full Text Available The authors compare several different classifier combination methods on a single task, namely speaker age classification. This task is well suited to combination strategies, since significantly different feature classes are employed. Support vector...

  16. Consistency Analysis of Nearest Subspace Classifier

    OpenAIRE

    Wang, Yi

    2015-01-01

    The Nearest subspace classifier (NSS) finds an estimation of the underlying subspace within each class and assigns data points to the class that corresponds to its nearest subspace. This paper mainly studies how well NSS can be generalized to new samples. It is proved that NSS is strongly consistent under certain assumptions. For completeness, NSS is evaluated through experiments on various simulated and real data sets, in comparison with some other linear model based classifiers. It is also ...

  17. NAC transcription factors: structurally distinct, functionally diverse

    DEFF Research Database (Denmark)

    Olsen, Addie Nina; Ernst, Heidi A; Leggio, Leila Lo

    2005-01-01

    level and localization, and to the first indications of NAC participation in transcription factor networks. The recent determination of the DNA and protein binding NAC domain structure offers insight into the molecular functions of the protein family. Research into NAC transcription factors has......NAC proteins constitute one of the largest families of plant-specific transcription factors, and the family is present in a wide range of land plants. Here, we summarize the biological and molecular functions of the NAC family, paying particular attention to the intricate regulation of NAC protein...

  18. Pixel Classification of SAR ice images using ANFIS-PSO Classifier

    Directory of Open Access Journals (Sweden)

    G. Vasumathi

    2016-12-01

    Full Text Available Synthetic Aperture Radar (SAR is playing a vital role in taking extremely high resolution radar images. It is greatly used to monitor the ice covered ocean regions. Sea monitoring is important for various purposes which includes global climate systems and ship navigation. Classification on the ice infested area gives important features which will be further useful for various monitoring process around the ice regions. Main objective of this paper is to classify the SAR ice image that helps in identifying the regions around the ice infested areas. In this paper three stages are considered in classification of SAR ice images. It starts with preprocessing in which the speckled SAR ice images are denoised using various speckle removal filters; comparison is made on all these filters to find the best filter in speckle removal. Second stage includes segmentation in which different regions are segmented using K-means and watershed segmentation algorithms; comparison is made between these two algorithms to find the best in segmenting SAR ice images. The last stage includes pixel based classification which identifies and classifies the segmented regions using various supervised learning classifiers. The algorithms includes Back propagation neural networks (BPN, Fuzzy Classifier, Adaptive Neuro Fuzzy Inference Classifier (ANFIS classifier and proposed ANFIS with Particle Swarm Optimization (PSO classifier; comparison is made on all these classifiers to propose which classifier is best suitable for classifying the SAR ice image. Various evaluation metrics are performed separately at all these three stages.

  19. Self-organizing map classifier for stressed speech recognition

    Science.gov (United States)

    Partila, Pavol; Tovarek, Jaromir; Voznak, Miroslav

    2016-05-01

    This paper presents a method for detecting speech under stress using Self-Organizing Maps. Most people who are exposed to stressful situations can not adequately respond to stimuli. Army, police, and fire department occupy the largest part of the environment that are typical of an increased number of stressful situations. The role of men in action is controlled by the control center. Control commands should be adapted to the psychological state of a man in action. It is known that the psychological changes of the human body are also reflected physiologically, which consequently means the stress effected speech. Therefore, it is clear that the speech stress recognizing system is required in the security forces. One of the possible classifiers, which are popular for its flexibility, is a self-organizing map. It is one type of the artificial neural networks. Flexibility means independence classifier on the character of the input data. This feature is suitable for speech processing. Human Stress can be seen as a kind of emotional state. Mel-frequency cepstral coefficients, LPC coefficients, and prosody features were selected for input data. These coefficients were selected for their sensitivity to emotional changes. The calculation of the parameters was performed on speech recordings, which can be divided into two classes, namely the stress state recordings and normal state recordings. The benefit of the experiment is a method using SOM classifier for stress speech detection. Results showed the advantage of this method, which is input data flexibility.

  20. General and Local: Averaged k-Dependence Bayesian Classifiers

    Directory of Open Access Journals (Sweden)

    Limin Wang

    2015-06-01

    Full Text Available The inference of a general Bayesian network has been shown to be an NP-hard problem, even for approximate solutions. Although k-dependence Bayesian (KDB classifier can construct at arbitrary points (values of k along the attribute dependence spectrum, it cannot identify the changes of interdependencies when attributes take different values. Local KDB, which learns in the framework of KDB, is proposed in this study to describe the local dependencies implicated in each test instance. Based on the analysis of functional dependencies, substitution-elimination resolution, a new type of semi-naive Bayesian operation, is proposed to substitute or eliminate generalization to achieve accurate estimation of conditional probability distribution while reducing computational complexity. The final classifier, averaged k-dependence Bayesian (AKDB classifiers, will average the output of KDB and local KDB. Experimental results on the repository of machine learning databases from the University of California Irvine (UCI showed that AKDB has significant advantages in zero-one loss and bias relative to naive Bayes (NB, tree augmented naive Bayes (TAN, Averaged one-dependence estimators (AODE, and KDB. Moreover, KDB and local KDB show mutually complementary characteristics with respect to variance.

  1. Classifier-Guided Sampling for Complex Energy System Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Backlund, Peter B. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Eddy, John P. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

    2015-09-01

    This report documents the results of a Laboratory Directed Research and Development (LDRD) effort enti tled "Classifier - Guided Sampling for Complex Energy System Optimization" that was conducted during FY 2014 and FY 2015. The goal of this proj ect was to develop, implement, and test major improvements to the classifier - guided sampling (CGS) algorithm. CGS is type of evolutionary algorithm for perform ing search and optimization over a set of discrete design variables in the face of one or more objective functions. E xisting evolutionary algorithms, such as genetic algorithms , may require a large number of o bjecti ve function evaluations to identify optimal or near - optimal solutions . Reducing the number of evaluations can result in significant time savings, especially if the objective function is computationally expensive. CGS reduce s the evaluation count by us ing a Bayesian network classifier to filter out non - promising candidate designs , prior to evaluation, based on their posterior probabilit ies . In this project, b oth the single - objective and multi - objective version s of the CGS are developed and tested on a set of benchm ark problems. As a domain - specific case study, CGS is used to design a microgrid for use in islanded mode during an extended bulk power grid outage.

  2. Entropy based classifier for cross-domain opinion mining

    Directory of Open Access Journals (Sweden)

    Jyoti S. Deshmukh

    2018-01-01

    Full Text Available In recent years, the growth of social network has increased the interest of people in analyzing reviews and opinions for products before they buy them. Consequently, this has given rise to the domain adaptation as a prominent area of research in sentiment analysis. A classifier trained from one domain often gives poor results on data from another domain. Expression of sentiment is different in every domain. The labeling cost of each domain separately is very high as well as time consuming. Therefore, this study has proposed an approach that extracts and classifies opinion words from one domain called source domain and predicts opinion words of another domain called target domain using a semi-supervised approach, which combines modified maximum entropy and bipartite graph clustering. A comparison of opinion classification on reviews on four different product domains is presented. The results demonstrate that the proposed method performs relatively well in comparison to the other methods. Comparison of SentiWordNet of domain-specific and domain-independent words reveals that on an average 72.6% and 88.4% words, respectively, are correctly classified.

  3. Reinforcement Learning Based Artificial Immune Classifier

    Directory of Open Access Journals (Sweden)

    Mehmet Karakose

    2013-01-01

    Full Text Available One of the widely used methods for classification that is a decision-making process is artificial immune systems. Artificial immune systems based on natural immunity system can be successfully applied for classification, optimization, recognition, and learning in real-world problems. In this study, a reinforcement learning based artificial immune classifier is proposed as a new approach. This approach uses reinforcement learning to find better antibody with immune operators. The proposed new approach has many contributions according to other methods in the literature such as effectiveness, less memory cell, high accuracy, speed, and data adaptability. The performance of the proposed approach is demonstrated by simulation and experimental results using real data in Matlab and FPGA. Some benchmark data and remote image data are used for experimental results. The comparative results with supervised/unsupervised based artificial immune system, negative selection classifier, and resource limited artificial immune classifier are given to demonstrate the effectiveness of the proposed new method.

  4. Classifier Fusion With Contextual Reliability Evaluation.

    Science.gov (United States)

    Liu, Zhunga; Pan, Quan; Dezert, Jean; Han, Jun-Wei; He, You

    2018-05-01

    Classifier fusion is an efficient strategy to improve the classification performance for the complex pattern recognition problem. In practice, the multiple classifiers to combine can have different reliabilities and the proper reliability evaluation plays an important role in the fusion process for getting the best classification performance. We propose a new method for classifier fusion with contextual reliability evaluation (CF-CRE) based on inner reliability and relative reliability concepts. The inner reliability, represented by a matrix, characterizes the probability of the object belonging to one class when it is classified to another class. The elements of this matrix are estimated from the -nearest neighbors of the object. A cautious discounting rule is developed under belief functions framework to revise the classification result according to the inner reliability. The relative reliability is evaluated based on a new incompatibility measure which allows to reduce the level of conflict between the classifiers by applying the classical evidence discounting rule to each classifier before their combination. The inner reliability and relative reliability capture different aspects of the classification reliability. The discounted classification results are combined with Dempster-Shafer's rule for the final class decision making support. The performance of CF-CRE have been evaluated and compared with those of main classical fusion methods using real data sets. The experimental results show that CF-CRE can produce substantially higher accuracy than other fusion methods in general. Moreover, CF-CRE is robust to the changes of the number of nearest neighbors chosen for estimating the reliability matrix, which is appealing for the applications.

  5. Classifying sows' activity types from acceleration patterns

    DEFF Research Database (Denmark)

    Cornou, Cecile; Lundbye-Christensen, Søren

    2008-01-01

    An automated method of classifying sow activity using acceleration measurements would allow the individual sow's behavior to be monitored throughout the reproductive cycle; applications for detecting behaviors characteristic of estrus and farrowing or to monitor illness and welfare can be foreseen....... This article suggests a method of classifying five types of activity exhibited by group-housed sows. The method involves the measurement of acceleration in three dimensions. The five activities are: feeding, walking, rooting, lying laterally and lying sternally. Four time series of acceleration (the three...

  6. Data characteristics that determine classifier performance

    CSIR Research Space (South Africa)

    Van der Walt, Christiaan M

    2006-11-01

    Full Text Available available at [11]. The kNN uses a LinearNN nearest neighbour search algorithm with an Euclidean distance metric [8]. The optimal k value is determined by performing 10-fold cross-validation. An optimal k value between 1 and 10 is used for Experiments 1... classifiers. 10-fold cross-validation is used to evaluate and compare the performance of the classifiers on the different data sets. 3.1. Artificial data generation Multivariate Gaussian distributions are used to generate artificial data sets. We use d...

  7. A Customizable Text Classifier for Text Mining

    Directory of Open Access Journals (Sweden)

    Yun-liang Zhang

    2007-12-01

    Full Text Available Text mining deals with complex and unstructured texts. Usually a particular collection of texts that is specified to one or more domains is necessary. We have developed a customizable text classifier for users to mine the collection automatically. It derives from the sentence category of the HNC theory and corresponding techniques. It can start with a few texts, and it can adjust automatically or be adjusted by user. The user can also control the number of domains chosen and decide the standard with which to choose the texts based on demand and abundance of materials. The performance of the classifier varies with the user's choice.

  8. A survey of decision tree classifier methodology

    Science.gov (United States)

    Safavian, S. R.; Landgrebe, David

    1991-01-01

    Decision tree classifiers (DTCs) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. Perhaps the most important feature of DTCs is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A survey of current methods is presented for DTC designs and the various existing issues. After considering potential advantages of DTCs over single-state classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed.

  9. A Novel Approach for Multi Class Fault Diagnosis in Induction Machine Based on Statistical Time Features and Random Forest Classifier

    Science.gov (United States)

    Sonje, M. Deepak; Kundu, P.; Chowdhury, A.

    2017-08-01

    Fault diagnosis and detection is the important area in health monitoring of electrical machines. This paper proposes the recently developed machine learning classifier for multi class fault diagnosis in induction machine. The classification is based on random forest (RF) algorithm. Initially, stator currents are acquired from the induction machine under various conditions. After preprocessing the currents, fourteen statistical time features are estimated for each phase of the current. These parameters are considered as inputs to the classifier. The main scope of the paper is to evaluate effectiveness of RF classifier for individual and mixed fault diagnosis in induction machine. The stator, rotor and mixed faults (stator and rotor faults) are classified using the proposed classifier. The obtained performance measures are compared with the multilayer perceptron neural network (MLPNN) classifier. The results show the much better performance measures and more accurate than MLPNN classifier. For demonstration of planned fault diagnosis algorithm, experimentally obtained results are considered to build the classifier more practical.

  10. Studying Dynamic Features in Myocardial Infarction Progression by Integrating miRNA-Transcription Factor Co-Regulatory Networks and Time-Series RNA Expression Data from Peripheral Blood Mononuclear Cells.

    Directory of Open Access Journals (Sweden)

    Hongbo Shi

    Full Text Available Myocardial infarction (MI is a serious heart disease and a leading cause of mortality and morbidity worldwide. Although some molecules (genes, miRNAs and transcription factors (TFs associated with MI have been studied in a specific pathological context, their dynamic characteristics in gene expressions, biological functions and regulatory interactions in MI progression have not been fully elucidated to date. In the current study, we analyzed time-series RNA expression data from peripheral blood mononuclear cells. We observed that significantly differentially expressed genes were sharply up- or down-regulated in the acute phase of MI, and then changed slowly until the chronic phase. Biological functions involved at each stage of MI were identified. Additionally, dynamic miRNA-TF co-regulatory networks were constructed based on the significantly differentially expressed genes and miRNA-TF co-regulatory motifs, and the dynamic interplay of miRNAs, TFs and target genes were investigated. Finally, a new panel of candidate diagnostic biomarkers (STAT3 and ICAM1 was identified to have discriminatory capability for patients with or without MI, especially the patients with or without recurrent events. The results of the present study not only shed new light on the understanding underlying regulatory mechanisms involved in MI progression, but also contribute to the discovery of true diagnostic biomarkers for MI.

  11. Identifying modules of coexpressed transcript units and their organization of Saccharopolyspora erythraea from time series gene expression profiles.

    Directory of Open Access Journals (Sweden)

    Xiao Chang

    Full Text Available BACKGROUND: The Saccharopolyspora erythraea genome sequence was released in 2007. In order to look at the gene regulations at whole transcriptome level, an expression microarray was specifically designed on the S. erythraea strain NRRL 2338 genome sequence. Based on these data, we set out to investigate the potential transcriptional regulatory networks and their organization. METHODOLOGY/PRINCIPAL FINDINGS: In view of the hierarchical structure of bacterial transcriptional regulation, we constructed a hierarchical coexpression network at whole transcriptome level. A total of 27 modules were identified from 1255 differentially expressed transcript units (TUs across time course, which were further classified in to four groups. Functional enrichment analysis indicated the biological significance of our hierarchical network. It was indicated that primary metabolism is activated in the first rapid growth phase (phase A, and secondary metabolism is induced when the growth is slowed down (phase B. Among the 27 modules, two are highly correlated to erythromycin production. One contains all genes in the erythromycin-biosynthetic (ery gene cluster and the other seems to be associated with erythromycin production by sharing common intermediate metabolites. Non-concomitant correlation between production and expression regulation was observed. Especially, by calculating the partial correlation coefficients and building the network based on Gaussian graphical model, intrinsic associations between modules were found, and the association between those two erythromycin production-correlated modules was included as expected. CONCLUSIONS: This work created a hierarchical model clustering transcriptome data into coordinated modules, and modules into groups across the time course, giving insight into the concerted transcriptional regulations especially the regulation corresponding to erythromycin production of S. erythraea. This strategy may be extendable to studies

  12. Identifying modules of coexpressed transcript units and their organization of Saccharopolyspora erythraea from time series gene expression profiles.

    Science.gov (United States)

    Chang, Xiao; Liu, Shuai; Yu, Yong-Tao; Li, Yi-Xue; Li, Yuan-Yuan

    2010-08-12

    The Saccharopolyspora erythraea genome sequence was released in 2007. In order to look at the gene regulations at whole transcriptome level, an expression microarray was specifically designed on the S. erythraea strain NRRL 2338 genome sequence. Based on these data, we set out to investigate the potential transcriptional regulatory networks and their organization. In view of the hierarchical structure of bacterial transcriptional regulation, we constructed a hierarchical coexpression network at whole transcriptome level. A total of 27 modules were identified from 1255 differentially expressed transcript units (TUs) across time course, which were further classified in to four groups. Functional enrichment analysis indicated the biological significance of our hierarchical network. It was indicated that primary metabolism is activated in the first rapid growth phase (phase A), and secondary metabolism is induced when the growth is slowed down (phase B). Among the 27 modules, two are highly correlated to erythromycin production. One contains all genes in the erythromycin-biosynthetic (ery) gene cluster and the other seems to be associated with erythromycin production by sharing common intermediate metabolites. Non-concomitant correlation between production and expression regulation was observed. Especially, by calculating the partial correlation coefficients and building the network based on Gaussian graphical model, intrinsic associations between modules were found, and the association between those two erythromycin production-correlated modules was included as expected. This work created a hierarchical model clustering transcriptome data into coordinated modules, and modules into groups across the time course, giving insight into the concerted transcriptional regulations especially the regulation corresponding to erythromycin production of S. erythraea. This strategy may be extendable to studies on other prokaryotic microorganisms.

  13. 75 FR 37253 - Classified National Security Information

    Science.gov (United States)

    2010-06-28

    ... ``Secret.'' (3) Each interior page of a classified document shall be marked at the top and bottom either... ``(TS)'' for Top Secret, ``(S)'' for Secret, and ``(C)'' for Confidential will be used. (2) Portions... from the informational text. (1) Conspicuously place the overall classification at the top and bottom...

  14. 75 FR 707 - Classified National Security Information

    Science.gov (United States)

    2010-01-05

    ... classified at one of the following three levels: (1) ``Top Secret'' shall be applied to information, the... exercise this authority. (2) ``Top Secret'' original classification authority may be delegated only by the... official has been delegated ``Top Secret'' original classification authority by the agency head. (4) Each...

  15. Histogram deconvolution - An aid to automated classifiers

    Science.gov (United States)

    Lorre, J. J.

    1983-01-01

    It is shown that N-dimensional histograms are convolved by the addition of noise in the picture domain. Three methods are described which provide the ability to deconvolve such noise-affected histograms. The purpose of the deconvolution is to provide automated classifiers with a higher quality N-dimensional histogram from which to obtain classification statistics.

  16. Classifying web pages with visual features

    NARCIS (Netherlands)

    de Boer, V.; van Someren, M.; Lupascu, T.; Filipe, J.; Cordeiro, J.

    2010-01-01

    To automatically classify and process web pages, current systems use the textual content of those pages, including both the displayed content and the underlying (HTML) code. However, a very important feature of a web page is its visual appearance. In this paper, we show that using generic visual

  17. Comparison of Transcription Factor Binding Site Models

    KAUST Repository

    Bhuyan, Sharifulislam

    2012-05-01

    Modeling of transcription factor binding sites (TFBSs) and TFBS prediction on genomic sequences are important steps to elucidate transcription regulatory mechanism. Dependency of transcription regulation on a great number of factors such as chemical specificity, molecular structure, genomic and epigenetic characteristics, long distance interaction, makes this a challenging problem. Different experimental procedures generate evidence that DNA-binding domains of transcription factors show considerable DNA sequence specificity. Probabilistic modeling of TFBSs has been moderately successful in identifying patterns from a family of sequences. In this study, we compare performances of different probabilistic models and try to estimate their efficacy over experimental TFBSs data. We build a pipeline to calculate sensitivity and specificity from aligned TFBS sequences for several probabilistic models, such as Markov chains, hidden Markov models, Bayesian networks. Our work, containing relevant statistics and evaluation for the models, can help researchers to choose the most appropriate model for the problem at hand.

  18. Disassembly and Sanitization of Classified Matter

    International Nuclear Information System (INIS)

    Stockham, Dwight J.; Saad, Max P.

    2008-01-01

    The Disassembly Sanitization Operation (DSO) process was implemented to support weapon disassembly and disposition by using recycling and waste minimization measures. This process was initiated by treaty agreements and reconfigurations within both the DOD and DOE Complexes. The DOE is faced with disassembling and disposing of a huge inventory of retired weapons, components, training equipment, spare parts, weapon maintenance equipment, and associated material. In addition, regulations have caused a dramatic increase in the need for information required to support the handling and disposition of these parts and materials. In the past, huge inventories of classified weapon components were required to have long-term storage at Sandia and at many other locations throughout the DoE Complex. These materials are placed in onsite storage unit due to classification issues and they may also contain radiological and/or hazardous components. Since no disposal options exist for this material, the only choice was long-term storage. Long-term storage is costly and somewhat problematic, requiring a secured storage area, monitoring, auditing, and presenting the potential for loss or theft of the material. Overall recycling rates for materials sent through the DSO process have enabled 70 to 80% of these components to be recycled. These components are made of high quality materials and once this material has been sanitized, the demand for the component metals for recycling efforts is very high. The DSO process for NGPF, classified components established the credibility of this technique for addressing the long-term storage requirements of the classified weapons component inventory. The success of this application has generated interest from other Sandia organizations and other locations throughout the complex. Other organizations are requesting the help of the DSO team and the DSO is responding to these requests by expanding its scope to include Work-for- Other projects. For example

  19. Networks in Cell Biology

    Science.gov (United States)

    Buchanan, Mark; Caldarelli, Guido; De Los Rios, Paolo; Rao, Francesco; Vendruscolo, Michele

    2010-05-01

    Introduction; 1. Network views of the cell Paolo De Los Rios and Michele Vendruscolo; 2. Transcriptional regulatory networks Sarath Chandra Janga and M. Madan Babu; 3. Transcription factors and gene regulatory networks Matteo Brilli, Elissa Calistri and Pietro Lió; 4. Experimental methods for protein interaction identification Peter Uetz, Björn Titz, Seesandra V. Rajagopala and Gerard Cagney; 5. Modeling protein interaction networks Francesco Rao; 6. Dynamics and evolution of metabolic networks Daniel Segré; 7. Hierarchical modularity in biological networks: the case of metabolic networks Erzsébet Ravasz Regan; 8. Signalling networks Gian Paolo Rossini; Appendix 1. Complex networks: from local to global properties D. Garlaschelli and G. Caldarelli; Appendix 2. Modelling the local structure of networks D. Garlaschelli and G. Caldarelli; Appendix 3. Higher-order topological properties S. Ahnert, T. Fink and G. Caldarelli; Appendix 4. Elementary mathematical concepts A. Gabrielli and G. Caldarelli; References.

  20. Supervised Sequence Labelling with Recurrent Neural Networks

    CERN Document Server

    Graves, Alex

    2012-01-01

    Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools—robust to input noise and distortion, able to exploit long-range contextual information—that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary.    The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional...

  1. WRKY transcription factors

    Science.gov (United States)

    Bakshi, Madhunita; Oelmüller, Ralf

    2014-01-01

    WRKY transcription factors are one of the largest families of transcriptional regulators found exclusively in plants. They have diverse biological functions in plant disease resistance, abiotic stress responses, nutrient deprivation, senescence, seed and trichome development, embryogenesis, as well as additional developmental and hormone-controlled processes. WRKYs can act as transcriptional activators or repressors, in various homo- and heterodimer combinations. Here we review recent progress on the function of WRKY transcription factors in Arabidopsis and other plant species such as rice, potato, and parsley, with a special focus on abiotic, developmental, and hormone-regulated processes. PMID:24492469

  2. Comparing cosmic web classifiers using information theory

    International Nuclear Information System (INIS)

    Leclercq, Florent; Lavaux, Guilhem; Wandelt, Benjamin; Jasche, Jens

    2016-01-01

    We introduce a decision scheme for optimally choosing a classifier, which segments the cosmic web into different structure types (voids, sheets, filaments, and clusters). Our framework, based on information theory, accounts for the design aims of different classes of possible applications: (i) parameter inference, (ii) model selection, and (iii) prediction of new observations. As an illustration, we use cosmographic maps of web-types in the Sloan Digital Sky Survey to assess the relative performance of the classifiers T-WEB, DIVA and ORIGAMI for: (i) analyzing the morphology of the cosmic web, (ii) discriminating dark energy models, and (iii) predicting galaxy colors. Our study substantiates a data-supported connection between cosmic web analysis and information theory, and paves the path towards principled design of analysis procedures for the next generation of galaxy surveys. We have made the cosmic web maps, galaxy catalog, and analysis scripts used in this work publicly available.

  3. Comparing cosmic web classifiers using information theory

    Energy Technology Data Exchange (ETDEWEB)

    Leclercq, Florent [Institute of Cosmology and Gravitation (ICG), University of Portsmouth, Dennis Sciama Building, Burnaby Road, Portsmouth PO1 3FX (United Kingdom); Lavaux, Guilhem; Wandelt, Benjamin [Institut d' Astrophysique de Paris (IAP), UMR 7095, CNRS – UPMC Université Paris 6, Sorbonne Universités, 98bis boulevard Arago, F-75014 Paris (France); Jasche, Jens, E-mail: florent.leclercq@polytechnique.org, E-mail: lavaux@iap.fr, E-mail: j.jasche@tum.de, E-mail: wandelt@iap.fr [Excellence Cluster Universe, Technische Universität München, Boltzmannstrasse 2, D-85748 Garching (Germany)

    2016-08-01

    We introduce a decision scheme for optimally choosing a classifier, which segments the cosmic web into different structure types (voids, sheets, filaments, and clusters). Our framework, based on information theory, accounts for the design aims of different classes of possible applications: (i) parameter inference, (ii) model selection, and (iii) prediction of new observations. As an illustration, we use cosmographic maps of web-types in the Sloan Digital Sky Survey to assess the relative performance of the classifiers T-WEB, DIVA and ORIGAMI for: (i) analyzing the morphology of the cosmic web, (ii) discriminating dark energy models, and (iii) predicting galaxy colors. Our study substantiates a data-supported connection between cosmic web analysis and information theory, and paves the path towards principled design of analysis procedures for the next generation of galaxy surveys. We have made the cosmic web maps, galaxy catalog, and analysis scripts used in this work publicly available.

  4. Detection of Fundus Lesions Using Classifier Selection

    Science.gov (United States)

    Nagayoshi, Hiroto; Hiramatsu, Yoshitaka; Sako, Hiroshi; Himaga, Mitsutoshi; Kato, Satoshi

    A system for detecting fundus lesions caused by diabetic retinopathy from fundus images is being developed. The system can screen the images in advance in order to reduce the inspection workload on doctors. One of the difficulties that must be addressed in completing this system is how to remove false positives (which tend to arise near blood vessels) without decreasing the detection rate of lesions in other areas. To overcome this difficulty, we developed classifier selection according to the position of a candidate lesion, and we introduced new features that can distinguish true lesions from false positives. A system incorporating classifier selection and these new features was tested in experiments using 55 fundus images with some lesions and 223 images without lesions. The results of the experiments confirm the effectiveness of the proposed system, namely, degrees of sensitivity and specificity of 98% and 81%, respectively.

  5. Learning for VMM + WTA Embedded Classifiers

    Science.gov (United States)

    2016-03-31

    Learning for VMM + WTA Embedded Classifiers Jennifer Hasler and Sahil Shah Electrical and Computer Engineering Georgia Institute of Technology...enabling correct classification of each novel acoustic signal (generator, idle car, and idle truck ). The classification structure requires, after...measured on our SoC FPAA IC. The test input is composed of signals from urban environment for 3 objects (generator, idle car, and idle truck

  6. Optimization of short amino acid sequences classifier

    Science.gov (United States)

    Barcz, Aleksy; Szymański, Zbigniew

    This article describes processing methods used for short amino acid sequences classification. The data processed are 9-symbols string representations of amino acid sequences, divided into 49 data sets - each one containing samples labeled as reacting or not with given enzyme. The goal of the classification is to determine for a single enzyme, whether an amino acid sequence would react with it or not. Each data set is processed separately. Feature selection is performed to reduce the number of dimensions for each data set. The method used for feature selection consists of two phases. During the first phase, significant positions are selected using Classification and Regression Trees. Afterwards, symbols appearing at the selected positions are substituted with numeric values of amino acid properties taken from the AAindex database. In the second phase the new set of features is reduced using a correlation-based ranking formula and Gram-Schmidt orthogonalization. Finally, the preprocessed data is used for training LS-SVM classifiers. SPDE, an evolutionary algorithm, is used to obtain optimal hyperparameters for the LS-SVM classifier, such as error penalty parameter C and kernel-specific hyperparameters. A simple score penalty is used to adapt the SPDE algorithm to the task of selecting classifiers with best performance measures values.

  7. SVM classifier on chip for melanoma detection.

    Science.gov (United States)

    Afifi, Shereen; GholamHosseini, Hamid; Sinha, Roopak

    2017-07-01

    Support Vector Machine (SVM) is a common classifier used for efficient classification with high accuracy. SVM shows high accuracy for classifying melanoma (skin cancer) clinical images within computer-aided diagnosis systems used by skin cancer specialists to detect melanoma early and save lives. We aim to develop a medical low-cost handheld device that runs a real-time embedded SVM-based diagnosis system for use in primary care for early detection of melanoma. In this paper, an optimized SVM classifier is implemented onto a recent FPGA platform using the latest design methodology to be embedded into the proposed device for realizing online efficient melanoma detection on a single system on chip/device. The hardware implementation results demonstrate a high classification accuracy of 97.9% and a significant acceleration factor of 26 from equivalent software implementation on an embedded processor, with 34% of resources utilization and 2 watts for power consumption. Consequently, the implemented system meets crucial embedded systems constraints of high performance and low cost, resources utilization and power consumption, while achieving high classification accuracy.

  8. Analysis and minimization of overtraining effect in rule-based classifiers for computer-aided diagnosis

    International Nuclear Information System (INIS)

    Li Qiang; Doi Kunio

    2006-01-01

    Computer-aided diagnostic (CAD) schemes have been developed to assist radiologists detect various lesions in medical images. In CAD schemes, classifiers play a key role in achieving a high lesion detection rate and a low false-positive rate. Although many popular classifiers such as linear discriminant analysis and artificial neural networks have been employed in CAD schemes for reduction of false positives, a rule-based classifier has probably been the simplest and most frequently used one since the early days of development of various CAD schemes. However, with existing rule-based classifiers, there are major disadvantages that significantly reduce their practicality and credibility. The disadvantages include manual design, poor reproducibility, poor evaluation methods such as resubstitution, and a large overtraining effect. An automated rule-based classifier with a minimized overtraining effect can overcome or significantly reduce the extent of the above-mentioned disadvantages. In this study, we developed an 'optimal' method for the selection of cutoff thresholds and a fully automated rule-based classifier. Experimental results performed with Monte Carlo simulation and a real lung nodule CT data set demonstrated that the automated threshold selection method can completely eliminate overtraining effect in the procedure of cutoff threshold selection, and thus can minimize overall overtraining effect in the constructed rule-based classifier. We believe that this threshold selection method is very useful in the construction of automated rule-based classifiers with minimized overtraining effect

  9. Characterization of novel heat-responsive transcription factor (TaHSFA6e) gene involved in regulation of heat shock proteins (HSPs) - A key member of heat stress-tolerance network of wheat.

    Science.gov (United States)

    Kumar, Ranjeet R; Goswami, Suneha; Singh, Khushboo; Dubey, Kavita; Rai, Gyanendra K; Singh, Bhupinder; Singh, Shivdhar; Grover, Monendra; Mishra, Dwijesh; Kumar, Sanjeev; Bakshi, Suman; Rai, Anil; Pathak, Himanshu; Chinnusamy, Viswanathan; Praveen, Shelly

    2018-08-10

    Heat stress has an adverse effect on the quality and quantity of agriculturally important crops, especially wheat. The tolerance mechanism has not been explored much in wheat and very few genes/ TFs responsive to heat stress is available on public domain. Here, we identified, cloned and characterized a putative TaHSFA6e TF gene of 1.3 kb from wheat cv. HD2985. We observed an ORF of 368 aa with Hsf DNA binding signature domain in the amino acid sequence. Single copy number of TaHSFA6e was observed integrated in the genome of wheat. Expression analysis of TaHSFA6e under differential HS showed maximum transcripts in wheat cv. Halna (thermotolerant) in response to 38 °C for 2 h during pollination and grain-filling stages, as compared to PBW343, HD2329 and HD2985. Putative target genes of TaHSFA6e (HSP17, HSP70 and HSP90) showed upregulation in response to differential HS (30 & 38 °C, 2 h) during pollination and grain-filling stages. Small HSP17 was observed most triggered in Halna under HS. We observed increase in the catalase, guaiacol peroxidase, total antioxidant capacity (TAC), and decrease in the lipid peroxidation in thermotolerant cvs. (Halna, HD2985), as compared to thermosusceptible (PBW343, HD2329) under differential HS. Multiple stresses (heat - 38 °C, 2 h, and drought - 100 mL of 20% polyethylene Glycol 6000) during seedling stage of wheat showed positive correlation between the expression of TaHSFA6e, putative targets (HSP70, HSP90, HSP17) and TAC. Halna (thermotolerant) performed better, as compared to other contrasting cvs. TaHSFA6e TF can be used as promising candidate gene for manipulating the heat stress-tolerance network. Copyright © 2018 Elsevier B.V. All rights reserved.

  10. NMD Classifier: A reliable and systematic classification tool for nonsense-mediated decay events.

    Directory of Open Access Journals (Sweden)

    Min-Kung Hsu

    Full Text Available Nonsense-mediated decay (NMD degrades mRNAs that include premature termination codons to avoid the translation and accumulation of truncated proteins. This mechanism has been found to participate in gene regulation and a wide spectrum of biological processes. However, the evolutionary and regulatory origins of NMD-targeted transcripts (NMDTs have been less studied, partly because of the complexity in analyzing NMD events. Here we report NMD Classifier, a tool for systematic classification of NMD events for either annotated or de novo assembled transcripts. This tool is based on the assumption of minimal evolution/regulation-an event that leads to the least change is the most likely to occur. Our simulation results indicate that NMD Classifier can correctly identify an average of 99.3% of the NMD-causing transcript structural changes, particularly exon inclusions/exclusions and exon boundary alterations. Researchers can apply NMD Classifier to evolutionary and regulatory studies by comparing NMD events of different biological conditions or in different organisms.

  11. Reconstructing the Prostate Cancer Transcriptional Regulatory Network

    Science.gov (United States)

    2010-09-01

    and disease prognosis. J Clin Oncol 2006;24:3763–70. 13. Klein CA, Schmidt- Kittler O, Schardt JA, Pantel K, Speicher MR, Riethmuller G. Comparative...Cancer Gene Discovery Jessica Kao1., Keyan Salari1,2., Melanie Bocanegra1, Yoon-La Choi1,3, Luc Girard4, Jeet Gandhi4, Kevin A. Kwei1, Tina Hernandez...JM, Klein RC, Oka M, Cowan KH (1995) Posttranscriptional regulation of the c-myb proto-oncogene in estrogen receptor-positive breast cancer cells

  12. Transcription regulatory networks analysis using CAGE

    KAUST Repository

    Tegné r, Jesper N.; Bjö rkegren, Johan L M; Ravasi, Timothy; Bajic, Vladimir

    2009-01-01

    and the fine interplay between regulatory proteins and the promoter structure governing the combinatorial regulation of gene expression. In this chapter we review how the CAGE data can be integrated with other measurements such as expression, physical

  13. Transcriptional regulatory network controlling secondary cell wall ...

    African Journals Online (AJOL)

    Secondary wall is an abundant component of plant biomass and has a potential to be a renewable resource of bioenergy and biomaterials. It is important to unravel the molecular mechanism underlying secondary wall formation and how it contributes to plant biomass production. In this review, we summarized the potential ...

  14. Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level

    Directory of Open Access Journals (Sweden)

    Shehzad Khalid

    2014-01-01

    Full Text Available We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes.

  15. Genome-wide Reconstruction of OxyR and SoxRS Transcriptional Regulatory Networks under Oxidative Stress in Escherichia coli K-12 MG1655

    DEFF Research Database (Denmark)

    Seo, Sang Woo; Kim, Donghyuk; Szubin, Richard

    2015-01-01

    Three transcription factors (TFs), OxyR, SoxR, and SoxS, play a critical role in transcriptional regulation of the defense system for oxidative stress in bacteria. However, their full genome-wide regulatory potential is unknown. Here, we perform a genome-scale reconstruction of the OxyR, SoxR, an...

  16. The Protection of Classified Information: The Legal Framework

    National Research Council Canada - National Science Library

    Elsea, Jennifer K

    2006-01-01

    Recent incidents involving leaks of classified information have heightened interest in the legal framework that governs security classification, access to classified information, and penalties for improper disclosure...

  17. Entropy Based Classifier Combination for Sentence Segmentation

    Science.gov (United States)

    2007-01-01

    speaker diarization system to divide the audio data into hypothetical speakers [17...the prosodic feature also includes turn-based features which describe the position of a word in relation to diarization seg- mentation. The speaker ...ro- bust speaker segmentation: the ICSI-SRI fall 2004 diarization system,” in Proc. RT-04F Workshop, 2004. [18] “The rich transcription fall 2003,” http://nist.gov/speech/tests/rt/rt2003/fall/docs/rt03-fall-eval- plan-v9.pdf.

  18. Navigating the transcriptional roadmap regulating plant secondary cell wall deposition

    Directory of Open Access Journals (Sweden)

    Steven Grant Hussey

    2013-08-01

    Full Text Available The current status of lignocellulosic biomass as an invaluable resource in industry, agriculture and health has spurred increased interest in understanding the transcriptional regulation of secondary cell wall (SCW biosynthesis. The last decade of research has revealed an extensive network of NAC, MYB and other families of transcription factors regulating Arabidopsis SCW biosynthesis, and numerous studies have explored SCW-related transcription factors in other dicots and monocots. Whilst the general structure of the Arabidopsis network has been a topic of several reviews, they have not comprehensively represented the detailed protein-DNA and protein-protein interactions described in the literature, and an understanding of network dynamics and functionality has not yet been achieved for SCW formation. Furthermore the methodologies employed in studies of SCW transcriptional regulation have not received much attention, especially in the case of non-model organisms. In this review, we have reconstructed the most exhaustive literature-based network representations to date of SCW transcriptional regulation in Arabidopsis. We include a manipulable Cytoscape representation of the Arabidopsis SCW transcriptional network to aid in future studies, along with a list of supporting literature for each documented interaction. Amongst other topics, we discuss the various components of the network, its evolutionary conservation in plants, putative modules and dynamic mechanisms that may influence network function, and the approaches that have been employed in network inference. Future research should aim to better understand network function and its response to dynamic perturbations, whilst the development and application of genome-wide approaches such as ChIP-seq and systems genetics are in progress for the study of SCW transcriptional regulation in non-model organisms.

  19. Classifying smoking urges via machine learning.

    Science.gov (United States)

    Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

    2016-12-01

    Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. Copyright © 2016 Elsevier Ireland Ltd. All rights

  20. Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers

    Directory of Open Access Journals (Sweden)

    Dawei Li

    2017-01-01

    Full Text Available This study develops a tree augmented naive Bayesian (TAN classifier based incident detection algorithm. Compared with the Bayesian networks based detection algorithms developed in the previous studies, this algorithm has less dependency on experts’ knowledge. The structure of TAN classifier for incident detection is learned from data. The discretization of continuous attributes is processed using an entropy-based method automatically. A simulation dataset on the section of the Ayer Rajah Expressway (AYE in Singapore is used to demonstrate the development of proposed algorithm, including wavelet denoising, normalization, entropy-based discretization, and structure learning. The performance of TAN based algorithm is evaluated compared with the previous developed Bayesian network (BN based and multilayer feed forward (MLF neural networks based algorithms with the same AYE data. The experiment results show that the TAN based algorithms perform better than the BN classifiers and have a similar performance to the MLF based algorithm. However, TAN based algorithm would have wider vista of applications because the theory of TAN classifiers is much less complicated than MLF. It should be found from the experiment that the TAN classifier based algorithm has a significant superiority over the speed of model training and calibration compared with MLF.

  1. Ensembles of novelty detection classifiers for structural health monitoring using guided waves

    Science.gov (United States)

    Dib, Gerges; Karpenko, Oleksii; Koricho, Ermias; Khomenko, Anton; Haq, Mahmoodul; Udpa, Lalita

    2018-01-01

    Guided wave structural health monitoring uses sparse sensor networks embedded in sophisticated structures for defect detection and characterization. The biggest challenge of those sensor networks is developing robust techniques for reliable damage detection under changing environmental and operating conditions (EOC). To address this challenge, we develop a novelty classifier for damage detection based on one class support vector machines. We identify appropriate features for damage detection and introduce a feature aggregation method which quadratically increases the number of available training observations. We adopt a two-level voting scheme by using an ensemble of classifiers and predictions. Each classifier is trained on a different segment of the guided wave signal, and each classifier makes an ensemble of predictions based on a single observation. Using this approach, the classifier can be trained using a small number of baseline signals. We study the performance using Monte-Carlo simulations of an analytical model and data from impact damage experiments on a glass fiber composite plate. We also demonstrate the classifier performance using two types of baseline signals: fixed and rolling baseline training set. The former requires prior knowledge of baseline signals from all EOC, while the latter does not and leverages the fact that EOC vary slowly over time and can be modeled as a Gaussian process.

  2. Classifying spaces of degenerating polarized Hodge structures

    CERN Document Server

    Kato, Kazuya

    2009-01-01

    In 1970, Phillip Griffiths envisioned that points at infinity could be added to the classifying space D of polarized Hodge structures. In this book, Kazuya Kato and Sampei Usui realize this dream by creating a logarithmic Hodge theory. They use the logarithmic structures begun by Fontaine-Illusie to revive nilpotent orbits as a logarithmic Hodge structure. The book focuses on two principal topics. First, Kato and Usui construct the fine moduli space of polarized logarithmic Hodge structures with additional structures. Even for a Hermitian symmetric domain D, the present theory is a refinem

  3. Cubical sets as a classifying topos

    DEFF Research Database (Denmark)

    Spitters, Bas

    Coquand’s cubical set model for homotopy type theory provides the basis for a computational interpretation of the univalence axiom and some higher inductive types, as implemented in the cubical proof assistant. We show that the underlying cube category is the opposite of the Lawvere theory of De...... Morgan algebras. The topos of cubical sets itself classifies the theory of ‘free De Morgan algebras’. This provides us with a topos with an internal ‘interval’. Using this interval we construct a model of type theory following van den Berg and Garner. We are currently investigating the precise relation...

  4. Double Ramp Loss Based Reject Option Classifier

    Science.gov (United States)

    2015-05-22

    of convex (DC) functions. To minimize it, we use DC programming approach [1]. The proposed method has following advantages: (1) the proposed loss LDR ...space constraints. We see that LDR does not put any restriction on ρ for it to be an upper bound of L0−d−1. 2.2 Risk Formulation Using LDR Let S = {(xn...classifier learnt using LDR based approach (C = 100, μ = 1, d = .2). Filled circles and triangles represent the support vectors. 4 Experimental Results We show

  5. Classifying Coding DNA with Nucleotide Statistics

    Directory of Open Access Journals (Sweden)

    Nicolas Carels

    2009-10-01

    Full Text Available In this report, we compared the success rate of classification of coding sequences (CDS vs. introns by Codon Structure Factor (CSF and by a method that we called Universal Feature Method (UFM. UFM is based on the scoring of purine bias (Rrr and stop codon frequency. We show that the success rate of CDS/intron classification by UFM is higher than by CSF. UFM classifies ORFs as coding or non-coding through a score based on (i the stop codon distribution, (ii the product of purine probabilities in the three positions of nucleotide triplets, (iii the product of Cytosine (C, Guanine (G, and Adenine (A probabilities in the 1st, 2nd, and 3rd positions of triplets, respectively, (iv the probabilities of G in 1st and 2nd position of triplets and (v the distance of their GC3 vs. GC2 levels to the regression line of the universal correlation. More than 80% of CDSs (true positives of Homo sapiens (>250 bp, Drosophila melanogaster (>250 bp and Arabidopsis thaliana (>200 bp are successfully classified with a false positive rate lower or equal to 5%. The method releases coding sequences in their coding strand and coding frame, which allows their automatic translation into protein sequences with 95% confidence. The method is a natural consequence of the compositional bias of nucleotides in coding sequences.

  6. A systematic comparison of supervised classifiers.

    Directory of Open Access Journals (Sweden)

    Diego Raphael Amancio

    Full Text Available Pattern recognition has been employed in a myriad of industrial, commercial and academic applications. Many techniques have been devised to tackle such a diversity of applications. Despite the long tradition of pattern recognition research, there is no technique that yields the best classification in all scenarios. Therefore, as many techniques as possible should be considered in high accuracy applications. Typical related works either focus on the performance of a given algorithm or compare various classification methods. In many occasions, however, researchers who are not experts in the field of machine learning have to deal with practical classification tasks without an in-depth knowledge about the underlying parameters. Actually, the adequate choice of classifiers and parameters in such practical circumstances constitutes a long-standing problem and is one of the subjects of the current paper. We carried out a performance study of nine well-known classifiers implemented in the Weka framework and compared the influence of the parameter configurations on the accuracy. The default configuration of parameters in Weka was found to provide near optimal performance for most cases, not including methods such as the support vector machine (SVM. In addition, the k-nearest neighbor method frequently allowed the best accuracy. In certain conditions, it was possible to improve the quality of SVM by more than 20% with respect to their default parameter configuration.

  7. Mercury⊕: An evidential reasoning image classifier

    Science.gov (United States)

    Peddle, Derek R.

    1995-12-01

    MERCURY⊕ is a multisource evidential reasoning classification software system based on the Dempster-Shafer theory of evidence. The design and implementation of this software package is described for improving the classification and analysis of multisource digital image data necessary for addressing advanced environmental and geoscience applications. In the remote-sensing context, the approach provides a more appropriate framework for classifying modern, multisource, and ancillary data sets which may contain a large number of disparate variables with different statistical properties, scales of measurement, and levels of error which cannot be handled using conventional Bayesian approaches. The software uses a nonparametric, supervised approach to classification, and provides a more objective and flexible interface to the evidential reasoning framework using a frequency-based method for computing support values from training data. The MERCURY⊕ software package has been implemented efficiently in the C programming language, with extensive use made of dynamic memory allocation procedures and compound linked list and hash-table data structures to optimize the storage and retrieval of evidence in a Knowledge Look-up Table. The software is complete with a full user interface and runs under Unix, Ultrix, VAX/VMS, MS-DOS, and Apple Macintosh operating system. An example of classifying alpine land cover and permafrost active layer depth in northern Canada is presented to illustrate the use and application of these ideas.

  8. Transcriptional and post-transcriptional regulation of nucleotide excision repair genes in human cells

    Energy Technology Data Exchange (ETDEWEB)

    Lefkofsky, Hailey B. [Translational Oncology Program, University of Michigan Medical School, Ann Arbor, MI (United States); Veloso, Artur [Translational Oncology Program, University of Michigan Medical School, Ann Arbor, MI (United States); Department of Radiation Oncology, University of Michigan Medical School, Ann Arbor, MI (United States); Bioinformatics Program, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI (United States); Ljungman, Mats, E-mail: ljungman@umich.edu [Translational Oncology Program, University of Michigan Medical School, Ann Arbor, MI (United States); Department of Radiation Oncology, University of Michigan Medical School, Ann Arbor, MI (United States); Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI (United States)

    2015-06-15

    Nucleotide excision repair (NER) removes DNA helix-distorting lesions induced by UV light and various chemotherapeutic agents such as cisplatin. These lesions efficiently block the elongation of transcription and need to be rapidly removed by transcription-coupled NER (TC-NER) to avoid the induction of apoptosis. Twenty-nine genes have been classified to code for proteins participating in nucleotide excision repair (NER) in human cells. Here we explored the transcriptional and post-transcriptional regulation of these NER genes across 13 human cell lines using Bru-seq and BruChase-seq, respectively. Many NER genes are relatively large in size and therefore will be easily inactivated by UV-induced transcription-blocking lesions. Furthermore, many of these genes produce transcripts that are rather unstable. Thus, these genes are expected to rapidly lose expression leading to a diminished function of NER. One such gene is ERCC6 that codes for the CSB protein critical for TC-NER. Due to its large gene size and high RNA turnover rate, the ERCC6 gene may act as dosimeter of DNA damage so that at high levels of damage, ERCC6 RNA levels would be diminished leading to the loss of CSB expression, inhibition of TC-NER and the promotion of cell death.

  9. Classifying galaxy spectra at 0.5 < z < 1 with self-organizing maps

    Science.gov (United States)

    Rahmani, S.; Teimoorinia, H.; Barmby, P.

    2018-05-01

    The spectrum of a galaxy contains information about its physical properties. Classifying spectra using templates helps elucidate the nature of a galaxy's energy sources. In this paper, we investigate the use of self-organizing maps in classifying galaxy spectra against templates. We trained semi-supervised self-organizing map networks using a set of templates covering the wavelength range from far ultraviolet to near infrared. The trained networks were used to classify the spectra of a sample of 142 galaxies with 0.5 K-means clustering, a supervised neural network, and chi-squared minimization. Spectra corresponding to quiescent galaxies were more likely to be classified similarly by all methods while starburst spectra showed more variability. Compared to classification using chi-squared minimization or the supervised neural network, the galaxies classed together by the self-organizing map had more similar spectra. The class ordering provided by the one-dimensional self-organizing maps corresponds to an ordering in physical properties, a potentially important feature for the exploration of large datasets.

  10. 36 CFR 1256.46 - National security-classified information.

    Science.gov (United States)

    2010-07-01

    ... 36 Parks, Forests, and Public Property 3 2010-07-01 2010-07-01 false National security-classified... Restrictions § 1256.46 National security-classified information. In accordance with 5 U.S.C. 552(b)(1), NARA... properly classified under the provisions of the pertinent Executive Order on Classified National Security...

  11. Comparative Transcriptional Profiling of Three Super-Hybrid Rice Combinations

    Directory of Open Access Journals (Sweden)

    Yonggang Peng

    2014-03-01

    Full Text Available Utilization of heterosis has significantly increased rice yields. However, its mechanism remains unclear. In this study, comparative transcriptional profiles of three super-hybrid rice combinations, LY2163, LY2186 and LYP9, at the flowering and filling stages, were created using rice whole-genome oligonucleotide microarray. The LY2163, LY2186 and LYP9 hybrids yielded 1193, 1630 and 1046 differentially expressed genes (DGs, accounting for 3.2%, 4.4% and 2.8% of the total number of genes (36,926, respectively, after using the z-test (p < 0.01. Functional category analysis showed that the DGs in each hybrid combination were mainly classified into the carbohydrate metabolism and energy metabolism categories. Further analysis of the metabolic pathways showed that DGs were significantly enriched in the carbon fixation pathway (p < 0.01 for all three combinations. Over 80% of the DGs were located in rice quantitative trait loci (QTLs of the Gramene database, of which more than 90% were located in the yield related QTLs in all three combinations, which suggested that there was a correlation between DGs and rice heterosis. Pathway Studio analysis showed the presence of DGs in the circadian regulatory network of all three hybrid combinations, which suggested that the circadian clock had a role in rice heterosis. Our results provide information that can help to elucidate the molecular mechanism underlying rice heterosis.

  12. Structurally distinct polycyclic aromatic hydrocarbons induce differential transcriptional responses in developing zebrafish

    International Nuclear Information System (INIS)

    Goodale, Britton C.; Tilton, Susan C.; Corvi, Margaret M.; Wilson, Glenn R.; Janszen, Derek B.; Anderson, Kim A.; Waters, Katrina M.; Tanguay, Robert L.

    2013-01-01

    Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous in the environment as components of fossil fuels and by-products of combustion. These multi-ring chemicals differentially activate the aryl hydrocarbon receptor (AHR) in a structurally dependent manner, and induce toxicity via both AHR-dependent and -independent mechanisms. PAH exposure is known to induce developmental malformations in zebrafish embryos, and recent studies have shown cardiac toxicity induced by compounds with low AHR affinity. Unraveling the potentially diverse molecular mechanisms of PAH toxicity is essential for understanding the hazard posed by complex PAH mixtures present in the environment. We analyzed transcriptional responses to PAH exposure in zebrafish embryos exposed to benz(a)anthracene (BAA), dibenzothiophene (DBT) and pyrene (PYR) at concentrations that induced developmental malformations by 120 h post-fertilization (hpf). Whole genome microarray analysis of mRNA expression at 24 and 48 hpf identified genes that were differentially regulated over time and in response to the three PAH structures. PAH body burdens were analyzed at both time points using GC–MS, and demonstrated differences in PAH uptake into the embryos. This was important for discerning dose-related differences from those that represented unique molecular mechanisms. While BAA misregulated the least number of transcripts, it caused strong induction of cyp1a and other genes known to be downstream of the AHR, which were not induced by the other two PAHs. Analysis of functional roles of misregulated genes and their predicted regulatory transcription factors also distinguished the BAA response from regulatory networks disrupted by DBT and PYR exposure. These results indicate that systems approaches can be used to classify the toxicity of PAHs based on the networks perturbed following exposure, and may provide a path for unraveling the toxicity of complex PAH mixtures. - Highlights: • Defined global mRNA expression

  13. Classifying and Analyzing 3d Cell Motion in Jammed Microgels

    Science.gov (United States)

    Bhattacharjee, Tapomoy; Sawyer, W. Gregory; Angelini, Thomas

    Soft granular polyelectrolyte microgels swell in liquid cell growth media to form a continuous elastic solid that can easily transition between solid to fluid state under a low shear stress. Such Liquid-like solids (LLS) have recently been used to create 3D cellular constructs as well as to support, culture and harvest cells in 3D. Current understanding of cell migration mechanics in 3D was established from experiments performed in natural and synthetic polymer networks. Spatial variation in network structure and the transience of degradable gels limit their usefulness in quantitative cell mechanics studies. By contrast, LLS growth media approximates a homogeneous continuum, enabling tractable cell mechanics measurements to be performed in 3D. Here, we introduce a process to understand and classify cytotoxic T cell motion in 3D by studying cellular motility in LLS media. General classification of T cell motion can be achieved with a very traditional statistical approach: the cell's mean squared displacement (MSD) as a function of delay time. We will also use Langevin approaches combined with the constitutive equations of the LLS medium to predict the statistics of T cell motion. National Science Foundation under Grant No. DMR-1352043.

  14. Genome-Wide Analyses of the NAC Transcription Factor Gene Family in Pepper (Capsicum annuum L.: Chromosome Location, Phylogeny, Structure, Expression Patterns, Cis-Elements in the Promoter, and Interaction Network

    Directory of Open Access Journals (Sweden)

    Weiping Diao

    2018-03-01

    Full Text Available The NAM, ATAF1/2, and CUC2 (NAC transcription factors form a large plant-specific gene family, which is involved in the regulation of tissue development in response to biotic and abiotic stress. To date, there have been no comprehensive studies investigating chromosomal location, gene structure, gene phylogeny, conserved motifs, or gene expression of NAC in pepper (Capsicum annuum L.. The recent release of the complete genome sequence of pepper allowed us to perform a genome-wide investigation of Capsicum annuum L. NAC (CaNAC proteins. In the present study, a comprehensive analysis of the CaNAC gene family in pepper was performed, and a total of 104 CaNAC genes were identified. Genome mapping analysis revealed that CaNAC genes were enriched on four chromosomes (chromosomes 1, 2, 3, and 6. In addition, phylogenetic analysis of the NAC domains from pepper, potato, Arabidopsis, and rice showed that CaNAC genes could be clustered into three groups (I, II, and III. Group III, which contained 24 CaNAC genes, was exclusive to the Solanaceae plant family. Gene structure and protein motif analyses showed that these genes were relatively conserved within each subgroup. The number of introns in CaNAC genes varied from 0 to 8, with 83 (78.9% of CaNAC genes containing two or less introns. Promoter analysis confirmed that CaNAC genes are involved in pepper growth, development, and biotic or abiotic stress responses. Further, the expression of 22 selected CaNAC genes in response to seven different biotic and abiotic stresses [salt, heat shock, drought, Phytophthora capsici, abscisic acid, salicylic acid (SA, and methyl jasmonate (MeJA] was evaluated by quantitative RT-PCR to determine their stress-related expression patterns. Several putative stress-responsive CaNAC genes, including CaNAC72 and CaNAC27, which are orthologs of the known stress-responsive Arabidopsis gene ANAC055 and potato gene StNAC30, respectively, were highly regulated by treatment with

  15. Classifying Drivers' Cognitive Load Using EEG Signals.

    Science.gov (United States)

    Barua, Shaibal; Ahmed, Mobyen Uddin; Begum, Shahina

    2017-01-01

    A growing traffic safety issue is the effect of cognitive loading activities on traffic safety and driving performance. To monitor drivers' mental state, understanding cognitive load is important since while driving, performing cognitively loading secondary tasks, for example talking on the phone, can affect the performance in the primary task, i.e. driving. Electroencephalography (EEG) is one of the reliable measures of cognitive load that can detect the changes in instantaneous load and effect of cognitively loading secondary task. In this driving simulator study, 1-back task is carried out while the driver performs three different simulated driving scenarios. This paper presents an EEG based approach to classify a drivers' level of cognitive load using Case-Based Reasoning (CBR). The results show that for each individual scenario as well as using data combined from the different scenarios, CBR based system achieved approximately over 70% of classification accuracy.

  16. Classifying prion and prion-like phenomena.

    Science.gov (United States)

    Harbi, Djamel; Harrison, Paul M

    2014-01-01

    The universe of prion and prion-like phenomena has expanded significantly in the past several years. Here, we overview the challenges in classifying this data informatically, given that terms such as "prion-like", "prion-related" or "prion-forming" do not have a stable meaning in the scientific literature. We examine the spectrum of proteins that have been described in the literature as forming prions, and discuss how "prion" can have a range of meaning, with a strict definition being for demonstration of infection with in vitro-derived recombinant prions. We suggest that although prion/prion-like phenomena can largely be apportioned into a small number of broad groups dependent on the type of transmissibility evidence for them, as new phenomena are discovered in the coming years, a detailed ontological approach might be necessary that allows for subtle definition of different "flavors" of prion / prion-like phenomena.

  17. Just-in-time adaptive classifiers-part II: designing the classifier.

    Science.gov (United States)

    Alippi, Cesare; Roveri, Manuel

    2008-12-01

    Aging effects, environmental changes, thermal drifts, and soft and hard faults affect physical systems by changing their nature and behavior over time. To cope with a process evolution adaptive solutions must be envisaged to track its dynamics; in this direction, adaptive classifiers are generally designed by assuming the stationary hypothesis for the process generating the data with very few results addressing nonstationary environments. This paper proposes a methodology based on k-nearest neighbor (NN) classifiers for designing adaptive classification systems able to react to changing conditions just-in-time (JIT), i.e., exactly when it is needed. k-NN classifiers have been selected for their computational-free training phase, the possibility to easily estimate the model complexity k and keep under control the computational complexity of the classifier through suitable data reduction mechanisms. A JIT classifier requires a temporal detection of a (possible) process deviation (aspect tackled in a companion paper) followed by an adaptive management of the knowledge base (KB) of the classifier to cope with the process change. The novelty of the proposed approach resides in the general framework supporting the real-time update of the KB of the classification system in response to novel information coming from the process both in stationary conditions (accuracy improvement) and in nonstationary ones (process tracking) and in providing a suitable estimate of k. It is shown that the classification system grants consistency once the change targets the process generating the data in a new stationary state, as it is the case in many real applications.

  18. Communication Behaviour-Based Big Data Application to Classify and Detect HTTP Automated Software

    Directory of Open Access Journals (Sweden)

    Manh Cong Tran

    2016-01-01

    Full Text Available HTTP is recognized as the most widely used protocol on the Internet when applications are being transferred more and more by developers onto the web. Due to increasingly complex computer systems, diversity HTTP automated software (autoware thrives. Unfortunately, besides normal autoware, HTTP malware and greyware are also spreading rapidly in web environment. Consequently, network communication is not just rigorously controlled by users intention. This raises the demand for analyzing HTTP autoware communication behaviour to detect and classify malicious and normal activities via HTTP traffic. Hence, in this paper, based on many studies and analysis of the autoware communication behaviour through access graph, a new method to detect and classify HTTP autoware communication at network level is presented. The proposal system includes combination of MapReduce of Hadoop and MarkLogic NoSQL database along with xQuery to deal with huge HTTP traffic generated each day in a large network. The method is examined with real outbound HTTP traffic data collected through a proxy server of a private network. Experimental results obtained for proposed method showed that promised outcomes are achieved since 95.1% of suspicious autoware are classified and detected. This finding may assist network and system administrator in inspecting early the internal threats caused by HTTP autoware.

  19. How salicylic acid takes transcriptional control over jasmonic acid signaling

    Directory of Open Access Journals (Sweden)

    Lotte eCaarls

    2015-03-01

    Full Text Available Transcriptional regulation is a central process in plant immunity. The induction or repression of defense genes is orchestrated by signaling networks that are directed by plant hormones of which salicylic acid (SA and jasmonic acid (JA are the major players. Extensive cross-communication between the hormone signaling pathways allows for fine tuning of transcriptional programs, determining resistance to invaders and trade-offs with plant development. Here, we give an overview of how SA can control transcriptional reprogramming of JA-induced genes in Arabidopsis thaliana. SA can influence activity and/or localization of transcriptional regulators by post-translational modifications of transcription factors and co-regulators. SA-induced redox changes, mediated by thioredoxins and glutaredoxins, modify transcriptional regulators that are involved in suppression of JA-dependent genes, such as NPR1 and TGA transcription factors, which affects their localization or DNA binding activity. Furthermore, SA can mediate sequestering of JA-responsive transcription factors away from their target genes by stalling them in the cytosol or in complexes with repressor proteins in the nucleus. SA also affects JA-induced transcription by inducing degradation of transcription factors with an activating role in JA signaling, as was shown for the ERF transcription factor ORA59. Additionally, SA can induce negative regulators, among which WRKY transcription factors, that can directly or indirectly inhibit JA-responsive gene expression. Finally, at the DNA level, modification of histones by SA-dependent factors can result in repression of JA-responsive genes. These diverse and complex regulatory mechanisms affect important signaling hubs in the integration of hormone signaling networks. Some pathogens have evolved effectors that highjack hormone crosstalk mechanisms for their own good, which are described in this review as well.

  20. Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier

    Directory of Open Access Journals (Sweden)

    C. V. Subbulakshmi

    2015-01-01

    Full Text Available Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO algorithm with the extreme learning machine (ELM classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN, proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.

  1. Collective Classification in Network Data

    OpenAIRE

    Sen, Prithviraj; Namata, Galileo; Bilgic, Mustafa; Getoor, Lise; University of Maryland; Galligher, Brian; Eliassi-Rad, Tina

    2008-01-01

    Many real-world applications produce networked data such as the world-wide web (hypertext documents connected via hyperlinks), social networks (for example, people connected by friendship links), communication networks (computers connected via communication links) and biological networks (for example, protein interaction networks). A recent focus in machine learning research has been to extend traditional machine learning classification techniques to classify nodes in such networks. In this a...

  2. Deep Learning to Classify Radiology Free-Text Reports.

    Science.gov (United States)

    Chen, Matthew C; Ball, Robyn L; Yang, Lingyao; Moradzadeh, Nathaniel; Chapman, Brian E; Larson, David B; Langlotz, Curtis P; Amrhein, Timothy J; Lungren, Matthew P

    2018-03-01

    Purpose To evaluate the performance of a deep learning convolutional neural network (CNN) model compared with a traditional natural language processing (NLP) model in extracting pulmonary embolism (PE) findings from thoracic computed tomography (CT) reports from two institutions. Materials and Methods Contrast material-enhanced CT examinations of the chest performed between January 1, 1998, and January 1, 2016, were selected. Annotations by two human radiologists were made for three categories: the presence, chronicity, and location of PE. Classification of performance of a CNN model with an unsupervised learning algorithm for obtaining vector representations of words was compared with the open-source application PeFinder. Sensitivity, specificity, accuracy, and F1 scores for both the CNN model and PeFinder in the internal and external validation sets were determined. Results The CNN model demonstrated an accuracy of 99% and an area under the curve value of 0.97. For internal validation report data, the CNN model had a statistically significant larger F1 score (0.938) than did PeFinder (0.867) when classifying findings as either PE positive or PE negative, but no significant difference in sensitivity, specificity, or accuracy was found. For external validation report data, no statistical difference between the performance of the CNN model and PeFinder was found. Conclusion A deep learning CNN model can classify radiology free-text reports with accuracy equivalent to or beyond that of an existing traditional NLP model. © RSNA, 2017 Online supplemental material is available for this article.

  3. Classifying Adverse Events in the Dental Office.

    Science.gov (United States)

    Kalenderian, Elsbeth; Obadan-Udoh, Enihomo; Maramaldi, Peter; Etolue, Jini; Yansane, Alfa; Stewart, Denice; White, Joel; Vaderhobli, Ram; Kent, Karla; Hebballi, Nutan B; Delattre, Veronique; Kahn, Maria; Tokede, Oluwabunmi; Ramoni, Rachel B; Walji, Muhammad F

    2017-06-30

    Dentists strive to provide safe and effective oral healthcare. However, some patients may encounter an adverse event (AE) defined as "unnecessary harm due to dental treatment." In this research, we propose and evaluate two systems for categorizing the type and severity of AEs encountered at the dental office. Several existing medical AE type and severity classification systems were reviewed and adapted for dentistry. Using data collected in previous work, two initial dental AE type and severity classification systems were developed. Eight independent reviewers performed focused chart reviews, and AEs identified were used to evaluate and modify these newly developed classifications. A total of 958 charts were independently reviewed. Among the reviewed charts, 118 prospective AEs were found and 101 (85.6%) were verified as AEs through a consensus process. At the end of the study, a final AE type classification comprising 12 categories, and an AE severity classification comprising 7 categories emerged. Pain and infection were the most common AE types representing 73% of the cases reviewed (56% and 17%, respectively) and 88% were found to cause temporary, moderate to severe harm to the patient. Adverse events found during the chart review process were successfully classified using the novel dental AE type and severity classifications. Understanding the type of AEs and their severity are important steps if we are to learn from and prevent patient harm in the dental office.

  4. Is it important to classify ischaemic stroke?

    LENUS (Irish Health Repository)

    Iqbal, M

    2012-02-01

    Thirty-five percent of all ischemic events remain classified as cryptogenic. This study was conducted to ascertain the accuracy of diagnosis of ischaemic stroke based on information given in the medical notes. It was tested by applying the clinical information to the (TOAST) criteria. Hundred and five patients presented with acute stroke between Jan-Jun 2007. Data was collected on 90 patients. Male to female ratio was 39:51 with age range of 47-93 years. Sixty (67%) patients had total\\/partial anterior circulation stroke; 5 (5.6%) had a lacunar stroke and in 25 (28%) the mechanism of stroke could not be identified. Four (4.4%) patients with small vessel disease were anticoagulated; 5 (5.6%) with atrial fibrillation received antiplatelet therapy and 2 (2.2%) patients with atrial fibrillation underwent CEA. This study revealed deficiencies in the clinical assessment of patients and treatment was not tailored to the mechanism of stroke in some patients.

  5. Stress fracture development classified by bone scintigraphy

    International Nuclear Information System (INIS)

    Zwas, S.T.; Elkanovich, R.; Frank, G.; Aharonson, Z.

    1985-01-01

    There is no consensus on classifying stress fractures (SF) appearing on bone scans. The authors present a system of classification based on grading the severity and development of bone lesions by visual inspection, according to three main scintigraphic criteria: focality and size, intensity of uptake compare to adjacent bone, and local medular extension. Four grades of development (I-IV) were ranked, ranging from ill defined slightly increased cortical uptake to well defined regions with markedly increased uptake extending transversely bicortically. 310 male subjects aged 19-2, suffering several weeks from leg pains occurring during intensive physical training underwent bone scans of the pelvis and lower extremities using Tc-99-m-MDP. 76% of the scans were positive with 354 lesions, of which 88% were in th4e mild (I-II) grades and 12% in the moderate (III) and severe (IV) grades. Post-treatment scans were obtained in 65 cases having 78 lesions during 1- to 6-month intervals. Complete resolution was found after 1-2 months in 36% of the mild lesions but in only 12% of the moderate and severe ones, and after 3-6 months in 55% of the mild lesions and 15% of the severe ones. 75% of the moderate and severe lesions showed residual uptake in various stages throughout the follow-up period. Early recognition and treatment of mild SF lesions in this study prevented protracted disability and progression of the lesions and facilitated complete healing

  6. Classifying the Diversity of Bus Mapping Systems

    Science.gov (United States)

    Said, Mohd Shahmy Mohd; Forrest, David

    2018-05-01

    This study represents the first stage of an investigation into understanding the nature of different approaches to mapping bus routes and bus network, and how they may best be applied in different public transport situations. In many cities, bus services represent an important facet of easing traffic congestion and reducing pollution. However, with the entrenched car culture in many countries, persuading people to change their mode of transport is a major challenge. To promote this modal shift, people need to know what services are available and where (and when) they go. Bus service maps provide an invaluable element of providing suitable public transport information, but are often overlooked by transport planners, and are under-researched by cartographers. The method here consists of the creation of a map evaluation form and performing assessment of published bus networks maps. The analyses were completed by a combination of quantitative and qualitative data analysis of various aspects of cartographic design and classification. This paper focuses on the resulting classification, which is illustrated by a series of examples. This classification will facilitate more in depth investigations into the details of cartographic design for such maps and help direct areas for user evaluation.

  7. A deep learning method for classifying mammographic breast density categories.

    Science.gov (United States)

    Mohamed, Aly A; Berg, Wendie A; Peng, Hong; Luo, Yahong; Jankowitz, Rachel C; Wu, Shandong

    2018-01-01

    Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI-RADS categories, i.e., "scattered density" and "heterogeneously dense". The aim of this work was to investigate a deep learning-based breast density classifier to consistently distinguish these two categories, aiming at providing a potential computerized tool to assist radiologists in assigning a BI-RADS category in current clinical workflow. In this study, we constructed a convolutional neural network (CNN)-based model coupled with a large (i.e., 22,000 images) digital mammogram imaging dataset to evaluate the classification performance between the two aforementioned breast density categories. All images were collected from a cohort of 1,427 women who underwent standard digital mammography screening from 2005 to 2016 at our institution. The truths of the density categories were based on standard clinical assessment made by board-certified breast imaging radiologists. Effects of direct training from scratch solely using digital mammogram images and transfer learning of a pretrained model on a large nonmedical imaging dataset were evaluated for the specific task of breast density classification. In order to measure the classification performance, the CNN classifier was also tested on a refined version of the mammogram image dataset by removing some potentially inaccurately labeled images. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to measure the accuracy of the classifier. The AUC was 0.9421 when the CNN-model was trained from scratch on our own mammogram images, and the accuracy increased gradually along with an increased size of training samples

  8. Comparison of Shallow and Deep Learning Methods on Classifying the Regional Pattern of Diffuse Lung Disease.

    Science.gov (United States)

    Kim, Guk Bae; Jung, Kyu-Hwan; Lee, Yeha; Kim, Hyun-Jun; Kim, Namkug; Jun, Sanghoon; Seo, Joon Beom; Lynch, David A

    2017-10-17

    This study aimed to compare shallow and deep learning of classifying the patterns of interstitial lung diseases (ILDs). Using high-resolution computed tomography images, two experienced radiologists marked 1200 regions of interest (ROIs), in which 600 ROIs were each acquired using a GE or Siemens scanner and each group of 600 ROIs consisted of 100 ROIs for subregions that included normal and five regional pulmonary disease patterns (ground-glass opacity, consolidation, reticular opacity, emphysema, and honeycombing). We employed the convolution neural network (CNN) with six learnable layers that consisted of four convolution layers and two fully connected layers. The classification results were compared with the results classified by a shallow learning of a support vector machine (SVM). The CNN classifier showed significantly better performance for accuracy compared with that of the SVM classifier by 6-9%. As the convolution layer increases, the classification accuracy of the CNN showed better performance from 81.27 to 95.12%. Especially in the cases showing pathological ambiguity such as between normal and emphysema cases or between honeycombing and reticular opacity cases, the increment of the convolution layer greatly drops the misclassification rate between each case. Conclusively, the CNN classifier showed significantly greater accuracy than the SVM classifier, and the results implied structural characteristics that are inherent to the specific ILD patterns.

  9. A Synthetic Biology Framework for Programming Eukaryotic Transcription Functions

    Science.gov (United States)

    Khalil, Ahmad S.; Lu, Timothy K.; Bashor, Caleb J.; Ramirez, Cherie L.; Pyenson, Nora C.; Joung, J. Keith; Collins, James J.

    2013-01-01

    SUMMARY Eukaryotic transcription factors (TFs) perform complex and combinatorial functions within transcriptional networks. Here, we present a synthetic framework for systematically constructing eukaryotic transcription functions using artificial zinc fingers, modular DNA-binding domains found within many eukaryotic TFs. Utilizing this platform, we construct a library of orthogonal synthetic transcription factors (sTFs) and use these to wire synthetic transcriptional circuits in yeast. We engineer complex functions, such as tunable output strength and transcriptional cooperativity, by rationally adjusting a decomposed set of key component properties, e.g., DNA specificity, affinity, promoter design, protein-protein interactions. We show that subtle perturbations to these properties can transform an individual sTF between distinct roles (activator, cooperative factor, inhibitory factor) within a transcriptional complex, thus drastically altering the signal processing behavior of multi-input systems. This platform provides new genetic components for synthetic biology and enables bottom-up approaches to understanding the design principles of eukaryotic transcriptional complexes and networks. PMID:22863014

  10. Neural network analysis in pharmacogenetics of mood disorders

    Directory of Open Access Journals (Sweden)

    Serretti Alessandro

    2004-12-01

    Full Text Available Abstract Background The increasing number of available genotypes for genetic studies in humans requires more advanced techniques of analysis. We previously reported significant univariate associations between gene polymorphisms and antidepressant response in mood disorders. However the combined analysis of multiple gene polymorphisms and clinical variables requires the use of non linear methods. Methods In the present study we tested a neural network strategy for a combined analysis of two gene polymorphisms. A Multi Layer Perceptron model showed the best performance and was therefore selected over the other networks. One hundred and twenty one depressed inpatients treated with fluvoxamine in the context of previously reported pharmacogenetic studies were included. The polymorphism in the transcriptional control region upstream of the 5HTT coding sequence (SERTPR and in the Tryptophan Hydroxylase (TPH gene were analysed simultaneously. Results A multi layer perceptron network composed by 1 hidden layer with 7 nodes was chosen. 77.5 % of responders and 51.2% of non responders were correctly classified (ROC area = 0.731 – empirical p value = 0.0082. Finally, we performed a comparison with traditional techniques. A discriminant function analysis correctly classified 34.1 % of responders and 68.1 % of non responders (F = 8.16 p = 0.0005. Conclusions Overall, our findings suggest that neural networks may be a valid technique for the analysis of gene polymorphisms in pharmacogenetic studies. The complex interactions modelled through NN may be eventually applied at the clinical level for the individualized therapy.

  11. The Transcription Factor Encyclopedia

    DEFF Research Database (Denmark)

    Yusuf, Dimas; Butland, Stefanie L; Swanson, Magdalena I

    2012-01-01

    mini review articles on pertinent human, mouse and rat TFs. Notable features of the TFe website include a high-quality PDF generator and web API for programmatic data retrieval. TFe aims to rapidly educate scientists about the TFs they encounter through the delivery of succinct summaries written......ABSTRACT: Here we present the Transcription Factor Encyclopedia (TFe), a new web-based compendium of mini review articles on transcription factors (TFs) that is founded on the principles of open access and collaboration. Our consortium of over 100 researchers has collectively contributed over 130...

  12. RRHGE: A Novel Approach to Classify the Estrogen Receptor Based Breast Cancer Subtypes

    Directory of Open Access Journals (Sweden)

    Ashish Saini

    2014-01-01

    Full Text Available Background. Breast cancer is the most common type of cancer among females with a high mortality rate. It is essential to classify the estrogen receptor based breast cancer subtypes into correct subclasses, so that the right treatments can be applied to lower the mortality rate. Using gene signatures derived from gene interaction networks to classify breast cancers has proven to be more reproducible and can achieve higher classification performance. However, the interactions in the gene interaction network usually contain many false-positive interactions that do not have any biological meanings. Therefore, it is a challenge to incorporate the reliability assessment of interactions when deriving gene signatures from gene interaction networks. How to effectively extract gene signatures from available resources is critical to the success of cancer classification. Methods. We propose a novel method to measure and extract the reliable (biologically true or valid interactions from gene interaction networks and incorporate the extracted reliable gene interactions into our proposed RRHGE algorithm to identify significant gene signatures from microarray gene expression data for classifying ER+ and ER− breast cancer samples. Results. The evaluation on real breast cancer samples showed that our RRHGE algorithm achieved higher classification accuracy than the existing approaches.

  13. 41 CFR 105-62.102 - Authority to originally classify.

    Science.gov (United States)

    2010-07-01

    ... originally classify. (a) Top secret, secret, and confidential. The authority to originally classify information as Top Secret, Secret, or Confidential may be exercised only by the Administrator and is delegable...

  14. Naive Bayesian classifiers for multinomial features: a theoretical analysis

    CSIR Research Space (South Africa)

    Van Dyk, E

    2007-11-01

    Full Text Available The authors investigate the use of naive Bayesian classifiers for multinomial feature spaces and derive error estimates for these classifiers. The error analysis is done by developing a mathematical model to estimate the probability density...

  15. Classifying quantum entanglement through topological links

    Science.gov (United States)

    Quinta, Gonçalo M.; André, Rui

    2018-04-01

    We propose an alternative classification scheme for quantum entanglement based on topological links. This is done by identifying a nonrigid ring to a particle, attributing the act of cutting and removing a ring to the operation of tracing out the particle, and associating linked rings to entangled particles. This analogy naturally leads us to a classification of multipartite quantum entanglement based on all possible distinct links for a given number of rings. To determine all different possibilities, we develop a formalism that associates any link to a polynomial, with each polynomial thereby defining a distinct equivalence class. To demonstrate the use of this classification scheme, we choose qubit quantum states as our example of physical system. A possible procedure to obtain qubit states from the polynomials is also introduced, providing an example state for each link class. We apply the formalism for the quantum systems of three and four qubits and demonstrate the potential of these tools in a context of qubit networks.

  16. Fast Most Similar Neighbor (MSN) classifiers for Mixed Data

    OpenAIRE

    Hernández Rodríguez, Selene

    2010-01-01

    The k nearest neighbor (k-NN) classifier has been extensively used in Pattern Recognition because of its simplicity and its good performance. However, in large datasets applications, the exhaustive k-NN classifier becomes impractical. Therefore, many fast k-NN classifiers have been developed; most of them rely on metric properties (usually the triangle inequality) to reduce the number of prototype comparisons. Hence, the existing fast k-NN classifiers are applicable only when the comparison f...

  17. Dynamic ad hoc networks

    CERN Document Server

    Rashvand, Habib

    2013-01-01

    Motivated by the exciting new application paradigm of using amalgamated technologies of the Internet and wireless, the next generation communication networks (also called 'ubiquitous', 'complex' and 'unstructured' networking) are changing the way we develop and apply our future systems and services at home and on local, national and global scales. Whatever the interconnection - a WiMAX enabled networked mobile vehicle, MEMS or nanotechnology enabled distributed sensor systems, Vehicular Ad hoc Networking (VANET) or Mobile Ad hoc Networking (MANET) - all can be classified under new networking s

  18. Three data partitioning strategies for building local classifiers (Chapter 14)

    NARCIS (Netherlands)

    Zliobaite, I.; Okun, O.; Valentini, G.; Re, M.

    2011-01-01

    Divide-and-conquer approach has been recognized in multiple classifier systems aiming to utilize local expertise of individual classifiers. In this study we experimentally investigate three strategies for building local classifiers that are based on different routines of sampling data for training.

  19. Recognition of pornographic web pages by classifying texts and images.

    Science.gov (United States)

    Hu, Weiming; Wu, Ou; Chen, Zhouyao; Fu, Zhouyu; Maybank, Steve

    2007-06-01

    With the rapid development of the World Wide Web, people benefit more and more from the sharing of information. However, Web pages with obscene, harmful, or illegal content can be easily accessed. It is important to recognize such unsuitable, offensive, or pornographic Web pages. In this paper, a novel framework for recognizing pornographic Web pages is described. A C4.5 decision tree is used to divide Web pages, according to content representations, into continuous text pages, discrete text pages, and image pages. These three categories of Web pages are handled, respectively, by a continuous text classifier, a discrete text classifier, and an algorithm that fuses the results from the image classifier and the discrete text classifier. In the continuous text classifier, statistical and semantic features are used to recognize pornographic texts. In the discrete text classifier, the naive Bayes rule is used to calculate the probability that a discrete text is pornographic. In the image classifier, the object's contour-based features are extracted to recognize pornographic images. In the text and image fusion algorithm, the Bayes theory is used to combine the recognition results from images and texts. Experimental results demonstrate that the continuous text classifier outperforms the traditional keyword-statistics-based classifier, the contour-based image classifier outperforms the traditional skin-region-based image classifier, the results obtained by our fusion algorithm outperform those by either of the individual classifiers, and our framework can be adapted to different categories of Web pages.

  20. 32 CFR 2400.28 - Dissemination of classified information.

    Science.gov (United States)

    2010-07-01

    ... 32 National Defense 6 2010-07-01 2010-07-01 false Dissemination of classified information. 2400.28... SECURITY PROGRAM Safeguarding § 2400.28 Dissemination of classified information. Heads of OSTP offices... originating official may prescribe specific restrictions on dissemination of classified information when...

  1. Basal transcription machinery

    Indian Academy of Sciences (India)

    2007-03-29

    Mar 29, 2007 ... The holoenzyme of prokaryotic RNA polymerase consists of the core enzyme, made of two , , ' and subunits, which lacks promoter selectivity and a sigma () subunit which enables the core enzyme to initiate transcription in a promoter dependent fashion. A stress sigma factor s, in prokaryotes ...

  2. Machine Dictation and Transcription.

    Science.gov (United States)

    Harvey, Evelyn; And Others

    This instructional package contains both an instructor's manual and a student's manual for a course in machine dictation and transcription. The instructor's manual contains an overview with tips on teaching the course, letters for dictation, and a key to the letters. The student's manual contains an overview of the course and of the skills needed…

  3. Transcriptional Regulation in Haematopoiesis:

    DEFF Research Database (Denmark)

    Lauridsen, Felicia K B

    with the capacity to both self-renew and differentiate. This thesis is built upon two studies, which investigate two different aspects of the haematopoietic system; heterogeneity within the HSC compartment (presented in manuscript I), and the interplay between transcription factors controlling granulocyte/ monocyte...

  4. Peat classified as slowly renewable biomass fuel

    International Nuclear Information System (INIS)

    2001-01-01

    thousands of years. The report states also that peat should be classified as biomass fuel instead of biofuels, such as wood, or fossil fuels such as coal. According to the report peat is a renewable biomass fuel like biofuels, but due to slow accumulation it should be considered as slowly renewable fuel. The report estimates that bonding of carbon in both virgin and forest drained peatlands are so high that it can compensate the emissions formed in combustion of energy peat

  5. Transcriptional Analysis of the MrpJ Network: Modulation of Diverse Virulence-Associated Genes and Direct Regulation of mrp Fimbrial and flhDC Flagellar Operons in Proteus mirabilis

    Science.gov (United States)

    Bode, Nadine J.; Debnath, Irina; Kuan, Lisa; Schulfer, Anjelique; Ty, Maureen

    2015-01-01

    The enteric bacterium Proteus mirabilis is associated with a significant number of catheter-associated urinary tract infections (UTIs). Strict regulation of the antagonistic processes of adhesion and motility, mediated by fimbriae and flagella, respectively, is essential for disease progression. Previously, the transcriptional regulator MrpJ, which is encoded by the mrp fimbrial operon, has been shown to repress both swimming and swarming motility. Here we show that MrpJ affects an array of cellular processes beyond adherence and motility. Microarray analysis found that expression of mrpJ mimicking levels observed during UTIs leads to differential expression of 217 genes related to, among other functions, bacterial virulence, type VI secretion, and metabolism. We probed the molecular mechanism of transcriptional regulation by MrpJ using transcriptional reporters and chromatin immunoprecipitation (ChIP). Binding of MrpJ to two virulence-associated target gene promoters, the promoters of the flagellar master regulator flhDC and mrp itself, appears to be affected by the condensation state of the native chromosome, although both targets share a direct MrpJ binding site proximal to the transcriptional start. Furthermore, an mrpJ deletion mutant colonized the bladders of mice at significantly lower levels in a transurethral model of infection. Additionally, we observed that mrpJ is widely conserved in a collection of recent clinical isolates. Altogether, these findings support a role of MrpJ as a global regulator of P. mirabilis virulence. PMID:25847961

  6. Transcriptional regulation of the operon encoding stress-responsive ECF sigma factor SigH and its anti-sigma factor RshA, and control of its regulatory network in Corynebacterium glutamicum.

    Science.gov (United States)

    Busche, Tobias; Silar, Radoslav; Pičmanová, Martina; Pátek, Miroslav; Kalinowski, Jörn

    2012-09-03

    The expression of genes in Corynebacterium glutamicum, a Gram-positive non-pathogenic bacterium used mainly for the industrial production of amino acids, is regulated by seven different sigma factors of RNA polymerase, including the stress-responsive ECF-sigma factor SigH. The sigH gene is located in a gene cluster together with the rshA gene, putatively encoding an anti-sigma factor. The aim of this study was to analyze the transcriptional regulation of the sigH and rshA gene cluster and the effects of RshA on the SigH regulon, in order to refine the model describing the role of SigH and RshA during stress response. Transcription analyses revealed that the sigH gene and rshA gene are cotranscribed from four sigH housekeeping promoters in C. glutamicum. In addition, a SigH-controlled rshA promoter was found to only drive the transcription of the rshA gene. To test the role of the putative anti-sigma factor gene rshA under normal growth conditions, a C. glutamicum rshA deletion strain was constructed and used for genome-wide transcription profiling with DNA microarrays. In total, 83 genes organized in 61 putative transcriptional units, including those previously detected using sigH mutant strains, exhibited increased transcript levels in the rshA deletion mutant compared to its parental strain. The genes encoding proteins related to disulphide stress response, heat stress proteins, components of the SOS-response to DNA damage and proteasome components were the most markedly upregulated gene groups. Altogether six SigH-dependent promoters upstream of the identified genes were determined by primer extension and a refined consensus promoter consisting of 45 original promoter sequences was constructed. The rshA gene codes for an anti-sigma factor controlling the function of the stress-responsive sigma factor SigH in C. glutamicum. Transcription of rshA from a SigH-dependent promoter may serve to quickly shutdown the SigH-dependent stress response after the cells have

  7. Transcriptional regulat