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Sample records for learned complex signals

  1. Brain signal complexity rises with repetition suppression in visual learning.

    Science.gov (United States)

    Lafontaine, Marc Philippe; Lacourse, Karine; Lina, Jean-Marc; McIntosh, Anthony R; Gosselin, Frédéric; Théoret, Hugo; Lippé, Sarah

    2016-06-21

    Neuronal activity associated with visual processing of an unfamiliar face gradually diminishes when it is viewed repeatedly. This process, known as repetition suppression (RS), is involved in the acquisition of familiarity. Current models suggest that RS results from interactions between visual information processing areas located in the occipito-temporal cortex and higher order areas, such as the dorsolateral prefrontal cortex (DLPFC). Brain signal complexity, which reflects information dynamics of cortical networks, has been shown to increase as unfamiliar faces become familiar. However, the complementarity of RS and increases in brain signal complexity have yet to be demonstrated within the same measurements. We hypothesized that RS and brain signal complexity increase occur simultaneously during learning of unfamiliar faces. Further, we expected alteration of DLPFC function by transcranial direct current stimulation (tDCS) to modulate RS and brain signal complexity over the occipito-temporal cortex. Participants underwent three tDCS conditions in random order: right anodal/left cathodal, right cathodal/left anodal and sham. Following tDCS, participants learned unfamiliar faces, while an electroencephalogram (EEG) was recorded. Results revealed RS over occipito-temporal electrode sites during learning, reflected by a decrease in signal energy, a measure of amplitude. Simultaneously, as signal energy decreased, brain signal complexity, as estimated with multiscale entropy (MSE), increased. In addition, prefrontal tDCS modulated brain signal complexity over the right occipito-temporal cortex during the first presentation of faces. These results suggest that although RS may reflect a brain mechanism essential to learning, complementary processes reflected by increases in brain signal complexity, may be instrumental in the acquisition of novel visual information. Such processes likely involve long-range coordinated activity between prefrontal and lower order visual

  2. Discrimination of Rock Fracture and Blast Events Based on Signal Complexity and Machine Learning

    Directory of Open Access Journals (Sweden)

    Zilong Zhou

    2018-01-01

    Full Text Available The automatic discrimination of rock fracture and blast events is complex and challenging due to the similar waveform characteristics. To solve this problem, a new method based on the signal complexity analysis and machine learning has been proposed in this paper. First, the permutation entropy values of signals at different scale factors are calculated to reflect complexity of signals and constructed into a feature vector set. Secondly, based on the feature vector set, back-propagation neural network (BPNN as a means of machine learning is applied to establish a discriminator for rock fracture and blast events. Then to evaluate the classification performances of the new method, the classifying accuracies of support vector machine (SVM, naive Bayes classifier, and the new method are compared, and the receiver operating characteristic (ROC curves are also analyzed. The results show the new method obtains the best classification performances. In addition, the influence of different scale factor q and number of training samples n on discrimination results is discussed. It is found that the classifying accuracy of the new method reaches the highest value when q = 8–15 or 8–20 and n=140.

  3. Chansporter complexes in cell signaling.

    Science.gov (United States)

    Abbott, Geoffrey W

    2017-09-01

    Ion channels facilitate diffusion of ions across cell membranes for such diverse purposes as neuronal signaling, muscular contraction, and fluid homeostasis. Solute transporters often utilize ionic gradients to move aqueous solutes up their concentration gradient, also fulfilling a wide variety of tasks. Recently, an increasing number of ion channel-transporter ('chansporter') complexes have been discovered. Chansporter complex formation may overcome what could otherwise be considerable spatial barriers to rapid signal integration and feedback between channels and transporters, the ions and other substrates they transport, and environmental factors to which they must respond. Here, current knowledge in this field is summarized, covering both heterologous expression structure/function findings and potential mechanisms by which chansporter complexes fulfill contrasting roles in cell signaling in vivo. © 2017 Federation of European Biochemical Societies.

  4. Proceedings of the IEEE Machine Learning for Signal Processing XVII

    DEFF Research Database (Denmark)

    The seventeenth of a series of workshops sponsored by the IEEE Signal Processing Society and organized by the Machine Learning for Signal Processing Technical Committee (MLSP-TC). The field of machine learning has matured considerably in both methodology and real-world application domains and has...... become particularly important for solution of problems in signal processing. As reflected in this collection, machine learning for signal processing combines many ideas from adaptive signal/image processing, learning theory and models, and statistics in order to solve complex real-world signal processing......, and two papers from the winners of the Data Analysis Competition. The program included papers in the following areas: genomic signal processing, pattern recognition and classification, image and video processing, blind signal processing, models, learning algorithms, and applications of machine learning...

  5. Supervised Learning with Complex-valued Neural Networks

    CERN Document Server

    Suresh, Sundaram; Savitha, Ramasamy

    2013-01-01

    Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks.  Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computati...

  6. Complexity control in statistical learning

    Indian Academy of Sciences (India)

    Then we describe how the method of regularization is used to control complexity in learning. We discuss two examples of regularization, one in which the function space used is finite dimensional, and another in which it is a reproducing kernel Hilbert space. Our exposition follows the formulation of Cucker and Smale.

  7. Learning sparse generative models of audiovisual signals

    OpenAIRE

    Monaci, Gianluca; Sommer, Friedrich T.; Vandergheynst, Pierre

    2008-01-01

    This paper presents a novel framework to learn sparse represen- tations for audiovisual signals. An audiovisual signal is modeled as a sparse sum of audiovisual kernels. The kernels are bimodal functions made of synchronous audio and video components that can be positioned independently and arbitrarily in space and time. We design an algorithm capable of learning sets of such audiovi- sual, synchronous, shift-invariant functions by alternatingly solving a coding and a learning pr...

  8. A Signal-Interleaving Complex Bandpass Sigma-Delta Converter

    DEFF Research Database (Denmark)

    Wad, Paul Emmanuel

    1997-01-01

    Complex or quadrature Sigma-Delta converters operate on complex signals, i.e. signals consisting of a real and an imaginary component, whereas conventional converters operate only on real signals. The advantage of complex signal processing in the discrete-time domain is that the entire sampling...

  9. A deep learning approach for fetal QRS complex detection.

    Science.gov (United States)

    Zhong, Wei; Liao, Lijuan; Guo, Xuemei; Wang, Guoli

    2018-04-20

    Non-invasive foetal electrocardiography (NI-FECG) has the potential to provide more additional clinical information for detecting and diagnosing fetal diseases. We propose and demonstrate a deep learning approach for fetal QRS complex detection from raw NI-FECG signals by using a convolutional neural network (CNN) model. The main objective is to investigate whether reliable fetal QRS complex detection performance can still be obtained from features of single-channel NI-FECG signals, without canceling maternal ECG (MECG) signals. A deep learning method is proposed for recognizing fetal QRS complexes. Firstly, we collect data from set-a of the PhysioNet/computing in Cardiology Challenge database. The sample entropy method is used for signal quality assessment. Part of the bad quality signals is excluded in the further analysis. Secondly, in the proposed method, the features of raw NI-FECG signals are normalized before they are fed to a CNN classifier to perform fetal QRS complex detection. We use precision, recall, F-measure and accuracy as the evaluation metrics to assess the performance of fetal QRS complex detection. The proposed deep learning method can achieve relatively high precision (75.33%), recall (80.54%), and F-measure scores (77.85%) compared with three other well-known pattern classification methods, namely KNN, naive Bayes and SVM. the proposed deep learning method can attain reliable fetal QRS complex detection performance from the raw NI-FECG signals without canceling MECG signals. In addition, the influence of different activation functions and signal quality assessment on classification performance are evaluated, and results show that Relu outperforms the Sigmoid and Tanh on this particular task, and better classification performance is obtained with the signal quality assessment step in this study.

  10. Exome localization of complex disease association signals

    Directory of Open Access Journals (Sweden)

    Lewis Cathryn M

    2011-02-01

    Full Text Available Abstract Background Genome-wide association studies (GWAS of common diseases have had a tremendous impact on genetic research over the last five years; the field is now moving from microarray-based technology towards next-generation sequencing. To evaluate the potential of association studies for complex diseases based on exome sequencing we analysed the distribution of association signal with respect to protein-coding genes based on GWAS data for seven diseases from the Wellcome Trust Case Control Consortium. Results We find significant concentration of association signal in exons and genes for Crohn's Disease, Type 1 Diabetes and Bipolar Disorder, but also observe enrichment from up to 40 kilobases upstream to 40 kilobases downstream of protein-coding genes for Crohn's Disease and Type 1 Diabetes; the exact extent of the distribution is disease dependent. Conclusions Our work suggests that exome sequencing may be a feasible approach to find genetic variation associated with complex disease. Extending the exome sequencing to include flanking regions therefore promises further improvement of covering disease-relevant variants.

  11. Neural Correlates of Success and Failure Signals During Neurofeedback Learning.

    Science.gov (United States)

    Radua, Joaquim; Stoica, Teodora; Scheinost, Dustin; Pittenger, Christopher; Hampson, Michelle

    2018-05-15

    Feedback-driven learning, observed across phylogeny and of clear adaptive value, is frequently operationalized in simple operant conditioning paradigms, but it can be much more complex, driven by abstract representations of success and failure. This study investigates the neural processes involved in processing success and failure during feedback learning, which are not well understood. Data analyzed were acquired during a multisession neurofeedback experiment in which ten participants were presented with, and instructed to modulate, the activity of their orbitofrontal cortex with the aim of decreasing their anxiety. We assessed the regional blood-oxygenation-level-dependent response to the individualized neurofeedback signals of success and failure across twelve functional runs acquired in two different magnetic resonance sessions in each of ten individuals. Neurofeedback signals of failure correlated early during learning with deactivation in the precuneus/posterior cingulate and neurofeedback signals of success correlated later during learning with deactivation in the medial prefrontal/anterior cingulate cortex. The intensity of the latter deactivations predicted the efficacy of the neurofeedback intervention in the reduction of anxiety. These findings indicate a role for regulation of the default mode network during feedback learning, and suggest a higher sensitivity to signals of failure during the early feedback learning and to signals of success subsequently. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

  12. Vicarious reinforcement learning signals when instructing others.

    Science.gov (United States)

    Apps, Matthew A J; Lesage, Elise; Ramnani, Narender

    2015-02-18

    Reinforcement learning (RL) theory posits that learning is driven by discrepancies between the predicted and actual outcomes of actions (prediction errors [PEs]). In social environments, learning is often guided by similar RL mechanisms. For example, teachers monitor the actions of students and provide feedback to them. This feedback evokes PEs in students that guide their learning. We report the first study that investigates the neural mechanisms that underpin RL signals in the brain of a teacher. Neurons in the anterior cingulate cortex (ACC) signal PEs when learning from the outcomes of one's own actions but also signal information when outcomes are received by others. Does a teacher's ACC signal PEs when monitoring a student's learning? Using fMRI, we studied brain activity in human subjects (teachers) as they taught a confederate (student) action-outcome associations by providing positive or negative feedback. We examined activity time-locked to the students' responses, when teachers infer student predictions and know actual outcomes. We fitted a RL-based computational model to the behavior of the student to characterize their learning, and examined whether a teacher's ACC signals when a student's predictions are wrong. In line with our hypothesis, activity in the teacher's ACC covaried with the PE values in the model. Additionally, activity in the teacher's insula and ventromedial prefrontal cortex covaried with the predicted value according to the student. Our findings highlight that the ACC signals PEs vicariously for others' erroneous predictions, when monitoring and instructing their learning. These results suggest that RL mechanisms, processed vicariously, may underpin and facilitate teaching behaviors. Copyright © 2015 Apps et al.

  13. Signalling chains with probe and adjust learning

    Science.gov (United States)

    Gosti, Giorgio

    2018-04-01

    Many models explain the evolution of signalling in repeated stage games on social networks, differently in this study each signalling game evolves a communication strategy to transmit information across the network. Specifically, I formalise signalling chain games as a generalisation of Lewis' signalling games, where a number of players are placed on a chain network and play a signalling game in which they have to propagate information across the network. I show that probe and adjust learning allows the system to develop communication conventions, but it may temporarily perturb the system out of conventions. Through simulations, I evaluate how long the system takes to evolve a signalling convention and the amount of time it stays in it. This discussion presents a mechanism in which simple players can evolve signalling across a social network without necessarily understanding the entire system.

  14. Financial signal processing and machine learning

    CERN Document Server

    Kulkarni,Sanjeev R; Dmitry M. Malioutov

    2016-01-01

    The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analy...

  15. Constructivist learning theories and complex learning environments

    NARCIS (Netherlands)

    R-J. Simons; Dr. S. Bolhuis

    2004-01-01

    Learning theories broadly characterised as constructivist, agree on the importance to learning of the environment, but differ on what exactly it is that constitutes this importance. Accordingly, they also differ on the educational consequences to be drawn from the theoretical perspective. Cognitive

  16. Adaptive learning and complex dynamics

    International Nuclear Information System (INIS)

    Gomes, Orlando

    2009-01-01

    In this paper, we explore the dynamic properties of a group of simple deterministic difference equation systems in which the conventional perfect foresight assumption gives place to a mechanism of adaptive learning. These systems have a common feature: under perfect foresight (or rational expectations) they all possess a unique fixed point steady state. This long-term outcome is obtained also under learning if the quality underlying the learning process is high. Otherwise, when the degree of inefficiency of the learning process is relatively strong, nonlinear dynamics (periodic and a-periodic cycles) arise. The specific properties of each one of the proposed systems is explored both in terms of local and global dynamics. One macroeconomic model is used to illustrate how the formation of expectations through learning may eventually lead to awkward long-term outcomes.

  17. Complexity control in statistical learning

    Indian Academy of Sciences (India)

    complexity of the class of models from which we are to choose our model. In this ... As is explained in §2, we use the concept of covering numbers to quantify the complexity of a class of ..... called structural risk minimization (SRM). Vapnik ...

  18. Sequence distance via parsing complexity: Heartbeat signals

    International Nuclear Information System (INIS)

    Degli Esposti, M.; Farinelli, C.; Menconi, G.

    2009-01-01

    We compare and discuss the use of different symbolic codings of electrocardiogram (ECG) signals in order to distinguish healthy patients from hospitalized ones. To this aim, we recall a parsing-based similarity distance and compare the performances of several methods of classification of data.

  19. Selective disruption of the AKAP signaling complexes.

    Science.gov (United States)

    Kennedy, Eileen J; Scott, John D

    2015-01-01

    Synthesis of the second messenger cAMP activates a variety of signaling pathways critical for all facets of intracellular regulation. Protein kinase A (PKA) is the major cAMP-responsive effector. Where and when this enzyme is activated has profound implications on the cellular role of PKA. A-Kinase Anchoring Proteins (AKAPs) play a critical role in this process by orchestrating spatial and temporal aspects of PKA action. A popular means of evaluating the impact of these anchored signaling events is to biochemically interfere with the PKA-AKAP interface. Hence, peptide disruptors of PKA anchoring are valuable tools in the investigation of local PKA action. This article outlines the development of PKA isoform-selective disruptor peptides, documents the optimization of cell-soluble peptide derivatives, and introduces alternative cell-based approaches that interrogate other aspects of the PKA-AKAP interface.

  20. Astrocyte Ca2+ signalling: an unexpected complexity

    OpenAIRE

    Volterra, Andrea; Liaudet, Nicolas; Savtchouk, Iaroslav

    2014-01-01

    Astrocyte Ca(2+) signalling has been proposed to link neuronal information in different spatial-temporal dimensions to achieve a higher level of brain integration. However, some discrepancies in the results of recent studies challenge this view and highlight key insufficiencies in our current understanding. In parallel, new experimental approaches that enable the study of astrocyte physiology at higher spatial-temporal resolution in intact brain preparations are beginning to reveal an unexpec...

  1. Leadership Learning for Complex Organizations

    Science.gov (United States)

    Ng, F. S. David

    2015-01-01

    Many school leadership programs are set and delivered in specific modules or workshops in order to achieve a pre-determined set of competencies, knowledge, and skills. In addition, these programs are driven by the faculty member and the prescribed content. As Singapore schools become more complex in the roles and responsibilities to educate the…

  2. Learning To Live with Complexity.

    Science.gov (United States)

    Dosa, Marta

    Neither the design of information systems and networks nor the delivery of library services can claim true user centricity without an understanding of the multifaceted psychological environment of users and potential users. The complexity of the political process, social problems, challenges to scientific inquiry, entrepreneurship, and…

  3. Deep Learning in Visual Computing and Signal Processing

    OpenAIRE

    Xie, Danfeng; Zhang, Lei; Bai, Li

    2017-01-01

    Deep learning is a subfield of machine learning, which aims to learn a hierarchy of features from input data. Nowadays, researchers have intensively investigated deep learning algorithms for solving challenging problems in many areas such as image classification, speech recognition, signal processing, and natural language processing. In this study, we not only review typical deep learning algorithms in computer vision and signal processing but also provide detailed information on how to apply...

  4. IEEE International Workshop on Machine Learning for Signal Processing: Preface

    DEFF Research Database (Denmark)

    Tao, Jianhua

    The 21st IEEE International Workshop on Machine Learning for Signal Processing will be held in Beijing, China, on September 18–21, 2011. The workshop series is the major annual technical event of the IEEE Signal Processing Society's Technical Committee on Machine Learning for Signal Processing...

  5. Modulation of EEG Theta Band Signal Complexity by Music Therapy

    Science.gov (United States)

    Bhattacharya, Joydeep; Lee, Eun-Jeong

    The primary goal of this study was to investigate the impact of monochord (MC) sounds, a type of archaic sounds used in music therapy, on the neural complexity of EEG signals obtained from patients undergoing chemotherapy. The secondary goal was to compare the EEG signal complexity values for monochords with those for progressive muscle relaxation (PMR), an alternative therapy for relaxation. Forty cancer patients were randomly allocated to one of the two relaxation groups, MC and PMR, over a period of six months; continuous EEG signals were recorded during the first and last sessions. EEG signals were analyzed by applying signal mode complexity, a measure of complexity of neuronal oscillations. Across sessions, both groups showed a modulation of complexity of beta-2 band (20-29Hz) at midfrontal regions, but only MC group showed a modulation of complexity of theta band (3.5-7.5Hz) at posterior regions. Therefore, the neuronal complexity patterns showed different changes in EEG frequency band specific complexity resulting in two different types of interventions. Moreover, the different neural responses to listening to monochords and PMR were observed after regular relaxation interventions over a short time span.

  6. Learning from Evidence in a Complex World

    Science.gov (United States)

    Sterman, John D.

    2006-01-01

    Policies to promote public health and welfare often fail or worsen the problems they are intended to solve. Evidence-based learning should prevent such policy resistance, but learning in complex systems is often weak and slow. Complexity hinders our ability to discover the delayed and distal impacts of interventions, generating unintended “side effects.” Yet learning often fails even when strong evidence is available: common mental models lead to erroneous but self-confirming inferences, allowing harmful beliefs and behaviors to persist and undermining implementation of beneficial policies. Here I show how systems thinking and simulation modeling can help expand the boundaries of our mental models, enhance our ability to generate and learn from evidence, and catalyze effective change in public health and beyond. PMID:16449579

  7. Learning to live with complexity.

    Science.gov (United States)

    Sargut, Gökçe; McGrath, Rita Gunther

    2011-09-01

    Business life has always featured the unpredictable, the surprising, and the unexpected. But in today's hyperconnected world, complexity is the norm. Systems that used to be separate are now intertwined and interdependent, and knowing the starting conditions is no guide to predicting outcomes; too many continuously changing interactive elements are in play. Managers looking to navigate these difficulties need to adopt new approaches. They should drop outmoded forecasting tools-for example, ones that rely on averages, which are often less important than outliers. Instead, they should use models that simulate the behavior of the system. They should also make sure that their data include a good amount of future-oriented information. Risk mitigation is crucial as well. Managers should minimize the need to rely on predictions-for instance, they can give users a say in product design. They can decouple elements in a system and build in redundancy to minimize the consequences of a partial system failure, and turn to outside partners to extend their own company's capabilities. They can complement hard analysis with "soft" methods such as storytelling to make potentially important future possibilities more real. And they can make trade-offs that keep early failures small and provide the diversity of thought needed in a nimble organization faced with complexity on virtually every front.

  8. Stochastic Learning and the Intuitive Criterion in Simple Signaling Games

    DEFF Research Database (Denmark)

    Sloth, Birgitte; Whitta-Jacobsen, Hans Jørgen

    A stochastic learning process for signaling games with two types, two signals, and two responses gives rise to equilibrium selection which is in remarkable accordance with the selection obtained by the intuitive criterion......A stochastic learning process for signaling games with two types, two signals, and two responses gives rise to equilibrium selection which is in remarkable accordance with the selection obtained by the intuitive criterion...

  9. Continuous residual reinforcement learning for traffic signal control optimization

    NARCIS (Netherlands)

    Aslani, Mohammad; Seipel, Stefan; Wiering, Marco

    2018-01-01

    Traffic signal control can be naturally regarded as a reinforcement learning problem. Unfortunately, it is one of the most difficult classes of reinforcement learning problems owing to its large state space. A straightforward approach to address this challenge is to control traffic signals based on

  10. Fractal Complexity-Based Feature Extraction Algorithm of Communication Signals

    Science.gov (United States)

    Wang, Hui; Li, Jingchao; Guo, Lili; Dou, Zheng; Lin, Yun; Zhou, Ruolin

    How to analyze and identify the characteristics of radiation sources and estimate the threat level by means of detecting, intercepting and locating has been the central issue of electronic support in the electronic warfare, and communication signal recognition is one of the key points to solve this issue. Aiming at accurately extracting the individual characteristics of the radiation source for the increasingly complex communication electromagnetic environment, a novel feature extraction algorithm for individual characteristics of the communication radiation source based on the fractal complexity of the signal is proposed. According to the complexity of the received signal and the situation of environmental noise, use the fractal dimension characteristics of different complexity to depict the subtle characteristics of the signal to establish the characteristic database, and then identify different broadcasting station by gray relation theory system. The simulation results demonstrate that the algorithm can achieve recognition rate of 94% even in the environment with SNR of -10dB, and this provides an important theoretical basis for the accurate identification of the subtle features of the signal at low SNR in the field of information confrontation.

  11. Trends in Machine Learning for Signal Processing

    DEFF Research Database (Denmark)

    Adali, Tulay; Miller, David J.; Diamantaras, Konstantinos I.

    2011-01-01

    By putting the accent on learning from the data and the environment, the Machine Learning for SP (MLSP) Technical Committee (TC) provides the essential bridge between the machine learning and SP communities. While the emphasis in MLSP is on learning and data-driven approaches, SP defines the main...... applications of interest, and thus the constraints and requirements on solutions, which include computational efficiency, online adaptation, and learning with limited supervision/reference data....

  12. Nonlinear complexity analysis of brain FMRI signals in schizophrenia.

    Directory of Open Access Journals (Sweden)

    Moses O Sokunbi

    Full Text Available We investigated the differences in brain fMRI signal complexity in patients with schizophrenia while performing the Cyberball social exclusion task, using measures of Sample entropy and Hurst exponent (H. 13 patients meeting diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM IV criteria for schizophrenia and 16 healthy controls underwent fMRI scanning at 1.5 T. The fMRI data of both groups of participants were pre-processed, the entropy characterized and the Hurst exponent extracted. Whole brain entropy and H maps of the groups were generated and analysed. The results after adjusting for age and sex differences together show that patients with schizophrenia exhibited higher complexity than healthy controls, at mean whole brain and regional levels. Also, both Sample entropy and Hurst exponent agree that patients with schizophrenia have more complex fMRI signals than healthy controls. These results suggest that schizophrenia is associated with more complex signal patterns when compared to healthy controls, supporting the increase in complexity hypothesis, where system complexity increases with age or disease, and also consistent with the notion that schizophrenia is characterised by a dysregulation of the nonlinear dynamics of underlying neuronal systems.

  13. Learning Latent Structure in Complex Networks

    DEFF Research Database (Denmark)

    Mørup, Morten; Hansen, Lars Kai

    such as the Modularity, it has recently been shown that latent structure in complex networks is learnable by Bayesian generative link distribution models (Airoldi et al., 2008, Hofman and Wiggins, 2008). In this paper we propose a new generative model that allows representation of latent community structure......Latent structure in complex networks, e.g., in the form of community structure, can help understand network dynamics, identify heterogeneities in network properties, and predict ‘missing’ links. While most community detection algorithms are based on optimizing heuristic clustering objectives...... as in the previous Bayesian approaches and in addition allows learning of node specific link properties similar to that in the modularity objective. We employ a new relaxation method for efficient inference in these generative models that allows us to learn the behavior of very large networks. We compare the link...

  14. Aliasing in the Complex Cepstrum of Linear-Phase Signals

    DEFF Research Database (Denmark)

    Bysted, Tommy Kristensen

    1997-01-01

    Assuming linear-phase of the associated time signal, this paper presents an approximated analytical description of the unavoidable aliasing in practical use of complex cepstrums. The linear-phase assumption covers two major applications of complex cepstrums which are linear- to minimum-phase FIR......-filter transformation and minimum-phase estimation from amplitude specifications. The description is made in the cepstrum domain, the Fourier transform of the complex cepstrum and in the frequency domain. Two examples are given, one for verification of the derived equations and one using the description to reduce...... aliasing in minimum-phase estimation...

  15. Proceedings of IEEE Machine Learning for Signal Processing Workshop XVI

    DEFF Research Database (Denmark)

    Larsen, Jan

    These proceedings contains refereed papers presented at the sixteenth IEEE Workshop on Machine Learning for Signal Processing (MLSP'2006), held in Maynooth, Co. Kildare, Ireland, September 6-8, 2006. This is a continuation of the IEEE Workshops on Neural Networks for Signal Processing (NNSP......). The name of the Technical Committee, hence of the Workshop, was changed to Machine Learning for Signal Processing in September 2003 to better reflect the areas represented by the Technical Committee. The conference is organized by the Machine Learning for Signal Processing Technical Committee...... the same standard as the printed version and facilitates the reading and searching of the papers. The field of machine learning has matured considerably in both methodology and real-world application domains and has become particularly important for solution of problems in signal processing. As reflected...

  16. Labor Inhibits Placental Mechanistic Target of Rapamycin Complex 1 Signaling

    Science.gov (United States)

    LAGER, Susanne; AYE, Irving L.M.H.; GACCIOLI, Francesca; RAMIREZ, Vanessa I.; JANSSON, Thomas; POWELL, Theresa L.

    2014-01-01

    Introduction Labor induces a myriad of changes in placental gene expression. These changes may represent a physiological adaptation inhibiting placental cellular processes associated with a high demand for oxygen and energy (e.g., protein synthesis and active transport) thereby promoting oxygen and glucose transfer to the fetus. We hypothesized that mechanistic target of rapamycin complex 1 (mTORC1) signaling, a positive regulator of trophoblast protein synthesis and amino acid transport, is inhibited by labor. Methods Placental tissue was collected from healthy, term pregnancies (n=15 no-labor; n=12 labor). Activation of Caspase-1, IRS1/Akt, STAT, mTOR, and inflammatory signaling pathways was determined by Western blot. NFκB p65 and PPARγ DNA binding activity was measured in isolated nuclei. Results Labor increased Caspase-1 activation and mTOR complex 2 signaling, as measured by phosphorylation of Akt (S473). However, mTORC1 signaling was inhibited in response to labor as evidenced by decreased phosphorylation of mTOR (S2448) and 4EBP1 (T37/46 and T70). Labor also decreased NFκB and PPARγ DNA binding activity, while having no effect on IRS1 or STAT signaling pathway. Discussion and conclusion Several placental signaling pathways are affected by labor, which has implications for experimental design in studies of placental signaling. Inhibition of placental mTORC1 signaling in response to labor may serve to down-regulate protein synthesis and amino acid transport, processes that account for a large share of placental oxygen and glucose consumption. We speculate that this response preserves glucose and oxygen for transfer to the fetus during the stressful events of labor. PMID:25454472

  17. Proceedings of IEEE Machine Learning for Signal Processing Workshop XV

    DEFF Research Database (Denmark)

    Larsen, Jan

    These proceedings contains refereed papers presented at the Fifteenth IEEE Workshop on Machine Learning for Signal Processing (MLSP’2005), held in Mystic, Connecticut, USA, September 28-30, 2005. This is a continuation of the IEEE Workshops on Neural Networks for Signal Processing (NNSP) organized...... by the NNSP Technical Committee of the IEEE Signal Processing Society. The name of the Technical Committee, hence of the Workshop, was changed to Machine Learning for Signal Processing in September 2003 to better reflect the areas represented by the Technical Committee. The conference is organized...... by the Machine Learning for Signal Processing Technical Committee with sponsorship of the IEEE Signal Processing Society. Following the practice started two years ago, the bound volume of the proceedings is going to be published by IEEE following the Workshop, and we are pleased to offer to conference attendees...

  18. Investing in Youth Work: Learning from Complexity

    Directory of Open Access Journals (Sweden)

    Kari Denissen Cunnien

    2017-04-01

    Full Text Available This article proposes key elements for a system of support for youth workers to develop their professional skills and capabilities by using a human development approach.  The article argues that narrowed and bureaucratic approaches to professional development can ignore the complex dynamics of human development that support engaged learning and continuous growth and improvement.  The author suggests a more dynamic system where professional development in grounded by practice; employs reflection, mentorship and coaching; and supports healthy organizational culture to foster high quality youth work.

  19. Assembly of Oligomeric Death Domain Complexes during Toll Receptor Signaling*

    OpenAIRE

    Moncrieffe, Martin C.; Grossmann, J. Günter; Gay, Nicholas J.

    2008-01-01

    The Drosophila Toll receptor is activated by the endogenous protein ligand Spätzle in response to microbial stimuli in immunity and spatial cues during embryonic development. Downstream signaling is mediated by the adaptor proteins Tube, the kinase Pelle, and the Drosophila homologue of myeloid differentiation primary response protein (dMyD88). Here we have characterized heterodimeric (dMyD88-Tube) and heterotrimeric (dMyD88-Tube-Pelle) death domain complexes. We show ...

  20. Assembly of Oligomeric Death Domain Complexes during Toll Receptor Signaling*

    Science.gov (United States)

    Moncrieffe, Martin C.; Grossmann, J. Günter; Gay, Nicholas J.

    2008-01-01

    The Drosophila Toll receptor is activated by the endogenous protein ligand Spätzle in response to microbial stimuli in immunity and spatial cues during embryonic development. Downstream signaling is mediated by the adaptor proteins Tube, the kinase Pelle, and the Drosophila homologue of myeloid differentiation primary response protein (dMyD88). Here we have characterized heterodimeric (dMyD88-Tube) and heterotrimeric (dMyD88-Tube-Pelle) death domain complexes. We show that both the heterodimeric and heterotrimeric complexes form kidney-shaped structures and that Tube is bivalent and has separate high affinity binding sites for dMyD88 and Pelle. Additionally we found no interaction between the isolated death domains of Pelle and dMyD88. These results indicate that the mode of assembly of the heterotrimeric dMyD88-Tube-Pelle complex downstream of the activated Toll receptor is unique. The measured dissociation constants for the interaction between the death domains of dMyD88 and Tube and of Pelle and a preformed dMyD88-Tube complex are used to propose a model of the early postreceptor events in Drosophila Toll receptor signaling. PMID:18829464

  1. Assembly of oligomeric death domain complexes during Toll receptor signaling.

    Science.gov (United States)

    Moncrieffe, Martin C; Grossmann, J Günter; Gay, Nicholas J

    2008-11-28

    The Drosophila Toll receptor is activated by the endogenous protein ligand Spätzle in response to microbial stimuli in immunity and spatial cues during embryonic development. Downstream signaling is mediated by the adaptor proteins Tube, the kinase Pelle, and the Drosophila homologue of myeloid differentiation primary response protein (dMyD88). Here we have characterized heterodimeric (dMyD88-Tube) and heterotrimeric (dMyD88-Tube-Pelle) death domain complexes. We show that both the heterodimeric and heterotrimeric complexes form kidney-shaped structures and that Tube is bivalent and has separate high affinity binding sites for dMyD88 and Pelle. Additionally we found no interaction between the isolated death domains of Pelle and dMyD88. These results indicate that the mode of assembly of the heterotrimeric dMyD88-Tube-Pelle complex downstream of the activated Toll receptor is unique. The measured dissociation constants for the interaction between the death domains of dMyD88 and Tube and of Pelle and a preformed dMyD88-Tube complex are used to propose a model of the early postreceptor events in Drosophila Toll receptor signaling.

  2. Simulation of Weak Signals of Nanotechnology Innovation in Complex System

    Directory of Open Access Journals (Sweden)

    Sun Hi Yoo

    2018-02-01

    Full Text Available It is especially indispensable for new businesses or industries to predict the innovation of new technologies. This requires an understanding of how the complex process of innovation, which is accomplished through more efficient products, processes, services, technologies, or ideas, is adopted and diffused in the market, government, and society. Furthermore, detecting “weak signals” (signs of change in science and technology (S&T is also important to foretell events associated with innovations in technology. Thus, we explore the dynamic behavior of weak signals of a specific technological innovation using the agent-based simulating tool NetLogo. This study provides a deeper understanding of the early stages of complex technology innovation, and the models are capable of analyzing initial complex interaction structures between components of technologies and between agents engaged in collective invention.

  3. Network affordances through online learning: Increasing use and complexity.

    OpenAIRE

    Hajhashemi, Karim; Anderson, Neil; Jackson, Cliff; Caltabiano, Nerina

    2013-01-01

    Computers, mobile devices and the Internet have enabled a learning environment described as online learning or a variety of other terms such as e-learning. Researchers believe that online learning has become more complex due to learners' sharing and acquiring knowledge at a variety of remote locations, in a variety of modalities. However, advances in technology and the integration of ICT with teaching and learning settings have quickened the growth of online learning and importantly have chan...

  4. Entropy for the Complexity of Physiological Signal Dynamics.

    Science.gov (United States)

    Zhang, Xiaohua Douglas

    2017-01-01

    Recently, the rapid development of large data storage technologies, mobile network technology, and portable medical devices makes it possible to measure, record, store, and track analysis of biological dynamics. Portable noninvasive medical devices are crucial to capture individual characteristics of biological dynamics. The wearable noninvasive medical devices and the analysis/management of related digital medical data will revolutionize the management and treatment of diseases, subsequently resulting in the establishment of a new healthcare system. One of the key features that can be extracted from the data obtained by wearable noninvasive medical device is the complexity of physiological signals, which can be represented by entropy of biological dynamics contained in the physiological signals measured by these continuous monitoring medical devices. Thus, in this chapter I present the major concepts of entropy that are commonly used to measure the complexity of biological dynamics. The concepts include Shannon entropy, Kolmogorov entropy, Renyi entropy, approximate entropy, sample entropy, and multiscale entropy. I also demonstrate an example of using entropy for the complexity of glucose dynamics.

  5. Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering

    Directory of Open Access Journals (Sweden)

    Xin Tian

    2017-06-01

    Full Text Available We introduce a seismic signal compression method based on nonparametric Bayesian dictionary learning method via clustering. The seismic data is compressed patch by patch, and the dictionary is learned online. Clustering is introduced for dictionary learning. A set of dictionaries could be generated, and each dictionary is used for one cluster’s sparse coding. In this way, the signals in one cluster could be well represented by their corresponding dictionaries. A nonparametric Bayesian dictionary learning method is used to learn the dictionaries, which naturally infers an appropriate dictionary size for each cluster. A uniform quantizer and an adaptive arithmetic coding algorithm are adopted to code the sparse coefficients. With comparisons to other state-of-the art approaches, the effectiveness of the proposed method could be validated in the experiments.

  6. Learning the lipid language of plant signalling.

    NARCIS (Netherlands)

    van Leeuwen, W.; Okresz, L.; Bogre, L.; Munnik, T.

    2004-01-01

    Plant cells respond to different biotic and abiotic stresses by producing various uncommon phospholipids that are believed to play key roles in cell signalling. We can predict how they work because animal and yeast proteins have been shown to have specific lipid-binding domains, which act as docking

  7. Complex Interplay of Hormonal Signals during Grape Berry Ripening

    Directory of Open Access Journals (Sweden)

    Ana Margarida Fortes

    2015-05-01

    Full Text Available Grape and wine production and quality is extremely dependent on the fruit ripening process. Sensory and nutritional characteristics are important aspects for consumers and their development during fruit ripening involves complex hormonal control. In this review, we explored data already published on grape ripening and compared it with the hormonal regulation of ripening of other climacteric and non-climacteric fruits. The roles of abscisic acid, ethylene, and brassinosteroids as promoters of ripening are discussed, as well as the role of auxins, cytokinins, gibberellins, jasmonates, and polyamines as inhibitors of ripening. In particular, the recently described role of polyamine catabolism in grape ripening is discussed, together with its putative interaction with other hormones. Furthermore, other recent examples of cross-talk among the different hormones are presented, revealing a complex interplay of signals during grape development and ripening.

  8. Design principles of nuclear receptor signaling: how complex networking improves signal transduction

    Science.gov (United States)

    Kolodkin, Alexey N; Bruggeman, Frank J; Plant, Nick; Moné, Martijn J; Bakker, Barbara M; Campbell, Moray J; van Leeuwen, Johannes P T M; Carlberg, Carsten; Snoep, Jacky L; Westerhoff, Hans V

    2010-01-01

    The topology of nuclear receptor (NR) signaling is captured in a systems biological graphical notation. This enables us to identify a number of ‘design' aspects of the topology of these networks that might appear unnecessarily complex or even functionally paradoxical. In realistic kinetic models of increasing complexity, calculations show how these features correspond to potentially important design principles, e.g.: (i) cytosolic ‘nuclear' receptor may shuttle signal molecules to the nucleus, (ii) the active export of NRs may ensure that there is sufficient receptor protein to capture ligand at the cytoplasmic membrane, (iii) a three conveyor belts design dissipating GTP-free energy, greatly aids response, (iv) the active export of importins may prevent sequestration of NRs by importins in the nucleus and (v) the unspecific nature of the nuclear pore may ensure signal-flux robustness. In addition, the models developed are suitable for implementation in specific cases of NR-mediated signaling, to predict individual receptor functions and differential sensitivity toward physiological and pharmacological ligands. PMID:21179018

  9. Neural signals of vicarious extinction learning.

    Science.gov (United States)

    Golkar, Armita; Haaker, Jan; Selbing, Ida; Olsson, Andreas

    2016-10-01

    Social transmission of both threat and safety is ubiquitous, but little is known about the neural circuitry underlying vicarious safety learning. This is surprising given that these processes are critical to flexibly adapt to a changeable environment. To address how the expression of previously learned fears can be modified by the transmission of social information, two conditioned stimuli (CS + s) were paired with shock and the third was not. During extinction, we held constant the amount of direct, non-reinforced, exposure to the CSs (i.e. direct extinction), and critically varied whether another individual-acting as a demonstrator-experienced safety (CS + vic safety) or aversive reinforcement (CS + vic reinf). During extinction, ventromedial prefrontal cortex (vmPFC) responses to the CS + vic reinf increased but decreased to the CS + vic safety This pattern of vmPFC activity was reversed during a subsequent fear reinstatement test, suggesting a temporal shift in the involvement of the vmPFC. Moreover, only the CS + vic reinf association recovered. Our data suggest that vicarious extinction prevents the return of conditioned fear responses, and that this efficacy is reflected by diminished vmPFC involvement during extinction learning. The present findings may have important implications for understanding how social information influences the persistence of fear memories in individuals suffering from emotional disorders. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  10. Association between increased EEG signal complexity and cannabis dependence.

    Science.gov (United States)

    Laprevote, Vincent; Bon, Laura; Krieg, Julien; Schwitzer, Thomas; Bourion-Bedes, Stéphanie; Maillard, Louis; Schwan, Raymund

    2017-12-01

    Both acute and regular cannabis use affects the functioning of the brain. While several studies have demonstrated that regular cannabis use can impair the capacity to synchronize neural assemblies during specific tasks, less is known about spontaneous brain activity. This can be explored by measuring EEG complexity, which reflects the spontaneous variability of human brain activity. A recent study has shown that acute cannabis use can affect that complexity. Since the characteristics of cannabis use can affect the impact on brain functioning, this study sets out to measure EEG complexity in regular cannabis users with or without dependence, in comparison with healthy controls. We recruited 26 healthy controls, 25 cannabis users without cannabis dependence and 14 cannabis users with cannabis dependence, based on DSM IV TR criteria. The EEG signal was extracted from at least 250 epochs of the 500ms pre-stimulation phase during a visual evoked potential paradigm. Brain complexity was estimated using Lempel-Ziv Complexity (LZC), which was compared across groups by non-parametric Kruskall-Wallis ANOVA. The analysis revealed a significant difference between the groups, with higher LZC in participants with cannabis dependence than in non-dependent cannabis users. There was no specific localization of this effect across electrodes. We showed that cannabis dependence is associated to an increased spontaneous brain complexity in regular users. This result is in line with previous results in acute cannabis users. It may reflect increased randomness of neural activity in cannabis dependence. Future studies should explore whether this effect is permanent or diminishes with cannabis cessation. Copyright © 2017 Elsevier B.V. and ECNP. All rights reserved.

  11. Age-Related Changes in Electroencephalographic Signal Complexity

    Science.gov (United States)

    Zappasodi, Filippo; Marzetti, Laura; Olejarczyk, Elzbieta; Tecchio, Franca; Pizzella, Vittorio

    2015-01-01

    The study of active and healthy aging is a primary focus for social and neuroscientific communities. Here, we move a step forward in assessing electrophysiological neuronal activity changes in the brain with healthy aging. To this end, electroencephalographic (EEG) resting state activity was acquired in 40 healthy subjects (age 16–85). We evaluated Fractal Dimension (FD) according to the Higuchi algorithm, a measure which quantifies the presence of statistical similarity at different scales in temporal fluctuations of EEG signals. Our results showed that FD increases from age twenty to age fifty and then decreases. The curve that best fits the changes in FD values across age over the whole sample is a parabola, with the vertex located around age fifty. Moreover, FD changes are site specific, with interhemispheric FD asymmetry being pronounced in elderly individuals in the frontal and central regions. The present results indicate that fractal dimension well describes the modulations of brain activity with age. Since fractal dimension has been proposed to be related to the complexity of the signal dynamics, our data demonstrate that the complexity of neuronal electric activity changes across the life span of an individual, with a steady increase during young adulthood and a decrease in the elderly population. PMID:26536036

  12. Enabling complex genetic circuits to respond to extrinsic environmental signals.

    Science.gov (United States)

    Hoynes-O'Connor, Allison; Shopera, Tatenda; Hinman, Kristina; Creamer, John Philip; Moon, Tae Seok

    2017-07-01

    Genetic circuits have the potential to improve a broad range of metabolic engineering processes and address a variety of medical and environmental challenges. However, in order to engineer genetic circuits that can meet the needs of these real-world applications, genetic sensors that respond to relevant extrinsic and intrinsic signals must be implemented in complex genetic circuits. In this work, we construct the first AND and NAND gates that respond to temperature and pH, two signals that have relevance in a variety of real-world applications. A previously identified pH-responsive promoter and a temperature-responsive promoter were extracted from the E. coli genome, characterized, and modified to suit the needs of the genetic circuits. These promoters were combined with components of the type III secretion system in Salmonella typhimurium and used to construct a set of AND gates with up to 23-fold change. Next, an antisense RNA was integrated into the circuit architecture to invert the logic of the AND gate and generate a set of NAND gates with up to 1168-fold change. These circuits provide the first demonstration of complex pH- and temperature-responsive genetic circuits, and lay the groundwork for the use of similar circuits in real-world applications. Biotechnol. Bioeng. 2017;114: 1626-1631. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  13. 2015 International Conference on Machine Learning and Signal Processing

    CERN Document Server

    Woo, Wai; Sulaiman, Hamzah; Othman, Mohd; Saat, Mohd

    2016-01-01

    This book presents important research findings and recent innovations in the field of machine learning and signal processing. A wide range of topics relating to machine learning and signal processing techniques and their applications are addressed in order to provide both researchers and practitioners with a valuable resource documenting the latest advances and trends. The book comprises a careful selection of the papers submitted to the 2015 International Conference on Machine Learning and Signal Processing (MALSIP 2015), which was held on 15–17 December 2015 in Ho Chi Minh City, Vietnam with the aim of offering researchers, academicians, and practitioners an ideal opportunity to disseminate their findings and achievements. All of the included contributions were chosen by expert peer reviewers from across the world on the basis of their interest to the community. In addition to presenting the latest in design, development, and research, the book provides access to numerous new algorithms for machine learni...

  14. Stochastic effects as a force to increase the complexity of signaling networks

    KAUST Repository

    Kuwahara, Hiroyuki; Gao, Xin

    2013-01-01

    Cellular signaling networks are complex and appear to include many nonfunctional elements. Recently, it was suggested that nonfunctional interactions of proteins cause signaling noise, which, perhaps, shapes the signal transduction mechanism

  15. Assembly of Slx4 signaling complexes behind DNA replication forks.

    Science.gov (United States)

    Balint, Attila; Kim, TaeHyung; Gallo, David; Cussiol, Jose Renato; Bastos de Oliveira, Francisco M; Yimit, Askar; Ou, Jiongwen; Nakato, Ryuichiro; Gurevich, Alexey; Shirahige, Katsuhiko; Smolka, Marcus B; Zhang, Zhaolei; Brown, Grant W

    2015-08-13

    Obstructions to replication fork progression, referred to collectively as DNA replication stress, challenge genome stability. In Saccharomyces cerevisiae, cells lacking RTT107 or SLX4 show genome instability and sensitivity to DNA replication stress and are defective in the completion of DNA replication during recovery from replication stress. We demonstrate that Slx4 is recruited to chromatin behind stressed replication forks, in a region that is spatially distinct from that occupied by the replication machinery. Slx4 complex formation is nucleated by Mec1 phosphorylation of histone H2A, which is recognized by the constitutive Slx4 binding partner Rtt107. Slx4 is essential for recruiting the Mec1 activator Dpb11 behind stressed replication forks, and Slx4 complexes are important for full activity of Mec1. We propose that Slx4 complexes promote robust checkpoint signaling by Mec1 by stably recruiting Dpb11 within a discrete domain behind the replication fork, during DNA replication stress. © 2015 The Authors.

  16. An imperfect dopaminergic error signal can drive temporal-difference learning.

    Directory of Open Access Journals (Sweden)

    Wiebke Potjans

    2011-05-01

    Full Text Available An open problem in the field of computational neuroscience is how to link synaptic plasticity to system-level learning. A promising framework in this context is temporal-difference (TD learning. Experimental evidence that supports the hypothesis that the mammalian brain performs temporal-difference learning includes the resemblance of the phasic activity of the midbrain dopaminergic neurons to the TD error and the discovery that cortico-striatal synaptic plasticity is modulated by dopamine. However, as the phasic dopaminergic signal does not reproduce all the properties of the theoretical TD error, it is unclear whether it is capable of driving behavior adaptation in complex tasks. Here, we present a spiking temporal-difference learning model based on the actor-critic architecture. The model dynamically generates a dopaminergic signal with realistic firing rates and exploits this signal to modulate the plasticity of synapses as a third factor. The predictions of our proposed plasticity dynamics are in good agreement with experimental results with respect to dopamine, pre- and post-synaptic activity. An analytical mapping from the parameters of our proposed plasticity dynamics to those of the classical discrete-time TD algorithm reveals that the biological constraints of the dopaminergic signal entail a modified TD algorithm with self-adapting learning parameters and an adapting offset. We show that the neuronal network is able to learn a task with sparse positive rewards as fast as the corresponding classical discrete-time TD algorithm. However, the performance of the neuronal network is impaired with respect to the traditional algorithm on a task with both positive and negative rewards and breaks down entirely on a task with purely negative rewards. Our model demonstrates that the asymmetry of a realistic dopaminergic signal enables TD learning when learning is driven by positive rewards but not when driven by negative rewards.

  17. Classifying BCI signals from novice users with extreme learning machine

    Directory of Open Access Journals (Sweden)

    Rodríguez-Bermúdez Germán

    2017-07-01

    Full Text Available Brain computer interface (BCI allows to control external devices only with the electrical activity of the brain. In order to improve the system, several approaches have been proposed. However it is usual to test algorithms with standard BCI signals from experts users or from repositories available on Internet. In this work, extreme learning machine (ELM has been tested with signals from 5 novel users to compare with standard classification algorithms. Experimental results show that ELM is a suitable method to classify electroencephalogram signals from novice users.

  18. Blind identification and separation of complex-valued signals

    CERN Document Server

    Moreau, Eric

    2013-01-01

    Blind identification consists of estimating a multi-dimensional system only through the use of its output, and source separation, the blind estimation of the inverse of the system. Estimation is generally carried out using different statistics of the output. The authors of this book consider the blind identification and source separation problem in the complex-domain, where the available statistical properties are richer and include non-circularity of the sources - underlying components. They define identifiability conditions and present state-of-the-art algorithms that are based on algebraic methods as well as iterative algorithms based on maximum likelihood theory. Contents 1. Mathematical Preliminaries. 2. Estimation by Joint Diagonalization. 3. Maximum Likelihood ICA. About the Authors Eric Moreau is Professor of Electrical Engineering at the University of Toulon, France. His research interests concern statistical signal processing, high order statistics and matrix/tensor decompositions with applic...

  19. Statistical mechanics of learning orthogonal signals for general covariance models

    International Nuclear Information System (INIS)

    Hoyle, David C

    2010-01-01

    Statistical mechanics techniques have proved to be useful tools in quantifying the accuracy with which signal vectors are extracted from experimental data. However, analysis has previously been limited to specific model forms for the population covariance C, which may be inappropriate for real world data sets. In this paper we obtain new statistical mechanical results for a general population covariance matrix C. For data sets consisting of p sample points in R N we use the replica method to study the accuracy of orthogonal signal vectors estimated from the sample data. In the asymptotic limit of N,p→∞ at fixed α = p/N, we derive analytical results for the signal direction learning curves. In the asymptotic limit the learning curves follow a single universal form, each displaying a retarded learning transition. An explicit formula for the location of the retarded learning transition is obtained and we find marked variation in the location of the retarded learning transition dependent on the distribution of population covariance eigenvalues. The results of the replica analysis are confirmed against simulation

  20. Denoising of gravitational wave signals via dictionary learning algorithms

    Science.gov (United States)

    Torres-Forné, Alejandro; Marquina, Antonio; Font, José A.; Ibáñez, José M.

    2016-12-01

    Gravitational wave astronomy has become a reality after the historical detections accomplished during the first observing run of the two advanced LIGO detectors. In the following years, the number of detections is expected to increase significantly with the full commissioning of the advanced LIGO, advanced Virgo and KAGRA detectors. The development of sophisticated data analysis techniques to improve the opportunities of detection for low signal-to-noise-ratio events is, hence, a most crucial effort. In this paper, we present one such technique, dictionary-learning algorithms, which have been extensively developed in the last few years and successfully applied mostly in the context of image processing. However, to the best of our knowledge, such algorithms have not yet been employed to denoise gravitational wave signals. By building dictionaries from numerical relativity templates of both binary black holes mergers and bursts of rotational core collapse, we show how machine-learning algorithms based on dictionaries can also be successfully applied for gravitational wave denoising. We use a subset of signals from both catalogs, embedded in nonwhite Gaussian noise, to assess our techniques with a large sample of tests and to find the best model parameters. The application of our method to the actual signal GW150914 shows promising results. Dictionary-learning algorithms could be a complementary addition to the gravitational wave data analysis toolkit. They may be used to extract signals from noise and to infer physical parameters if the data are in good enough agreement with the morphology of the dictionary atoms.

  1. Machine learning approaches for the prediction of signal peptides and otherprotein sorting signals

    DEFF Research Database (Denmark)

    Nielsen, Henrik; Brunak, Søren; von Heijne, Gunnar

    1999-01-01

    Prediction of protein sorting signals from the sequence of amino acids has great importance in the field of proteomics today. Recently,the growth of protein databases, combined with machine learning approaches, such as neural networks and hidden Markov models, havemade it possible to achieve...

  2. Learning to manage complexity through simulation: students' challenges and possible strategies.

    Science.gov (United States)

    Gormley, Gerard J; Fenwick, Tara

    2016-06-01

    Many have called for medical students to learn how to manage complexity in healthcare. This study examines the nuances of students' challenges in coping with a complex simulation learning activity, using concepts from complexity theory, and suggests strategies to help them better understand and manage complexity.Wearing video glasses, participants took part in a simulation ward-based exercise that incorporated characteristics of complexity. Video footage was used to elicit interviews, which were transcribed. Using complexity theory as a theoretical lens, an iterative approach was taken to identify the challenges that participants faced and possible coping strategies using both interview transcripts and video footage.Students' challenges in coping with clinical complexity included being: a) unprepared for 'diving in', b) caught in an escalating system, c) captured by the patient, and d) unable to assert boundaries of acceptable practice.Many characteristics of complexity can be recreated in a ward-based simulation learning activity, affording learners an embodied and immersive experience of these complexity challenges. Possible strategies for managing complexity themes include: a) taking time to size up the system, b) attuning to what emerges, c) reducing complexity, d) boundary practices, and e) working with uncertainty. This study signals pedagogical opportunities for recognizing and dealing with complexity.

  3. Dangerous mating systems: signal complexity, signal content and neural capacity in spiders.

    Science.gov (United States)

    Herberstein, M E; Wignall, A E; Hebets, E A; Schneider, J M

    2014-10-01

    Spiders are highly efficient predators in possession of exquisite sensory capacities for ambushing prey, combined with machinery for launching rapid and determined attacks. As a consequence, any sexually motivated approach carries a risk of ending up as prey rather than as a mate. Sexual selection has shaped courtship to effectively communicate the presence, identity, motivation and/or quality of potential mates, which help ameliorate these risks. Spiders communicate this information via several sensory channels, including mechanical (e.g. vibrational), visual and/or chemical, with examples of multimodal signalling beginning to emerge in the literature. The diverse environments that spiders inhabit have further shaped courtship content and form. While our understanding of spider neurobiology remains in its infancy, recent studies are highlighting the unique and considerable capacities of spiders to process and respond to complex sexual signals. As a result, the dangerous mating systems of spiders are providing important insights into how ecology shapes the evolution of communication systems, with future work offering the potential to link this complex communication with its neural processes. Copyright © 2014 Elsevier Ltd. All rights reserved.

  4. Feature economy vs. logical complexity in phonological pattern learning

    NARCIS (Netherlands)

    Seinhorst, K.T.

    Complexity has been linked to ease of learning. This article explores the roles of two measures of complexity – feature economy and logical complexity – in the acquisition of sets of signs, taken from a small sign language that serves as an analogue of plosive inventories in spoken language. In a

  5. Optimal quantum sample complexity of learning algorithms

    NARCIS (Netherlands)

    Arunachalam, S.; de Wolf, R.

    2017-01-01

    In learning theory, the VC dimension of a concept class C is the most common way to measure its "richness." A fundamental result says that the number of examples needed to learn an unknown target concept c 2 C under an unknown distribution D, is tightly determined by the VC dimension d of the

  6. Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms

    Science.gov (United States)

    Zhang, Zhiwen; Duan, Feng; Zhou, Xin; Meng, Zixuan

    2017-01-01

    Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing. We propose a regularized common spatial pattern (R-CSP) algorithm for EEG feature extraction by incorporating the principle of generic learning. A new classifier combining the K-nearest neighbor (KNN) and support vector machine (SVM) approaches is used to classify four anisomerous states, namely, imaginary movements with the left hand, right foot, and right shoulder and the resting state. The highest classification accuracy rate is 92.5%, and the average classification accuracy rate is 87%. The proposed complex algorithm identification method can significantly improve the identification rate of the minority samples and the overall classification performance. PMID:28874909

  7. Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms.

    Science.gov (United States)

    Liu, Rensong; Zhang, Zhiwen; Duan, Feng; Zhou, Xin; Meng, Zixuan

    2017-01-01

    Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing. We propose a regularized common spatial pattern (R-CSP) algorithm for EEG feature extraction by incorporating the principle of generic learning. A new classifier combining the K -nearest neighbor (KNN) and support vector machine (SVM) approaches is used to classify four anisomerous states, namely, imaginary movements with the left hand, right foot, and right shoulder and the resting state. The highest classification accuracy rate is 92.5%, and the average classification accuracy rate is 87%. The proposed complex algorithm identification method can significantly improve the identification rate of the minority samples and the overall classification performance.

  8. A new generative complexity science of learning for a complex pedagogy

    NARCIS (Netherlands)

    Jörg, T.

    2007-01-01

    Proposal for the SIG Chaos and Complexity Theories at AERA 2007 Title: A New Generative Complexity Science of Learning for a Complex Pedagogy Ton Jörg IVLOS Institute of Education University of Utrecht The Netherlands A.G.D.Jorg@ivlos.uu.nl Introduction My paper focuses on the link between thinking

  9. Complexity Theory and CALL Curriculum in Foreign Language Learning

    Directory of Open Access Journals (Sweden)

    Hassan Soleimani

    2014-05-01

    Full Text Available Complexity theory literally indicates the complexity of a system, behavior, or a process. Its connotative meaning, while, implies dynamism, openness, sensitivity to initial conditions and feedback, and adaptation properties of a system. Regarding English as a Foreign/ Second Language (EFL/ESL this theory emphasizes on the complexity of the process of teaching and learning, including all the properties of a complex system. The purpose of the current study is to discuss the role of CALL as a modern technology in simplifying the process of teaching and learning a new language while integrating into the complexity theory. Nonetheless, the findings obtained from reviewing previously conducted studies in this field confirmed the usefulness of CALL curriculum in EFL/ESL contexts. These findings can also provide pedagogical implications for employing computer as an effective teaching and learning tool.

  10. Early Language Learning: Complexity and Mixed Methods

    Science.gov (United States)

    Enever, Janet, Ed.; Lindgren, Eva, Ed.

    2017-01-01

    This is the first collection of research studies to explore the potential for mixed methods to shed light on foreign or second language learning by young learners in instructed contexts. It brings together recent studies undertaken in Cameroon, China, Croatia, Ethiopia, France, Germany, Italy, Kenya, Mexico, Slovenia, Spain, Sweden, Tanzania and…

  11. Lessons Learned from Crowdsourcing Complex Engineering Tasks.

    Science.gov (United States)

    Staffelbach, Matthew; Sempolinski, Peter; Kijewski-Correa, Tracy; Thain, Douglas; Wei, Daniel; Kareem, Ahsan; Madey, Gregory

    2015-01-01

    Crowdsourcing is the practice of obtaining needed ideas, services, or content by requesting contributions from a large group of people. Amazon Mechanical Turk is a web marketplace for crowdsourcing microtasks, such as answering surveys and image tagging. We explored the limits of crowdsourcing by using Mechanical Turk for a more complicated task: analysis and creation of wind simulations. Our investigation examined the feasibility of using crowdsourcing for complex, highly technical tasks. This was done to determine if the benefits of crowdsourcing could be harnessed to accurately and effectively contribute to solving complex real world engineering problems. Of course, untrained crowds cannot be used as a mere substitute for trained expertise. Rather, we sought to understand how crowd workers can be used as a large pool of labor for a preliminary analysis of complex data. We compared the skill of the anonymous crowd workers from Amazon Mechanical Turk with that of civil engineering graduate students, making a first pass at analyzing wind simulation data. For the first phase, we posted analysis questions to Amazon crowd workers and to two groups of civil engineering graduate students. A second phase of our experiment instructed crowd workers and students to create simulations on our Virtual Wind Tunnel website to solve a more complex task. With a sufficiently comprehensive tutorial and compensation similar to typical crowd-sourcing wages, we were able to enlist crowd workers to effectively complete longer, more complex tasks with competence comparable to that of graduate students with more comprehensive, expert-level knowledge. Furthermore, more complex tasks require increased communication with the workers. As tasks become more complex, the employment relationship begins to become more akin to outsourcing than crowdsourcing. Through this investigation, we were able to stretch and explore the limits of crowdsourcing as a tool for solving complex problems.

  12. Prototypes and matrix relevance learning in complex fourier space

    NARCIS (Netherlands)

    Straat, M.; Kaden, M.; Gay, M.; Villmann, T.; Lampe, Alexander; Seiffert, U.; Biehl, M.; Melchert, F.

    2017-01-01

    In this contribution, we consider the classification of time-series and similar functional data which can be represented in complex Fourier coefficient space. We apply versions of Learning Vector Quantization (LVQ) which are suitable for complex-valued data, based on the so-called Wirtinger

  13. Forgetting in Reinforcement Learning Links Sustained Dopamine Signals to Motivation.

    Science.gov (United States)

    Kato, Ayaka; Morita, Kenji

    2016-10-01

    It has been suggested that dopamine (DA) represents reward-prediction-error (RPE) defined in reinforcement learning and therefore DA responds to unpredicted but not predicted reward. However, recent studies have found DA response sustained towards predictable reward in tasks involving self-paced behavior, and suggested that this response represents a motivational signal. We have previously shown that RPE can sustain if there is decay/forgetting of learned-values, which can be implemented as decay of synaptic strengths storing learned-values. This account, however, did not explain the suggested link between tonic/sustained DA and motivation. In the present work, we explored the motivational effects of the value-decay in self-paced approach behavior, modeled as a series of 'Go' or 'No-Go' selections towards a goal. Through simulations, we found that the value-decay can enhance motivation, specifically, facilitate fast goal-reaching, albeit counterintuitively. Mathematical analyses revealed that underlying potential mechanisms are twofold: (1) decay-induced sustained RPE creates a gradient of 'Go' values towards a goal, and (2) value-contrasts between 'Go' and 'No-Go' are generated because while chosen values are continually updated, unchosen values simply decay. Our model provides potential explanations for the key experimental findings that suggest DA's roles in motivation: (i) slowdown of behavior by post-training blockade of DA signaling, (ii) observations that DA blockade severely impairs effortful actions to obtain rewards while largely sparing seeking of easily obtainable rewards, and (iii) relationships between the reward amount, the level of motivation reflected in the speed of behavior, and the average level of DA. These results indicate that reinforcement learning with value-decay, or forgetting, provides a parsimonious mechanistic account for the DA's roles in value-learning and motivation. Our results also suggest that when biological systems for value-learning

  14. Forgetting in Reinforcement Learning Links Sustained Dopamine Signals to Motivation.

    Directory of Open Access Journals (Sweden)

    Ayaka Kato

    2016-10-01

    Full Text Available It has been suggested that dopamine (DA represents reward-prediction-error (RPE defined in reinforcement learning and therefore DA responds to unpredicted but not predicted reward. However, recent studies have found DA response sustained towards predictable reward in tasks involving self-paced behavior, and suggested that this response represents a motivational signal. We have previously shown that RPE can sustain if there is decay/forgetting of learned-values, which can be implemented as decay of synaptic strengths storing learned-values. This account, however, did not explain the suggested link between tonic/sustained DA and motivation. In the present work, we explored the motivational effects of the value-decay in self-paced approach behavior, modeled as a series of 'Go' or 'No-Go' selections towards a goal. Through simulations, we found that the value-decay can enhance motivation, specifically, facilitate fast goal-reaching, albeit counterintuitively. Mathematical analyses revealed that underlying potential mechanisms are twofold: (1 decay-induced sustained RPE creates a gradient of 'Go' values towards a goal, and (2 value-contrasts between 'Go' and 'No-Go' are generated because while chosen values are continually updated, unchosen values simply decay. Our model provides potential explanations for the key experimental findings that suggest DA's roles in motivation: (i slowdown of behavior by post-training blockade of DA signaling, (ii observations that DA blockade severely impairs effortful actions to obtain rewards while largely sparing seeking of easily obtainable rewards, and (iii relationships between the reward amount, the level of motivation reflected in the speed of behavior, and the average level of DA. These results indicate that reinforcement learning with value-decay, or forgetting, provides a parsimonious mechanistic account for the DA's roles in value-learning and motivation. Our results also suggest that when biological systems

  15. Conceptualizing Debates in Learning and Educational Research: Toward a Complex Systems Conceptual Framework of Learning

    Science.gov (United States)

    Jacobson, Michael J.; Kapur, Manu; Reimann, Peter

    2016-01-01

    This article proposes a conceptual framework of learning based on perspectives and methodologies being employed in the study of complex physical and social systems to inform educational research. We argue that the contexts in which learning occurs are complex systems with elements or agents at different levels--including neuronal, cognitive,…

  16. Evolutionary and adaptive learning in complex markets: a brief summary

    Science.gov (United States)

    Hommes, Cars H.

    2007-06-01

    We briefly review some work on expectations and learning in complex markets, using the familiar demand-supply cobweb model. We discuss and combine two different approaches on learning. According to the adaptive learning approach, agents behave as econometricians using time series observations to form expectations, and update the parameters as more observations become available. This approach has become popular in macro. The second approach has an evolutionary flavor and is sometimes referred to as reinforcement learning. Agents employ different forecasting strategies and evaluate these strategies based upon a fitness measure, e.g. past realized profits. In this framework, boundedly rational agents switch between different, but fixed behavioral rules. This approach has become popular in finance. We combine evolutionary and adaptive learning to model complex markets and discuss whether this theory can match empirical facts and forecasting behavior in laboratory experiments with human subjects.

  17. SOCIAL COMPLEXITY AND LEARNING FORAGING TASKS IN BEES

    Directory of Open Access Journals (Sweden)

    AMAYA-MÁRQUEZ MARISOL

    2008-12-01

    Full Text Available Social complexity and models concerning central place foraging were tested with respect to learning predictions using the social honey bee (Apis mellifera and solitary blue orchard bee (Osmia lignaria when given foraging problems. Both species were presented the same foraging problems, where 1 only reward molarity varied between flower morphs, and 2 only reward volume varied between flower morphs. Experiments utilized blue vs. white flower patches to standardize rewards in each experimental situation. Although honey bees learned faster than blue orchard bees when given a molarity difference reward problem, there was no significant difference in learning rate when presented a volume difference reward problem. Further, the rate at which blue orchard bees learned the volume difference problem was not significantly different from that with which honey bees learned about reward molarity differences. The results do not support the predictions of the social complexity theory, but do support those of the central place model

  18. Assessing Complex Learning Objectives through Analytics

    Science.gov (United States)

    Horodyskyj, L.; Mead, C.; Buxner, S.; Semken, S. C.; Anbar, A. D.

    2016-12-01

    A significant obstacle to improving the quality of education is the lack of easy-to-use assessments of higher-order thinking. Most existing assessments focus on recall and understanding questions, which demonstrate lower-order thinking. Traditionally, higher-order thinking is assessed with practical tests and written responses, which are time-consuming to analyze and are not easily scalable. Computer-based learning environments offer the possibility of assessing such learning outcomes based on analysis of students' actions within an adaptive learning environment. Our fully online introductory science course, Habitable Worlds, uses an intelligent tutoring system that collects and responds to a range of behavioral data, including actions within the keystone project. This central project is a summative, game-like experience in which students synthesize and apply what they have learned throughout the course to identify and characterize a habitable planet from among hundreds of stars. Student performance is graded based on completion and accuracy, but two additional properties can be utilized to gauge higher-order thinking: (1) how efficient a student is with the virtual currency within the project and (2) how many of the optional milestones a student reached. In the project, students can use the currency to check their work and "unlock" convenience features. High-achieving students spend close to the minimum amount required to reach these goals, indicating a high-level of concept mastery and efficient methodology. Average students spend more, indicating effort, but lower mastery. Low-achieving students were more likely to spend very little, which indicates low effort. Differences on these metrics were statistically significant between all three of these populations. We interpret this as evidence that high-achieving students develop and apply efficient problem-solving skills as compared to lower-achieving student who use more brute-force approaches.

  19. Addressing complex design problems through inductive learning

    OpenAIRE

    Hanna, S.

    2012-01-01

    Optimisation and related techniques are well suited to clearly defined problems involving systems that can be accurately simulated, but not to tasks in which the phenomena in question are highly complex or the problem ill-defined. These latter are typical of architecture and particularly creative design tasks, which therefore currently lack viable computational tools. It is argued that as design teams and construction projects of unprecedented scale are increasingly frequent, this is just whe...

  20. Seizure Classification From EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System.

    Science.gov (United States)

    Jiang, Yizhang; Wu, Dongrui; Deng, Zhaohong; Qian, Pengjiang; Wang, Jun; Wang, Guanjin; Chung, Fu-Lai; Choi, Kup-Sze; Wang, Shitong

    2017-12-01

    Recognition of epileptic seizures from offline EEG signals is very important in clinical diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine learning approaches can be faster and more consistent. However, the classification accuracy is usually not satisfactory for two main reasons: the distributions of the data used for training and testing may be different, and the amount of training data may not be enough. In addition, most machine learning approaches generate black-box models that are difficult to interpret. In this paper, we integrate transductive transfer learning, semi-supervised learning and TSK fuzzy system to tackle these three problems. More specifically, we use transfer learning to reduce the discrepancy in data distribution between the training and testing data, employ semi-supervised learning to use the unlabeled testing data to remedy the shortage of training data, and adopt TSK fuzzy system to increase model interpretability. Two learning algorithms are proposed to train the system. Our experimental results show that the proposed approaches can achieve better performance than many state-of-the-art seizure classification algorithms.

  1. Machine Learning Techniques for Optical Performance Monitoring from Directly Detected PDM-QAM Signals

    DEFF Research Database (Denmark)

    Thrane, Jakob; Wass, Jesper; Piels, Molly

    2017-01-01

    Linear signal processing algorithms are effective in dealing with linear transmission channel and linear signal detection, while the nonlinear signal processing algorithms, from the machine learning community, are effective in dealing with nonlinear transmission channel and nonlinear signal...... detection. In this paper, a brief overview of the various machine learning methods and their application in optical communication is presented and discussed. Moreover, supervised machine learning methods, such as neural networks and support vector machine, are experimentally demonstrated for in-band optical...

  2. NSP-CAS Protein Complexes: Emerging Signaling Modules in Cancer.

    Science.gov (United States)

    Wallez, Yann; Mace, Peter D; Pasquale, Elena B; Riedl, Stefan J

    2012-05-01

    The CAS (CRK-associated substrate) family of adaptor proteins comprises 4 members, which share a conserved modular domain structure that enables multiple protein-protein interactions, leading to the assembly of intracellular signaling platforms. Besides their physiological role in signal transduction downstream of a variety of cell surface receptors, CAS proteins are also critical for oncogenic transformation and cancer cell malignancy through associations with a variety of regulatory proteins and downstream effectors. Among the regulatory partners, the 3 recently identified adaptor proteins constituting the NSP (novel SH2-containing protein) family avidly bind to the conserved carboxy-terminal focal adhesion-targeting (FAT) domain of CAS proteins. NSP proteins use an anomalous nucleotide exchange factor domain that lacks catalytic activity to form NSP-CAS signaling modules. Additionally, the NSP SH2 domain can link NSP-CAS signaling assemblies to tyrosine-phosphorylated cell surface receptors. NSP proteins can potentiate CAS function by affecting key CAS attributes such as expression levels, phosphorylation state, and subcellular localization, leading to effects on cell adhesion, migration, and invasion as well as cell growth. The consequences of these activities are well exemplified by the role that members of both families play in promoting breast cancer cell invasiveness and resistance to antiestrogens. In this review, we discuss the intriguing interplay between the NSP and CAS families, with a particular focus on cancer signaling networks.

  3. The Neural Feedback Response to Error As a Teaching Signal for the Motor Learning System

    Science.gov (United States)

    Shadmehr, Reza

    2016-01-01

    When we experience an error during a movement, we update our motor commands to partially correct for this error on the next trial. How does experience of error produce the improvement in the subsequent motor commands? During the course of an erroneous reaching movement, proprioceptive and visual sensory pathways not only sense the error, but also engage feedback mechanisms, resulting in corrective motor responses that continue until the hand arrives at its goal. One possibility is that this feedback response is co-opted by the learning system and used as a template to improve performance on the next attempt. Here we used electromyography (EMG) to compare neural correlates of learning and feedback to test the hypothesis that the feedback response to error acts as a template for learning. We designed a task in which mixtures of error-clamp and force-field perturbation trials were used to deconstruct EMG time courses into error-feedback and learning components. We observed that the error-feedback response was composed of excitation of some muscles, and inhibition of others, producing a complex activation/deactivation pattern during the reach. Despite this complexity, across muscles the learning response was consistently a scaled version of the error-feedback response, but shifted 125 ms earlier in time. Across people, individuals who produced a greater feedback response to error, also learned more from error. This suggests that the feedback response to error serves as a teaching signal for the brain. Individuals who learn faster have a better teacher in their feedback control system. SIGNIFICANCE STATEMENT Our sensory organs transduce errors in behavior. To improve performance, we must generate better motor commands. How does the nervous system transform an error in sensory coordinates into better motor commands in muscle coordinates? Here we show that when an error occurs during a movement, the reflexes transform the sensory representation of error into motor

  4. Signal transduction by the major histocompatibility complex class I molecule

    DEFF Research Database (Denmark)

    Pedersen, A E; Skov, Svend; Bregenholt, S

    1999-01-01

    Ligation of cell surface major histocompatibility class I (MHC-I) proteins by antibodies, or by their native counter receptor, the CD8 molecule, mediates transduction of signals into the cells. MHC-I-mediated signaling can lead to both increased and decreased activity of the MHC-I-expressing cell...... and functioning, MHC-I molecules might be of importance for the maintenance of cellular homeostasis not only within the immune system, but also in the interplay between the immune system and other organ systems....

  5. Nonlinear signal processing for ultrasonic imaging of material complexity

    Czech Academy of Sciences Publication Activity Database

    Dos Santos, S.; Vejvodová, Šárka; Převorovský, Zdeněk

    2010-01-01

    Roč. 59, č. 2 (2010), s. 108-117 ISSN 1736-6046 Institutional research plan: CEZ:AV0Z20760514 Keywords : nonlinear signal processing * TR-NEWS * symmetry analysis * DORT Subject RIV: BI - Acoustics Impact factor: 0.464, year: 2010 www.eap.ee/proceedings

  6. Modulation of learning and memory by cytokines: signaling mechanisms and long term consequences.

    Science.gov (United States)

    Donzis, Elissa J; Tronson, Natalie C

    2014-11-01

    This review describes the role of cytokines and their downstream signaling cascades on the modulation of learning and memory. Immune proteins are required for many key neural processes and dysregulation of these functions by systemic inflammation can result in impairments of memory that persist long after the resolution of inflammation. Recent research has demonstrated that manipulations of individual cytokines can modulate learning, memory, and synaptic plasticity. The many conflicting findings, however, have prevented a clear understanding of the precise role of cytokines in memory. Given the complexity of inflammatory signaling, understanding its modulatory role requires a shift in focus from single cytokines to a network of cytokine interactions and elucidation of the cytokine-dependent intracellular signaling cascades. Finally, we propose that whereas signal transduction and transcription may mediate short-term modulation of memory, long-lasting cellular and molecular mechanisms such as epigenetic modifications and altered neurogenesis may be required for the long lasting impact of inflammation on memory and cognition. Copyright © 2014 Elsevier Inc. All rights reserved.

  7. Learning about knowledge: A complex network approach

    International Nuclear Information System (INIS)

    Fontoura Costa, Luciano da

    2006-01-01

    An approach to modeling knowledge acquisition in terms of walks along complex networks is described. Each subset of knowledge is represented as a node, and relations between such knowledge are expressed as edges. Two types of edges are considered, corresponding to free and conditional transitions. The latter case implies that a node can only be reached after visiting previously a set of nodes (the required conditions). The process of knowledge acquisition can then be simulated by considering the number of nodes visited as a single agent moves along the network, starting from its lowest layer. It is shown that hierarchical networks--i.e., networks composed of successive interconnected layers--are related to compositions of the prerequisite relationships between the nodes. In order to avoid deadlocks--i.e., unreachable nodes--the subnetwork in each layer is assumed to be a connected component. Several configurations of such hierarchical knowledge networks are simulated and the performance of the moving agent quantified in terms of the percentage of visited nodes after each movement. The Barabasi-Albert and random models are considered for the layer and interconnecting subnetworks. Although all subnetworks in each realization have the same number of nodes, several interconnectivities, defined by the average node degree of the interconnection networks, have been considered. Two visiting strategies are investigated: random choice among the existing edges and preferential choice to so far untracked edges. A series of interesting results are obtained, including the identification of a series of plateaus of knowledge stagnation in the case of the preferential movement strategy in the presence of conditional edges

  8. Dynamic time warping and machine learning for signal quality assessment of pulsatile signals

    International Nuclear Information System (INIS)

    Li, Q; Clifford, G D

    2012-01-01

    In this work, we describe a beat-by-beat method for assessing the clinical utility of pulsatile waveforms, primarily recorded from cardiovascular blood volume or pressure changes, concentrating on the photoplethysmogram (PPG). Physiological blood flow is nonstationary, with pulses changing in height, width and morphology due to changes in heart rate, cardiac output, sensor type and hardware or software pre-processing requirements. Moreover, considerable inter-individual and sensor-location variability exists. Simple template matching methods are therefore inappropriate, and a patient-specific adaptive initialization is therefore required. We introduce dynamic time warping to stretch each beat to match a running template and combine it with several other features related to signal quality, including correlation and the percentage of the beat that appeared to be clipped. The features were then presented to a multi-layer perceptron neural network to learn the relationships between the parameters in the presence of good- and bad-quality pulses. An expert-labeled database of 1055 segments of PPG, each 6 s long, recorded from 104 separate critical care admissions during both normal and verified arrhythmic events, was used to train and test our algorithms. An accuracy of 97.5% on the training set and 95.2% on test set was found. The algorithm could be deployed as a stand-alone signal quality assessment algorithm for vetting the clinical utility of PPG traces or any similar quasi-periodic signal. (paper)

  9. Dynamic time warping and machine learning for signal quality assessment of pulsatile signals.

    Science.gov (United States)

    Li, Q; Clifford, G D

    2012-09-01

    In this work, we describe a beat-by-beat method for assessing the clinical utility of pulsatile waveforms, primarily recorded from cardiovascular blood volume or pressure changes, concentrating on the photoplethysmogram (PPG). Physiological blood flow is nonstationary, with pulses changing in height, width and morphology due to changes in heart rate, cardiac output, sensor type and hardware or software pre-processing requirements. Moreover, considerable inter-individual and sensor-location variability exists. Simple template matching methods are therefore inappropriate, and a patient-specific adaptive initialization is therefore required. We introduce dynamic time warping to stretch each beat to match a running template and combine it with several other features related to signal quality, including correlation and the percentage of the beat that appeared to be clipped. The features were then presented to a multi-layer perceptron neural network to learn the relationships between the parameters in the presence of good- and bad-quality pulses. An expert-labeled database of 1055 segments of PPG, each 6 s long, recorded from 104 separate critical care admissions during both normal and verified arrhythmic events, was used to train and test our algorithms. An accuracy of 97.5% on the training set and 95.2% on test set was found. The algorithm could be deployed as a stand-alone signal quality assessment algorithm for vetting the clinical utility of PPG traces or any similar quasi-periodic signal.

  10. Criteria for the Development of Complex Teaching-Learning Environments.

    Science.gov (United States)

    Achtenhagen, Frank

    2001-01-01

    Relates aspects of the didactic tradition, especially the German didactic tradition, to the theory and practice of instructional design. Focuses on processes that are necessary to the modeling of reality and describes the design and development of a virtual enterprise as a complex teaching-learning environment in a German business school.…

  11. Multimodal versus Unimodal Instructions in a Complex Learning Context.

    NARCIS (Netherlands)

    Gellevij, M.R.M.; van der Meij, Hans; de Jong, Anthonius J.M.; Pieters, Julius Marie

    2002-01-01

    Multimodal instruction with text and pictures was compared with unimodal, text-only instruction. More specifically, 44 students used a visual or a textual manual to learn a complex software application. During 2 103–116-min training sessions, cognitive load, and time and ability to recover from

  12. Multimodal versus Unimodal Instruction in a Complex Learning Context.

    Science.gov (United States)

    Gellevij, Mark; van der Meij, Hans; de Jong, Ton; Pieters, Jules

    2002-01-01

    Compared multimodal instruction with text and pictures with unimodal text-only instruction as 44 college students used a visual or textual manual to learn a complex software application. Results initially support dual coding theory and indicate that multimodal instruction led to better performance than unimodal instruction. (SLD)

  13. SOFTWARE COMPLEX FOR CREATION AND ACCUMULATION OF MODERN LEARNING MATERIALS

    Directory of Open Access Journals (Sweden)

    V. V. Polinovskyi

    2010-08-01

    Full Text Available The article analyzes weaknesses of existing tools for lecture materials creation and suggests new complex with modular architecture, which supports different types of lecture materials, templates, interactive elements, includes lecture material database with searching, sorting, and grouping capabilities and can be used for creating lectures courses for distance learning, as well as for interactive lectures for full-time courses.

  14. Representational scripting for carrying out complex learning tasks

    NARCIS (Netherlands)

    Slof, B.

    2011-01-01

    Learning to solve complex problems is important because in our rapidly changing modern society and work environments knowing the answer is often not possible. Although educators and instructional designers acknowledge the benefits of problem solving, they also realize that learners need good

  15. Variability in Second Language Learning: The Roles of Individual Differences, Learning Conditions, and Linguistic Complexity

    Science.gov (United States)

    Tagarelli, Kaitlyn M.; Ruiz, Simón; Vega, José Luis Moreno; Rebuschat, Patrick

    2016-01-01

    Second language learning outcomes are highly variable, due to a variety of factors, including individual differences, exposure conditions, and linguistic complexity. However, exactly how these factors interact to influence language learning is unknown. This article examines the relationship between these three variables in language learners.…

  16. Spared internal but impaired external reward prediction error signals in major depressive disorder during reinforcement learning.

    Science.gov (United States)

    Bakic, Jasmina; Pourtois, Gilles; Jepma, Marieke; Duprat, Romain; De Raedt, Rudi; Baeken, Chris

    2017-01-01

    Major depressive disorder (MDD) creates debilitating effects on a wide range of cognitive functions, including reinforcement learning (RL). In this study, we sought to assess whether reward processing as such, or alternatively the complex interplay between motivation and reward might potentially account for the abnormal reward-based learning in MDD. A total of 35 treatment resistant MDD patients and 44 age matched healthy controls (HCs) performed a standard probabilistic learning task. RL was titrated using behavioral, computational modeling and event-related brain potentials (ERPs) data. MDD patients showed comparable learning rate compared to HCs. However, they showed decreased lose-shift responses as well as blunted subjective evaluations of the reinforcers used during the task, relative to HCs. Moreover, MDD patients showed normal internal (at the level of error-related negativity, ERN) but abnormal external (at the level of feedback-related negativity, FRN) reward prediction error (RPE) signals during RL, selectively when additional efforts had to be made to establish learning. Collectively, these results lend support to the assumption that MDD does not impair reward processing per se during RL. Instead, it seems to alter the processing of the emotional value of (external) reinforcers during RL, when additional intrinsic motivational processes have to be engaged. © 2016 Wiley Periodicals, Inc.

  17. Study of Environmental Data Complexity using Extreme Learning Machine

    Science.gov (United States)

    Leuenberger, Michael; Kanevski, Mikhail

    2017-04-01

    The main goals of environmental data science using machine learning algorithm deal, in a broad sense, around the calibration, the prediction and the visualization of hidden relationship between input and output variables. In order to optimize the models and to understand the phenomenon under study, the characterization of the complexity (at different levels) should be taken into account. Therefore, the identification of the linear or non-linear behavior between input and output variables adds valuable information for the knowledge of the phenomenon complexity. The present research highlights and investigates the different issues that can occur when identifying the complexity (linear/non-linear) of environmental data using machine learning algorithm. In particular, the main attention is paid to the description of a self-consistent methodology for the use of Extreme Learning Machines (ELM, Huang et al., 2006), which recently gained a great popularity. By applying two ELM models (with linear and non-linear activation functions) and by comparing their efficiency, quantification of the linearity can be evaluated. The considered approach is accompanied by simulated and real high dimensional and multivariate data case studies. In conclusion, the current challenges and future development in complexity quantification using environmental data mining are discussed. References - Huang, G.-B., Zhu, Q.-Y., Siew, C.-K., 2006. Extreme learning machine: theory and applications. Neurocomputing 70 (1-3), 489-501. - Kanevski, M., Pozdnoukhov, A., Timonin, V., 2009. Machine Learning for Spatial Environmental Data. EPFL Press; Lausanne, Switzerland, p.392. - Leuenberger, M., Kanevski, M., 2015. Extreme Learning Machines for spatial environmental data. Computers and Geosciences 85, 64-73.

  18. Machines vs. ensembles: effective MAPK signaling through heterogeneous sets of protein complexes.

    Directory of Open Access Journals (Sweden)

    Ryan Suderman

    Full Text Available Despite the importance of intracellular signaling networks, there is currently no consensus regarding the fundamental nature of the protein complexes such networks employ. One prominent view involves stable signaling machines with well-defined quaternary structures. The combinatorial complexity of signaling networks has led to an opposing perspective, namely that signaling proceeds via heterogeneous pleiomorphic ensembles of transient complexes. Since many hypotheses regarding network function rely on how we conceptualize signaling complexes, resolving this issue is a central problem in systems biology. Unfortunately, direct experimental characterization of these complexes has proven technologically difficult, while combinatorial complexity has prevented traditional modeling methods from approaching this question. Here we employ rule-based modeling, a technique that overcomes these limitations, to construct a model of the yeast pheromone signaling network. We found that this model exhibits significant ensemble character while generating reliable responses that match experimental observations. To contrast the ensemble behavior, we constructed a model that employs hierarchical assembly pathways to produce scaffold-based signaling machines. We found that this machine model could not replicate the experimentally observed combinatorial inhibition that arises when the scaffold is overexpressed. This finding provides evidence against the hierarchical assembly of machines in the pheromone signaling network and suggests that machines and ensembles may serve distinct purposes in vivo. In some cases, e.g. core enzymatic activities like protein synthesis and degradation, machines assembled via hierarchical energy landscapes may provide functional stability for the cell. In other cases, such as signaling, ensembles may represent a form of weak linkage, facilitating variation and plasticity in network evolution. The capacity of ensembles to signal effectively

  19. Everyday complexities and sociomaterialities of learning, technology, affects and effects

    DEFF Research Database (Denmark)

    Hansbøl, Mikala

    design with particular intended educational purposes (e.g. educational technology and technology education), the everyday complexities and sociomaterialities of learning and technology intermingles with how students/professionals become affected by digital technology and hence also which matters......This paper starts out with the challenge of establishing and researching relationships between educational design, digital technology and professional learning. The paper is empirical and takes point of departure in case examples from two development projects with a focus on professional education....... Both projects focus on new waysto build relationships between digital technologies, professional education and learning. Each project takes a different take on how to approach and position digital technology and it’s relationships with the educational programs and students’ learning. Project Wellfare...

  20. Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning

    KAUST Repository

    Teng, Haotian; Cao, Minh Duc; Hall, Michael B; Duarte, Tania; Wang, Sheng; Coin, Lachlan J M

    2018-01-01

    Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology that offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report Chiron, the first deep learning model to achieve end-to-end basecalling and directly translate the raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4,000 reads, we show that our model provides state-of-the-art basecalling accuracy, even on previously unseen species. Chiron achieves basecalling speeds of more than 2,000 bases per second using desktop computer graphics processing units.

  1. Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning

    KAUST Repository

    Teng, Haotian

    2018-04-10

    Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology that offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report Chiron, the first deep learning model to achieve end-to-end basecalling and directly translate the raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4,000 reads, we show that our model provides state-of-the-art basecalling accuracy, even on previously unseen species. Chiron achieves basecalling speeds of more than 2,000 bases per second using desktop computer graphics processing units.

  2. Hyaluronan activates Hyal-2/WWOX/Smad4 signaling and causes bubbling cell death when the signaling complex is overexpressed

    Science.gov (United States)

    Hsu, Li-Jin; Hong, Qunying; Chen, Shur-Tzu; Kuo, Hsiang-Lin; Schultz, Lori; Heath, John; Lin, Sing-Ru; Lee, Ming-Hui; Li, Dong-Zhang; Li, Zih-Ling; Cheng, Hui-Ching; Armand, Gerard; Chang, Nan-Shan

    2017-01-01

    Malignant cancer cells frequently secrete significant amounts of transforming growth factor beta (TGF-β), hyaluronan (HA) and hyaluronidases to facilitate metastasizing to target organs. In a non-canonical signaling, TGF-β binds membrane hyaluronidase Hyal-2 for recruiting tumor suppressors WWOX and Smad4, and the resulting Hyal-2/WWOX/Smad4 complex is accumulated in the nucleus to enhance SMAD-promoter dependent transcriptional activity. Yeast two-hybrid analysis showed that WWOX acts as a bridge to bind both Hyal-2 and Smad4. When WWOX-expressing cells were stimulated with high molecular weight HA, an increased formation of endogenous Hyal-2/WWOX/Smad4 complex occurred rapidly, followed by relocating to the nuclei in 20-40 min. In WWOX-deficient cells, HA failed to induce Smad2/3/4 relocation to the nucleus. To prove the signaling event, we designed a real time tri-molecular FRET analysis and revealed that HA induces the signaling pathway from ectopic Smad4 to WWOX and finally to p53, as well as from Smad4 to Hyal-2 and then to WWOX. An increased binding of the Smad4/Hyal-2/WWOX complex occurs with time in the nucleus that leads to bubbling cell death. In contrast, HA increases the binding of Smad4/WWOX/p53, which causes membrane blebbing but without cell death. In traumatic brain injury-induced neuronal death, the Hyal-2/WWOX complex was accumulated in the apoptotic nuclei of neurons in the rat brains in 24 hr post injury, as determined by immunoelectron microscopy. Together, HA activates the Hyal-2/WWOX/Smad4 signaling and causes bubbling cell death when the signaling complex is overexpressed. PMID:27845895

  3. Hyaluronan activates Hyal-2/WWOX/Smad4 signaling and causes bubbling cell death when the signaling complex is overexpressed.

    Science.gov (United States)

    Hsu, Li-Jin; Hong, Qunying; Chen, Shur-Tzu; Kuo, Hsiang-Lin; Schultz, Lori; Heath, John; Lin, Sing-Ru; Lee, Ming-Hui; Li, Dong-Zhang; Li, Zih-Ling; Cheng, Hui-Ching; Armand, Gerard; Chang, Nan-Shan

    2017-03-21

    Malignant cancer cells frequently secrete significant amounts of transforming growth factor beta (TGF-β), hyaluronan (HA) and hyaluronidases to facilitate metastasizing to target organs. In a non-canonical signaling, TGF-β binds membrane hyaluronidase Hyal-2 for recruiting tumor suppressors WWOX and Smad4, and the resulting Hyal-2/WWOX/Smad4 complex is accumulated in the nucleus to enhance SMAD-promoter dependent transcriptional activity. Yeast two-hybrid analysis showed that WWOX acts as a bridge to bind both Hyal-2 and Smad4. When WWOX-expressing cells were stimulated with high molecular weight HA, an increased formation of endogenous Hyal-2/WWOX/Smad4 complex occurred rapidly, followed by relocating to the nuclei in 20-40 min. In WWOX-deficient cells, HA failed to induce Smad2/3/4 relocation to the nucleus. To prove the signaling event, we designed a real time tri-molecular FRET analysis and revealed that HA induces the signaling pathway from ectopic Smad4 to WWOX and finally to p53, as well as from Smad4 to Hyal-2 and then to WWOX. An increased binding of the Smad4/Hyal-2/WWOX complex occurs with time in the nucleus that leads to bubbling cell death. In contrast, HA increases the binding of Smad4/WWOX/p53, which causes membrane blebbing but without cell death. In traumatic brain injury-induced neuronal death, the Hyal-2/WWOX complex was accumulated in the apoptotic nuclei of neurons in the rat brains in 24 hr post injury, as determined by immunoelectron microscopy. Together, HA activates the Hyal-2/WWOX/Smad4 signaling and causes bubbling cell death when the signaling complex is overexpressed.

  4. Reinforcement learning agents providing advice in complex video games

    Science.gov (United States)

    Taylor, Matthew E.; Carboni, Nicholas; Fachantidis, Anestis; Vlahavas, Ioannis; Torrey, Lisa

    2014-01-01

    This article introduces a teacher-student framework for reinforcement learning, synthesising and extending material that appeared in conference proceedings [Torrey, L., & Taylor, M. E. (2013)]. Teaching on a budget: Agents advising agents in reinforcement learning. {Proceedings of the international conference on autonomous agents and multiagent systems}] and in a non-archival workshop paper [Carboni, N., &Taylor, M. E. (2013, May)]. Preliminary results for 1 vs. 1 tactics in StarCraft. {Proceedings of the adaptive and learning agents workshop (at AAMAS-13)}]. In this framework, a teacher agent instructs a student agent by suggesting actions the student should take as it learns. However, the teacher may only give such advice a limited number of times. We present several novel algorithms that teachers can use to budget their advice effectively, and we evaluate them in two complex video games: StarCraft and Pac-Man. Our results show that the same amount of advice, given at different moments, can have different effects on student learning, and that teachers can significantly affect student learning even when students use different learning methods and state representations.

  5. Motor-related signals in the auditory system for listening and learning.

    Science.gov (United States)

    Schneider, David M; Mooney, Richard

    2015-08-01

    In the auditory system, corollary discharge signals are theorized to facilitate normal hearing and the learning of acoustic behaviors, including speech and music. Despite clear evidence of corollary discharge signals in the auditory cortex and their presumed importance for hearing and auditory-guided motor learning, the circuitry and function of corollary discharge signals in the auditory cortex are not well described. In this review, we focus on recent developments in the mouse and songbird that provide insights into the circuitry that transmits corollary discharge signals to the auditory system and the function of these signals in the context of hearing and vocal learning. Copyright © 2015 Elsevier Ltd. All rights reserved.

  6. SPATA2-Mediated Binding of CYLD to HOIP Enables CYLD Recruitment to Signaling Complexes

    Directory of Open Access Journals (Sweden)

    Sebastian Kupka

    2016-08-01

    Full Text Available Recruitment of the deubiquitinase CYLD to signaling complexes is mediated by its interaction with HOIP, the catalytically active component of the linear ubiquitin chain assembly complex (LUBAC. Here, we identify SPATA2 as a constitutive direct binding partner of HOIP that bridges the interaction between CYLD and HOIP. SPATA2 recruitment to TNFR1- and NOD2-signaling complexes is dependent on HOIP, and loss of SPATA2 abolishes CYLD recruitment. Deficiency in SPATA2 exerts limited effects on gene activation pathways but diminishes necroptosis induced by tumor necrosis factor (TNF, resembling loss of CYLD. In summary, we describe SPATA2 as a previously unrecognized factor in LUBAC-dependent signaling pathways that serves as an adaptor between HOIP and CYLD, thereby enabling recruitment of CYLD to signaling complexes.

  7. Improved Empirical Mode Decomposition Algorithm of Processing Complex Signal for IoT Application

    OpenAIRE

    Yang, Xianzhao; Cheng, Gengguo; Liu, Huikang

    2015-01-01

    Hilbert-Huang transform is widely used in signal analysis. However, due to its inadequacy in estimating both the maximum and the minimum values of the signals at both ends of the border, traditional HHT is easy to produce boundary error in empirical mode decomposition (EMD) process. To overcome this deficiency, this paper proposes an enhanced empirical mode decomposition algorithm for processing complex signal. Our work mainly focuses on two aspects. On one hand, we develop a technique to obt...

  8. Unraveling the Complexities of Androgen Receptor Signaling in Prostate Cancer Cells

    OpenAIRE

    Heemers, Hannelore V.; Tindall, Donald J.

    2009-01-01

    Androgen signaling is critical for proliferation of prostate cancer cells but cannot be fully inhibited by current androgen deprivation therapies. A study by Xu et al. in this issue of Cancer Cell provides insights into the complexities of androgen signaling in prostate cancer and suggests avenues to target a subset of androgen-sensitive genes.

  9. Normalization of informatisation parameter on airfield light-signal bar at flights in complex meteorological conditions

    Directory of Open Access Journals (Sweden)

    П.В. Попов

    2005-03-01

    Full Text Available  The technique of maintenance of the set level of flights safetivness is developed by normalization of informatisation parameters functional groups of light-signal lightings at technological stages of interaction of crew of the airplane with the airfield light-signals bar at flights in a complex weathercast conditions.

  10. SCScore: Synthetic Complexity Learned from a Reaction Corpus.

    Science.gov (United States)

    Coley, Connor W; Rogers, Luke; Green, William H; Jensen, Klavs F

    2018-02-26

    Several definitions of molecular complexity exist to facilitate prioritization of lead compounds, to identify diversity-inducing and complexifying reactions, and to guide retrosynthetic searches. In this work, we focus on synthetic complexity and reformalize its definition to correlate with the expected number of reaction steps required to produce a target molecule, with implicit knowledge about what compounds are reasonable starting materials. We train a neural network model on 12 million reactions from the Reaxys database to impose a pairwise inequality constraint enforcing the premise of this definition: that on average, the products of published chemical reactions should be more synthetically complex than their corresponding reactants. The learned metric (SCScore) exhibits highly desirable nonlinear behavior, particularly in recognizing increases in synthetic complexity throughout a number of linear synthetic routes.

  11. Complex Signal Kurtosis and Independent Component Analysis for Wideband Radio Frequency Interference Detection

    Science.gov (United States)

    Schoenwald, Adam; Mohammed, Priscilla; Bradley, Damon; Piepmeier, Jeffrey; Wong, Englin; Gholian, Armen

    2016-01-01

    Radio-frequency interference (RFI) has negatively implicated scientific measurements across a wide variation passive remote sensing satellites. This has been observed in the L-band radiometers SMOS, Aquarius and more recently, SMAP [1, 2]. RFI has also been observed at higher frequencies such as K band [3]. Improvements in technology have allowed wider bandwidth digital back ends for passive microwave radiometry. A complex signal kurtosis radio frequency interference detector was developed to help identify corrupted measurements [4]. This work explores the use of ICA (Independent Component Analysis) as a blind source separation technique to pre-process radiometric signals for use with the previously developed real and complex signal kurtosis detectors.

  12. The effect of hypobaric hypoxia on multichannel EEG signal complexity.

    Science.gov (United States)

    Papadelis, Christos; Kourtidou-Papadeli, Chrysoula; Bamidis, Panagiotis D; Maglaveras, Nikos; Pappas, Konstantinos

    2007-01-01

    The objective of this study was the development and evaluation of nonlinear electroencephalography parameters which assess hypoxia-induced EEG alterations, and describe the temporal characteristics of different hypoxic levels' residual effect upon the brain electrical activity. Multichannel EEG, pO2, pCO2, ECG, and respiration measurements were recorded from 10 subjects exposed to three experimental conditions (100% oxygen, hypoxia, recovery) at three-levels of reduced barometric pressure. The mean spectral power of EEG under each session and altitude were estimated for the standard bands. Approximate Entropy (ApEn) of EEG segments was calculated, and the ApEn's time-courses were smoothed by a moving average filter. On the smoothed diagrams, parameters were defined. A significant increase in total power and power of theta and alpha bands was observed during hypoxia. Visual interpretation of ApEn time-courses revealed a characteristic pattern (decreasing during hypoxia and recovering after oxygen re-administration). The introduced qEEG parameters S1 and K1 distinguished successfully the three hypoxic conditions. The introduced parameters based on ApEn time-courses are assessing reliably and effectively the different hypoxic levels. ApEn decrease may be explained by neurons' functional isolation due to hypoxia since decreased complexity corresponds to greater autonomy of components, although this interpretation should be further supported by electrocorticographic animal studies. The introduced qEEG parameters seem to be appropriate for assessing the hypoxia-related neurophysiological state of patients in the hyperbaric chambers in the treatment of decompression sickness, carbon dioxide poisoning, and mountaineering.

  13. Clinical quality needs complex adaptive systems and machine learning.

    Science.gov (United States)

    Marsland, Stephen; Buchan, Iain

    2004-01-01

    The vast increase in clinical data has the potential to bring about large improvements in clinical quality and other aspects of healthcare delivery. However, such benefits do not come without cost. The analysis of such large datasets, particularly where the data may have to be merged from several sources and may be noisy and incomplete, is a challenging task. Furthermore, the introduction of clinical changes is a cyclical task, meaning that the processes under examination operate in an environment that is not static. We suggest that traditional methods of analysis are unsuitable for the task, and identify complexity theory and machine learning as areas that have the potential to facilitate the examination of clinical quality. By its nature the field of complex adaptive systems deals with environments that change because of the interactions that have occurred in the past. We draw parallels between health informatics and bioinformatics, which has already started to successfully use machine learning methods.

  14. Use of multiple singular value decompositions to analyze complex intracellular calcium ion signals

    KAUST Repository

    Martinez, Josue G.; Huang, Jianhua Z.; Burghardt, Robert C.; Barhoumi, Rola; Carroll, Raymond J.

    2009-01-01

    ) to extract the signals from such movies, in a way that is semi-automatic and tuned closely to the actual data and their many complexities. These complexities include the following. First, the images themselves are of no interest: all interest focuses

  15. The amygdala complex: multiple roles in associative learning and attention.

    OpenAIRE

    Gallagher, M; Holland, P C

    1994-01-01

    Although certain neurophysiological functions of the amygdala complex in learning seem well established, the purpose of this review is to propose that an additional conceptualization of amygdala function is now needed. The research we review provides evidence that a subsystem within the amygdala provides a coordinated regulation of attentional processes. An important aspect of this additional neuropsychology of the amygdala is that it may aid in understanding the importance of connections bet...

  16. Learning with Generalization Capability by Kernel Methods of Bounded Complexity

    Czech Academy of Sciences Publication Activity Database

    Kůrková, Věra; Sanguineti, M.

    2005-01-01

    Roč. 21, č. 3 (2005), s. 350-367 ISSN 0885-064X R&D Projects: GA AV ČR 1ET100300419 Institutional research plan: CEZ:AV0Z10300504 Keywords : supervised learning * generalization * model complexity * kernel methods * minimization of regularized empirical errors * upper bounds on rates of approximate optimization Subject RIV: BA - General Mathematics Impact factor: 1.186, year: 2005

  17. Non-linear dynamical signal characterization for prediction of defibrillation success through machine learning

    Directory of Open Access Journals (Sweden)

    Shandilya Sharad

    2012-10-01

    Full Text Available Abstract Background Ventricular Fibrillation (VF is a common presenting dysrhythmia in the setting of cardiac arrest whose main treatment is defibrillation through direct current countershock to achieve return of spontaneous circulation. However, often defibrillation is unsuccessful and may even lead to the transition of VF to more nefarious rhythms such as asystole or pulseless electrical activity. Multiple methods have been proposed for predicting defibrillation success based on examination of the VF waveform. To date, however, no analytical technique has been widely accepted. We developed a unique approach of computational VF waveform analysis, with and without addition of the signal of end-tidal carbon dioxide (PetCO2, using advanced machine learning algorithms. We compare these results with those obtained using the Amplitude Spectral Area (AMSA technique. Methods A total of 90 pre-countershock ECG signals were analyzed form an accessible preshosptial cardiac arrest database. A unified predictive model, based on signal processing and machine learning, was developed with time-series and dual-tree complex wavelet transform features. Upon selection of correlated variables, a parametrically optimized support vector machine (SVM model was trained for predicting outcomes on the test sets. Training and testing was performed with nested 10-fold cross validation and 6–10 features for each test fold. Results The integrative model performs real-time, short-term (7.8 second analysis of the Electrocardiogram (ECG. For a total of 90 signals, 34 successful and 56 unsuccessful defibrillations were classified with an average Accuracy and Receiver Operator Characteristic (ROC Area Under the Curve (AUC of 82.2% and 85%, respectively. Incorporation of the end-tidal carbon dioxide signal boosted Accuracy and ROC AUC to 83.3% and 93.8%, respectively, for a smaller dataset containing 48 signals. VF analysis using AMSA resulted in accuracy and ROC AUC of 64

  18. Prediction of preterm deliveries from EHG signals using machine learning.

    Directory of Open Access Journals (Sweden)

    Paul Fergus

    Full Text Available There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. In extreme cases, this can also lead to long-term conditions, such as cerebral palsy, mental retardation, learning difficulties, including poor health and growth. In the US alone, the societal and economic cost of preterm births, in 2005, was estimated to be $26.2 billion, per annum. In the UK, this value was close to £2.95 billion, in 2009. Many believe that a better understanding of why preterm births occur, and a strategic focus on prevention, will help to improve the health of children and reduce healthcare costs. At present, most methods of preterm birth prediction are subjective. However, a strong body of evidence suggests the analysis of uterine electrical signals (Electrohysterography, could provide a viable way of diagnosing true labour and predict preterm deliveries. Most Electrohysterography studies focus on true labour detection during the final seven days, before labour. The challenge is to utilise Electrohysterography techniques to predict preterm delivery earlier in the pregnancy. This paper explores this idea further and presents a supervised machine learning approach that classifies term and preterm records, using an open source dataset containing 300 records (38 preterm and 262 term. The synthetic minority oversampling technique is used to oversample the minority preterm class, and cross validation techniques, are used to evaluate the dataset against other similar studies. Our approach shows an improvement on existing studies with 96% sensitivity, 90% specificity, and a 95% area under the curve value with 8% global error using the polynomial

  19. Interaction and Technological Resources to Support Learning of Complex Numbers

    Directory of Open Access Journals (Sweden)

    Cassiano Scott Puhl

    2016-02-01

    Full Text Available This article presents a didactic proposal, a workshop for the introduction of the study of complex numbers. Unlike recurrent practices, the workshop began developing the geometric shape of the complex number, implicitly, through vectors. Eliminating student formal vision and algebraic, enriching the teaching practice. The main objective of the strategy was to build the concept of imaginary unit without causing a feeling of strangeness or insignificance of number. The theory of David Ausubel, meaningful learning, the workshop was based on a strategy developed to analyze the subsumers of students and develop a learning by subject. Combined with dynamic and interactive activities in the workshop, there is the use of a learning object (http://matematicacomplexa.meximas.com/. An environment created and basing on the theory of meaningful learning, making students reflect and interact in developed applications sometimes being challenged and other testing hypotheses and, above all, building knowledge. This proposal provided a rich environment for exchange of information between participants and deepening of ideas and concepts that served as subsumers. The result of the experience was very positive, as evidenced by the comments and data submitted by the participants, thus demonstrating that the objectives of this didactic proposal have been achieved.

  20. A simple iterative independent component analysis algorithm for vibration source signal identification of complex structures

    Directory of Open Access Journals (Sweden)

    Dong-Sup Lee

    2015-01-01

    Full Text Available Independent Component Analysis (ICA, one of the blind source separation methods, can be applied for extracting unknown source signals only from received signals. This is accomplished by finding statistical independence of signal mixtures and has been successfully applied to myriad fields such as medical science, image processing, and numerous others. Nevertheless, there are inherent problems that have been reported when using this technique: insta- bility and invalid ordering of separated signals, particularly when using a conventional ICA technique in vibratory source signal identification of complex structures. In this study, a simple iterative algorithm of the conventional ICA has been proposed to mitigate these problems. The proposed method to extract more stable source signals having valid order includes an iterative and reordering process of extracted mixing matrix to reconstruct finally converged source signals, referring to the magnitudes of correlation coefficients between the intermediately separated signals and the signals measured on or nearby sources. In order to review the problems of the conventional ICA technique and to vali- date the proposed method, numerical analyses have been carried out for a virtual response model and a 30 m class submarine model. Moreover, in order to investigate applicability of the proposed method to real problem of complex structure, an experiment has been carried out for a scaled submarine mockup. The results show that the proposed method could resolve the inherent problems of a conventional ICA technique.

  1. Aiding the Detection of QRS Complex in ECG Signals by Detecting S Peaks Independently.

    Science.gov (United States)

    Sabherwal, Pooja; Singh, Latika; Agrawal, Monika

    2018-03-30

    In this paper, a novel algorithm for the accurate detection of QRS complex by combining the independent detection of R and S peaks, using fusion algorithm is proposed. R peak detection has been extensively studied and is being used to detect the QRS complex. Whereas, S peaks, which is also part of QRS complex can be independently detected to aid the detection of QRS complex. In this paper, we suggest a method to first estimate S peak from raw ECG signal and then use them to aid the detection of QRS complex. The amplitude of S peak in ECG signal is relatively weak than corresponding R peak, which is traditionally used for the detection of QRS complex, therefore, an appropriate digital filter is designed to enhance the S peaks. These enhanced S peaks are then detected by adaptive thresholding. The algorithm is validated on all the signals of MIT-BIH arrhythmia database and noise stress database taken from physionet.org. The algorithm performs reasonably well even for the signals highly corrupted by noise. The algorithm performance is confirmed by sensitivity and positive predictivity of 99.99% and the detection accuracy of 99.98% for QRS complex detection. The number of false positives and false negatives resulted while analysis has been drastically reduced to 80 and 42 against the 98 and 84 the best results reported so far.

  2. Mannotriose regulates learning and memory signal transduction in the hippocampus

    Institute of Scientific and Technical Information of China (English)

    Lina Zhang; Weiwei Dai; Xueli Zhang; Zhangbin Gong; Guoqin Jin

    2013-01-01

    Rehmannia is a commonly used Chinese herb, which improves learning and memory. However, the crucial components of the signal transduction pathway associated with this effect remain elusive. Pri-mary hippocampal neurons were cultured in vitro, insulted with high-concentration (1 × 10-4 mol/L) cor-ticosterone, and treated with 1 × 10-4 mol/L mannotriose. Thiazolyl blue tetrazolium bromide assay and western blot analysis showed that hippocampal neuron survival rates and protein levels of glucocorti-coid receptor, serum and glucocorticoid-regulated protein kinase, and brain-derived neurotrophic factor were al dramatical y decreased after high-concentration corticosterone-induced injury. This effect was reversed by mannotriose, to a similar level as RU38486 and donepezil. Our findings indicate that mannotriose could protect hippocampal neurons from high-concentration corticosterone-induced injury. The mechanism by which this occurred was associated with levels of glucocorticoid receptor protein, serum and glucocorticoid-regulated protein kinase, and brain-derived neurotrophic factor.

  3. Application of complex discrete wavelet transform in classification of Doppler signals using complex-valued artificial neural network.

    Science.gov (United States)

    Ceylan, Murat; Ceylan, Rahime; Ozbay, Yüksel; Kara, Sadik

    2008-09-01

    In biomedical signal classification, due to the huge amount of data, to compress the biomedical waveform data is vital. This paper presents two different structures formed using feature extraction algorithms to decrease size of feature set in training and test data. The proposed structures, named as wavelet transform-complex-valued artificial neural network (WT-CVANN) and complex wavelet transform-complex-valued artificial neural network (CWT-CVANN), use real and complex discrete wavelet transform for feature extraction. The aim of using wavelet transform is to compress data and to reduce training time of network without decreasing accuracy rate. In this study, the presented structures were applied to the problem of classification in carotid arterial Doppler ultrasound signals. Carotid arterial Doppler ultrasound signals were acquired from left carotid arteries of 38 patients and 40 healthy volunteers. The patient group included 22 males and 16 females with an established diagnosis of the early phase of atherosclerosis through coronary or aortofemoropopliteal (lower extremity) angiographies (mean age, 59 years; range, 48-72 years). Healthy volunteers were young non-smokers who seem to not bear any risk of atherosclerosis, including 28 males and 12 females (mean age, 23 years; range, 19-27 years). Sensitivity, specificity and average detection rate were calculated for comparison, after training and test phases of all structures finished. These parameters have demonstrated that training times of CVANN and real-valued artificial neural network (RVANN) were reduced using feature extraction algorithms without decreasing accuracy rate in accordance to our aim.

  4. Machinery vibration signal denoising based on learned dictionary and sparse representation

    International Nuclear Information System (INIS)

    Guo, Liang; Gao, Hongli; Li, Jun; Huang, Haifeng; Zhang, Xiaochen

    2015-01-01

    Mechanical vibration signal denoising has been an import problem for machine damage assessment and health monitoring. Wavelet transfer and sparse reconstruction are the powerful and practical methods. However, those methods are based on the fixed basis functions or atoms. In this paper, a novel method is presented. The atoms used to represent signals are learned from the raw signal. And in order to satisfy the requirements of real-time signal processing, an online dictionary learning algorithm is adopted. Orthogonal matching pursuit is applied to extract the most pursuit column in the dictionary. At last, denoised signal is calculated with the sparse vector and learned dictionary. A simulation signal and real bearing fault signal are utilized to evaluate the improved performance of the proposed method through the comparison with kinds of denoising algorithms. Then Its computing efficiency is demonstrated by an illustrative runtime example. The results show that the proposed method outperforms current algorithms with efficiency calculation. (paper)

  5. Hydrogen Exchange Differences between Chemoreceptor Signaling Complexes Localize to Functionally Important Subdomains

    Science.gov (United States)

    2015-01-01

    The goal of understanding mechanisms of transmembrane signaling, one of many key life processes mediated by membrane proteins, has motivated numerous studies of bacterial chemotaxis receptors. Ligand binding to the receptor causes a piston motion of an α helix in the periplasmic and transmembrane domains, but it is unclear how the signal is then propagated through the cytoplasmic domain to control the activity of the associated kinase CheA. Recent proposals suggest that signaling in the cytoplasmic domain involves opposing changes in dynamics in different subdomains. However, it has been difficult to measure dynamics within the functional system, consisting of extended arrays of receptor complexes with two other proteins, CheA and CheW. We have combined hydrogen exchange mass spectrometry with vesicle template assembly of functional complexes of the receptor cytoplasmic domain to reveal that there are significant signaling-associated changes in exchange, and these changes localize to key regions of the receptor involved in the excitation and adaptation responses. The methylation subdomain exhibits complex changes that include slower hydrogen exchange in complexes in a kinase-activating state, which may be partially consistent with proposals that this subdomain is stabilized in this state. The signaling subdomain exhibits significant protection from hydrogen exchange in complexes in a kinase-activating state, suggesting a tighter and/or larger interaction interface with CheA and CheW in this state. These first measurements of the stability of protein subdomains within functional signaling complexes demonstrate the promise of this approach for measuring functionally important protein dynamics within the various physiologically relevant states of multiprotein complexes. PMID:25420045

  6. Simulation study on effects of signaling network structure on the developmental increase in complexity

    Energy Technology Data Exchange (ETDEWEB)

    Keranen, Soile V.E.

    2003-04-02

    The developmental increase in structural complexity in multicellular life forms depends on local, often non-periodic differences in gene expression. These depend on a network of gene-gene interactions coded within the organismal genome. To better understand how genomic information generates complex expression patterns, I have modeled the pattern forming behavior of small artificial genomes in virtual blastoderm embryos. I varied several basic properties of these genomic signaling networks, such as the number of genes, the distributions of positive (inductive) and negative (repressive) interactions, and the strengths of gene-gene interactions, and analyzed their effects on developmental pattern formation. The results show how even simple genomes can generate complex non-periodic patterns under suitable conditions. They also show how the frequency of complex patterns depended on the numbers and relative arrangements of positive and negative interactions. For example, negative co-regulation of signaling pathway components increased the likelihood of (complex) patterns relative to differential negative regulation of the pathway components. Interestingly, neither quantitative differences either in strengths of signaling interactions nor multiple response thresholds to signal concentration (as in morphogen gradients) were essential for formation of multiple, spatially unique cell types. Thus, with combinatorial code of gene regulation and hierarchical signaling interactions, it is theoretically possible to organize metazoan embryogenesis with just a small fraction of the metazoan genome. Because even small networks can generate complex patterns when they contain a suitable set of connections, evolution of metazoan complexity may have depended more on selection for favourable configurations of signaling interactions than on the increase in numbers of regulatory genes.

  7. Assessing Complexity in Learning Outcomes--A Comparison between the SOLO Taxonomy and the Model of Hierarchical Complexity

    Science.gov (United States)

    Stålne, Kristian; Kjellström, Sofia; Utriainen, Jukka

    2016-01-01

    An important aspect of higher education is to educate students who can manage complex relationships and solve complex problems. Teachers need to be able to evaluate course content with regard to complexity, as well as evaluate students' ability to assimilate complex content and express it in the form of a learning outcome. One model for evaluating…

  8. Complexity of EEG-signal in Time Domain - Possible Biomedical Application

    Science.gov (United States)

    Klonowski, Wlodzimierz; Olejarczyk, Elzbieta; Stepien, Robert

    2002-07-01

    Human brain is a highly complex nonlinear system. So it is not surprising that in analysis of EEG-signal, which represents overall activity of the brain, the methods of Nonlinear Dynamics (or Chaos Theory as it is commonly called) can be used. Even if the signal is not chaotic these methods are a motivating tool to explore changes in brain activity due to different functional activation states, e.g. different sleep stages, or to applied therapy, e.g. exposure to chemical agents (drugs) and physical factors (light, magnetic field). The methods supplied by Nonlinear Dynamics reveal signal characteristics that are not revealed by linear methods like FFT. Better understanding of principles that govern dynamics and complexity of EEG-signal can help to find `the signatures' of different physiological and pathological states of human brain, quantitative characteristics that may find applications in medical diagnostics.

  9. Extracting meaning from audio signals - a machine learning approach

    DEFF Research Database (Denmark)

    Larsen, Jan

    2007-01-01

    * Machine learning framework for sound search * Genre classification * Music and audio separation * Wind noise suppression......* Machine learning framework for sound search * Genre classification * Music and audio separation * Wind noise suppression...

  10. The role of learning-related dopamine signals in addiction vulnerability.

    Science.gov (United States)

    Huys, Quentin J M; Tobler, Philippe N; Hasler, Gregor; Flagel, Shelly B

    2014-01-01

    Dopaminergic signals play a mathematically precise role in reward-related learning, and variations in dopaminergic signaling have been implicated in vulnerability to addiction. Here, we provide a detailed overview of the relationship between theoretical, mathematical, and experimental accounts of phasic dopamine signaling, with implications for the role of learning-related dopamine signaling in addiction and related disorders. We describe the theoretical and behavioral characteristics of model-free learning based on errors in the prediction of reward, including step-by-step explanations of the underlying equations. We then use recent insights from an animal model that highlights individual variation in learning during a Pavlovian conditioning paradigm to describe overlapping aspects of incentive salience attribution and model-free learning. We argue that this provides a computationally coherent account of some features of addiction. © 2014 Elsevier B.V. All rights reserved.

  11. Supervised machine learning algorithms to diagnose stress for vehicle drivers based on physiological sensor signals.

    Science.gov (United States)

    Barua, Shaibal; Begum, Shahina; Ahmed, Mobyen Uddin

    2015-01-01

    Machine learning algorithms play an important role in computer science research. Recent advancement in sensor data collection in clinical sciences lead to a complex, heterogeneous data processing, and analysis for patient diagnosis and prognosis. Diagnosis and treatment of patients based on manual analysis of these sensor data are difficult and time consuming. Therefore, development of Knowledge-based systems to support clinicians in decision-making is important. However, it is necessary to perform experimental work to compare performances of different machine learning methods to help to select appropriate method for a specific characteristic of data sets. This paper compares classification performance of three popular machine learning methods i.e., case-based reasoning, neutral networks and support vector machine to diagnose stress of vehicle drivers using finger temperature and heart rate variability. The experimental results show that case-based reasoning outperforms other two methods in terms of classification accuracy. Case-based reasoning has achieved 80% and 86% accuracy to classify stress using finger temperature and heart rate variability. On contrary, both neural network and support vector machine have achieved less than 80% accuracy by using both physiological signals.

  12. Bio-Signal Complexity Analysis in Epileptic Seizure Monitoring: A Topic Review

    Directory of Open Access Journals (Sweden)

    Zhenning Mei

    2018-05-01

    Full Text Available Complexity science has provided new perspectives and opportunities for understanding a variety of complex natural or social phenomena, including brain dysfunctions like epilepsy. By delving into the complexity in electrophysiological signals and neuroimaging, new insights have emerged. These discoveries have revealed that complexity is a fundamental aspect of physiological processes. The inherent nonlinearity and non-stationarity of physiological processes limits the methods based on simpler underlying assumptions to point out the pathway to a more comprehensive understanding of their behavior and relation with certain diseases. The perspective of complexity may benefit both the research and clinical practice through providing novel data analytics tools devoted for the understanding of and the intervention about epilepsies. This review aims to provide a sketchy overview of the methods derived from different disciplines lucubrating to the complexity of bio-signals in the field of epilepsy monitoring. Although the complexity of bio-signals is still not fully understood, bundles of new insights have been already obtained. Despite the promising results about epileptic seizure detection and prediction through offline analysis, we are still lacking robust, tried-and-true real-time applications. Multidisciplinary collaborations and more high-quality data accessible to the whole community are needed for reproducible research and the development of such applications.

  13. Receptor density balances signal stimulation and attenuation in membrane-assembled complexes of bacterial chemotaxis signaling proteins

    Science.gov (United States)

    Besschetnova, Tatiana Y.; Montefusco, David J.; Asinas, Abdalin E.; Shrout, Anthony L.; Antommattei, Frances M.; Weis, Robert M.

    2008-01-01

    All cells possess transmembrane signaling systems that function in the environment of the lipid bilayer. In the Escherichia coli chemotaxis pathway, the binding of attractants to a two-dimensional array of receptors and signaling proteins simultaneously inhibits an associated kinase and stimulates receptor methylation—a slower process that restores kinase activity. These two opposing effects lead to robust adaptation toward stimuli through a physical mechanism that is not understood. Here, we provide evidence of a counterbalancing influence exerted by receptor density on kinase stimulation and receptor methylation. Receptor signaling complexes were reconstituted over a range of defined surface concentrations by using a template-directed assembly method, and the kinase and receptor methylation activities were measured. Kinase activity and methylation rates were both found to vary significantly with surface concentration—yet in opposite ways: samples prepared at high surface densities stimulated kinase activity more effectively than low-density samples, whereas lower surface densities produced greater methylation rates than higher densities. FRET experiments demonstrated that the cooperative change in kinase activity coincided with a change in the arrangement of the membrane-associated receptor domains. The counterbalancing influence of density on receptor methylation and kinase stimulation leads naturally to a model for signal regulation that is compatible with the known logic of the E. coli pathway. Density-dependent mechanisms are likely to be general and may operate when two or more membrane-related processes are influenced differently by the two-dimensional concentration of pathway elements. PMID:18711126

  14. Fuzzy approximate entropy analysis of chaotic and natural complex systems: detecting muscle fatigue using electromyography signals.

    Science.gov (United States)

    Xie, Hong-Bo; Guo, Jing-Yi; Zheng, Yong-Ping

    2010-04-01

    In the present contribution, a complexity measure is proposed to assess surface electromyography (EMG) in the study of muscle fatigue during sustained, isometric muscle contractions. Approximate entropy (ApEn) is believed to provide quantitative information about the complexity of experimental data that is often corrupted with noise, short data length, and in many cases, has inherent dynamics that exhibit both deterministic and stochastic behaviors. We developed an improved ApEn measure, i.e., fuzzy approximate entropy (fApEn), which utilizes the fuzzy membership function to define the vectors' similarity. Tests were conducted on independent, identically distributed (i.i.d.) Gaussian and uniform noises, a chirp signal, MIX processes, Rossler equation, and Henon map. Compared with the standard ApEn, the fApEn showed better monotonicity, relative consistency, and more robustness to noise when characterizing signals with different complexities. Performance analysis on experimental EMG signals demonstrated that the fApEn significantly decreased during the development of muscle fatigue, which is a similar trend to that of the mean frequency (MNF) of the EMG signal, while the standard ApEn failed to detect this change. Moreover, fApEn of EMG demonstrated a better robustness to the length of the analysis window in comparison with the MNF of EMG. The results suggest that the fApEn of an EMG signal may potentially become a new reliable method for muscle fatigue assessment and be applicable to other short noisy physiological signal analysis.

  15. Complex motion of a vehicle through a series of signals controlled by power-law phase

    Science.gov (United States)

    Nagatani, Takashi

    2017-07-01

    We study the dynamic motion of a vehicle moving through the series of traffic signals controlled by the position-dependent phase of power law. All signals are controlled by both cycle time and position-dependent phase. The dynamic model of the vehicular motion is described in terms of the nonlinear map. The vehicular motion varies in a complex manner by varying cycle time for various values of the power of the position-dependent phase. The vehicle displays the periodic motion with a long cycle for the integer power of the phase, while the vehicular motion exhibits the very complex behavior for the non-integer power of the phase.

  16. With you or against you: social orientation dependent learning signals guide actions made for others.

    Science.gov (United States)

    Christopoulos, George I; King-Casas, Brooks

    2015-01-01

    In social environments, it is crucial that decision-makers take account of the impact of their actions not only for oneself, but also on other social agents. Previous work has identified neural signals in the striatum encoding value-based prediction errors for outcomes to oneself; also, recent work suggests that neural activity in prefrontal cortex may similarly encode value-based prediction errors related to outcomes to others. However, prior work also indicates that social valuations are not isomorphic, with social value orientations of decision-makers ranging on a cooperative to competitive continuum; this variation has not been examined within social learning environments. Here, we combine a computational model of learning with functional neuroimaging to examine how individual differences in orientation impact neural mechanisms underlying 'other-value' learning. Across four experimental conditions, reinforcement learning signals for other-value were identified in medial prefrontal cortex, and were distinct from self-value learning signals identified in striatum. Critically, the magnitude and direction of the other-value learning signal depended strongly on an individual's cooperative or competitive orientation toward others. These data indicate that social decisions are guided by a social orientation-dependent learning system that is computationally similar but anatomically distinct from self-value learning. The sensitivity of the medial prefrontal learning signal to social preferences suggests a mechanism linking such preferences to biases in social actions and highlights the importance of incorporating heterogeneous social predispositions in neurocomputational models of social behavior. Published by Elsevier Inc.

  17. Synthesis of high-complexity rhythmic signals for closed-loop electrical neuromodulation.

    Science.gov (United States)

    Zalay, Osbert C; Bardakjian, Berj L

    2013-06-01

    We propose an approach to synthesizing high-complexity rhythmic signals for closed-loop electrical neuromodulation using cognitive rhythm generator (CRG) networks, wherein the CRG is a hybrid oscillator comprised of (1) a bank of neuronal modes, (2) a ring device (clock), and (3) a static output nonlinearity (mapper). Networks of coupled CRGs have been previously implemented to simulate the electrical activity of biological neural networks, including in silico models of epilepsy, producing outputs of similar waveform and complexity to the biological system. This has enabled CRG network models to be used as platforms for testing seizure control strategies. Presently, we take the application one step further, envisioning therapeutic CRG networks as rhythmic signal generators creating neuromimetic signals for stimulation purposes, motivated by recent research indicating that stimulus complexity and waveform characteristics influence neuromodulation efficacy. To demonstrate this concept, an epileptiform CRG network generating spontaneous seizure-like events (SLEs) was coupled to a therapeutic CRG network, forming a closed-loop neuromodulation system. SLEs are associated with low-complexity dynamics and high phase coherence in the network. The tuned therapeutic network generated a high-complexity, multi-banded rhythmic stimulation signal with prominent theta and gamma-frequency power that suppressed SLEs and increased dynamic complexity in the epileptiform network, as measured by a relative increase in the maximum Lyapunov exponent and decrease in phase coherence. CRG-based neuromodulation outperformed both low and high-frequency periodic pulse stimulation, suggesting that neuromodulation using complex, biomimetic signals may provide an improvement over conventional electrical stimulation techniques for treating neurological disorders such as epilepsy. Copyright © 2013 Elsevier Ltd. All rights reserved.

  18. Students' explanations in complex learning of disciplinary programming

    Science.gov (United States)

    Vieira, Camilo

    Computational Science and Engineering (CSE) has been denominated as the third pillar of science and as a set of important skills to solve the problems of a global society. Along with the theoretical and the experimental approaches, computation offers a third alternative to solve complex problems that require processing large amounts of data, or representing complex phenomena that are not easy to experiment with. Despite the relevance of CSE, current professionals and scientists are not well prepared to take advantage of this set of tools and methods. Computation is usually taught in an isolated way from engineering disciplines, and therefore, engineers do not know how to exploit CSE affordances. This dissertation intends to introduce computational tools and methods contextualized within the Materials Science and Engineering curriculum. Considering that learning how to program is a complex task, the dissertation explores effective pedagogical practices that can support student disciplinary and computational learning. Two case studies will be evaluated to identify the characteristics of effective worked examples in the context of CSE. Specifically, this dissertation explores students explanations of these worked examples in two engineering courses with different levels of transparency: a programming course in materials science and engineering glass box and a thermodynamics course involving computational representations black box. Results from this study suggest that students benefit in different ways from writing in-code comments. These benefits include but are not limited to: connecting xv individual lines of code to the overall problem, getting familiar with the syntax, learning effective algorithm design strategies, and connecting computation with their discipline. Students in the glass box context generate higher quality explanations than students in the black box context. These explanations are related to students prior experiences. Specifically, students with

  19. Controlling Uncertainty Decision Making and Learning in Complex Worlds

    CERN Document Server

    Osman, Magda

    2010-01-01

    Controlling Uncertainty: Decision Making and Learning in Complex Worlds reviews and discusses the most current research relating to the ways we can control the uncertain world around us.: Features reviews and discussions of the most current research in a number of fields relevant to controlling uncertainty, such as psychology, neuroscience, computer science and engineering; Presents a new framework that is designed to integrate a variety of disparate fields of research; Represents the first book of its kind to provide a general overview of work related to understanding control

  20. LC-MS/MS signal suppression effects in the analysis of pesticides in complex environmental matrices.

    Science.gov (United States)

    Choi, B K; Hercules, D M; Gusev, A I

    2001-02-01

    The application of LC separation and mobile phase additives in addressing LC-MS/MS matrix signal suppression effects for the analysis of pesticides in a complex environmental matrix was investigated. It was shown that signal suppression is most significant for analytes eluting early in the LC-MS analysis. Introduction of different buffers (e.g. ammonium formate, ammonium hydroxide, formic acid) into the LC mobile phase was effective in improving signal correlation between the matrix and standard samples. The signal improvement is dependent on buffer concentration as well as LC separation of the matrix components. The application of LC separation alone was not effective in addressing suppression effects when characterizing complex matrix samples. Overloading of the LC column by matrix components was found to significantly contribute to analyte-matrix co-elution and suppression of signal. This signal suppression effect can be efficiently compensated by 2D LC (LC-LC) separation techniques. The effectiveness of buffers and LC separation in improving signal correlation between standard and matrix samples is discussed.

  1. Use of multiple singular value decompositions to analyze complex intracellular calcium ion signals

    KAUST Repository

    Martinez, Josue G.

    2009-12-01

    We compare calcium ion signaling (Ca(2+)) between two exposures; the data are present as movies, or, more prosaically, time series of images. This paper describes novel uses of singular value decompositions (SVD) and weighted versions of them (WSVD) to extract the signals from such movies, in a way that is semi-automatic and tuned closely to the actual data and their many complexities. These complexities include the following. First, the images themselves are of no interest: all interest focuses on the behavior of individual cells across time, and thus, the cells need to be segmented in an automated manner. Second, the cells themselves have 100+ pixels, so that they form 100+ curves measured over time, so that data compression is required to extract the features of these curves. Third, some of the pixels in some of the cells are subject to image saturation due to bit depth limits, and this saturation needs to be accounted for if one is to normalize the images in a reasonably un-biased manner. Finally, the Ca(2+) signals have oscillations or waves that vary with time and these signals need to be extracted. Thus, our aim is to show how to use multiple weighted and standard singular value decompositions to detect, extract and clarify the Ca(2+) signals. Our signal extraction methods then lead to simple although finely focused statistical methods to compare Ca(2+) signals across experimental conditions.

  2. Intelligent sensor networks the integration of sensor networks, signal processing and machine learning

    CERN Document Server

    Hu, Fei

    2012-01-01

    Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, incl

  3. Isolation and structure–function characterization of a signaling-active rhodopsin–G protein complex

    Science.gov (United States)

    Gao, Yang; Westfield, Gerwin; Erickson, Jon W.; Cerione, Richard A.; Skiniotis, Georgios; Ramachandran, Sekar

    2017-01-01

    The visual photo-transduction cascade is a prototypical G protein–coupled receptor (GPCR) signaling system, in which light-activated rhodopsin (Rho*) is the GPCR catalyzing the exchange of GDP for GTP on the heterotrimeric G protein transducin (GT). This results in the dissociation of GT into its component αT–GTP and β1γ1 subunit complex. Structural information for the Rho*–GT complex will be essential for understanding the molecular mechanism of visual photo-transduction. Moreover, it will shed light on how GPCRs selectively couple to and activate their G protein signaling partners. Here, we report on the preparation of a stable detergent-solubilized complex between Rho* and a heterotrimer (GT*) comprising a GαT/Gαi1 chimera (αT*) and β1γ1. The complex was formed on native rod outer segment membranes upon light activation, solubilized in lauryl maltose neopentyl glycol, and purified with a combination of affinity and size-exclusion chromatography. We found that the complex is fully functional and that the stoichiometry of Rho* to GαT* is 1:1. The molecular weight of the complex was calculated from small-angle X-ray scattering data and was in good agreement with a model consisting of one Rho* and one GT*. The complex was visualized by negative-stain electron microscopy, which revealed an architecture similar to that of the β2-adrenergic receptor–GS complex, including a flexible αT* helical domain. The stability and high yield of the purified complex should allow for further efforts toward obtaining a high-resolution structure of this important signaling complex. PMID:28655769

  4. Isolation and structure-function characterization of a signaling-active rhodopsin-G protein complex.

    Science.gov (United States)

    Gao, Yang; Westfield, Gerwin; Erickson, Jon W; Cerione, Richard A; Skiniotis, Georgios; Ramachandran, Sekar

    2017-08-25

    The visual photo-transduction cascade is a prototypical G protein-coupled receptor (GPCR) signaling system, in which light-activated rhodopsin (Rho*) is the GPCR catalyzing the exchange of GDP for GTP on the heterotrimeric G protein transducin (G T ). This results in the dissociation of G T into its component α T -GTP and β 1 γ 1 subunit complex. Structural information for the Rho*-G T complex will be essential for understanding the molecular mechanism of visual photo-transduction. Moreover, it will shed light on how GPCRs selectively couple to and activate their G protein signaling partners. Here, we report on the preparation of a stable detergent-solubilized complex between Rho* and a heterotrimer (G T *) comprising a Gα T /Gα i1 chimera (α T *) and β 1 γ 1 The complex was formed on native rod outer segment membranes upon light activation, solubilized in lauryl maltose neopentyl glycol, and purified with a combination of affinity and size-exclusion chromatography. We found that the complex is fully functional and that the stoichiometry of Rho* to Gα T * is 1:1. The molecular weight of the complex was calculated from small-angle X-ray scattering data and was in good agreement with a model consisting of one Rho* and one G T *. The complex was visualized by negative-stain electron microscopy, which revealed an architecture similar to that of the β 2 -adrenergic receptor-G S complex, including a flexible α T * helical domain. The stability and high yield of the purified complex should allow for further efforts toward obtaining a high-resolution structure of this important signaling complex. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

  5. Influence of learning strategy on response time during complex value-based learning and choice.

    Directory of Open Access Journals (Sweden)

    Shiva Farashahi

    Full Text Available Measurements of response time (RT have long been used to infer neural processes underlying various cognitive functions such as working memory, attention, and decision making. However, it is currently unknown if RT is also informative about various stages of value-based choice, particularly how reward values are constructed. To investigate these questions, we analyzed the pattern of RT during a set of multi-dimensional learning and decision-making tasks that can prompt subjects to adopt different learning strategies. In our experiments, subjects could use reward feedback to directly learn reward values associated with possible choice options (object-based learning. Alternatively, they could learn reward values of options' features (e.g. color, shape and combine these values to estimate reward values for individual options (feature-based learning. We found that RT was slower when the difference between subjects' estimates of reward probabilities for the two alternative objects on a given trial was smaller. Moreover, RT was overall faster when the preceding trial was rewarded or when the previously selected object was present. These effects, however, were mediated by an interaction between these factors such that subjects were faster when the previously selected object was present rather than absent but only after unrewarded trials. Finally, RT reflected the learning strategy (i.e. object-based or feature-based approach adopted by the subject on a trial-by-trial basis, indicating an overall faster construction of reward value and/or value comparison during object-based learning. Altogether, these results demonstrate that the pattern of RT can be informative about how reward values are learned and constructed during complex value-based learning and decision making.

  6. Active Learning for Directed Exploration of Complex Systems

    Science.gov (United States)

    Burl, Michael C.; Wang, Esther

    2009-01-01

    Physics-based simulation codes are widely used in science and engineering to model complex systems that would be infeasible to study otherwise. Such codes provide the highest-fidelity representation of system behavior, but are often so slow to run that insight into the system is limited. For example, conducting an exhaustive sweep over a d-dimensional input parameter space with k-steps along each dimension requires k(sup d) simulation trials (translating into k(sup d) CPU-days for one of our current simulations). An alternative is directed exploration in which the next simulation trials are cleverly chosen at each step. Given the results of previous trials, supervised learning techniques (SVM, KDE, GP) are applied to build up simplified predictive models of system behavior. These models are then used within an active learning framework to identify the most valuable trials to run next. Several active learning strategies are examined including a recently-proposed information-theoretic approach. Performance is evaluated on a set of thirteen synthetic oracles, which serve as surrogates for the more expensive simulations and enable the experiments to be replicated by other researchers.

  7. A functional TOC complex contributes to gravity signal transduction in Arabidopsis.

    Science.gov (United States)

    Strohm, Allison K; Barrett-Wilt, Greg A; Masson, Patrick H

    2014-01-01

    Although plastid sedimentation has long been recognized as important for a plant's perception of gravity, it was recently shown that plastids play an additional function in gravitropism. The Translocon at the Outer envelope membrane of Chloroplasts (TOC) complex transports nuclear-encoded proteins into plastids, and a receptor of this complex, Toc132, was previously hypothesized to contribute to gravitropism either by directly functioning as a gravity signal transducer or by indirectly mediating the plastid localization of a gravity signal transducer. Here we show that mutations in multiple genes encoding TOC complex components affect gravitropism in a genetically sensitized background and that the cytoplasmic acidic domain of Toc132 is not required for its involvement in this process. Furthermore, mutations in TOC132 enhance the gravitropic defect of a mutant whose amyloplasts lack starch. Finally, we show that the levels of several nuclear-encoded root proteins are altered in toc132 mutants. These data suggest that the TOC complex indirectly mediates gravity signal transduction in Arabidopsis and support the idea that plastids are involved in gravitropism not only through their ability to sediment but also as part of the signal transduction mechanism.

  8. An ecological method for the sampling of nonverbal signalling behaviours of young children with profound and multiple learning disabilities (PMLD).

    Science.gov (United States)

    Atkin, Keith; Lorch, Marjorie Perlman

    2016-08-01

    Profound and multiple learning disabilities (PMLD) are a complex range of disabilities that affect the general health and well-being of the individual and their capacity to interact and learn. We developed a new methodology to capture the non-symbolic signalling behaviours of children with PMLD within the context of a face-to-face interaction with a caregiver to provide analysis at a micro-level of descriptive detail incorporating the use of the ELAN digital video software. The signalling behaviours of participants in a natural, everyday interaction can be better understood with the use of this innovation in methodology, which is predicated on the ecology of communication. Recognition of the developmental ability of the participants is an integral factor within that ecology. The method presented establishes an advanced account of the modalities through which a child affected by PMLD is able to communicate.

  9. Identifying deterministic signals in simulated gravitational wave data: algorithmic complexity and the surrogate data method

    International Nuclear Information System (INIS)

    Zhao Yi; Small, Michael; Coward, David; Howell, Eric; Zhao Chunnong; Ju Li; Blair, David

    2006-01-01

    We describe the application of complexity estimation and the surrogate data method to identify deterministic dynamics in simulated gravitational wave (GW) data contaminated with white and coloured noises. The surrogate method uses algorithmic complexity as a discriminating statistic to decide if noisy data contain a statistically significant level of deterministic dynamics (the GW signal). The results illustrate that the complexity method is sensitive to a small amplitude simulated GW background (SNR down to 0.08 for white noise and 0.05 for coloured noise) and is also more robust than commonly used linear methods (autocorrelation or Fourier analysis)

  10. Machine Learning for Optical Performance Monitoring from Directly Detected PDM-QAM Signals

    DEFF Research Database (Denmark)

    Wass, J.; Thrane, Jakob; Piels, Molly

    2016-01-01

    Supervised machine learning methods are applied and demonstrated experimentally for inband OSNR estimation and modulation format classification in optical communication systems. The proposed methods accurately evaluate coherent signals up to 64QAM using only intensity information....

  11. Bose-Einstein condensates form in heuristics learned by ciliates deciding to signal 'social' commitments.

    Science.gov (United States)

    Clark, Kevin B

    2010-03-01

    Fringe quantum biology theories often adopt the concept of Bose-Einstein condensation when explaining how consciousness, emotion, perception, learning, and reasoning emerge from operations of intact animal nervous systems and other computational media. However, controversial empirical evidence and mathematical formalism concerning decoherence rates of bioprocesses keep these frameworks from satisfactorily accounting for the physical nature of cognitive-like events. This study, inspired by the discovery that preferential attachment rules computed by complex technological networks obey Bose-Einstein statistics, is the first rigorous attempt to examine whether analogues of Bose-Einstein condensation precipitate learned decision making in live biological systems as bioenergetics optimization predicts. By exploiting the ciliate Spirostomum ambiguum's capacity to learn and store behavioral strategies advertising mating availability into heuristics of topologically invariant computational networks, three distinct phases of strategy use were found to map onto statistical distributions described by Bose-Einstein, Fermi-Dirac, and classical Maxwell-Boltzmann behavior. Ciliates that sensitized or habituated signaling patterns to emit brief periods of either deceptive 'harder-to-get' or altruistic 'easier-to-get' serial escape reactions began testing condensed on initially perceived fittest 'courting' solutions. When these ciliates switched from their first strategy choices, Bose-Einstein condensation of strategy use abruptly dissipated into a Maxwell-Boltzmann computational phase no longer dominated by a single fittest strategy. Recursive trial-and-error strategy searches annealed strategy use back into a condensed phase consistent with performance optimization. 'Social' decisions performed by ciliates showing no nonassociative learning were largely governed by Fermi-Dirac statistics, resulting in degenerate distributions of strategy choices. These findings corroborate

  12. Analysis of the Influence of Complexity and Entropy of Odorant on Fractal Dynamics and Entropy of EEG Signal

    OpenAIRE

    Hamidreza Namazi; Amin Akrami; Sina Nazeri; Vladimir V. Kulish

    2016-01-01

    An important challenge in brain research is to make out the relation between the features of olfactory stimuli and the electroencephalogram (EEG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the EEG signal. This study investigates the relation between the structures of EEG signal and the olfactory stimulus (odorant). We show that the complexity of the EEG signal is coupled with the molecular complexity of the odorant, where more structurally ...

  13. Stochastic effects as a force to increase the complexity of signaling networks

    KAUST Repository

    Kuwahara, Hiroyuki

    2013-07-29

    Cellular signaling networks are complex and appear to include many nonfunctional elements. Recently, it was suggested that nonfunctional interactions of proteins cause signaling noise, which, perhaps, shapes the signal transduction mechanism. However, the conditions under which molecular noise influences cellular information processing remain unclear. Here, we explore a large number of simple biological models of varying network sizes to understand the architectural conditions under which the interactions of signaling proteins can exhibit specific stochastic effects - called deviant effects - in which the average behavior of a biological system is substantially altered in the presence of molecular noise. We find that a small fraction of these networks does exhibit deviant effects and shares a common architectural feature whereas most of the networks show only insignificant levels of deviations. Interestingly, addition of seemingly unimportant interactions into protein networks gives rise to deviant effects.

  14. Weak signal transmission in complex networks and its application in detecting connectivity.

    Science.gov (United States)

    Liang, Xiaoming; Liu, Zonghua; Li, Baowen

    2009-10-01

    We present a network model of coupled oscillators to study how a weak signal is transmitted in complex networks. Through both theoretical analysis and numerical simulations, we find that the response of other nodes to the weak signal decays exponentially with their topological distance to the signal source and the coupling strength between two neighboring nodes can be figured out by the responses. This finding can be conveniently used to detect the topology of unknown network, such as the degree distribution, clustering coefficient and community structure, etc., by repeatedly choosing different nodes as the signal source. Through four typical networks, i.e., the regular one dimensional, small world, random, and scale-free networks, we show that the features of network can be approximately given by investigating many fewer nodes than the network size, thus our approach to detect the topology of unknown network may be efficient in practical situations with large network size.

  15. Chronically Increased G[subscript s][alpha] Signaling Disrupts Associative and Spatial Learning

    Science.gov (United States)

    Bourtchouladze, Rusiko; Patterson, Susan L.; Kelly, Michele P.; Kreibich, Arati; Kandel, Eric R.; Abel, Ted

    2006-01-01

    The cAMP/PKA pathway plays a critical role in learning and memory systems in animals ranging from mice to "Drosophila" to "Aplysia." Studies of olfactory learning in "Drosophila" suggest that altered expression of either positive or negative regulators of the cAMP/PKA signaling pathway beyond a certain optimum range may be deleterious. Here we…

  16. Supporting Multimedia Learning with Visual Signalling and Animated Pedagogical Agent: Moderating Effects of Prior Knowledge

    Science.gov (United States)

    Johnson, A. M.; Ozogul, G.; Reisslein, M.

    2015-01-01

    An experiment examined the effects of visual signalling to relevant information in multiple external representations and the visual presence of an animated pedagogical agent (APA). Students learned electric circuit analysis using a computer-based learning environment that included Cartesian graphs, equations and electric circuit diagrams. The…

  17. Disentangling the Complexity of HGF Signaling by Combining Qualitative and Quantitative Modeling.

    Directory of Open Access Journals (Sweden)

    Lorenza A D'Alessandro

    2015-04-01

    Full Text Available Signaling pathways are characterized by crosstalk, feedback and feedforward mechanisms giving rise to highly complex and cell-context specific signaling networks. Dissecting the underlying relations is crucial to predict the impact of targeted perturbations. However, a major challenge in identifying cell-context specific signaling networks is the enormous number of potentially possible interactions. Here, we report a novel hybrid mathematical modeling strategy to systematically unravel hepatocyte growth factor (HGF stimulated phosphoinositide-3-kinase (PI3K and mitogen activated protein kinase (MAPK signaling, which critically contribute to liver regeneration. By combining time-resolved quantitative experimental data generated in primary mouse hepatocytes with interaction graph and ordinary differential equation modeling, we identify and experimentally validate a network structure that represents the experimental data best and indicates specific crosstalk mechanisms. Whereas the identified network is robust against single perturbations, combinatorial inhibition strategies are predicted that result in strong reduction of Akt and ERK activation. Thus, by capitalizing on the advantages of the two modeling approaches, we reduce the high combinatorial complexity and identify cell-context specific signaling networks.

  18. Formative assessment in an online learning environment to support flexible on-the-job learning in complex professional domains

    NARCIS (Netherlands)

    Tamara van Gog; Desirée Joosten-ten Brinke; F. J. Prins; Dominique Sluijsmans

    2010-01-01

    This article describes a blueprint for an online learning environment that is based on prominent instructional design and assessment theories for supporting learning in complex domains. The core of this environment consists of formative assessment tasks (i.e., assessment for learning) that center on

  19. Complexity, Training Paradigm Design, and the Contribution of Memory Subsystems to Grammar Learning.

    Science.gov (United States)

    Antoniou, Mark; Ettlinger, Marc; Wong, Patrick C M

    2016-01-01

    Although there is variability in nonnative grammar learning outcomes, the contributions of training paradigm design and memory subsystems are not well understood. To examine this, we presented learners with an artificial grammar that formed words via simple and complex morphophonological rules. Across three experiments, we manipulated training paradigm design and measured subjects' declarative, procedural, and working memory subsystems. Experiment 1 demonstrated that passive, exposure-based training boosted learning of both simple and complex grammatical rules, relative to no training. Additionally, procedural memory correlated with simple rule learning, whereas declarative memory correlated with complex rule learning. Experiment 2 showed that presenting corrective feedback during the test phase did not improve learning. Experiment 3 revealed that structuring the order of training so that subjects are first exposed to the simple rule and then the complex improved learning. The cumulative findings shed light on the contributions of grammatical complexity, training paradigm design, and domain-general memory subsystems in determining grammar learning success.

  20. Complexity, Training Paradigm Design, and the Contribution of Memory Subsystems to Grammar Learning

    Science.gov (United States)

    Ettlinger, Marc; Wong, Patrick C. M.

    2016-01-01

    Although there is variability in nonnative grammar learning outcomes, the contributions of training paradigm design and memory subsystems are not well understood. To examine this, we presented learners with an artificial grammar that formed words via simple and complex morphophonological rules. Across three experiments, we manipulated training paradigm design and measured subjects' declarative, procedural, and working memory subsystems. Experiment 1 demonstrated that passive, exposure-based training boosted learning of both simple and complex grammatical rules, relative to no training. Additionally, procedural memory correlated with simple rule learning, whereas declarative memory correlated with complex rule learning. Experiment 2 showed that presenting corrective feedback during the test phase did not improve learning. Experiment 3 revealed that structuring the order of training so that subjects are first exposed to the simple rule and then the complex improved learning. The cumulative findings shed light on the contributions of grammatical complexity, training paradigm design, and domain-general memory subsystems in determining grammar learning success. PMID:27391085

  1. Slowness and sparseness have diverging effects on complex cell learning.

    Directory of Open Access Journals (Sweden)

    Jörn-Philipp Lies

    2014-03-01

    Full Text Available Following earlier studies which showed that a sparse coding principle may explain the receptive field properties of complex cells in primary visual cortex, it has been concluded that the same properties may be equally derived from a slowness principle. In contrast to this claim, we here show that slowness and sparsity drive the representations towards substantially different receptive field properties. To do so, we present complete sets of basis functions learned with slow subspace analysis (SSA in case of natural movies as well as translations, rotations, and scalings of natural images. SSA directly parallels independent subspace analysis (ISA with the only difference that SSA maximizes slowness instead of sparsity. We find a large discrepancy between the filter shapes learned with SSA and ISA. We argue that SSA can be understood as a generalization of the Fourier transform where the power spectrum corresponds to the maximally slow subspace energies in SSA. Finally, we investigate the trade-off between slowness and sparseness when combined in one objective function.

  2. Does formal complexity reflect cognitive complexity? Investigating aspects of the Chomsky Hierarchy in an artificial language learning study.

    Science.gov (United States)

    Öttl, Birgit; Jäger, Gerhard; Kaup, Barbara

    2015-01-01

    This study investigated whether formal complexity, as described by the Chomsky Hierarchy, corresponds to cognitive complexity during language learning. According to the Chomsky Hierarchy, nested dependencies (context-free) are less complex than cross-serial dependencies (mildly context-sensitive). In two artificial grammar learning (AGL) experiments participants were presented with a language containing either nested or cross-serial dependencies. A learning effect for both types of dependencies could be observed, but no difference between dependency types emerged. These behavioral findings do not seem to reflect complexity differences as described in the Chomsky Hierarchy. This study extends previous findings in demonstrating learning effects for nested and cross-serial dependencies with more natural stimulus materials in a classical AGL paradigm after only one hour of exposure. The current findings can be taken as a starting point for further exploring the degree to which the Chomsky Hierarchy reflects cognitive processes.

  3. Signaling and Adaptation Modulate the Dynamics of the Photosensoric Complex of Natronomonas pharaonis.

    Directory of Open Access Journals (Sweden)

    Philipp S Orekhov

    2015-10-01

    Full Text Available Motile bacteria and archaea respond to chemical and physical stimuli seeking optimal conditions for survival. To this end transmembrane chemo- and photoreceptors organized in large arrays initiate signaling cascades and ultimately regulate the rotation of flagellar motors. To unravel the molecular mechanism of signaling in an archaeal phototaxis complex we performed coarse-grained molecular dynamics simulations of a trimer of receptor/transducer dimers, namely NpSRII/NpHtrII from Natronomonas pharaonis. Signaling is regulated by a reversible methylation mechanism called adaptation, which also influences the level of basal receptor activation. Mimicking two extreme methylation states in our simulations we found conformational changes for the transmembrane region of NpSRII/NpHtrII which resemble experimentally observed light-induced changes. Further downstream in the cytoplasmic domain of the transducer the signal propagates via distinct changes in the dynamics of HAMP1, HAMP2, the adaptation domain and the binding region for the kinase CheA, where conformational rearrangements were found to be subtle. Overall these observations suggest a signaling mechanism based on dynamic allostery resembling models previously proposed for E. coli chemoreceptors, indicating similar properties of signal transduction for archaeal photoreceptors and bacterial chemoreceptors.

  4. Comparison of GFL–GFRα complexes: further evidence relating GFL bend angle to RET signalling

    International Nuclear Information System (INIS)

    Parkash, Vimal; Goldman, Adrian

    2009-01-01

    The second crystal structure of the GDNF-GFRα1 complex provides further evidence that GFL signalling through RET is determined by the bend angle in the GFL. Glial cell line-derived neurotrophic factor (GDNF) activates the receptor tyrosine kinase RET by binding to the GDNF-family receptor α1 (GFRα1) and forming the GDNF 2 –GFRα1 2 –RET 2 heterohexamer complex. A previous crystal structure of the GDNF 2 –GFRα1 2 complex suggested that differences in signalling in GDNF-family ligand (GFL) complexes might arise from differences in the bend angle between the two monomers in the GFL homodimer. Here, a 2.35 Å resolution structure of the GDNF 2 –GFRα1 2 complex crystallized with new cell dimensions is reported. The structure was refined to a final R factor of 22.5% (R free = 28%). The structures of both biological tetrameric complexes in the asymmetric unit are very similar to 2v5e and different from the artemin–GFRα3 structure, even though there is a small change in the structure of the GDNF. By comparison of all known GDNF and artemin structures, it is concluded that GDNF is more bent and more flexible than artemin and that this may be related to RET signalling. Comparisons also suggest that the differences between artemin and GDNF arise from the increased curvature of the artemin ‘fingers’, which both increases the buried surface area in the monomer–monomer interface and changes the intermonomer bend angle. From sequence comparison, it is suggested that neuturin (the second GFL) adopts an artemin-like conformation, while persephin has a different conformation to the other three

  5. Binding of the Ras activator son of sevenless to insulin receptor substrate-1 signaling complexes.

    Science.gov (United States)

    Baltensperger, K; Kozma, L M; Cherniack, A D; Klarlund, J K; Chawla, A; Banerjee, U; Czech, M P

    1993-06-25

    Signal transmission by insulin involves tyrosine phosphorylation of a major insulin receptor substrate (IRS-1) and exchange of Ras-bound guanosine diphosphate for guanosine triphosphate. Proteins containing Src homology 2 and 3 (SH2 and SH3) domains, such as the p85 regulatory subunit of phosphatidylinositol-3 kinase and growth factor receptor-bound protein 2 (GRB2), bind tyrosine phosphate sites on IRS-1 through their SH2 regions. Such complexes in COS cells were found to contain the heterologously expressed putative guanine nucleotide exchange factor encoded by the Drosophila son of sevenless gene (dSos). Thus, GRB2, p85, or other proteins with SH2-SH3 adapter sequences may link Sos proteins to IRS-1 signaling complexes as part of the mechanism by which insulin activates Ras.

  6. Characterizing scaling properties of complex signals with missed data segments using the multifractal analysis

    Science.gov (United States)

    Pavlov, A. N.; Pavlova, O. N.; Abdurashitov, A. S.; Sindeeva, O. A.; Semyachkina-Glushkovskaya, O. V.; Kurths, J.

    2018-01-01

    The scaling properties of complex processes may be highly influenced by the presence of various artifacts in experimental recordings. Their removal produces changes in the singularity spectra and the Hölder exponents as compared with the original artifacts-free data, and these changes are significantly different for positively correlated and anti-correlated signals. While signals with power-law correlations are nearly insensitive to the loss of significant parts of data, the removal of fragments of anti-correlated signals is more crucial for further data analysis. In this work, we study the ability of characterizing scaling features of chaotic and stochastic processes with distinct correlation properties using a wavelet-based multifractal analysis, and discuss differences between the effect of missed data for synchronous and asynchronous oscillatory regimes. We show that even an extreme data loss allows characterizing physiological processes such as the cerebral blood flow dynamics.

  7. Interference in Ballistic Motor Learning: Specificity and Role of Sensory Error Signals

    Science.gov (United States)

    Lundbye-Jensen, Jesper; Petersen, Tue Hvass; Rothwell, John C.; Nielsen, Jens Bo

    2011-01-01

    Humans are capable of learning numerous motor skills, but newly acquired skills may be abolished by subsequent learning. Here we ask what factors determine whether interference occurs in motor learning. We speculated that interference requires competing processes of synaptic plasticity in overlapping circuits and predicted specificity. To test this, subjects learned a ballistic motor task. Interference was observed following subsequent learning of an accuracy-tracking task, but only if the competing task involved the same muscles and movement direction. Interference was not observed from a non-learning task suggesting that interference requires competing learning. Subsequent learning of the competing task 4 h after initial learning did not cause interference suggesting disruption of early motor memory consolidation as one possible mechanism underlying interference. Repeated transcranial magnetic stimulation (rTMS) of corticospinal motor output at intensities below movement threshold did not cause interference, whereas suprathreshold rTMS evoking motor responses and (re)afferent activation did. Finally, the experiments revealed that suprathreshold repetitive electrical stimulation of the agonist (but not antagonist) peripheral nerve caused interference. The present study is, to our knowledge, the first to demonstrate that peripheral nerve stimulation may cause interference. The finding underscores the importance of sensory feedback as error signals in motor learning. We conclude that interference requires competing plasticity in overlapping circuits. Interference is remarkably specific for circuits involved in a specific movement and it may relate to sensory error signals. PMID:21408054

  8. Mixed signal learning by spike correlation propagation in feedback inhibitory circuits.

    Directory of Open Access Journals (Sweden)

    Naoki Hiratani

    2015-04-01

    Full Text Available The brain can learn and detect mixed input signals masked by various types of noise, and spike-timing-dependent plasticity (STDP is the candidate synaptic level mechanism. Because sensory inputs typically have spike correlation, and local circuits have dense feedback connections, input spikes cause the propagation of spike correlation in lateral circuits; however, it is largely unknown how this secondary correlation generated by lateral circuits influences learning processes through STDP, or whether it is beneficial to achieve efficient spike-based learning from uncertain stimuli. To explore the answers to these questions, we construct models of feedforward networks with lateral inhibitory circuits and study how propagated correlation influences STDP learning, and what kind of learning algorithm such circuits achieve. We derive analytical conditions at which neurons detect minor signals with STDP, and show that depending on the origin of the noise, different correlation timescales are useful for learning. In particular, we show that non-precise spike correlation is beneficial for learning in the presence of cross-talk noise. We also show that by considering excitatory and inhibitory STDP at lateral connections, the circuit can acquire a lateral structure optimal for signal detection. In addition, we demonstrate that the model performs blind source separation in a manner similar to the sequential sampling approximation of the Bayesian independent component analysis algorithm. Our results provide a basic understanding of STDP learning in feedback circuits by integrating analyses from both dynamical systems and information theory.

  9. A Signal for the Need to Restructure the Learning Process.

    Science.gov (United States)

    Breivik, Patricia Senn

    1991-01-01

    Although the U.S. will not disintegrate tomorrow if information literacy and resource-based learning remain underfunded, today's disadvantaged groups will fall further behind, as a new "information elite" emerges. The American Library Association's 1989 information literacy report is one step toward creating a national agenda for…

  10. Improving EEG signal peak detection using feature weight learning ...

    Indian Academy of Sciences (India)

    Therefore, we aimed to develop a general procedure for eye event-related applications based on feature weight learning (FWL), through the use of a neural network with random weights (NNRW) as the classifier. The FWL is performed using a particle swarm optimization algorithm, applied to the well-studied Dumpala, Acir, ...

  11. Sign(al)s: Living and Learning as Semiotic Engagement

    Science.gov (United States)

    Stables, Andrew

    2006-01-01

    Cartesian mind-body dualism, while often explicitly denied, has left a legacy of conceptions that remain highly influential in education. I argue that trends in both analytic and continental philosophy of language point towards a post-Cartesian settlement in which the distinction between "signs" and "signals" is collapsed, and which thus construes…

  12. TOR Complex 2-Ypk1 Signaling Maintains Sphingolipid Homeostasis by Sensing and Regulating ROS Accumulation

    Directory of Open Access Journals (Sweden)

    Brad J. Niles

    2014-02-01

    Full Text Available Reactive oxygen species (ROS are produced during normal metabolism and can function as signaling molecules. However, ROS at elevated levels can damage cells. Here, we identify the conserved target of rapamycin complex 2 (TORC2/Ypk1 signaling module as an important regulator of ROS in the model eukaryotic organism, S. cerevisiae. We show that TORC2/Ypk1 suppresses ROS produced both by mitochondria as well as by nonmitochondrial sources, including changes in acidification of the vacuole. Furthermore, we link vacuole-related ROS to sphingolipids, essential components of cellular membranes, whose synthesis is also controlled by TORC2/Ypk1 signaling. In total, our data reveal that TORC2/Ypk1 act within a homeostatic feedback loop to maintain sphingolipid levels and that ROS are a critical regulatory signal within this system. Thus, ROS sensing and signaling by TORC2/Ypk1 play a central physiological role in sphingolipid biosynthesis and in the maintenance of cell growth and viability.

  13. Precise signal amplitude retrieval for a non-homogeneous diagnostic beam using complex interferometry approach

    Science.gov (United States)

    Krupka, M.; Kalal, M.; Dostal, J.; Dudzak, R.; Juha, L.

    2017-08-01

    Classical interferometry became widely used method of active optical diagnostics. Its more advanced version, allowing reconstruction of three sets of data from just one especially designed interferogram (so called complex interferogram) was developed in the past and became known as complex interferometry. Along with the phase shift, which can be also retrieved using classical interferometry, the amplitude modifications of the probing part of the diagnostic beam caused by the object under study (to be called the signal amplitude) as well as the contrast of the interference fringes can be retrieved using the complex interferometry approach. In order to partially compensate for errors in the reconstruction due to imperfections in the diagnostic beam intensity structure as well as for errors caused by a non-ideal optical setup of the interferometer itself (including the quality of its optical components), a reference interferogram can be put to a good use. This method of interferogram analysis of experimental data has been successfully implemented in practice. However, in majority of interferometer setups (especially in the case of the ones employing the wavefront division) the probe and the reference part of the diagnostic beam would feature different intensity distributions over their respective cross sections. This introduces additional error into the reconstruction of the signal amplitude and the fringe contrast, which cannot be resolved using the reference interferogram only. In order to deal with this error it was found that additional separately recorded images of the intensity distribution of the probe and the reference part of the diagnostic beam (with no signal present) are needed. For the best results a sufficient shot-to-shot stability of the whole diagnostic system is required. In this paper, efficiency of the complex interferometry approach for obtaining the highest possible accuracy of the signal amplitude reconstruction is verified using the computer

  14. Social signals and aversive learning in honey bee drones and workers

    Science.gov (United States)

    Pérez, Eddie; Vallejo, Lianna; Pérez, María E.; Abramson, Charles I.; Giray, Tugrul

    2017-01-01

    ABSTRACT The dissemination of information is a basic element of group cohesion. In honey bees (Apis mellifera Linnaeus 1758), like in other social insects, the principal method for colony-wide information exchange is communication via pheromones. This medium of communication allows multiple individuals to conduct tasks critical to colony survival. Social signaling also establishes conflict at the level of the individual who must trade-off between attending to the immediate environment or the social demand. In this study we examined this conflict by challenging highly social worker honey bees, and less social male drone honey bees undergoing aversive training by presenting them with a social stress signal (isopentyl acetate, IPA). We utilized IPA exposure methods that caused lower learning performance in appetitive learning in workers. Exposure to isopentyl acetate (IPA) did not affect performance of drones and had a dose-specific effect on worker response, with positive effects diminishing at higher IPA doses. The IPA effects are specific because non-social cues, such as the odor cineole, improve learning performance in drones, and social homing signals (geraniol) did not have a discernible effect on drone or worker performance. We conclude that social signals do generate conflict and that response to them is dependent on signal relevance to the individual as well as the context. We discuss the effect of social signal on learning both related to its social role and potential evolutionary history. PMID:27895050

  15. Social signals and aversive learning in honey bee drones and workers

    Directory of Open Access Journals (Sweden)

    Arian Avalos

    2017-01-01

    Full Text Available The dissemination of information is a basic element of group cohesion. In honey bees (Apis mellifera Linnaeus 1758, like in other social insects, the principal method for colony-wide information exchange is communication via pheromones. This medium of communication allows multiple individuals to conduct tasks critical to colony survival. Social signaling also establishes conflict at the level of the individual who must trade-off between attending to the immediate environment or the social demand. In this study we examined this conflict by challenging highly social worker honey bees, and less social male drone honey bees undergoing aversive training by presenting them with a social stress signal (isopentyl acetate, IPA. We utilized IPA exposure methods that caused lower learning performance in appetitive learning in workers. Exposure to isopentyl acetate (IPA did not affect performance of drones and had a dose-specific effect on worker response, with positive effects diminishing at higher IPA doses. The IPA effects are specific because non-social cues, such as the odor cineole, improve learning performance in drones, and social homing signals (geraniol did not have a discernible effect on drone or worker performance. We conclude that social signals do generate conflict and that response to them is dependent on signal relevance to the individual as well as the context. We discuss the effect of social signal on learning both related to its social role and potential evolutionary history.

  16. Social signals and aversive learning in honey bee drones and workers.

    Science.gov (United States)

    Avalos, Arian; Pérez, Eddie; Vallejo, Lianna; Pérez, María E; Abramson, Charles I; Giray, Tugrul

    2017-01-15

    The dissemination of information is a basic element of group cohesion. In honey bees (Apis mellifera Linnaeus 1758), like in other social insects, the principal method for colony-wide information exchange is communication via pheromones. This medium of communication allows multiple individuals to conduct tasks critical to colony survival. Social signaling also establishes conflict at the level of the individual who must trade-off between attending to the immediate environment or the social demand. In this study we examined this conflict by challenging highly social worker honey bees, and less social male drone honey bees undergoing aversive training by presenting them with a social stress signal (isopentyl acetate, IPA). We utilized IPA exposure methods that caused lower learning performance in appetitive learning in workers. Exposure to isopentyl acetate (IPA) did not affect performance of drones and had a dose-specific effect on worker response, with positive effects diminishing at higher IPA doses. The IPA effects are specific because non-social cues, such as the odor cineole, improve learning performance in drones, and social homing signals (geraniol) did not have a discernible effect on drone or worker performance. We conclude that social signals do generate conflict and that response to them is dependent on signal relevance to the individual as well as the context. We discuss the effect of social signal on learning both related to its social role and potential evolutionary history. © 2017. Published by The Company of Biologists Ltd.

  17. Method to predict process signals to learn for SVM

    International Nuclear Information System (INIS)

    Minowa, Hirotsugu; Gofuku, Akio

    2013-01-01

    Study of diagnostic system using machine learning to reduce the incidents of the plant is in advance because an accident causes large damage about human, economic and social loss. There is a problem that 2 performances between a classification performance and generalization performance on the machine diagnostic machine is exclusive. However, multi agent diagnostic system makes it possible to use a diagnostic machine specialized either performance by multi diagnostic machines can be used. We propose method to select optimized variables to improve classification performance. The method can also be used for other supervised learning machine but Support Vector Machine. This paper reports that our method and result of evaluation experiment applied our method to output 40% of Monju. (author)

  18. Nitric Oxide Synthase and Cyclooxygenase Pathways: A Complex Interplay in Cellular Signaling.

    Science.gov (United States)

    Sorokin, Andrey

    2016-01-01

    The cellular reaction to external challenges is a tightly regulated process consisting of integrated processes mediated by a variety of signaling molecules, generated as a result of modulation of corresponding biosynthetic systems. Both, nitric oxide synthase (NOS) and cyclooxygenase (COX) systems, consist of constitutive forms (NOS1, NOS3 and COX-1), which are mostly involved in housekeeping tasks, and inducible forms (NOS2 and COX-2), which shape the cellular response to stress and variety of bioactive agents. The complex interplay between NOS and COX pathways can be observed at least at three levels. Firstly, products of NOS and Cox systems can mediate the regulation and the expression of inducible forms (NOS2 and COX-2) in response of similar and dissimilar stimulus. Secondly, the reciprocal modulation of cyclooxygenase activity by nitric oxide and NOS activity by prostaglandins at the posttranslational level has been shown to occur. Mechanisms by which nitric oxide can modulate prostaglandin synthesis include direct S-nitrosylation of COX and inactivation of prostaglandin I synthase by peroxynitrite, product of superoxide reaction with nitric oxide. Prostaglandins, conversely, can promote an increased association of dynein light chain (DLC) (also known as protein inhibitor of neuronal nitric oxide synthase) with NOS1, thereby reducing its activity. The third level of interplay is provided by intracellular crosstalk of signaling pathways stimulated by products of NOS and COX which contributes significantly to the complexity of cellular signaling. Since modulation of COX and NOS pathways was shown to be principally involved in a variety of pathological conditions, the dissection of their complex relationship is needed for better understanding of possible therapeutic strategies. This review focuses on implications of interplay between NOS and COX for cellular function and signal integration.

  19. Hurst Exponent Analysis of Resting-State fMRI Signal Complexity across the Adult Lifespan

    Directory of Open Access Journals (Sweden)

    Jianxin Dong

    2018-02-01

    Full Text Available Exploring functional information among various brain regions across time enables understanding of healthy aging process and holds great promise for age-related brain disease diagnosis. This paper proposed a method to explore fractal complexity of the resting-state functional magnetic resonance imaging (rs-fMRI signal in the human brain across the adult lifespan using Hurst exponent (HE. We took advantage of the examined rs-fMRI data from 116 adults 19 to 85 years of age (44.3 ± 19.4 years, 49 females from NKI/Rockland sample. Region-wise and voxel-wise analyses were performed to investigate the effects of age, gender, and their interaction on complexity. In region-wise analysis, we found that the healthy aging is accompanied by a loss of complexity in frontal and parietal lobe and increased complexity in insula, limbic, and temporal lobe. Meanwhile, differences in HE between genders were found to be significant in parietal lobe (p = 0.04, corrected. However, there was no interaction between gender and age. In voxel-wise analysis, the significant complexity decrease with aging was found in frontal and parietal lobe, and complexity increase was found in insula, limbic lobe, occipital lobe, and temporal lobe with aging. Meanwhile, differences in HE between genders were found to be significant in frontal, parietal, and limbic lobe. Furthermore, we found age and sex interaction in right parahippocampal gyrus (p = 0.04, corrected. Our findings reveal HE variations of the rs-fMRI signal across the human adult lifespan and show that HE may serve as a new parameter to assess healthy aging process.

  20. Design and Use of a Learning Object for Finding Complex Polynomial Roots

    Science.gov (United States)

    Benitez, Julio; Gimenez, Marcos H.; Hueso, Jose L.; Martinez, Eulalia; Riera, Jaime

    2013-01-01

    Complex numbers are essential in many fields of engineering, but students often fail to have a natural insight of them. We present a learning object for the study of complex polynomials that graphically shows that any complex polynomials has a root and, furthermore, is useful to find the approximate roots of a complex polynomial. Moreover, we…

  1. Microscopic insight into thermodynamics of conformational changes of SAP-SLAM complex in signal transduction cascade

    Science.gov (United States)

    Samanta, Sudipta; Mukherjee, Sanchita

    2017-04-01

    The signalling lymphocytic activation molecule (SLAM) family of receptors, expressed by an array of immune cells, associate with SLAM-associated protein (SAP)-related molecules, composed of single SH2 domain architecture. SAP activates Src-family kinase Fyn after SLAM ligation, resulting in a SLAM-SAP-Fyn complex, where, SAP binds the Fyn SH3 domain that does not involve canonical SH3 or SH2 interactions. This demands insight into this SAP mediated signalling cascade. Thermodynamics of the conformational changes are extracted from the histograms of dihedral angles obtained from the all-atom molecular dynamics simulations of this structurally well characterized SAP-SLAM complex. The results incorporate the binding induced thermodynamic changes of individual amino acid as well as the secondary structural elements of the protein and the solvent. Stabilization of the peptide partially comes through a strong hydrogen bonding network with the protein, while hydrophobic interactions also play a significant role where the peptide inserts itself into a hydrophobic cavity of the protein. SLAM binding widens SAP's second binding site for Fyn, which is the next step in the signal transduction cascade. The higher stabilization and less fluctuation of specific residues of SAP in the Fyn binding site, induced by SAP-SLAM complexation, emerge as the key structural elements to trigger the recognition of SAP by the SH3 domain of Fyn. The thermodynamic quantification of the protein due to complexation not only throws deeper understanding in the established mode of SAP-SLAM interaction but also assists in the recognition of the relevant residues of the protein responsible for alterations in its activity.

  2. Agent-specific learning signals for self-other distinction during mentalising.

    Directory of Open Access Journals (Sweden)

    Sam Ereira

    2018-04-01

    Full Text Available Humans have a remarkable ability to simulate the minds of others. How the brain distinguishes between mental states attributed to self and mental states attributed to someone else is unknown. Here, we investigated how fundamental neural learning signals are selectively attributed to different agents. Specifically, we asked whether learning signals are encoded in agent-specific neural patterns or whether a self-other distinction depends on encoding agent identity separately from this learning signal. To examine this, we tasked subjects to learn continuously 2 models of the same environment, such that one was selectively attributed to self and the other was selectively attributed to another agent. Combining computational modelling with magnetoencephalography (MEG enabled us to track neural representations of prediction errors (PEs and beliefs attributed to self, and of simulated PEs and beliefs attributed to another agent. We found that the representational pattern of a PE reliably predicts the identity of the agent to whom the signal is attributed, consistent with a neural self-other distinction implemented via agent-specific learning signals. Strikingly, subjects exhibiting a weaker neural self-other distinction also had a reduced behavioural capacity for self-other distinction and displayed more marked subclinical psychopathological traits. The neural self-other distinction was also modulated by social context, evidenced in a significantly reduced decoding of agent identity in a nonsocial control task. Thus, we show that self-other distinction is realised through an encoding of agent identity intrinsic to fundamental learning signals. The observation that the fidelity of this encoding predicts psychopathological traits is of interest as a potential neurocomputational psychiatric biomarker.

  3. Larger error signals in major depression are associated with better avoidance learning

    Directory of Open Access Journals (Sweden)

    James F eCavanagh

    2011-11-01

    Full Text Available The medial prefrontal cortex (mPFC is particularly reactive to signals of error, punishment, and conflict in the service of behavioral adaptation and it is consistently implicated in the etiology of Major Depressive Disorder (MDD. This association makes conceptual sense, given that MDD has been associated with hyper-reactivity in neural systems associated with punishment processing. Yet in practice, depression-related variance in measures of mPFC functioning often fails to relate to performance. For example, neuroelectric reflections of mediofrontal error signals are often found to be larger in MDD, but a deficit in post-error performance suggests that these error signals are not being used to rapidly adapt behavior. Thus, it remains unknown if depression-related variance in error signals reflects a meaningful alteration in the use of error or punishment information. However, larger mediofrontal error signals have also been related to another behavioral tendency: increased accuracy in avoidance learning. The integrity of this error-avoidance system remains untested in MDD. In this study, EEG was recorded as 21 symptomatic, drug-free participants with current or past MDD and 24 control participants performed a probabilistic reinforcement learning task. Depressed participants had larger mPFC EEG responses to error feedback than controls. The direct relationship between error signal amplitudes and avoidance learning accuracy was replicated. Crucially, this relationship was stronger in depressed participants for high conflict lose-lose situations, demonstrating a selective alteration of avoidance learning. This investigation provided evidence that larger error signal amplitudes in depression are associated with increased avoidance learning, identifying a candidate mechanistic model for hypersensitivity to negative outcomes in depression.

  4. Analysis of the Influence of Complexity and Entropy of Odorant on Fractal Dynamics and Entropy of EEG Signal.

    Science.gov (United States)

    Namazi, Hamidreza; Akrami, Amin; Nazeri, Sina; Kulish, Vladimir V

    2016-01-01

    An important challenge in brain research is to make out the relation between the features of olfactory stimuli and the electroencephalogram (EEG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the EEG signal. This study investigates the relation between the structures of EEG signal and the olfactory stimulus (odorant). We show that the complexity of the EEG signal is coupled with the molecular complexity of the odorant, where more structurally complex odorant causes less fractal EEG signal. Also, odorant having higher entropy causes the EEG signal to have lower approximate entropy. The method discussed here can be applied and investigated in case of patients with brain diseases as the rehabilitation purpose.

  5. Community Learning Campus: It Takes a Simple Message to Build a Complex Project

    Science.gov (United States)

    Pearson, George

    2012-01-01

    Education Canada asked Tom Thompson, president of Olds College and a prime mover behind the Community Learning Campus (CLC): What were the lessons learned from this unusually ambitious education project? Thompson mentions six lessons he learned from this complex project which include: (1) Dream big, build small, act now; (2) Keep a low profile at…

  6. Improvement of Inquiry in a Complex Technology-Enhanced Learning Environment

    NARCIS (Netherlands)

    Pedaste, Margus; Kori, Külli; Maeots, Mario; de Jong, Anthonius J.M.; Riopel, Martin; Smyrnaiou, Zacharoula

    2016-01-01

    Inquiry learning is an effective approach in science education. Complex technology-enhanced learning environments are needed to apply inquiry worldwide to support knowledge gain and improvement of inquiry skills. In our study, we applied an ecology mission in the SCY-Lab learning environment and

  7. Multi-signal sedimentation velocity analysis with mass conservation for determining the stoichiometry of protein complexes.

    Directory of Open Access Journals (Sweden)

    Chad A Brautigam

    Full Text Available Multi-signal sedimentation velocity analytical ultracentrifugation (MSSV is a powerful tool for the determination of the number, stoichiometry, and hydrodynamic shape of reversible protein complexes in two- and three-component systems. In this method, the evolution of sedimentation profiles of macromolecular mixtures is recorded simultaneously using multiple absorbance and refractive index signals and globally transformed into both spectrally and diffusion-deconvoluted component sedimentation coefficient distributions. For reactions with complex lifetimes comparable to the time-scale of sedimentation, MSSV reveals the number and stoichiometry of co-existing complexes. For systems with short complex lifetimes, MSSV reveals the composition of the reaction boundary of the coupled reaction/migration process, which we show here may be used to directly determine an association constant. A prerequisite for MSSV is that the interacting components are spectrally distinguishable, which may be a result, for example, of extrinsic chromophores or of different abundances of aromatic amino acids contributing to the UV absorbance. For interacting components that are spectrally poorly resolved, here we introduce a method for additional regularization of the spectral deconvolution by exploiting approximate knowledge of the total loading concentrations. While this novel mass conservation principle does not discriminate contributions to different species, it can be effectively combined with constraints in the sedimentation coefficient range of uncomplexed species. We show in theory, computer simulations, and experiment, how mass conservation MSSV as implemented in SEDPHAT can enhance or even substitute for the spectral discrimination of components. This should broaden the applicability of MSSV to the analysis of the composition of reversible macromolecular complexes.

  8. Ethylene Regulates Levels of Ethylene Receptor/CTR1 Signaling Complexes in Arabidopsis thaliana*

    Science.gov (United States)

    Shakeel, Samina N.; Gao, Zhiyong; Amir, Madiha; Chen, Yi-Feng; Rai, Muneeza Iqbal; Haq, Noor Ul; Schaller, G. Eric

    2015-01-01

    The plant hormone ethylene is perceived by a five-member family of receptors in Arabidopsis thaliana. The receptors function in conjunction with the Raf-like kinase CTR1 to negatively regulate ethylene signal transduction. CTR1 interacts with multiple members of the receptor family based on co-purification analysis, interacting more strongly with receptors containing a receiver domain. Levels of membrane-associated CTR1 vary in response to ethylene, doing so in a post-transcriptional manner that correlates with ethylene-mediated changes in levels of the ethylene receptors ERS1, ERS2, EIN4, and ETR2. Interactions between CTR1 and the receptor ETR1 protect ETR1 from ethylene-induced turnover. Kinetic and dose-response analyses support a model in which two opposing factors control levels of the ethylene receptor/CTR1 complexes. Ethylene stimulates the production of new complexes largely through transcriptional induction of the receptors. However, ethylene also induces turnover of receptors, such that levels of ethylene receptor/CTR1 complexes decrease at higher ethylene concentrations. Implications of this model for ethylene signaling are discussed. PMID:25814663

  9. The mediator complex in genomic and non-genomic signaling in cancer.

    Science.gov (United States)

    Weber, Hannah; Garabedian, Michael J

    2018-05-01

    Mediator is a conserved, multi-subunit macromolecular machine divided structurally into head, middle, and tail modules, along with a transiently associating kinase module. Mediator functions as an integrator of transcriptional regulatory activity by interacting with DNA-bound transcription factors and with RNA polymerase II (Pol II) to both activate and repress gene expression. Mediator has been shown to affect multiple steps in transcription, including chromatin looping between enhancers and promoters, pre-initiation complex formation, transcriptional elongation, and mRNA splicing. Individual Mediator subunits participate in regulation of gene expression by the estrogen and androgen receptors and are altered in a number of endocrine cancers, including breast and prostate cancer. In addition to its role in genomic signaling, MED12 has been implicated in non-genomic signaling by interacting with and activating TGF-beta receptor 2 in the cytoplasm. Recent structural studies have revealed extensive inter-domain interactions and complex architecture of the Mediator-Pol II complex, suggesting that Mediator is capable of reorganizing its conformation and composition to fit cellular needs. We propose that alterations in Mediator subunit expression that occur in various cancers could impact the organization and function of Mediator, resulting in changes in gene expression that promote malignancy. A better understanding of the role of Mediator in cancer could reveal new approaches to the diagnosis and treatment of Mediator-dependent endocrine cancers, especially in settings of therapy resistance. Copyright © 2017 Elsevier Inc. All rights reserved.

  10. Jasmonate signalling in Arabidopsis involves SGT1b-HSP70-HSP90 chaperone complexes.

    Science.gov (United States)

    Zhang, Xue-Cheng; Millet, Yves A; Cheng, Zhenyu; Bush, Jenifer; Ausubel, Frederick M

    Plant hormones play pivotal roles in growth, development and stress responses. Although it is essential to our understanding of hormone signalling, how plants maintain a steady state level of hormone receptors is poorly understood. We show that mutation of the Arabidopsis thaliana co-chaperone SGT1b impairs responses to the plant hormones jasmonate, auxin and gibberellic acid, but not brassinolide and abscisic acid, and that SGT1b and its homologue SGT1a are involved in maintaining the steady state levels of the F-box proteins COI1 and TIR1, receptors for jasmonate and auxin, respectively. The association of SGT1b with COI1 is direct and is independent of the Arabidopsis SKP1 protein, ASK1. We further show that COI1 is a client protein of SGT1b-HSP70-HSP90 chaperone complexes and that the complexes function in hormone signalling by stabilizing the COI1 protein. This study extends the SGT1b-HSP90 client protein list and broadens the functional scope of SGT1b-HSP70-HSP90 chaperone complexes.

  11. A complex symbol signal-to-noise ratio estimator and its performance

    Science.gov (United States)

    Feria, Y.

    1994-01-01

    This article presents an algorithm for estimating the signal-to-noise ratio (SNR) of signals that contain data on a downconverted suppressed carrier or the first harmonic of a square-wave subcarrier. This algorithm can be used to determine the performance of the full-spectrum combiner for the Galileo S-band (2.2- to 2.3-GHz) mission by measuring the input and output symbol SNR. A performance analysis of the algorithm shows that the estimator can estimate the complex symbol SNR using 10,000 symbols at a true symbol SNR of -5 dB with a mean of -4.9985 dB and a standard deviation of 0.2454 dB, and these analytical results are checked by simulations of 100 runs with a mean of -5.06 dB and a standard deviation of 0.2506 dB.

  12. Phase dynamics of complex-valued neural networks and its application to traffic signal control.

    Science.gov (United States)

    Nishikawa, Ikuko; Iritani, Takeshi; Sakakibara, Kazutoshi; Kuroe, Yasuaki

    2005-01-01

    Complex-valued Hopfield networks which possess the energy function are analyzed. The dynamics of the network with certain forms of an activation function is de-composable into the dynamics of the amplitude and phase of each neuron. Then the phase dynamics is described as a coupled system of phase oscillators with a pair-wise sinusoidal interaction. Therefore its phase synchronization mechanism is useful for the area-wide offset control of the traffic signals. The computer simulations show the effectiveness under the various traffic conditions.

  13. Auto-Encoder based Deep Learning for Surface Electromyography Signal Processing

    Directory of Open Access Journals (Sweden)

    Marwa Farouk Ibrahim Ibrahim

    2018-01-01

    Full Text Available Feature extraction is taking a very vital and essential part of bio-signal processing. We need to choose one of two paths to identify and select features in any system. The most popular track is engineering handcrafted, which mainly depends on the user experience and the field of application. While the other path is feature learning, which depends on training the system on recognising and picking the best features that match the application. The main concept of feature learning is to create a model that is expected to be able to learn the best features without any human intervention instead of recourse the traditional methods for feature extraction or reduction and avoid dealing with feature extraction that depends on researcher experience. In this paper, Auto-Encoder will be utilised as a feature learning algorithm to practice the recommended model to excerpt the useful features from the surface electromyography signal. Deep learning method will be suggested by using Auto-Encoder to learn features. Wavelet Packet, Spectrogram, and Wavelet will be employed to represent the surface electromyography signal in our recommended model. Then, the newly represented bio-signal will be fed to stacked autoencoder (2 stages to learn features and finally, the behaviour of the proposed algorithm will be estimated by hiring different classifiers such as Extreme Learning Machine, Support Vector Machine, and SoftMax Layer. The Rectified Linear Unit (ReLU will be created as an activation function for extreme learning machine classifier besides existing functions such as sigmoid and radial basis function. ReLU will show a better classification ability than sigmoid and Radial basis function (RBF for wavelet, Wavelet scale 5 and wavelet packet signal representations implemented techniques. ReLU will illustrate better classification ability, as an activation function, than sigmoid and poorer than RBF for spectrogram signal representation. Both confidence interval and

  14. Effect of signal noise on the learning capability of an artificial neural network

    International Nuclear Information System (INIS)

    Vega, J.J.; Reynoso, R.; Calvet, H. Carrillo

    2009-01-01

    Digital Pulse Shape Analysis (DPSA) by artificial neural networks (ANN) is becoming an important tool to extract relevant information from digitized signals in different areas. In this paper, we present a systematic evidence of how the concomitant noise that distorts the signals or patterns to be identified by an ANN set limits to its learning capability. Also, we present evidence that explains overtraining as a competition between the relevant pattern features, on the one side, against the signal noise, on the other side, as the main cause defining the shape of the error surface in weight space and, consequently, determining the steepest descent path that controls the ANN adaptation process.

  15. Mixed-complexity artificial grammar learning in humans and macaque monkeys: evaluating learning strategies.

    Science.gov (United States)

    Wilson, Benjamin; Smith, Kenny; Petkov, Christopher I

    2015-03-01

    Artificial grammars (AG) can be used to generate rule-based sequences of stimuli. Some of these can be used to investigate sequence-processing computations in non-human animals that might be related to, but not unique to, human language. Previous AG learning studies in non-human animals have used different AGs to separately test for specific sequence-processing abilities. However, given that natural language and certain animal communication systems (in particular, song) have multiple levels of complexity, mixed-complexity AGs are needed to simultaneously evaluate sensitivity to the different features of the AG. Here, we tested humans and Rhesus macaques using a mixed-complexity auditory AG, containing both adjacent (local) and non-adjacent (longer-distance) relationships. Following exposure to exemplary sequences generated by the AG, humans and macaques were individually tested with sequences that were either consistent with the AG or violated specific adjacent or non-adjacent relationships. We observed a considerable level of cross-species correspondence in the sensitivity of both humans and macaques to the adjacent AG relationships and to the statistical properties of the sequences. We found no significant sensitivity to the non-adjacent AG relationships in the macaques. A subset of humans was sensitive to this non-adjacent relationship, revealing interesting between- and within-species differences in AG learning strategies. The results suggest that humans and macaques are largely comparably sensitive to the adjacent AG relationships and their statistical properties. However, in the presence of multiple cues to grammaticality, the non-adjacent relationships are less salient to the macaques and many of the humans. © 2015 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  16. Circadian modulation of complex learning in diurnal and nocturnal Aplysia

    OpenAIRE

    Lyons, Lisa C.; Rawashdeh, Oliver; Katzoff, Ayelet; Susswein, Abraham J.; Eskin, Arnold

    2005-01-01

    Understanding modulation of memory, as well as the mechanisms underlying memory formation, has become a key issue in neuroscience research. Previously, we found that the formation of long-term, but not short-term, memory for a nonassociative form of learning, sensitization, was modulated by the circadian clock in the diurnal Aplysia californica. To define the scope of circadian modulation of memory, we examined an associative operant learning paradigm, learning that food is inedible (LFI). Si...

  17. A novel deep learning approach for classification of EEG motor imagery signals.

    Science.gov (United States)

    Tabar, Yousef Rezaei; Halici, Ugur

    2017-02-01

    Signal classification is an important issue in brain computer interface (BCI) systems. Deep learning approaches have been used successfully in many recent studies to learn features and classify different types of data. However, the number of studies that employ these approaches on BCI applications is very limited. In this study we aim to use deep learning methods to improve classification performance of EEG motor imagery signals. In this study we investigate convolutional neural networks (CNN) and stacked autoencoders (SAE) to classify EEG Motor Imagery signals. A new form of input is introduced to combine time, frequency and location information extracted from EEG signal and it is used in CNN having one 1D convolutional and one max-pooling layers. We also proposed a new deep network by combining CNN and SAE. In this network, the features that are extracted in CNN are classified through the deep network SAE. The classification performance obtained by the proposed method on BCI competition IV dataset 2b in terms of kappa value is 0.547. Our approach yields 9% improvement over the winner algorithm of the competition. Our results show that deep learning methods provide better classification performance compared to other state of art approaches. These methods can be applied successfully to BCI systems where the amount of data is large due to daily recording.

  18. Application of «Sensor signal analysis network» complex for distributed, time synchronized analysis of electromagnetic radiation

    Science.gov (United States)

    Mochalov, Vladimir; Mochalova, Anastasia

    2017-10-01

    The paper considers a developing software-hardware complex «Sensor signal analysis network» for distributed and time synchronized analysis of electromagnetic radiations. The areas of application and the main features of the complex are described. An example of application of the complex to monitor natural electromagnetic radiation sources is considered based on the data recorded in VLF range. A generalized functional scheme of stream analysis of signals by a complex functional node is suggested and its application for stream detection of atmospherics, whistlers and tweaks is considered.

  19. The Fyn tyrosine kinase binds Irs-1 and forms a distinct signaling complex during insulin stimulation.

    Science.gov (United States)

    Sun, X J; Pons, S; Asano, T; Myers, M G; Glasheen, E; White, M F

    1996-05-03

    Irs-proteins link the receptors for insulin/IGF-1, growth hormones, and several interleukins and interferons to signaling proteins that contain Src homology-2 (SH2). To identify new Irs-1-binding proteins, we screened a mouse embryo expression library with recombinant [32P]Irs-1, which revealed a specific association between p59fyn and Irs-1. The SH2 domain in p59fyn bound to phosphorylated Tyr895 and Tyr1172, which are located in YXX(L/I) motifs. Mutation of p59fyn at the COOH-terminal tyrosine phosphorylation site (Tyr531) enhanced its binding to Irs-1 during insulin stimulation. Binding experiments with various SH2 protein revealed that Grb-2 was largely excluded from Irs-1 complexes containing p59fyn, whereas Grb-2 and p85 occurred in the same Irs-1 complex. By comparison with the insulin receptor, p59fyn kinase phosphorylated a unique cohort of tyrosine residues in Irs-1. These results outline a role for p59fyn or other related Src-kinases during insulin and cytokine signaling.

  20. Design Implementation and Testing of a VLSI High Performance ASIC for Extracting the Phase of a Complex Signal

    National Research Council Canada - National Science Library

    Altmeyer, Ronald

    2002-01-01

    This thesis documents the research, circuit design, and simulation testing of a VLSI ASIC which extracts phase angle information from a complex sampled signal using the arctangent relationship: (phi=tan/-1 (Q/1...

  1. Regulation of Drosophila Brain Wiring by Neuropil Interactions via a Slit-Robo-RPTP Signaling Complex.

    Science.gov (United States)

    Oliva, Carlos; Soldano, Alessia; Mora, Natalia; De Geest, Natalie; Claeys, Annelies; Erfurth, Maria-Luise; Sierralta, Jimena; Ramaekers, Ariane; Dascenco, Dan; Ejsmont, Radoslaw K; Schmucker, Dietmar; Sanchez-Soriano, Natalia; Hassan, Bassem A

    2016-10-24

    The axonal wiring molecule Slit and its Round-About (Robo) receptors are conserved regulators of nerve cord patterning. Robo receptors also contribute to wiring brain circuits. Whether molecular mechanisms regulating these signals are modified to fit more complex brain wiring processes is unclear. We investigated the role of Slit and Robo receptors in wiring Drosophila higher-order brain circuits and identified differences in the cellular and molecular mechanisms of Robo/Slit function. First, we find that signaling by Robo receptors in the brain is regulated by the Receptor Protein Tyrosine Phosphatase RPTP69d. RPTP69d increases membrane availability of Robo3 without affecting its phosphorylation state. Second, we detect no midline localization of Slit during brain development. Instead, Slit is enriched in the mushroom body, a neuronal structure covering large areas of the brain. Thus, a divergent molecular mechanism regulates neuronal circuit wiring in the Drosophila brain, partly in response to signals from the mushroom body. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  2. Dynamic mesolimbic dopamine signaling during action sequence learning and expectation violation

    Science.gov (United States)

    Collins, Anne L.; Greenfield, Venuz Y.; Bye, Jeffrey K.; Linker, Kay E.; Wang, Alice S.; Wassum, Kate M.

    2016-01-01

    Prolonged mesolimbic dopamine concentration changes have been detected during spatial navigation, but little is known about the conditions that engender this signaling profile or how it develops with learning. To address this, we monitored dopamine concentration changes in the nucleus accumbens core of rats throughout acquisition and performance of an instrumental action sequence task. Prolonged dopamine concentration changes were detected that ramped up as rats executed each action sequence and declined after earned reward collection. With learning, dopamine concentration began to rise increasingly earlier in the execution of the sequence and ultimately backpropagated away from stereotyped sequence actions, becoming only transiently elevated by the most distal and unexpected reward predictor. Action sequence-related dopamine signaling was reactivated in well-trained rats if they became disengaged in the task and in response to an unexpected change in the value, but not identity of the earned reward. Throughout training and test, dopamine signaling correlated with sequence performance. These results suggest that action sequences can engender a prolonged mode of dopamine signaling in the nucleus accumbens core and that such signaling relates to elements of the motivation underlying sequence execution and is dynamic with learning, overtraining and violations in reward expectation. PMID:26869075

  3. Learning-based traffic signal control algorithms with neighborhood information sharing: An application for sustainable mobility

    Energy Technology Data Exchange (ETDEWEB)

    Aziz, H. M. Abdul [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Zhu, Feng [Purdue University, West Lafayette, IN (United States). Lyles School of Civil Engineering; Ukkusuri, Satish V. [Purdue University, West Lafayette, IN (United States). Lyles School of Civil Engineering

    2017-10-04

    Here, this research applies R-Markov Average Reward Technique based reinforcement learning (RL) algorithm, namely RMART, for vehicular signal control problem leveraging information sharing among signal controllers in connected vehicle environment. We implemented the algorithm in a network of 18 signalized intersections and compare the performance of RMART with fixed, adaptive, and variants of the RL schemes. Results show significant improvement in system performance for RMART algorithm with information sharing over both traditional fixed signal timing plans and real time adaptive control schemes. Additionally, the comparison with reinforcement learning algorithms including Q learning and SARSA indicate that RMART performs better at higher congestion levels. Further, a multi-reward structure is proposed that dynamically adjusts the reward function with varying congestion states at the intersection. Finally, the results from test networks show significant reduction in emissions (CO, CO2, NOx, VOC, PM10) when RL algorithms are implemented compared to fixed signal timings and adaptive schemes.

  4. Facilitating the Evaluation of Complexity in the Public Sector: Learning from the NHS in Scotland

    Science.gov (United States)

    Connolly, John; Reid, Garth; Mooney, Allan

    2015-01-01

    It is necessary for public managers to be able to evaluate programmes in the context of complexity. This article offers key learning and reflections based on the experience of facilitating the evaluation of complexity with a range of public sector partners in Scotland. There have been several articles that consider evaluating complexity and…

  5. Classification of EEG signals using a genetic-based machine learning classifier.

    Science.gov (United States)

    Skinner, B T; Nguyen, H T; Liu, D K

    2007-01-01

    This paper investigates the efficacy of the genetic-based learning classifier system XCS, for the classification of noisy, artefact-inclusive human electroencephalogram (EEG) signals represented using large condition strings (108bits). EEG signals from three participants were recorded while they performed four mental tasks designed to elicit hemispheric responses. Autoregressive (AR) models and Fast Fourier Transform (FFT) methods were used to form feature vectors with which mental tasks can be discriminated. XCS achieved a maximum classification accuracy of 99.3% and a best average of 88.9%. The relative classification performance of XCS was then compared against four non-evolutionary classifier systems originating from different learning techniques. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices.

  6. Complex Mobile Learning That Adapts to Learners' Cognitive Load

    Science.gov (United States)

    Deegan, Robin

    2015-01-01

    Mobile learning is cognitively demanding and frequently the ubiquitous nature of mobile computing means that mobile devices are used in cognitively demanding environments. This paper examines the use of mobile devices from a Learning, Usability and Cognitive Load Theory perspective. It suggests scenarios where these fields interact and presents an…

  7. A Holistic Approach to Scoring in Complex Mobile Learning Scenarios

    Science.gov (United States)

    Gebbe, Marcel; Teine, Matthias; Beutner, Marc

    2016-01-01

    Interactive dialogues are key elements for designing authentic and motivating learning situations, and in combination with learning analysis they provide educators and users with the opportunity to track information related to professional competences, but mind-sets as well. This paper offers exemplary insights into the project NetEnquiry that is…

  8. Bounds on the sample complexity for private learning and private data release

    Energy Technology Data Exchange (ETDEWEB)

    Kasiviswanathan, Shiva [Los Alamos National Laboratory; Beime, Amos [BEN-GURION UNIV.; Nissim, Kobbi [BEN-GURION UNIV.

    2009-01-01

    Learning is a task that generalizes many of the analyses that are applied to collections of data, and in particular, collections of sensitive individual information. Hence, it is natural to ask what can be learned while preserving individual privacy. [Kasiviswanathan, Lee, Nissim, Raskhodnikova, and Smith; FOCS 2008] initiated such a discussion. They formalized the notion of private learning, as a combination of PAC learning and differential privacy, and investigated what concept classes can be learned privately. Somewhat surprisingly, they showed that, ignoring time complexity, every PAC learning task could be performed privately with polynomially many samples, and in many natural cases this could even be done in polynomial time. While these results seem to equate non-private and private learning, there is still a significant gap: the sample complexity of (non-private) PAC learning is crisply characterized in terms of the VC-dimension of the concept class, whereas this relationship is lost in the constructions of private learners, which exhibit, generally, a higher sample complexity. Looking into this gap, we examine several private learning tasks and give tight bounds on their sample complexity. In particular, we show strong separations between sample complexities of proper and improper private learners (such separation does not exist for non-private learners), and between sample complexities of efficient and inefficient proper private learners. Our results show that VC-dimension is not the right measure for characterizing the sample complexity of proper private learning. We also examine the task of private data release (as initiated by [Blum, Ligett, and Roth; STOC 2008]), and give new lower bounds on the sample complexity. Our results show that the logarithmic dependence on size of the instance space is essential for private data release.

  9. Generation of Long-time Complex Signals for Testing the Instruments for Detection of Voltage Quality Disturbances

    Science.gov (United States)

    Živanović, Dragan; Simić, Milan; Kokolanski, Zivko; Denić, Dragan; Dimcev, Vladimir

    2018-04-01

    Software supported procedure for generation of long-time complex test sentences, suitable for testing the instruments for detection of standard voltage quality (VQ) disturbances is presented in this paper. This solution for test signal generation includes significant improvements of computer-based signal generator presented and described in the previously published paper [1]. The generator is based on virtual instrumentation software for defining the basic signal parameters, data acquisition card NI 6343, and power amplifier for amplification of output voltage level to the nominal RMS voltage value of 230 V. Definition of basic signal parameters in LabVIEW application software is supported using Script files, which allows simple repetition of specific test signals and combination of more different test sequences in the complex composite test waveform. The basic advantage of this generator compared to the similar solutions for signal generation is the possibility for long-time test sequence generation according to predefined complex test scenarios, including various combinations of VQ disturbances defined in accordance with the European standard EN50160. Experimental verification of the presented signal generator capability is performed by testing the commercial power quality analyzer Fluke 435 Series II. In this paper are shown some characteristic complex test signals with various disturbances and logged data obtained from the tested power quality analyzer.

  10. JNK Signaling: Regulation and Functions Based on Complex Protein-Protein Partnerships

    Science.gov (United States)

    Zeke, András; Misheva, Mariya

    2016-01-01

    SUMMARY The c-Jun N-terminal kinases (JNKs), as members of the mitogen-activated protein kinase (MAPK) family, mediate eukaryotic cell responses to a wide range of abiotic and biotic stress insults. JNKs also regulate important physiological processes, including neuronal functions, immunological actions, and embryonic development, via their impact on gene expression, cytoskeletal protein dynamics, and cell death/survival pathways. Although the JNK pathway has been under study for >20 years, its complexity is still perplexing, with multiple protein partners of JNKs underlying the diversity of actions. Here we review the current knowledge of JNK structure and isoforms as well as the partnerships of JNKs with a range of intracellular proteins. Many of these proteins are direct substrates of the JNKs. We analyzed almost 100 of these target proteins in detail within a framework of their classification based on their regulation by JNKs. Examples of these JNK substrates include a diverse assortment of nuclear transcription factors (Jun, ATF2, Myc, Elk1), cytoplasmic proteins involved in cytoskeleton regulation (DCX, Tau, WDR62) or vesicular transport (JIP1, JIP3), cell membrane receptors (BMPR2), and mitochondrial proteins (Mcl1, Bim). In addition, because upstream signaling components impact JNK activity, we critically assessed the involvement of signaling scaffolds and the roles of feedback mechanisms in the JNK pathway. Despite a clarification of many regulatory events in JNK-dependent signaling during the past decade, many other structural and mechanistic insights are just beginning to be revealed. These advances open new opportunities to understand the role of JNK signaling in diverse physiological and pathophysiological states. PMID:27466283

  11. Nicotinic modulation of hippocampal cell signaling and associated effects on learning and memory.

    Science.gov (United States)

    Kutlu, Munir Gunes; Gould, Thomas J

    2016-03-01

    The hippocampus is a key brain structure involved in synaptic plasticity associated with long-term declarative memory formation. Importantly, nicotine and activation of nicotinic acetylcholine receptors (nAChRs) can alter hippocampal plasticity and these changes may occur through modulation of hippocampal kinases and transcription factors. Hippocampal kinases such as cAMP-dependent protein kinase (PKA), calcium/calmodulin-dependent protein kinases (CAMKs), extracellular signal-regulated kinases 1 and 2 (ERK1/2), and c-jun N-terminal kinase 1 (JNK1), and the transcription factor cAMP-response element-binding protein (CREB) that are activated either directly or indirectly by nicotine may modulate hippocampal plasticity and in parallel hippocampus-dependent learning and memory. Evidence suggests that nicotine may alter hippocampus-dependent learning by changing the time and magnitude of activation of kinases and transcription factors normally involved in learning and by recruiting additional cell signaling molecules. Understanding how nicotine alters learning and memory will advance basic understanding of the neural substrates of learning and aid in understanding mental disorders that involve cognitive and learning deficits. Copyright © 2015 Elsevier Inc. All rights reserved.

  12. Binding of Signal Recognition Particle Gives Ribosome/Nascent Chain Complexes a Competitive Advantage in Endoplasmic Reticulum Membrane Interaction

    Science.gov (United States)

    Neuhof, Andrea; Rolls, Melissa M.; Jungnickel, Berit; Kalies, Kai-Uwe; Rapoport, Tom A.

    1998-01-01

    Most secretory and membrane proteins are sorted by signal sequences to the endoplasmic reticulum (ER) membrane early during their synthesis. Targeting of the ribosome-nascent chain complex (RNC) involves the binding of the signal sequence to the signal recognition particle (SRP), followed by an interaction of ribosome-bound SRP with the SRP receptor. However, ribosomes can also independently bind to the ER translocation channel formed by the Sec61p complex. To explain the specificity of membrane targeting, it has therefore been proposed that nascent polypeptide-associated complex functions as a cytosolic inhibitor of signal sequence- and SRP-independent ribosome binding to the ER membrane. We report here that SRP-independent binding of RNCs to the ER membrane can occur in the presence of all cytosolic factors, including nascent polypeptide-associated complex. Nontranslating ribosomes competitively inhibit SRP-independent membrane binding of RNCs but have no effect when SRP is bound to the RNCs. The protective effect of SRP against ribosome competition depends on a functional signal sequence in the nascent chain and is also observed with reconstituted proteoliposomes containing only the Sec61p complex and the SRP receptor. We conclude that cytosolic factors do not prevent the membrane binding of ribosomes. Instead, specific ribosome targeting to the Sec61p complex is provided by the binding of SRP to RNCs, followed by an interaction with the SRP receptor, which gives RNC–SRP complexes a selective advantage in membrane targeting over nontranslating ribosomes. PMID:9436994

  13. Importance of Mediator complex in the regulation and integration of diverse signaling pathways in plants

    Directory of Open Access Journals (Sweden)

    Subhasis eSamanta

    2015-09-01

    Full Text Available Basic transcriptional machinery in eukaryotes is assisted by a number of cofactors, which either increase or decrease the rate of transcription. Mediator complex is one such cofactor, and recently has drawn a lot of interest because of its integrative power to converge different signaling pathways before channelling the transcription instructions to the RNA polymerase II machinery. Like yeast and metazoans, plants do possess the Mediator complex across the kingdom, and its isolation and subunit analyses have been reported from the model plant, Arabidopsis. Genetic and molecular analyses have unravelled important regulatory roles of Mediator subunits at every stage of plant life cycle starting from flowering to embryo and organ development, to even size determination. It also contributes immensely to the survival of plants against different environmental vagaries by the timely activation of its resistance mechanisms. Here, we have provided an overview of plant Mediator complex starting from its discovery to regulation of stoichiometry of its subunits. We have also reviewed involvement of different Mediator subunits in different processes and pathways including defense response pathways evoked by diverse biotic cues. Wherever possible, attempts have been made to provide mechanistic insight of Mediator’s involvement in these processes.

  14. Importance of Mediator complex in the regulation and integration of diverse signaling pathways in plants.

    Science.gov (United States)

    Samanta, Subhasis; Thakur, Jitendra K

    2015-01-01

    Basic transcriptional machinery in eukaryotes is assisted by a number of cofactors, which either increase or decrease the rate of transcription. Mediator complex is one such cofactor, and recently has drawn a lot of interest because of its integrative power to converge different signaling pathways before channeling the transcription instructions to the RNA polymerase II machinery. Like yeast and metazoans, plants do possess the Mediator complex across the kingdom, and its isolation and subunit analyses have been reported from the model plant, Arabidopsis. Genetic, and molecular analyses have unraveled important regulatory roles of Mediator subunits at every stage of plant life cycle starting from flowering to embryo and organ development, to even size determination. It also contributes immensely to the survival of plants against different environmental vagaries by the timely activation of its resistance mechanisms. Here, we have provided an overview of plant Mediator complex starting from its discovery to regulation of stoichiometry of its subunits. We have also reviewed involvement of different Mediator subunits in different processes and pathways including defense response pathways evoked by diverse biotic cues. Wherever possible, attempts have been made to provide mechanistic insight of Mediator's involvement in these processes.

  15. Cell–cell signaling drives the evolution of complex traits: introduction—lung evo-devo

    Science.gov (United States)

    Torday, John S.; Rehan, V. K.

    2009-01-01

    Physiology integrates biology with the environment through cell–cell interactions at multiple levels. The evolution of the respiratory system has been “deconvoluted” (Torday and Rehan in Am J Respir Cell Mol Biol 31:8–12, 2004) through Gene Regulatory Networks (GRNs) applied to cell–cell communication for all aspects of lung biology development, homeostasis, regeneration, and aging. Using this approach, we have predicted the phenotypic consequences of failed signaling for lung development, homeostasis, and regeneration based on evolutionary principles. This cell–cell communication model predicts other aspects of vertebrate physiology as adaptational responses. For example, the oxygen-induced differentiation of alveolar myocytes into alveolar adipocytes was critical for the evolution of the lung in land dwelling animals adapting to fluctuating Phanarezoic oxygen levels over the past 500 million years. Adipocytes prevent lung injury due to oxygen radicals and facilitate the rise of endothermy. In addition, they produce the class I cytokine leptin, which augments pulmonary surfactant activity and alveolar surface area, increasing selection pressure for both respiratory oxygenation and metabolic demand initially constrained by high-systemic vascular pressure, but subsequently compensated by the evolution of the adrenomedullary beta-adrenergic receptor mechanism. Conserted positive selection for the lung and adrenals created further selection pressure for the heart, which becomes progressively more complex phylogenetically in tandem with the lung. Developmentally, increasing heart complexity and size impinges precociously on the gut mesoderm to induce the liver. That evolutionary-developmental interaction is significant because the liver provides regulated sources of glucose and glycogen to the evolving physiologic system, which is necessary for the evolution of the neocortex. Evolution of neocortical control furthers integration of physiologic systems. Such

  16. The Hrs/Stam complex acts as a positive and negative regulator of RTK signaling during Drosophila development.

    Directory of Open Access Journals (Sweden)

    Hélène Chanut-Delalande

    Full Text Available BACKGROUND: Endocytosis is a key regulatory step of diverse signalling pathways, including receptor tyrosine kinase (RTK signalling. Hrs and Stam constitute the ESCRT-0 complex that controls the initial selection of ubiquitinated proteins, which will subsequently be degraded in lysosomes. It has been well established ex vivo and during Drosophila embryogenesis that Hrs promotes EGFR down regulation. We have recently isolated the first mutations of stam in flies and shown that Stam is required for air sac morphogenesis, a larval respiratory structure whose formation critically depends on finely tuned levels of FGFR activity. This suggest that Stam, putatively within the ESCRT-0 complex, modulates FGF signalling, a possibility that has not been examined in Drosophila yet. PRINCIPAL FINDINGS: Here, we assessed the role of the Hrs/Stam complex in the regulation of signalling activity during Drosophila development. We show that stam and hrs are required for efficient FGFR signalling in the tracheal system, both during cell migration in the air sac primordium and during the formation of fine cytoplasmic extensions in terminal cells. We find that stam and hrs mutant cells display altered FGFR/Btl localisation, likely contributing to impaired signalling levels. Electron microscopy analyses indicate that endosome maturation is impaired at distinct steps by hrs and stam mutations. These somewhat unexpected results prompted us to further explore the function of stam and hrs in EGFR signalling. We show that while stam and hrs together downregulate EGFR signalling in the embryo, they are required for full activation of EGFR signalling during wing development. CONCLUSIONS/SIGNIFICANCE: Our study shows that the ESCRT-0 complex differentially regulates RTK signalling, either positively or negatively depending on tissues and developmental stages, further highlighting the importance of endocytosis in modulating signalling pathways during development.

  17. Which is the best intrinsic motivation signal for learning multiple skills?

    Directory of Open Access Journals (Sweden)

    Vieri Giuliano Santucci

    2013-11-01

    Full Text Available Humans and other biological agents are able to autonomously learn and cache different skills in the absence of any biological pressure or any assigned task. In this respect, Intrinsic Motivations (i.e. motivations not connected to reward-related stimuli play a cardinal role in animal learning, and can be considered as a fundamental tool for developing more autonomous and more adaptive artificial agents. In this work, we provide an exhaustive analysis of a scarcely investigated problem: which kind of IM reinforcement signal is the most suitable for driving the acquisition of multiple skills in the shortest time? To this purpose we implemented an artificial agent with a hierarchical architecture that allows to learn and cache different skills. We tested the system in a setup with continuous states and actions, in particular, with a cinematic robotic arm that has to learn different reaching tasks. We compare the results of different versions of the system driven by several different intrinsic motivation signals. The results show a that intrinsic reinforcements purely based on the knowledge of the system are not appropriate to guide the acquisition of multiple skills, and b that the stronger the link between the IM signal and the competence of the system, the better the performance.

  18. Hippocampal dendritic spines remodeling and fear memory are modulated by GABAergic signaling within the basolateral amygdala complex.

    Science.gov (United States)

    Giachero, Marcelo; Calfa, Gaston D; Molina, Victor A

    2015-05-01

    GABAergic signaling in the basolateral amygdala complex (BLA) plays a crucial role on the modulation of the stress influence on fear memory. Moreover, accumulating evidence suggests that the dorsal hippocampus (DH) is a downstream target of BLA neurons in contextual fear. Given that hippocampal structural plasticity is proposed to provide a substrate for the storage of long-term memories, the main aim of this study is to evaluate the modulation of GABA neurotransmission in the BLA on spine density in the DH following stress on contextual fear learning. The present findings show that prior stressful experience promoted contextual fear memory and enhanced spine density in the DH. Intra-BLA infusion of midazolam, a positive modulator of GABAa sites, prevented the facilitating influence of stress on both fear retention and hippocampal dendritic spine remodeling. Similarly to the stress-induced effects, the blockade of GABAa sites within the BLA ameliorated fear memory emergence and induced structural remodeling in the DH. These findings suggest that GABAergic transmission in BLA modulates the structural changes in DH associated to the influence of stress on fear memory. © 2015 Wiley Periodicals, Inc.

  19. Employing the Hilbert-Huang Transform to analyze observed natural complex signals: Calm wind meandering cases

    Science.gov (United States)

    Martins, Luis Gustavo Nogueira; Stefanello, Michel Baptistella; Degrazia, Gervásio Annes; Acevedo, Otávio Costa; Puhales, Franciano Scremin; Demarco, Giuliano; Mortarini, Luca; Anfossi, Domenico; Roberti, Débora Regina; Costa, Felipe Denardin; Maldaner, Silvana

    2016-11-01

    In this study we analyze natural complex signals employing the Hilbert-Huang spectral analysis. Specifically, low wind meandering meteorological data are decomposed into turbulent and non turbulent components. These non turbulent movements, responsible for the absence of a preferential direction of the horizontal wind, provoke negative lobes in the meandering autocorrelation functions. The meandering characteristic time scales (meandering periods) are determined from the spectral peak provided by the Hilbert-Huang marginal spectrum. The magnitudes of the temperature and horizontal wind meandering period obtained agree with the results found from the best fit of the heuristic meandering autocorrelation functions. Therefore, the new method represents a new procedure to evaluate meandering periods that does not employ mathematical expressions to represent observed meandering autocorrelation functions.

  20. Ant Queen Egg-Marking Signals: Matching Deceptive Laboratory Simplicity with Natural Complexity

    DEFF Research Database (Denmark)

    van Zweden, Jelle Stijn; Heinze, Jürgen; Boomsma, Jacobus Jan

    2009-01-01

    Background Experiments under controlled laboratory conditions can produce decisive evidence for testing biological hypotheses, provided they are representative of the more complex natural conditions. However, whether this requirement is fulfilled is seldom tested explicitly. Here we provide a lab....../field comparison to investigate the identity of an egg-marking signal of ant queens. Our study was based on ant workers resolving conflict over male production by destroying each other's eggs, but leaving queen eggs unharmed. For this, the workers need a proximate cue to discriminate between the two egg types...... that this compound by itself is not the natural queen egg-marking pheromone. We subsequently investigated the overall differences of entire chemical profiles of eggs, and found that queen-laid eggs in field colonies are more distinct from worker-laid eggs than in lab colonies, have more variation in profiles...

  1. Low-complexity camera digital signal imaging for video document projection system

    Science.gov (United States)

    Hsia, Shih-Chang; Tsai, Po-Shien

    2011-04-01

    We present high-performance and low-complexity algorithms for real-time camera imaging applications. The main functions of the proposed camera digital signal processing (DSP) involve color interpolation, white balance, adaptive binary processing, auto gain control, and edge and color enhancement for video projection systems. A series of simulations demonstrate that the proposed method can achieve good image quality while keeping computation cost and memory requirements low. On the basis of the proposed algorithms, the cost-effective hardware core is developed using Verilog HDL. The prototype chip has been verified with one low-cost programmable device. The real-time camera system can achieve 1270 × 792 resolution with the combination of extra components and can demonstrate each DSP function.

  2. Optimal sensor configuration for complex systems with application to signal detection in structures

    DEFF Research Database (Denmark)

    Sadegh, Payman; Spall, J. C.

    2000-01-01

    sensor outputs. Secondly, we describe an efficient and practical algorithm to achieve the optimization goals, based on simultaneous perturbation stochastic approximation (SPSA). SPSA avoids the need for detailed modeling of the sensor response by simply relying on observed responses as obtained......The paper considers the problem of sensor configuration for complex systems. The contribution of the paper is twofold. Firstly, we define an appropriate criterion that is based on maximizing overall sensor responses while minimizing redundant information as measured by correlations between multiple...... by limited experimentation with test sensor configurations. We illustrate the application of the approach to optimal placement of acoustic sensors for signal detection in structures. This includes both a computer simulation study for an aluminum plate, and real experimentations on a steel I-beam....

  3. CD25 and CD69 induction by α4β1 outside-in signalling requires TCR early signalling complex proteins

    Science.gov (United States)

    Cimo, Ann-Marie; Ahmed, Zamal; McIntyre, Bradley W.; Lewis, Dorothy E.; Ladbury, John E.

    2013-01-01

    Distinct signalling pathways producing diverse cellular outcomes can utilize similar subsets of proteins. For example, proteins from the TCR (T-cell receptor) ESC (early signalling complex) are also involved in interferon-α receptor signalling. Defining the mechanism for how these proteins function within a given pathway is important in understanding the integration and communication of signalling networks with one another. We investigated the contributions of the TCR ESC proteins Lck (lymphocyte-specific kinase), ZAP-70 (ζ-chain-associated protein of 70 kDa), Vav1, SLP-76 [SH2 (Src homology 2)-domain-containing leukocyte protein of 76 kDa] and LAT (linker for activation of T-cells) to integrin outside-in signalling in human T-cells. Lck, ZAP-70, SLP-76, Vav1 and LAT were activated by α4β1 outside-in signalling, but in a manner different from TCR signalling. TCR stimulation recruits ESC proteins to activate the mitogen-activated protein kinase ERK (extracellular-signal-regulated kinase). α4β1 outside-in-mediated ERK activation did not require TCR ESC proteins. However, α4β1 outside-in signalling induced CD25 and co-stimulated CD69 and this was dependent on TCR ESC proteins. TCR and α4β1 outside-in signalling are integrated through the common use of TCR ESC proteins; however, these proteins display functionally distinct roles in these pathways. These novel insights into the cross-talk between integrin outside-in and TCR signalling pathways are highly relevant to the development of therapeutic strategies to overcome disease associated with T-cell deregulation. PMID:23758320

  4. Task complexity as a driver for collaborative learning efficiency: The collective working-memory effect

    NARCIS (Netherlands)

    Kirschner, Femke; Paas, Fred; Kirschner, Paul A.

    2010-01-01

    Kirschner, F., Paas, F., & Kirschner, P. A. (2011). Task complexity as a driver for collaborative learning efficiency: The collective working-memory effect. Applied Cognitive Psychology, 25, 615–624. doi: 10.1002/acp.1730.

  5. Roles of NO signaling in long-term memory formation in visual learning in an insect.

    Directory of Open Access Journals (Sweden)

    Yukihisa Matsumoto

    Full Text Available Many insects exhibit excellent capability of visual learning, but the molecular and neural mechanisms are poorly understood. This is in contrast to accumulation of information on molecular and neural mechanisms of olfactory learning in insects. In olfactory learning in insects, it has been shown that cyclic AMP (cAMP signaling critically participates in the formation of protein synthesis-dependent long-term memory (LTM and, in some insects, nitric oxide (NO-cyclic GMP (cGMP signaling also plays roles in LTM formation. In this study, we examined the possible contribution of NO-cGMP signaling and cAMP signaling to LTM formation in visual pattern learning in crickets. Crickets that had been subjected to 8-trial conditioning to associate a visual pattern with water reward exhibited memory retention 1 day after conditioning, whereas those subjected to 4-trial conditioning exhibited 30-min memory retention but not 1-day retention. Injection of cycloheximide, a protein synthesis inhibitor, into the hemolymph prior to 8-trial conditioning blocked formation of 1-day memory, whereas it had no effect on 30-min memory formation, indicating that 1-day memory can be characterized as protein synthesis-dependent long-term memory (LTM. Injection of an inhibitor of the enzyme producing an NO or cAMP prior to 8-trial visual conditioning blocked LTM formation, whereas it had no effect on 30-min memory formation. Moreover, injection of an NO donor, cGMP analogue or cAMP analogue prior to 4-trial conditioning induced LTM. Induction of LTM by an NO donor was blocked by DDA, an inhibitor of adenylyl cyclase, an enzyme producing cAMP, but LTM induction by a cAMP analogue was not impaired by L-NAME, an inhibitor of NO synthase. The results indicate that cAMP signaling is downstream of NO signaling for visual LTM formation. We conclude that visual learning and olfactory learning share common biochemical cascades for LTM formation.

  6. Interdependence of free zinc changes and protein complex assembly - insights into zinc signal regulation.

    Science.gov (United States)

    Kocyła, Anna; Adamczyk, Justyna; Krężel, Artur

    2018-01-24

    Cellular zinc (Zn(ii)) is bound with proteins that are part of the proteomes of all domains of life. It is mostly utilized as a catalytic or structural protein cofactor, which results in a vast number of binding architectures. The Zn(ii) ion is also important for the formation of transient protein complexes with a Zn(ii)-dependent quaternary structure that is formed upon cellular zinc signals. The mechanisms by which proteins associate with and dissociate from Zn(ii) and the connection with cellular Zn(ii) changes remain incompletely understood. In this study, we aimed to examine how zinc protein domains with various Zn(ii)-binding architectures are formed under free Zn(ii) concentration changes and how formation of the Zn(ii)-dependent assemblies is related to the protein concentration and reactivity. To accomplish these goals we chose four zinc domains with different Zn(ii)-to-protein binding stoichiometries: classical zinc finger (ZnP), LIM domain (Zn 2 P), zinc hook (ZnP 2 ) and zinc clasp (ZnP 1 P 2 ) folds. Our research demonstrated a lack of changes in the saturation level of intraprotein zinc binding sites, despite various peptide concentrations, while homo- and heterodimers indicated a concentration-dependent tendency. In other words, at a certain free Zn(ii) concentration, the fraction of a formed dimeric complex increases or decreases with subunit concentration changes. Secondly, even small or local changes in free Zn(ii) may significantly affect protein saturation depending on its architecture, function and subcellular concentration. In our paper, we indicate the importance of interdependence of free Zn(ii) availability and protein subunit concentrations for cellular zinc signal regulation.

  7. Micro-earthquake signal analysis and hypocenter determination around Lokon volcano complex

    Energy Technology Data Exchange (ETDEWEB)

    Firmansyah, Rizky, E-mail: rizkyfirmansyah@hotmail.com [Geophysical Engineering, Faculty of Mining and Petroleum Engineering, Institut Teknologi Bandung, Bandung, 40132 (Indonesia); Nugraha, Andri Dian, E-mail: nugraha@gf.itb.ac.id [Global Geophysical Group, Faculty of Mining and Petroleum Engineering, Institut Teknologi Bandung, Bandung, 40132 (Indonesia); Kristianto, E-mail: kris@vsi.esdm.go.id [Center for Volcanology and Geological Hazard Mitigation (CVGHM), Geological Agency, Bandung, 40122 (Indonesia)

    2015-04-24

    Mount Lokon is one of five active volcanoes which is located in the North Sulawesi region. Since June 26{sup th}, 2011, standby alert set by the Center for Volcanology and Geological Hazard Mitigation (CVGHM) for this mountain. The Mount Lokon volcano erupted on July 4{sup th}, 2011 and still continuously erupted until August 28{sup th}, 2011. Due to its high seismic activity, this study is focused to analysis of micro-earthquake signal and determine the micro-earthquake hypocenter location around the complex area of Lokon-Empung Volcano before eruption phase in 2011 (time periods of January, 2009 up to March, 2010). Determination of the hypocenter location was conducted with Geiger Adaptive Damping (GAD) method. We used initial model from previous study in Volcan de Colima, Mexico. The reason behind the model selection was based on the same characteristics that shared between Mount Lokon and Colima including andesitic stratovolcano and small-plinian explosions volcanian types. In this study, a picking events was limited to the volcano-tectonics of A and B types, hybrid, long-period that has a clear signal onset, and local tectonic with different maximum S – P time are not more than three seconds. As a result, we observed the micro-earthquakes occurred in the area north-west of Mount Lokon region.

  8. Micro-earthquake signal analysis and hypocenter determination around Lokon volcano complex

    International Nuclear Information System (INIS)

    Firmansyah, Rizky; Nugraha, Andri Dian; Kristianto

    2015-01-01

    Mount Lokon is one of five active volcanoes which is located in the North Sulawesi region. Since June 26 th , 2011, standby alert set by the Center for Volcanology and Geological Hazard Mitigation (CVGHM) for this mountain. The Mount Lokon volcano erupted on July 4 th , 2011 and still continuously erupted until August 28 th , 2011. Due to its high seismic activity, this study is focused to analysis of micro-earthquake signal and determine the micro-earthquake hypocenter location around the complex area of Lokon-Empung Volcano before eruption phase in 2011 (time periods of January, 2009 up to March, 2010). Determination of the hypocenter location was conducted with Geiger Adaptive Damping (GAD) method. We used initial model from previous study in Volcan de Colima, Mexico. The reason behind the model selection was based on the same characteristics that shared between Mount Lokon and Colima including andesitic stratovolcano and small-plinian explosions volcanian types. In this study, a picking events was limited to the volcano-tectonics of A and B types, hybrid, long-period that has a clear signal onset, and local tectonic with different maximum S – P time are not more than three seconds. As a result, we observed the micro-earthquakes occurred in the area north-west of Mount Lokon region

  9. Learning in Complex Environments: The Effects of Background Speech on Early Word Learning

    Science.gov (United States)

    McMillan, Brianna T. M.; Saffran, Jenny R.

    2016-01-01

    Although most studies of language learning take place in quiet laboratory settings, everyday language learning occurs under noisy conditions. The current research investigated the effects of background speech on word learning. Both younger (22- to 24-month-olds; n = 40) and older (28- to 30-month-olds; n = 40) toddlers successfully learned novel…

  10. Complexity, Training Paradigm Design, and the Contribution of Memory Subsystems to Grammar Learning.

    Directory of Open Access Journals (Sweden)

    Mark Antoniou

    Full Text Available Although there is variability in nonnative grammar learning outcomes, the contributions of training paradigm design and memory subsystems are not well understood. To examine this, we presented learners with an artificial grammar that formed words via simple and complex morphophonological rules. Across three experiments, we manipulated training paradigm design and measured subjects' declarative, procedural, and working memory subsystems. Experiment 1 demonstrated that passive, exposure-based training boosted learning of both simple and complex grammatical rules, relative to no training. Additionally, procedural memory correlated with simple rule learning, whereas declarative memory correlated with complex rule learning. Experiment 2 showed that presenting corrective feedback during the test phase did not improve learning. Experiment 3 revealed that structuring the order of training so that subjects are first exposed to the simple rule and then the complex improved learning. The cumulative findings shed light on the contributions of grammatical complexity, training paradigm design, and domain-general memory subsystems in determining grammar learning success.

  11. The Effect of Contextualized Conversational Feedback in a Complex Open-Ended Learning Environment

    Science.gov (United States)

    Segedy, James R.; Kinnebrew, John S.; Biswas, Gautam

    2013-01-01

    Betty's Brain is an open-ended learning environment in which students learn about science topics by teaching a virtual agent named Betty through the construction of a visual causal map that represents the relevant science phenomena. The task is complex, and success requires the use of metacognitive strategies that support knowledge acquisition,…

  12. A Recollection of mTOR Signaling in Learning and Memory

    Science.gov (United States)

    Graber, Tyson E.; McCamphill, Patrick K.; Sossin, Wayne S.

    2013-01-01

    Mechanistic target of rapamcyin (mTOR) is a central player in cell growth throughout the organism. However, mTOR takes on an additional, more specialized role in the developed neuron, where it regulates the protein synthesis-dependent, plastic changes underlying learning and memory. mTOR is sequestered in two multiprotein complexes (mTORC1 and…

  13. Learning by Preparing to Teach: Fostering Self-Regulatory Processes and Achievement during Complex Mathematics Problem Solving

    Science.gov (United States)

    Muis, Krista R.; Psaradellis, Cynthia; Chevrier, Marianne; Di Leo, Ivana; Lajoie, Susanne P.

    2016-01-01

    We developed an intervention based on the learning by teaching paradigm to foster self-regulatory processes and better learning outcomes during complex mathematics problem solving in a technology-rich learning environment. Seventy-eight elementary students were randomly assigned to 1 of 2 conditions: learning by preparing to teach, or learning for…

  14. Efficient transmission of subthreshold signals in complex networks of spiking neurons.

    Science.gov (United States)

    Torres, Joaquin J; Elices, Irene; Marro, J

    2015-01-01

    We investigate the efficient transmission and processing of weak, subthreshold signals in a realistic neural medium in the presence of different levels of the underlying noise. Assuming Hebbian weights for maximal synaptic conductances--that naturally balances the network with excitatory and inhibitory synapses--and considering short-term synaptic plasticity affecting such conductances, we found different dynamic phases in the system. This includes a memory phase where population of neurons remain synchronized, an oscillatory phase where transitions between different synchronized populations of neurons appears and an asynchronous or noisy phase. When a weak stimulus input is applied to each neuron, increasing the level of noise in the medium we found an efficient transmission of such stimuli around the transition and critical points separating different phases for well-defined different levels of stochasticity in the system. We proved that this intriguing phenomenon is quite robust, as it occurs in different situations including several types of synaptic plasticity, different type and number of stored patterns and diverse network topologies, namely, diluted networks and complex topologies such as scale-free and small-world networks. We conclude that the robustness of the phenomenon in different realistic scenarios, including spiking neurons, short-term synaptic plasticity and complex networks topologies, make very likely that it could also occur in actual neural systems as recent psycho-physical experiments suggest.

  15. Efficient transmission of subthreshold signals in complex networks of spiking neurons.

    Directory of Open Access Journals (Sweden)

    Joaquin J Torres

    Full Text Available We investigate the efficient transmission and processing of weak, subthreshold signals in a realistic neural medium in the presence of different levels of the underlying noise. Assuming Hebbian weights for maximal synaptic conductances--that naturally balances the network with excitatory and inhibitory synapses--and considering short-term synaptic plasticity affecting such conductances, we found different dynamic phases in the system. This includes a memory phase where population of neurons remain synchronized, an oscillatory phase where transitions between different synchronized populations of neurons appears and an asynchronous or noisy phase. When a weak stimulus input is applied to each neuron, increasing the level of noise in the medium we found an efficient transmission of such stimuli around the transition and critical points separating different phases for well-defined different levels of stochasticity in the system. We proved that this intriguing phenomenon is quite robust, as it occurs in different situations including several types of synaptic plasticity, different type and number of stored patterns and diverse network topologies, namely, diluted networks and complex topologies such as scale-free and small-world networks. We conclude that the robustness of the phenomenon in different realistic scenarios, including spiking neurons, short-term synaptic plasticity and complex networks topologies, make very likely that it could also occur in actual neural systems as recent psycho-physical experiments suggest.

  16. Cadherin complexes recruit mRNAs and RISC to regulate epithelial cell signaling.

    Science.gov (United States)

    Kourtidis, Antonis; Necela, Brian; Lin, Wan-Hsin; Lu, Ruifeng; Feathers, Ryan W; Asmann, Yan W; Thompson, E Aubrey; Anastasiadis, Panos Z

    2017-10-02

    Cumulative evidence demonstrates that most RNAs exhibit specific subcellular distribution. However, the mechanisms regulating this phenomenon and its functional consequences are still under investigation. Here, we reveal that cadherin complexes at the apical zonula adherens (ZA) of epithelial adherens junctions recruit the core components of the RNA-induced silencing complex (RISC) Ago2, GW182, and PABPC1, as well as a set of 522 messenger RNAs (mRNAs) and 28 mature microRNAs (miRNAs or miRs), via PLEKHA7. Top canonical pathways represented by these mRNAs include Wnt/β-catenin, TGF-β, and stem cell signaling. We specifically demonstrate the presence and silencing of MYC, JUN, and SOX2 mRNAs by miR-24 and miR-200c at the ZA. PLEKHA7 knockdown dissociates RISC from the ZA, decreases loading of the ZA-associated mRNAs and miRNAs to Ago2, and results in a corresponding increase of MYC, JUN, and SOX2 protein expression. The present work reveals a mechanism that directly links junction integrity to the silencing of a set of mRNAs that critically affect epithelial homeostasis. © 2017 Kourtidis et al.

  17. A potential neural substrate for processing functional classes of complex acoustic signals.

    Directory of Open Access Journals (Sweden)

    Isabelle George

    Full Text Available Categorization is essential to all cognitive processes, but identifying the neural substrates underlying categorization processes is a real challenge. Among animals that have been shown to be able of categorization, songbirds are particularly interesting because they provide researchers with clear examples of categories of acoustic signals allowing different levels of recognition, and they possess a system of specialized brain structures found only in birds that learn to sing: the song system. Moreover, an avian brain nucleus that is analogous to the mammalian secondary auditory cortex (the caudo-medial nidopallium, or NCM has recently emerged as a plausible site for sensory representation of birdsong, and appears as a well positioned brain region for categorization of songs. Hence, we tested responses in this non-primary, associative area to clear and distinct classes of songs with different functions and social values, and for a possible correspondence between these responses and the functional aspects of songs, in a highly social songbird species: the European starling. Our results clearly show differential neuronal responses to the ethologically defined classes of songs, both in the number of neurons responding, and in the response magnitude of these neurons. Most importantly, these differential responses corresponded to the functional classes of songs, with increasing activation from non-specific to species-specific and from species-specific to individual-specific sounds. These data therefore suggest a potential neural substrate for sorting natural communication signals into categories, and for individual vocal recognition of same-species members. Given the many parallels that exist between birdsong and speech, these results may contribute to a better understanding of the neural bases of speech.

  18. The Hedgehog Signalling Pathway in Cell Migration and Guidance: What We Have Learned from Drosophila melanogaster

    Directory of Open Access Journals (Sweden)

    Sofia J. Araújo

    2015-10-01

    Full Text Available Cell migration and guidance are complex processes required for morphogenesis, the formation of tumor metastases, and the progression of human cancer. During migration, guidance molecules induce cell directionality and movement through complex intracellular mechanisms. Expression of these molecules has to be tightly regulated and their signals properly interpreted by the receiving cells so as to ensure correct navigation. This molecular control is fundamental for both normal morphogenesis and human disease. The Hedgehog (Hh signaling pathway is evolutionarily conserved and known to be crucial for normal cellular growth and differentiation throughout the animal kingdom. The relevance of Hh signaling for human disease is emphasized by its activation in many cancers. Here, I review the current knowledge regarding the involvement of the Hh pathway in cell migration and guidance during Drosophila development and discuss its implications for human cancer origin and progression.

  19. Satlc model lesson for teaching and learning complex ...

    African Journals Online (AJOL)

    Environmental chemistry is one of the disciplines of Science. For the goal of the deep learning of the subject, it is indispensable to present perception and models of chemical behaviour explicitly. This can be accomplished by giving careful consideration to the development of concepts such that newer approaches are given ...

  20. Implications of Complexity and Chaos Theories for Organizations that Learn

    Science.gov (United States)

    Smith, Peter A. C.

    2003-01-01

    In 1996 Hubert Saint-Onge and Smith published an article ("The evolutionary organization: avoiding a Titanic fate", in The Learning Organization, Vol. 3 No. 4), based on their experience at the Canadian Imperial Bank of Commerce (CIBC). It was established at CIBC that change could be successfully facilitated through blended application…

  1. Patients with Parkinson's disease learn to control complex systems-an indication for intact implicit cognitive skill learning.

    Science.gov (United States)

    Witt, Karsten; Daniels, Christine; Daniel, Victoria; Schmitt-Eliassen, Julia; Volkmann, Jens; Deuschl, Günther

    2006-01-01

    Implicit memory and learning mechanisms are composed of multiple processes and systems. Previous studies demonstrated a basal ganglia involvement in purely cognitive tasks that form stimulus response habits by reinforcement learning such as implicit classification learning. We will test the basal ganglia influence on two cognitive implicit tasks previously described by Berry and Broadbent, the sugar production task and the personal interaction task. Furthermore, we will investigate the relationship between certain aspects of an executive dysfunction and implicit learning. To this end, we have tested 22 Parkinsonian patients and 22 age-matched controls on two implicit cognitive tasks, in which participants learned to control a complex system. They interacted with the system by choosing an input value and obtaining an output that was related in a complex manner to the input. The objective was to reach and maintain a specific target value across trials (dynamic system learning). The two tasks followed the same underlying complex rule but had different surface appearances. Subsequently, participants performed an executive test battery including the Stroop test, verbal fluency and the Wisconsin card sorting test (WCST). The results demonstrate intact implicit learning in patients, despite an executive dysfunction in the Parkinsonian group. They lead to the conclusion that the basal ganglia system affected in Parkinson's disease does not contribute to the implicit acquisition of a new cognitive skill. Furthermore, the Parkinsonian patients were able to reach a specific goal in an implicit learning context despite impaired goal directed behaviour in the WCST, a classic test of executive functions. These results demonstrate a functional independence of implicit cognitive skill learning and certain aspects of executive functions.

  2. Explaining neural signals in human visual cortex with an associative learning model.

    Science.gov (United States)

    Jiang, Jiefeng; Schmajuk, Nestor; Egner, Tobias

    2012-08-01

    "Predictive coding" models posit a key role for associative learning in visual cognition, viewing perceptual inference as a process of matching (learned) top-down predictions (or expectations) against bottom-up sensory evidence. At the neural level, these models propose that each region along the visual processing hierarchy entails one set of processing units encoding predictions of bottom-up input, and another set computing mismatches (prediction error or surprise) between predictions and evidence. This contrasts with traditional views of visual neurons operating purely as bottom-up feature detectors. In support of the predictive coding hypothesis, a recent human neuroimaging study (Egner, Monti, & Summerfield, 2010) showed that neural population responses to expected and unexpected face and house stimuli in the "fusiform face area" (FFA) could be well-described as a summation of hypothetical face-expectation and -surprise signals, but not by feature detector responses. Here, we used computer simulations to test whether these imaging data could be formally explained within the broader framework of a mathematical neural network model of associative learning (Schmajuk, Gray, & Lam, 1996). Results show that FFA responses could be fit very closely by model variables coding for conditional predictions (and their violations) of stimuli that unconditionally activate the FFA. These data document that neural population signals in the ventral visual stream that deviate from classic feature detection responses can formally be explained by associative prediction and surprise signals.

  3. Situated learning theory: adding rate and complexity effects via Kauffman's NK model.

    Science.gov (United States)

    Yuan, Yu; McKelvey, Bill

    2004-01-01

    For many firms, producing information, knowledge, and enhancing learning capability have become the primary basis of competitive advantage. A review of organizational learning theory identifies two approaches: (1) those that treat symbolic information processing as fundamental to learning, and (2) those that view the situated nature of cognition as fundamental. After noting that the former is inadequate because it focuses primarily on behavioral and cognitive aspects of individual learning, this paper argues the importance of studying learning as interactions among people in the context of their environment. It contributes to organizational learning in three ways. First, it argues that situated learning theory is to be preferred over traditional behavioral and cognitive learning theories, because it treats organizations as complex adaptive systems rather than mere information processors. Second, it adds rate and nonlinear learning effects. Third, following model-centered epistemology, it uses an agent-based computational model, in particular a "humanized" version of Kauffman's NK model, to study the situated nature of learning. Using simulation results, we test eight hypotheses extending situated learning theory in new directions. The paper ends with a discussion of possible extensions of the current study to better address key issues in situated learning.

  4. Learning Science in a Virtual Reality Application: The Impacts of Animated-Virtual Actors' Visual Complexity

    Science.gov (United States)

    Kartiko, Iwan; Kavakli, Manolya; Cheng, Ken

    2010-01-01

    As the technology in computer graphics advances, Animated-Virtual Actors (AVAs) in Virtual Reality (VR) applications become increasingly rich and complex. Cognitive Theory of Multimedia Learning (CTML) suggests that complex visual materials could hinder novice learners from attending to the lesson properly. On the other hand, previous studies have…

  5. Joined up Thinking? Evaluating the Use of Concept-Mapping to Develop Complex System Learning

    Science.gov (United States)

    Stewart, Martyn

    2012-01-01

    In the physical and natural sciences, the complexity of natural systems and their interactions is becoming better understood. With increased emphasis on learning about complex systems, students will be encountering concepts that are dynamic, ill-structured and interconnected. Concept-mapping is a method considered particularly valuable for…

  6. Predicting protein complexes using a supervised learning method combined with local structural information.

    Science.gov (United States)

    Dong, Yadong; Sun, Yongqi; Qin, Chao

    2018-01-01

    The existing protein complex detection methods can be broadly divided into two categories: unsupervised and supervised learning methods. Most of the unsupervised learning methods assume that protein complexes are in dense regions of protein-protein interaction (PPI) networks even though many true complexes are not dense subgraphs. Supervised learning methods utilize the informative properties of known complexes; they often extract features from existing complexes and then use the features to train a classification model. The trained model is used to guide the search process for new complexes. However, insufficient extracted features, noise in the PPI data and the incompleteness of complex data make the classification model imprecise. Consequently, the classification model is not sufficient for guiding the detection of complexes. Therefore, we propose a new robust score function that combines the classification model with local structural information. Based on the score function, we provide a search method that works both forwards and backwards. The results from experiments on six benchmark PPI datasets and three protein complex datasets show that our approach can achieve better performance compared with the state-of-the-art supervised, semi-supervised and unsupervised methods for protein complex detection, occasionally significantly outperforming such methods.

  7. Cognitive Bias for Learning Speech Sounds From a Continuous Signal Space Seems Nonlinguistic

    Directory of Open Access Journals (Sweden)

    Sabine van der Ham

    2015-10-01

    Full Text Available When learning language, humans have a tendency to produce more extreme distributions of speech sounds than those observed most frequently: In rapid, casual speech, vowel sounds are centralized, yet cross-linguistically, peripheral vowels occur almost universally. We investigate whether adults’ generalization behavior reveals selective pressure for communication when they learn skewed distributions of speech-like sounds from a continuous signal space. The domain-specific hypothesis predicts that the emergence of sound categories is driven by a cognitive bias to make these categories maximally distinct, resulting in more skewed distributions in participants’ reproductions. However, our participants showed more centered distributions, which goes against this hypothesis, indicating that there are no strong innate linguistic biases that affect learning these speech-like sounds. The centralization behavior can be explained by a lack of communicative pressure to maintain categories.

  8. Latent memory facilitates relearning through molecular signaling mechanisms that are distinct from original learning.

    Science.gov (United States)

    Menges, Steven A; Riepe, Joshua R; Philips, Gary T

    2015-09-01

    A highly conserved feature of memory is that it can exist in a latent, non-expressed state which is revealed during subsequent learning by its ability to significantly facilitate (savings) or inhibit (latent inhibition) subsequent memory formation. Despite the ubiquitous nature of latent memory, the mechanistic nature of the latent memory trace and its ability to influence subsequent learning remains unclear. The model organism Aplysia californica provides the unique opportunity to make strong links between behavior and underlying cellular and molecular mechanisms. Using Aplysia, we have studied the mechanisms of savings due to latent memory for a prior, forgotten experience. We previously reported savings in the induction of three distinct temporal domains of memory: short-term (10min), intermediate-term (2h) and long-term (24h). Here we report that savings memory formation utilizes molecular signaling pathways that are distinct from original learning: whereas the induction of both original intermediate- and long-term memory in naïve animals requires mitogen activated protein kinase (MAPK) activation and ongoing protein synthesis, 2h savings memory is not disrupted by inhibitors of MAPK or protein synthesis, and 24h savings memory is not dependent on MAPK activation. Collectively, these findings reveal that during forgetting, latent memory for the original experience can facilitate relearning through molecular signaling mechanisms that are distinct from original learning. Copyright © 2015 Elsevier Inc. All rights reserved.

  9. Differential dependence of Pavlovian incentive motivation and instrumental incentive learning processes on dopamine signaling

    Science.gov (United States)

    Wassum, Kate M.; Ostlund, Sean B.; Balleine, Bernard W.; Maidment, Nigel T.

    2011-01-01

    Here we attempted to clarify the role of dopamine signaling in reward seeking. In Experiment 1, we assessed the effects of the dopamine D1/D2 receptor antagonist flupenthixol (0.5 mg/kg i.p.) on Pavlovian incentive motivation and found that flupenthixol blocked the ability of a conditioned stimulus to enhance both goal approach and instrumental performance (Pavlovian-to-instrumental transfer). In Experiment 2 we assessed the effects of flupenthixol on reward palatability during post-training noncontingent re-exposure to the sucrose reward in either a control 3-h or novel 23-h food-deprived state. Flupenthixol, although effective in blocking the Pavlovian goal approach, was without effect on palatability or the increase in reward palatability induced by the upshift in motivational state. This noncontingent re-exposure provided an opportunity for instrumental incentive learning, the process by which rats encode the value of a reward for use in updating reward-seeking actions. Flupenthixol administered prior to the instrumental incentive learning opportunity did not affect the increase in subsequent off-drug reward-seeking actions induced by that experience. These data suggest that although dopamine signaling is necessary for Pavlovian incentive motivation, it is not necessary for changes in reward experience, or for the instrumental incentive learning process that translates this experience into the incentive value used to drive reward-seeking actions, and provide further evidence that Pavlovian and instrumental incentive learning processes are dissociable. PMID:21693635

  10. Regulation of protease-activated receptor 1 signaling by the adaptor protein complex 2 and R4 subfamily of regulator of G protein signaling proteins.

    Science.gov (United States)

    Chen, Buxin; Siderovski, David P; Neubig, Richard R; Lawson, Mark A; Trejo, Joann

    2014-01-17

    The G protein-coupled protease-activated receptor 1 (PAR1) is irreversibly proteolytically activated by thrombin. Hence, the precise regulation of PAR1 signaling is important for proper cellular responses. In addition to desensitization, internalization and lysosomal sorting of activated PAR1 are critical for the termination of signaling. Unlike most G protein-coupled receptors, PAR1 internalization is mediated by the clathrin adaptor protein complex 2 (AP-2) and epsin-1, rather than β-arrestins. However, the function of AP-2 and epsin-1 in the regulation of PAR1 signaling is not known. Here, we report that AP-2, and not epsin-1, regulates activated PAR1-stimulated phosphoinositide hydrolysis via two different mechanisms that involve, in part, a subset of R4 subfamily of "regulator of G protein signaling" (RGS) proteins. A significantly greater increase in activated PAR1 signaling was observed in cells depleted of AP-2 using siRNA or in cells expressing a PAR1 (420)AKKAA(424) mutant with defective AP-2 binding. This effect was attributed to AP-2 modulation of PAR1 surface expression and efficiency of G protein coupling. We further found that ectopic expression of R4 subfamily members RGS2, RGS3, RGS4, and RGS5 reduced activated PAR1 wild-type signaling, whereas signaling by the PAR1 AKKAA mutant was minimally affected. Intriguingly, siRNA-mediated depletion analysis revealed a function for RGS5 in the regulation of signaling by the PAR1 wild type but not the AKKAA mutant. Moreover, activation of the PAR1 wild type, and not the AKKAA mutant, induced Gαq association with RGS3 via an AP-2-dependent mechanism. Thus, AP-2 regulates activated PAR1 signaling by altering receptor surface expression and through recruitment of RGS proteins.

  11. β-Catenin destruction complex-independent regulation of Hippo–YAP signaling by APC in intestinal tumorigenesis

    Science.gov (United States)

    Cai, Jing; Maitra, Anirban; Anders, Robert A.; Taketo, Makoto M.; Pan, Duojia

    2015-01-01

    Mutations in Adenomatous polyposis coli (APC) underlie familial adenomatous polyposis (FAP), an inherited cancer syndrome characterized by the widespread development of colorectal polyps. APC is best known as a scaffold protein in the β-catenin destruction complex, whose activity is antagonized by canonical Wnt signaling. Whether other effector pathways mediate APC's tumor suppressor function is less clear. Here we report that activation of YAP, the downstream effector of the Hippo signaling pathway, is a general hallmark of tubular adenomas from FAP patients. We show that APC functions as a scaffold protein that facilitates the Hippo kinase cascade by interacting with Sav1 and Lats1. Consistent with the molecular link between APC and the Hippo signaling pathway, genetic analysis reveals that YAP is absolutely required for the development of APC-deficient adenomas. These findings establish Hippo–YAP signaling as a critical effector pathway downstream from APC, independent from its involvement in the β-catenin destruction complex. PMID:26193883

  12. Exploring Complex Engineering Learning Over Time with Epistemic Network Analysis

    OpenAIRE

    Svarovsky, Gina Navoa

    2011-01-01

    Recently, K-12 engineering education has received increased attention as a pathway to building stronger foundations in math andscience and introducing young people to the profession. However, the National Academy of Engineering found that many K-12engineering programs focus heavily on engineering design and science and math learning while minimizing the development ofengineering habits of mind. This narrowly-focused engineering activity can leave young people – and in particular, girls – with...

  13. Multimodal Learning Analytics and Education Data Mining: Using Computational Technologies to Measure Complex Learning Tasks

    Science.gov (United States)

    Blikstein, Paulo; Worsley, Marcelo

    2016-01-01

    New high-frequency multimodal data collection technologies and machine learning analysis techniques could offer new insights into learning, especially when students have the opportunity to generate unique, personalized artifacts, such as computer programs, robots, and solutions engineering challenges. To date most of the work on learning analytics…

  14. Kurtosis based blind source extraction of complex noncircular signals with application in EEG artifact removal in real-time

    Directory of Open Access Journals (Sweden)

    Soroush eJavidi

    2011-10-01

    Full Text Available A new class of complex domain blind source extraction (BSE algorithms suitable for the extraction of both circular and noncircular complex signals is proposed. This is achieved through sequential extraction based on the degree of kurtosis and in the presence of noncircular measurement noise. The existence and uniqueness analysis of the solution is followed by a study of fast converging variants of the algorithm. The performance is first assessed through simulations on well understood benchmark signals, followed by a case study on real-time artifact removal from EEG signals, verified using both qualitative and quantitative metrics. The results illustrate the power of the proposed approach in real-time blind extraction of general complex-valued sources.

  15. Extinction of avoidance behavior by safety learning depends on endocannabinoid signaling in the hippocampus.

    Science.gov (United States)

    Micale, Vincenzo; Stepan, Jens; Jurik, Angela; Pamplona, Fabricio A; Marsch, Rudolph; Drago, Filippo; Eder, Matthias; Wotjak, Carsten T

    2017-07-01

    The development of exaggerated avoidance behavior is largely responsible for the decreased quality of life in patients suffering from anxiety disorders. Studies using animal models have contributed to the understanding of the neural mechanisms underlying the acquisition of avoidance responses. However, much less is known about its extinction. Here we provide evidence in mice that learning about the safety of an environment (i.e., safety learning) rather than repeated execution of the avoided response in absence of negative consequences (i.e., response extinction) allowed the animals to overcome their avoidance behavior in a step-down avoidance task. This process was context-dependent and could be blocked by pharmacological (3 mg/kg, s.c.; SR141716) or genetic (lack of cannabinoid CB1 receptors in neurons expressing dopamine D1 receptors) inactivation of CB1 receptors. In turn, the endocannabinoid reuptake inhibitor AM404 (3 mg/kg, i.p.) facilitated safety learning in a CB1-dependent manner and attenuated the relapse of avoidance behavior 28 days after conditioning. Safety learning crucially depended on endocannabinoid signaling at level of the hippocampus, since intrahippocampal SR141716 treatment impaired, whereas AM404 facilitated safety learning. Other than AM404, treatment with diazepam (1 mg/kg, i.p.) impaired safety learning. Drug effects on behavior were directly mirrored by drug effects on evoked activity propagation through the hippocampal trisynaptic circuit in brain slices: As revealed by voltage-sensitive dye imaging, diazepam impaired whereas AM404 facilitated activity propagation to CA1 in a CB1-dependent manner. In line with this, systemic AM404 enhanced safety learning-induced expression of Egr1 at level of CA1. Together, our data render it likely that AM404 promotes safety learning by enhancing information flow through the trisynaptic circuit to CA1. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. A mechanism for vertebrate Hedgehog signaling: recruitment to cilia and dissociation of SuFu–Gli protein complexes

    OpenAIRE

    Tukachinsky, Hanna; Lopez, Lyle V.; Salic, Adrian

    2010-01-01

    In vertebrates, Hedgehog (Hh) signaling initiated in primary cilia activates the membrane protein Smoothened (Smo) and leads to activation of Gli proteins, the transcriptional effectors of the pathway. In the absence of signaling, Gli proteins are inhibited by the cytoplasmic protein Suppressor of Fused (SuFu). It is unclear how Hh activates Gli and whether it directly regulates SuFu. We find that Hh stimulation quickly recruits endogenous SuFu–Gli complexes to cilia, suggesting a model in wh...

  17. Complex on the base of the ISKRA 226.6 personal computer for nuclear quadrupole resonance signal processing

    International Nuclear Information System (INIS)

    Morgunov, V.G.; Kravchenko, Eh.A.

    1988-01-01

    Complex, designed to conduct investigations by means of nuclear quadrupole resonance (NQR) method, which includes radiospectrometer, multichannel spectrum analyzer and ISKRA 226.6 personal computer, is developed. Analog-to-digital converter (ADC) with buffer storage device, interface and microcomputer are used to process NQR-signals. ADS conversion time is no more, than 50 ns, linearity - 1%. Programs on Fourier analysis of NQR-signals and calculation of relaxation times are developed

  18. A mechanism for vertebrate Hedgehog signaling: recruitment to cilia and dissociation of SuFu-Gli protein complexes.

    Science.gov (United States)

    Tukachinsky, Hanna; Lopez, Lyle V; Salic, Adrian

    2010-10-18

    In vertebrates, Hedgehog (Hh) signaling initiated in primary cilia activates the membrane protein Smoothened (Smo) and leads to activation of Gli proteins, the transcriptional effectors of the pathway. In the absence of signaling, Gli proteins are inhibited by the cytoplasmic protein Suppressor of Fused (SuFu). It is unclear how Hh activates Gli and whether it directly regulates SuFu. We find that Hh stimulation quickly recruits endogenous SuFu-Gli complexes to cilia, suggesting a model in which Smo activates Gli by relieving inhibition by SuFu. In support of this model, we find that Hh causes rapid dissociation of the SuFu-Gli complex, thus allowing Gli to enter the nucleus and activate transcription. Activation of protein kinase A (PKA), an inhibitor of Hh signaling, blocks ciliary localization of SuFu-Gli complexes, which in turn prevents their dissociation by signaling. Our results support a simple mechanism in which Hh signals at vertebrate cilia cause dissociation of inactive SuFu-Gli complexes, a process inhibited by PKA.

  19. A mechanism for vertebrate Hedgehog signaling: recruitment to cilia and dissociation of SuFu–Gli protein complexes

    Science.gov (United States)

    Tukachinsky, Hanna; Lopez, Lyle V.

    2010-01-01

    In vertebrates, Hedgehog (Hh) signaling initiated in primary cilia activates the membrane protein Smoothened (Smo) and leads to activation of Gli proteins, the transcriptional effectors of the pathway. In the absence of signaling, Gli proteins are inhibited by the cytoplasmic protein Suppressor of Fused (SuFu). It is unclear how Hh activates Gli and whether it directly regulates SuFu. We find that Hh stimulation quickly recruits endogenous SuFu–Gli complexes to cilia, suggesting a model in which Smo activates Gli by relieving inhibition by SuFu. In support of this model, we find that Hh causes rapid dissociation of the SuFu–Gli complex, thus allowing Gli to enter the nucleus and activate transcription. Activation of protein kinase A (PKA), an inhibitor of Hh signaling, blocks ciliary localization of SuFu–Gli complexes, which in turn prevents their dissociation by signaling. Our results support a simple mechanism in which Hh signals at vertebrate cilia cause dissociation of inactive SuFu–Gli complexes, a process inhibited by PKA. PMID:20956384

  20. Rheb may complex with RASSF1A to coordinate Hippo and TOR signaling.

    Science.gov (United States)

    Nelson, Nicholas; Clark, Geoffrey J

    2016-06-07

    The TOR pathway is a vital component of cellular homeostasis that controls the synthesis of proteins, nucleic acids and lipids. Its core is the TOR kinase. Activation of the TOR pathway suppresses autophagy, which plays a vital but complex role in tumorigenesis. The TOR pathway is regulated by activation of the Ras-related protein Rheb, which can bind mTOR. The Hippo pathway is a major growth control module that regulates cell growth, differentiation and apoptosis. Its core consists of an MST/LATS kinase cascade that can be activated by the RASSF1A tumor suppressor. The TOR and Hippo pathways may be coordinately regulated to promote cellular homeostasis. However, the links between the pathways remain only partially understood. We now demonstrate that in addition to mTOR regulation, Rheb also impacts the Hippo pathway by forming a complex with RASSF1A. Using stable clones of two human lung tumor cell lines (NCI-H1792 and NCI-H1299) with shRNA-mediated silencing or ectopic overexpression of RASSF1A, we show that activated Rheb stimulates the Hippo pathway, but is suppressed in its ability to stimulate the TOR pathway. Moreover, by selectively labeling autophagic vacuoles we show that RASSF1A inhibits the ability of Rheb to suppress autophagy and enhance cell growth. Thus, we identify a new connection that impacts coordination of Hippo and TOR signaling. As RASSF1A expression is frequently lost in human tumors, the RASSF1A status of a tumor may impact not just its Hippo pathway status, but also its TOR pathway status.

  1. satlc model lesson for teaching and learning complex environmental ...

    African Journals Online (AJOL)

    IICBA01

    Greenhouse-gas-induced temperature increase is one of the main reasons of ... The relation between altitude and density is a fairly complex exponential that has been ... in to ocean by which water becomes acidic also when water is heated it ...

  2. Toward a Learning Science for Complex Crowdsourcing Tasks

    Science.gov (United States)

    Doroudi, Shayan; Kamar, Ece; Brunskill, Emma; Horvitz, Eric

    2016-01-01

    We explore how crowdworkers can be trained to tackle complex crowdsourcing tasks. We are particularly interested in training novice workers to perform well on solving tasks in situations where the space of strategies is large and workers need to discover and try different strategies to be successful. In a first experiment, we perform a comparison…

  3. Play, learn, explore: grasping complexity through gaming and ...

    African Journals Online (AJOL)

    Increased demand for agricultural products, the aspirations of rural communities and a growing recognition of planetary boundaries outline the complex trade-offs resource users are facing on a daily basis. Management problems typically involve multiple stakeholders with diverse and often conflicting worldviews, needs ...

  4. Cueing Complex Animations: Does Direction of Attention Foster Learning Processes?

    Science.gov (United States)

    Lowe, Richard; Boucheix, Jean-Michel

    2011-01-01

    The time course of learners' processing of a complex animation was studied using a dynamic diagram of a piano mechanism. Over successive repetitions of the material, two forms of cueing (standard colour cueing and anti-cueing) were administered either before or during the animated segment of the presentation. An uncued group and two other control…

  5. Therapeutic Targeting of the IL-6 Trans-Signaling/Mechanistic Target of Rapamycin Complex 1 Axis in Pulmonary Emphysema.

    Science.gov (United States)

    Ruwanpura, Saleela M; McLeod, Louise; Dousha, Lovisa F; Seow, Huei J; Alhayyani, Sultan; Tate, Michelle D; Deswaerte, Virginie; Brooks, Gavin D; Bozinovski, Steven; MacDonald, Martin; Garbers, Christoph; King, Paul T; Bardin, Philip G; Vlahos, Ross; Rose-John, Stefan; Anderson, Gary P; Jenkins, Brendan J

    2016-12-15

    The potent immunomodulatory cytokine IL-6 is consistently up-regulated in human lungs with emphysema and in mouse emphysema models; however, the mechanisms by which IL-6 promotes emphysema remain obscure. IL-6 signals using two distinct modes: classical signaling via its membrane-bound IL-6 receptor (IL-6R), and trans-signaling via a naturally occurring soluble IL-6R. To identify whether IL-6 trans-signaling and/or classical signaling contribute to the pathogenesis of emphysema. We used the gp130 F/F genetic mouse model for spontaneous emphysema and cigarette smoke-induced emphysema models. Emphysema in mice was quantified by various methods including in vivo lung function and stereology, and terminal deoxynucleotidyl transferase dUTP nick end labeling assay was used to assess alveolar cell apoptosis. In mouse and human lung tissues, the expression level and location of IL-6 signaling-related genes and proteins were measured, and the levels of IL-6 and related proteins in sera from emphysematous mice and patients were also assessed. Lung tissues from patients with emphysema, and from spontaneous and cigarette smoke-induced emphysema mouse models, were characterized by excessive production of soluble IL-6R. Genetic blockade of IL-6 trans-signaling in emphysema mouse models and therapy with the IL-6 trans-signaling antagonist sgp130Fc ameliorated emphysema by suppressing augmented alveolar type II cell apoptosis. Furthermore, IL-6 trans-signaling-driven emphysematous changes in the lung correlated with mechanistic target of rapamycin complex 1 hyperactivation, and treatment of emphysema mouse models with the mechanistic target of rapamycin complex 1 inhibitor rapamycin attenuated emphysematous changes. Collectively, our data reveal that specific targeting of IL-6 trans-signaling may represent a novel treatment strategy for emphysema.

  6. Compressive sensing of electrocardiogram signals by promoting sparsity on the second-order difference and by using dictionary learning.

    Science.gov (United States)

    Pant, Jeevan K; Krishnan, Sridhar

    2014-04-01

    A new algorithm for the reconstruction of electrocardiogram (ECG) signals and a dictionary learning algorithm for the enhancement of its reconstruction performance for a class of signals are proposed. The signal reconstruction algorithm is based on minimizing the lp pseudo-norm of the second-order difference, called as the lp(2d) pseudo-norm, of the signal. The optimization involved is carried out using a sequential conjugate-gradient algorithm. The dictionary learning algorithm uses an iterative procedure wherein a signal reconstruction and a dictionary update steps are repeated until a convergence criterion is satisfied. The signal reconstruction step is implemented by using the proposed signal reconstruction algorithm and the dictionary update step is implemented by using the linear least-squares method. Extensive simulation results demonstrate that the proposed algorithm yields improved reconstruction performance for temporally correlated ECG signals relative to the state-of-the-art lp(1d)-regularized least-squares and Bayesian learning based algorithms. Also for a known class of signals, the reconstruction performance of the proposed algorithm can be improved by applying it in conjunction with a dictionary obtained using the proposed dictionary learning algorithm.

  7. Overcoming complexities: Damage detection using dictionary learning framework

    Science.gov (United States)

    Alguri, K. Supreet; Melville, Joseph; Deemer, Chris; Harley, Joel B.

    2018-04-01

    For in situ damage detection, guided wave structural health monitoring systems have been widely researched due to their ability to evaluate large areas and their ability detect many types of damage. These systems often evaluate structural health by recording initial baseline measurements from a pristine (i.e., undamaged) test structure and then comparing later measurements with that baseline. Yet, it is not always feasible to have a pristine baseline. As an alternative, substituting the baseline with data from a surrogate (nearly identical and pristine) structure is a logical option. While effective in some circumstance, surrogate data is often still a poor substitute for pristine baseline measurements due to minor differences between the structures. To overcome this challenge, we present a dictionary learning framework to adapt surrogate baseline data to better represent an undamaged test structure. We compare the performance of our framework with two other surrogate-based damage detection strategies: (1) using raw surrogate data for comparison and (2) using sparse wavenumber analysis, a precursor to our framework for improving the surrogate data. We apply our framework to guided wave data from two 108 mm by 108 mm aluminum plates. With 20 measurements, we show that our dictionary learning framework achieves a 98% accuracy, raw surrogate data achieves a 92% accuracy, and sparse wavenumber analysis achieves a 57% accuracy.

  8. SUMO-, MAPK- and resistance protein-signaling converge at transcription complexes that regulate plant innate immunity

    NARCIS (Netherlands)

    Burg, van den H.A.; Takken, F.L.W.

    2010-01-01

    Upon pathogen perception plant innate immune receptors activate various signaling pathways that trigger host defenses. PAMP-triggered defense signaling requires mitogen-activated protein kinase (MAPK) pathways, which modulate the activity of transcription factors through phosphorylation. Here, we

  9. SUMO-, MAPK-, and resistance protein-signaling converge at transcription complexes that regulate plant innate immunity

    NARCIS (Netherlands)

    van den Burg, H.A.; Takken, F.L.W.

    2010-01-01

    Upon pathogen perception plant innate immune receptors activate various signaling pathways that trigger host defenses. PAMP-triggered defense signaling requires mitogen-activated protein kinase (MAPK) pathways, which modulate the activity of transcription factors through phosphorylation. Here, we

  10. Four wind speed multi-step forecasting models using extreme learning machines and signal decomposing algorithms

    International Nuclear Information System (INIS)

    Liu, Hui; Tian, Hong-qi; Li, Yan-fei

    2015-01-01

    Highlights: • A hybrid architecture is proposed for the wind speed forecasting. • Four algorithms are used for the wind speed multi-scale decomposition. • The extreme learning machines are employed for the wind speed forecasting. • All the proposed hybrid models can generate the accurate results. - Abstract: Realization of accurate wind speed forecasting is important to guarantee the safety of wind power utilization. In this paper, a new hybrid forecasting architecture is proposed to realize the wind speed accurate forecasting. In this architecture, four different hybrid models are presented by combining four signal decomposing algorithms (e.g., Wavelet Decomposition/Wavelet Packet Decomposition/Empirical Mode Decomposition/Fast Ensemble Empirical Mode Decomposition) and Extreme Learning Machines. The originality of the study is to investigate the promoted percentages of the Extreme Learning Machines by those mainstream signal decomposing algorithms in the multiple step wind speed forecasting. The results of two forecasting experiments indicate that: (1) the method of Extreme Learning Machines is suitable for the wind speed forecasting; (2) by utilizing the decomposing algorithms, all the proposed hybrid algorithms have better performance than the single Extreme Learning Machines; (3) in the comparisons of the decomposing algorithms in the proposed hybrid architecture, the Fast Ensemble Empirical Mode Decomposition has the best performance in the three-step forecasting results while the Wavelet Packet Decomposition has the best performance in the one and two step forecasting results. At the same time, the Wavelet Packet Decomposition and the Fast Ensemble Empirical Mode Decomposition are better than the Wavelet Decomposition and the Empirical Mode Decomposition in all the step predictions, respectively; and (4) the proposed algorithms are effective in the wind speed accurate predictions

  11. Optimizing the number of steps in learning tasks for complex skills.

    Science.gov (United States)

    Nadolski, Rob J; Kirschner, Paul A; van Merriënboer, Jeroen J G

    2005-06-01

    Carrying out whole tasks is often too difficult for novice learners attempting to acquire complex skills. The common solution is to split up the tasks into a number of smaller steps. The number of steps must be optimized for efficient and effective learning. The aim of the study is to investigate the relation between the number of steps provided to learners and the quality of their learning of complex skills. It is hypothesized that students receiving an optimized number of steps will learn better than those receiving either the whole task in only one step or those receiving a large number of steps. Participants were 35 sophomore law students studying at Dutch universities, mean age=22.8 years (SD=3.5), 63% were female. Participants were randomly assigned to 1 of 3 computer-delivered versions of a multimedia programme on how to prepare and carry out a law plea. The versions differed only in the number of learning steps provided. Videotaped plea-performance results were determined, various related learning measures were acquired and all computer actions were logged and analyzed. Participants exposed to an intermediate (i.e. optimized) number of steps outperformed all others on the compulsory learning task. No differences in performance on a transfer task were found. A high number of steps proved to be less efficient for carrying out the learning task. An intermediate number of steps is the most effective, proving that the number of steps can be optimized for improving learning.

  12. Loss of Signal, Aeromedical Lessons Learned from the STS-107 Columbia Space Shuttle Mishap

    Science.gov (United States)

    Stepaniak, Phillip C.; Patlach, Robert

    2014-01-01

    Loss of Signal, a NASA publication to be available in May 2014 presents the aeromedical lessons learned from the Columbia accident that will enhance crew safety and survival on human space flight missions. These lessons were presented to limited audiences at three separate Aerospace Medical Association (AsMA) conferences: in 2004 in Anchorage, Alaska, on the causes of the accident; in 2005 in Kansas City, Missouri, on the response, recovery, and identification aspects of the investigation; and in 2011, again in Anchorage, Alaska, on future implications for human space flight. As we embark on the development of new spacefaring vehicles through both government and commercial efforts, the NASA Johnson Space Center Human Health and Performance Directorate is continuing to make this information available to a wider audience engaged in the design and development of future space vehicles. Loss of Signal summarizes and consolidates the aeromedical impacts of the Columbia mishap process-the response, recovery, identification, investigative studies, medical and legal forensic analysis, and future preparation that are needed to respond to spacecraft mishaps. The goal of this book is to provide an account of the aeromedical aspects of the Columbia accident and the investigation that followed, and to encourage aerospace medical specialists to continue to capture information, learn from it, and improve procedures and spacecraft designs for the safety of future crews. This poster presents an outline of Loss of Signal contents and highlights from each of five sections - the mission and mishap, the response, the investigation, the analysis and the future.

  13. Task complexity and maximal isometric strength gains through motor learning

    Science.gov (United States)

    McGuire, Jessica; Green, Lara A.; Gabriel, David A.

    2014-01-01

    Abstract This study compared the effects of a simple versus complex contraction pattern on the acquisition, retention, and transfer of maximal isometric strength gains and reductions in force variability. A control group (N = 12) performed simple isometric contractions of the wrist flexors. An experimental group (N = 12) performed complex proprioceptive neuromuscular facilitation (PNF) contractions consisting of maximal isometric wrist extension immediately reversing force direction to wrist flexion within a single trial. Ten contractions were completed on three consecutive days with a retention and transfer test 2‐weeks later. For the retention test, the groups performed their assigned contraction pattern followed by a transfer test that consisted of the other contraction pattern for a cross‐over design. Both groups exhibited comparable increases in strength (20.2%, P < 0.01) and reductions in mean torque variability (26.2%, P < 0.01), which were retained and transferred. There was a decrease in the coactivation ratio (antagonist/agonist muscle activity) for both groups, which was retained and transferred (35.2%, P < 0.01). The experimental group exhibited a linear decrease in variability of the torque‐ and sEMG‐time curves, indicating transfer to the simple contraction pattern (P < 0.01). The control group underwent a decrease in variability of the torque‐ and sEMG‐time curves from the first day of training to retention, but participants returned to baseline levels during the transfer condition (P < 0.01). However, the difference between torque RMS error versus the variability in torque‐ and sEMG‐time curves suggests the demands of the complex task were transferred, but could not be achieved in a reproducible way. PMID:25428951

  14. Analysis of Maneuvering Targets with Complex Motions by Two-Dimensional Product Modified Lv's Distribution for Quadratic Frequency Modulation Signals.

    Science.gov (United States)

    Jing, Fulong; Jiao, Shuhong; Hou, Changbo; Si, Weijian; Wang, Yu

    2017-06-21

    For targets with complex motion, such as ships fluctuating with oceanic waves and high maneuvering airplanes, azimuth echo signals can be modeled as multicomponent quadratic frequency modulation (QFM) signals after migration compensation and phase adjustment. For the QFM signal model, the chirp rate (CR) and the quadratic chirp rate (QCR) are two important physical quantities, which need to be estimated. For multicomponent QFM signals, the cross terms create a challenge for detection, which needs to be addressed. In this paper, by employing a novel multi-scale parametric symmetric self-correlation function (PSSF) and modified scaled Fourier transform (mSFT), an effective parameter estimation algorithm is proposed-referred to as the Two-Dimensional product modified Lv's distribution (2D-PMLVD)-for QFM signals. The 2D-PMLVD is simple and can be easily implemented by using fast Fourier transform (FFT) and complex multiplication. These measures are analyzed in the paper, including the principle, the cross term, anti-noise performance, and computational complexity. Compared to the other three representative methods, the 2D-PMLVD can achieve better anti-noise performance. The 2D-PMLVD, which is free of searching and has no identifiability problems, is more suitable for multicomponent situations. Through several simulations and analyses, the effectiveness of the proposed estimation algorithm is verified.

  15. Requirement of the Mre11 complex and exonuclease 1 for activation of the Mec1 signaling pathway.

    Science.gov (United States)

    Nakada, Daisuke; Hirano, Yukinori; Sugimoto, Katsunori

    2004-11-01

    The large protein kinases, ataxia-telangiectasia mutated (ATM) and ATM-Rad3-related (ATR), orchestrate DNA damage checkpoint pathways. In budding yeast, ATM and ATR homologs are encoded by TEL1 and MEC1, respectively. The Mre11 complex consists of two highly related proteins, Mre11 and Rad50, and a third protein, Xrs2 in budding yeast or Nbs1 in mammals. The Mre11 complex controls the ATM/Tel1 signaling pathway in response to double-strand break (DSB) induction. We show here that the Mre11 complex functions together with exonuclease 1 (Exo1) in activation of the Mec1 signaling pathway after DNA damage and replication block. Mec1 controls the checkpoint responses following UV irradiation as well as DSB induction. Correspondingly, the Mre11 complex and Exo1 play an overlapping role in activation of DSB- and UV-induced checkpoints. The Mre11 complex and Exo1 collaborate in producing long single-stranded DNA (ssDNA) tails at DSB ends and promote Mec1 association with the DSBs. The Ddc1-Mec3-Rad17 complex associates with sites of DNA damage and modulates the Mec1 signaling pathway. However, Ddc1 association with DSBs does not require the function of the Mre11 complex and Exo1. Mec1 controls checkpoint responses to stalled DNA replication as well. Accordingly, the Mre11 complex and Exo1 contribute to activation of the replication checkpoint pathway. Our results provide a model in which the Mre11 complex and Exo1 cooperate in generating long ssDNA tracts and thereby facilitate Mec1 association with sites of DNA damage or replication block.

  16. A lossless multichannel bio-signal compression based on low-complexity joint coding scheme for portable medical devices.

    Science.gov (United States)

    Kim, Dong-Sun; Kwon, Jin-San

    2014-09-18

    Research on real-time health systems have received great attention during recent years and the needs of high-quality personal multichannel medical signal compression for personal medical product applications are increasing. The international MPEG-4 audio lossless coding (ALS) standard supports a joint channel-coding scheme for improving compression performance of multichannel signals and it is very efficient compression method for multi-channel biosignals. However, the computational complexity of such a multichannel coding scheme is significantly greater than that of other lossless audio encoders. In this paper, we present a multichannel hardware encoder based on a low-complexity joint-coding technique and shared multiplier scheme for portable devices. A joint-coding decision method and a reference channel selection scheme are modified for a low-complexity joint coder. The proposed joint coding decision method determines the optimized joint-coding operation based on the relationship between the cross correlation of residual signals and the compression ratio. The reference channel selection is designed to select a channel for the entropy coding of the joint coding. The hardware encoder operates at a 40 MHz clock frequency and supports two-channel parallel encoding for the multichannel monitoring system. Experimental results show that the compression ratio increases by 0.06%, whereas the computational complexity decreases by 20.72% compared to the MPEG-4 ALS reference software encoder. In addition, the compression ratio increases by about 11.92%, compared to the single channel based bio-signal lossless data compressor.

  17. Fixed-Point Algorithms for the Blind Separation of Arbitrary Complex-Valued Non-Gaussian Signal Mixtures

    Directory of Open Access Journals (Sweden)

    Douglas Scott C

    2007-01-01

    Full Text Available We derive new fixed-point algorithms for the blind separation of complex-valued mixtures of independent, noncircularly symmetric, and non-Gaussian source signals. Leveraging recently developed results on the separability of complex-valued signal mixtures, we systematically construct iterative procedures on a kurtosis-based contrast whose evolutionary characteristics are identical to those of the FastICA algorithm of Hyvarinen and Oja in the real-valued mixture case. Thus, our methods inherit the fast convergence properties, computational simplicity, and ease of use of the FastICA algorithm while at the same time extending this class of techniques to complex signal mixtures. For extracting multiple sources, symmetric and asymmetric signal deflation procedures can be employed. Simulations for both noiseless and noisy mixtures indicate that the proposed algorithms have superior finite-sample performance in data-starved scenarios as compared to existing complex ICA methods while performing about as well as the best of these techniques for larger data-record lengths.

  18. Bcl-xL acts downstream of caspase-8 activation by the CD95 death-inducing signaling complex

    NARCIS (Netherlands)

    Medema, J. P.; Scaffidi, C.; Krammer, P. H.; Peter, M. E.

    1998-01-01

    The Bcl-2 family member Bcl-xL has often been correlated with apoptosis resistance. We have shown recently that in peripheral human T cells resistance to CD95-mediated apoptosis is characterized by a lack of caspase-8 recruitment to the CD95 death-inducing signaling complex (DISC) and by increased

  19. Grasping the Dynamic Complexity of Team Learning: An Integrative Model for Effective Team Learning in Organisations

    Science.gov (United States)

    Decuyper, Stefan; Dochy, Filip; Van den Bossche, Piet

    2010-01-01

    In this article we present an integrative model of team learning. Literature shows that effective team learning requires the establishment of a dialogical space amongst team members, in which communicative behaviours such as "sharing", "co-construction" and "constructive conflict" are balanced. However, finding this balance is not enough.…

  20. Employees' and Managers' Accounts of Interactive Workplace Learning: A Grounded Theory of "Complex Integrative Learning"

    Science.gov (United States)

    Armson, Genevieve; Whiteley, Alma

    2010-01-01

    Purpose: The purpose of this paper is to investigate employees' and managers' accounts of interactive learning and what might encourage or inhibit emergent learning. Design/methodology/approach: The approach taken was a constructivist/social constructivist ontology, interpretive epistemology and qualitative methodology, using grounded theory…

  1. Pseudo-stokes vector from complex signal representation of a speckle pattern and its applications to micro-displacement measurement

    DEFF Research Database (Denmark)

    Wang, W.; Ishijima, R.; Matsuda, A.

    2010-01-01

    As an improvement of the intensity correlation used widely in conventional electronic speckle photography, we propose a new technique for displacement measurement based on correlating Stokes-like parameters derivatives for transformed speckle patterns. The method is based on a Riesz transform of ...... are presented that demonstrate the validity and advantage of the proposed pseudo-Stokes vector correlation technique over conventional intensity correlation technique....... of the intensity speckle pattern, which converts the original real-valued signal into a complex signal. In closest analogy to the polarisation of a vector wave, the Stokes-like vector constructed from the spatial derivative of the generated complex signal has been applied for correlation. Experimental results...

  2. Analysis of progression of fatigue conditions in biceps brachii muscles using surface electromyography signals and complexity based features.

    Science.gov (United States)

    Karthick, P A; Makaram, Navaneethakrishna; Ramakrishnan, S

    2014-01-01

    Muscle fatigue is a neuromuscular condition where muscle performance decreases due to sustained or intense contraction. It is experienced by both normal and abnormal subjects. In this work, an attempt has been made to analyze the progression of muscle fatigue in biceps brachii muscles using surface electromyography (sEMG) signals. The sEMG signals are recorded from fifty healthy volunteers during dynamic contractions under well defined protocol. The acquired signals are preprocessed and segmented in to six equal parts for further analysis. The features, such as activity, mobility, complexity, sample entropy and spectral entropy are extracted from all six zones. The results are found showing that the extracted features except complexity feature have significant variations in differentiating non-fatigue and fatigue zone respectively. Thus, it appears that, these features are useful in automated analysis of various neuromuscular activities in normal and pathological conditions.

  3. PARTICLE FILTERING WITH SEQUENTIAL PARAMETER LEARNING FOR NONLINEAR BOLD fMRI SIGNALS.

    Science.gov (United States)

    Xia, Jing; Wang, Michelle Yongmei

    Analyzing the blood oxygenation level dependent (BOLD) effect in the functional magnetic resonance imaging (fMRI) is typically based on recent ground-breaking time series analysis techniques. This work represents a significant improvement over existing approaches to system identification using nonlinear hemodynamic models. It is important for three reasons. First, instead of using linearized approximations of the dynamics, we present a nonlinear filtering based on the sequential Monte Carlo method to capture the inherent nonlinearities in the physiological system. Second, we simultaneously estimate the hidden physiological states and the system parameters through particle filtering with sequential parameter learning to fully take advantage of the dynamic information of the BOLD signals. Third, during the unknown static parameter learning, we employ the low-dimensional sufficient statistics for efficiency and avoiding potential degeneration of the parameters. The performance of the proposed method is validated using both the simulated data and real BOLD fMRI data.

  4. Dissociation between judgments and outcome-expectancy measures in covariation learning: a signal detection theory approach.

    Science.gov (United States)

    Perales, José C; Catena, Andrés; Shanks, David R; González, José A

    2005-09-01

    A number of studies using trial-by-trial learning tasks have shown that judgments of covariation between a cue c and an outcome o deviate from normative metrics. Parameters based on trial-by-trial predictions were estimated from signal detection theory (SDT) in a standard causal learning task. Results showed that manipulations of P(c) when contingency (deltaP) was held constant did not affect participants' ability to predict the appearance of the outcome (d') but had a significant effect on response criterion (c) and numerical causal judgments. The association between criterion c and judgment was further demonstrated in 2 experiments in which the criterion was directly manipulated by linking payoffs to the predictive responses made by learners. In all cases, the more liberal the criterion c was, the higher judgments were. The results imply that the mechanisms underlying the elaboration of judgments and those involved in the elaboration of predictive responses are partially dissociable.

  5. An Adaptive Noise Cancellation System Based on Linear and Widely Linear Complex Valued Least Mean Square Algorithms for Removing Electrooculography Artifacts from Electroencephalography Signals

    Directory of Open Access Journals (Sweden)

    Engin Cemal MENGÜÇ

    2018-03-01

    Full Text Available In this study, an adaptive noise cancellation (ANC system based on linear and widely linear (WL complex valued least mean square (LMS algorithms is designed for removing electrooculography (EOG artifacts from electroencephalography (EEG signals. The real valued EOG and EEG signals (Fp1 and Fp2 given in dataset are primarily expressed as a complex valued signal in the complex domain. Then, using the proposed ANC system, the EOG artifacts are eliminated in the complex domain from the EEG signals. Expression of these signals in the complex domain allows us to remove EOG artifacts from two EEG channels simultaneously. Moreover, in this study, it has been shown that the complex valued EEG signal exhibits noncircular behavior, and in the case, the WL-CLMS algorithm enhances the performance of the ANC system compared to real-valued LMS and CLMS algorithms. Simulation results support the proposed approach.

  6. Variable complexity online sequential extreme learning machine, with applications to streamflow prediction

    Science.gov (United States)

    Lima, Aranildo R.; Hsieh, William W.; Cannon, Alex J.

    2017-12-01

    In situations where new data arrive continually, online learning algorithms are computationally much less costly than batch learning ones in maintaining the model up-to-date. The extreme learning machine (ELM), a single hidden layer artificial neural network with random weights in the hidden layer, is solved by linear least squares, and has an online learning version, the online sequential ELM (OSELM). As more data become available during online learning, information on the longer time scale becomes available, so ideally the model complexity should be allowed to change, but the number of hidden nodes (HN) remains fixed in OSELM. A variable complexity VC-OSELM algorithm is proposed to dynamically add or remove HN in the OSELM, allowing the model complexity to vary automatically as online learning proceeds. The performance of VC-OSELM was compared with OSELM in daily streamflow predictions at two hydrological stations in British Columbia, Canada, with VC-OSELM significantly outperforming OSELM in mean absolute error, root mean squared error and Nash-Sutcliffe efficiency at both stations.

  7. Perceptual learning increases the strength of the earliest signals in visual cortex.

    Science.gov (United States)

    Bao, Min; Yang, Lin; Rios, Cristina; He, Bin; Engel, Stephen A

    2010-11-10

    Training improves performance on most visual tasks. Such perceptual learning can modify how information is read out from, and represented in, later visual areas, but effects on early visual cortex are controversial. In particular, it remains unknown whether learning can reshape neural response properties in early visual areas independent from feedback arising in later cortical areas. Here, we tested whether learning can modify feedforward signals in early visual cortex as measured by the human electroencephalogram. Fourteen subjects were trained for >24 d to detect a diagonal grating pattern in one quadrant of the visual field. Training improved performance, reducing the contrast needed for reliable detection, and also reliably increased the amplitude of the earliest component of the visual evoked potential, the C1. Control orientations and locations showed smaller effects of training. Because the C1 arises rapidly and has a source in early visual cortex, our results suggest that learning can increase early visual area response through local receptive field changes without feedback from later areas.

  8. Global and local missions of cAMP signaling in neural plasticity, learning and memory

    Directory of Open Access Journals (Sweden)

    Daewoo eLee

    2015-08-01

    Full Text Available The fruit fly Drosophila melanogaster has been a popular model to study cAMP signaling and resultant behaviors due to its powerful genetic approaches. All molecular components (AC, PDE, PKA, CREB, etc essential for cAMP signaling have been identified in the fly. Among them, adenylyl cyclase (AC gene rutabaga and phosphodiesterase (PDE gene dunce have been intensively studied to understand the role of cAMP signaling. Interestingly, these two mutant genes were originally identified on the basis of associative learning deficits. This commentary summarizes findings on the role of cAMP in Drosophila neuronal excitability, synaptic plasticity and memory. It mainly focuses on two distinct mechanisms (global versus local regulating excitatory and inhibitory synaptic plasticity related to cAMP homeostasis. This dual regulatory role of cAMP is to increase the strength of excitatory neural circuits on one hand, but to act locally on postsynaptic GABA receptors to decrease inhibitory synaptic plasticity on the other. Thus the action of cAMP could result in a global increase in the neural circuit excitability and memory. Implications of this cAMP signaling related to drug discovery for neural diseases are also described.

  9. Using virtual humans and computer animations to learn complex motor skills: a case study in karate

    Directory of Open Access Journals (Sweden)

    Spanlang Bernhard

    2011-12-01

    Full Text Available Learning motor skills is a complex task involving a lot of cognitive issues. One of the main issues consists in retrieving the relevant information from the learning environment. In a traditional learning situation, a teacher gives oral explanations and performs actions to provide the learner with visual examples. Using virtual reality (VR as a tool for learning motor tasks is promising. However, it raises questions about the type of information this kind of environments can offer. In this paper, we propose to analyze the impact of virtual humans on the perception of the learners. As a case study, we propose to apply this research problem to karate gestures. The results of this study show no significant difference on the after training performance of learners confronted to three different learning environments (traditional group, video and VR.

  10. Assessment for Complex Learning Resources: Development and Validation of an Integrated Model

    Directory of Open Access Journals (Sweden)

    Gudrun Wesiak

    2013-01-01

    Full Text Available Today’s e-learning systems meet the challenge to provide interactive, personalized environments that support self-regulated learning as well as social collaboration and simulation. At the same time assessment procedures have to be adapted to the new learning environments by moving from isolated summative assessments to integrated assessment forms. Therefore, learning experiences enriched with complex didactic resources - such as virtualized collaborations and serious games - have emerged. In this extension of [1] an integrated model for e-assessment (IMA is outlined, which incorporates complex learning resources and assessment forms as main components for the development of an enriched learning experience. For a validation the IMA was presented to a group of experts from the fields of cognitive science, pedagogy, and e-learning. The findings from the validation lead to several refinements of the model, which mainly concern the component forms of assessment and the integration of social aspects. Both aspects are accounted for in the revised model, the former by providing a detailed sub-model for assessment forms.

  11. n-Order and maximum fuzzy similarity entropy for discrimination of signals of different complexity: Application to fetal heart rate signals.

    Science.gov (United States)

    Zaylaa, Amira; Oudjemia, Souad; Charara, Jamal; Girault, Jean-Marc

    2015-09-01

    This paper presents two new concepts for discrimination of signals of different complexity. The first focused initially on solving the problem of setting entropy descriptors by varying the pattern size instead of the tolerance. This led to the search for the optimal pattern size that maximized the similarity entropy. The second paradigm was based on the n-order similarity entropy that encompasses the 1-order similarity entropy. To improve the statistical stability, n-order fuzzy similarity entropy was proposed. Fractional Brownian motion was simulated to validate the different methods proposed, and fetal heart rate signals were used to discriminate normal from abnormal fetuses. In all cases, it was found that it was possible to discriminate time series of different complexity such as fractional Brownian motion and fetal heart rate signals. The best levels of performance in terms of sensitivity (90%) and specificity (90%) were obtained with the n-order fuzzy similarity entropy. However, it was shown that the optimal pattern size and the maximum similarity measurement were related to intrinsic features of the time series. Copyright © 2015 Elsevier Ltd. All rights reserved.

  12. Task complexity, student perceptions of vocabulary learning in EFL, and task performance.

    Science.gov (United States)

    Wu, Xiaoli; Lowyck, Joost; Sercu, Lies; Elen, Jan

    2013-03-01

    The study deepened our understanding of how students' self-efficacy beliefs contribute to the context of teaching English as a foreign language in the framework of cognitive mediational paradigm at a fine-tuned task-specific level. The aim was to examine the relationship among task complexity, self-efficacy beliefs, domain-related prior knowledge, learning strategy use, and task performance as they were applied to English vocabulary learning from reading tasks. Participants were 120 second-year university students (mean age 21) from a Chinese university. This experiment had two conditions (simple/complex). A vocabulary level test was first conducted to measure participants' prior knowledge of English vocabulary. Participants were then randomly assigned to one of the learning tasks. Participants were administered task booklets together with the self-efficacy scales, measures of learning strategy use, and post-tests. Data obtained were submitted to multivariate analysis of variance (MANOVA) and path analysis. Results from the MANOVA model showed a significant effect of vocabulary level on self-efficacy beliefs, learning strategy use, and task performance. Task complexity showed no significant effect; however, an interaction effect between vocabulary level and task complexity emerged. Results from the path analysis showed self-efficacy beliefs had an indirect effect on performance. Our results highlighted the mediating role of self-efficacy beliefs and learning strategy use. Our findings indicate that students' prior knowledge plays a crucial role on both self-efficacy beliefs and task performance, and the predictive power of self-efficacy on task performance may lie in its association with learning strategy use. © 2011 The British Psychological Society.

  13. Hsp90α forms a stable complex at the cilium neck for the interaction of signalling molecules in IGF-1 receptor signalling.

    Science.gov (United States)

    Wang, Hongzhong; Zou, Xinle; Wei, Zhuang; Wu, Yuan; Li, Rongxia; Zeng, Rong; Chen, Zhengjun; Liao, Kan

    2015-01-01

    The primary cilium is composed of an axoneme that protrudes from the cell surface, a basal body beneath the membrane and a transition neck in between. It is a sensory organelle on the plasma membrane, involved in mediating extracellular signals. In the transition neck region of the cilium, the microtubules change from triplet to doublet microtubules. This region also contains the transition fibres that crosslink the axoneme with the membrane and the necklace proteins that regulate molecules being transported into and out of the cilium. In this protein-enriched, complex area it is important to maintain the correct assembly of all of these proteins. Here, through immunofluorescent staining and protein isolation, we identify the molecular chaperone Hsp90α clustered at the periciliary base. At the transition neck region, phosphorylated Hsp90α forms a stable ring around the axoneme. Heat shock treatment causes Hsp90α to dissipate and induces resorption of cilia. We further identify that Hsp90α at the transition neck region represents a signalling platform on which IRS-1 interacts with intracellular downstream signalling molecules involved in IGF-1 receptor signalling. © 2015. Published by The Company of Biologists Ltd.

  14. ComplexContact: a web server for inter-protein contact prediction using deep learning

    KAUST Repository

    Zeng, Hong; Wang, Sheng; Zhou, Tianming; Zhao, Feifeng; Li, Xiufeng; Wu, Qing; Xu, Jinbo

    2018-01-01

    ComplexContact (http://raptorx2.uchicago.edu/ComplexContact/) is a web server for sequence-based interfacial residue-residue contact prediction of a putative protein complex. Interfacial residue-residue contacts are critical for understanding how proteins form complex and interact at residue level. When receiving a pair of protein sequences, ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA), then it applies co-evolution analysis and a CASP-winning deep learning (DL) method to predict interfacial contacts from paired MSAs and visualizes the prediction as an image. The DL method was originally developed for intra-protein contact prediction and performed the best in CASP12. Our large-scale experimental test further shows that ComplexContact greatly outperforms pure co-evolution methods for inter-protein contact prediction, regardless of the species.

  15. ComplexContact: a web server for inter-protein contact prediction using deep learning

    KAUST Repository

    Zeng, Hong

    2018-05-20

    ComplexContact (http://raptorx2.uchicago.edu/ComplexContact/) is a web server for sequence-based interfacial residue-residue contact prediction of a putative protein complex. Interfacial residue-residue contacts are critical for understanding how proteins form complex and interact at residue level. When receiving a pair of protein sequences, ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA), then it applies co-evolution analysis and a CASP-winning deep learning (DL) method to predict interfacial contacts from paired MSAs and visualizes the prediction as an image. The DL method was originally developed for intra-protein contact prediction and performed the best in CASP12. Our large-scale experimental test further shows that ComplexContact greatly outperforms pure co-evolution methods for inter-protein contact prediction, regardless of the species.

  16. ComplexContact: a web server for inter-protein contact prediction using deep learning.

    Science.gov (United States)

    Zeng, Hong; Wang, Sheng; Zhou, Tianming; Zhao, Feifeng; Li, Xiufeng; Wu, Qing; Xu, Jinbo

    2018-05-22

    ComplexContact (http://raptorx2.uchicago.edu/ComplexContact/) is a web server for sequence-based interfacial residue-residue contact prediction of a putative protein complex. Interfacial residue-residue contacts are critical for understanding how proteins form complex and interact at residue level. When receiving a pair of protein sequences, ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA), then it applies co-evolution analysis and a CASP-winning deep learning (DL) method to predict interfacial contacts from paired MSAs and visualizes the prediction as an image. The DL method was originally developed for intra-protein contact prediction and performed the best in CASP12. Our large-scale experimental test further shows that ComplexContact greatly outperforms pure co-evolution methods for inter-protein contact prediction, regardless of the species.

  17. Preparing new nurses with complexity science and problem-based learning.

    Science.gov (United States)

    Hodges, Helen F

    2011-01-01

    Successful nurses function effectively with adaptability, improvability, and interconnectedness, and can see emerging and unpredictable complex problems. Preparing new nurses for complexity requires a significant change in prevalent but dated nursing education models for rising graduates. The science of complexity coupled with problem-based learning and peer review contributes a feasible framework for a constructivist learning environment to examine real-time systems data; explore uncertainty, inherent patterns, and ambiguity; and develop skills for unstructured problem solving. This article describes a pilot study of a problem-based learning strategy guided by principles of complexity science in a community clinical nursing course. Thirty-five senior nursing students participated during a 3-year period. Assessments included peer review, a final project paper, reflection, and a satisfaction survey. Results were higher than expected levels of student satisfaction, increased breadth and analysis of complex data, acknowledgment of community as complex adaptive systems, and overall higher level thinking skills than in previous years. 2011, SLACK Incorporated.

  18. Low complexity iterative MLSE equalization of M-QAM signals in extremely long rayleigh fading channels

    CSIR Research Space (South Africa)

    Myburgh, HC

    2009-05-01

    Full Text Available long channels. Its computational complexity is linear in the data block length and approximately independent of the channel memory length, whereas conventional equalization algorithms have computational complexity linear in the data block length...

  19. From PII signaling to metabolite sensing: a novel 2-oxoglutarate sensor that details PII-NAGK complex formation.

    Directory of Open Access Journals (Sweden)

    Jan Lüddecke

    Full Text Available The widespread PII signal transduction proteins are known for integrating signals of nitrogen and energy supply and regulating cellular behavior by interacting with a multitude of target proteins. The PII protein of the cyanobacterium Synechococcus elongatus forms complexes with the controlling enzyme of arginine synthesis, N-acetyl-L-glutamate kinase (NAGK in a 2-oxoglutarate- and ATP/ADP-dependent manner. Fusing NAGK and PII proteins to either CFP or YFP yielded a FRET sensor that specifically responded to 2-oxoglutarate. The impact of the fluorescent tags on PII and NAGK was evaluated by enzyme assays, surface plasmon resonance spectroscopy and isothermal calorimetric experiments. The developed FRET sensor provides real-time data on PII - NAGK interaction and its modulation by the effector molecules ATP, ADP and 2-oxoglutarate in vitro. Additionally to its utility to monitor 2-oxoglutarate levels, the FRET assay provided novel insights into PII - NAGK complex formation: (i It revealed the formation of an encounter-complex between PII and NAGK, which holds the proteins in proximity even in the presence of inhibitors of complex formation; (ii It revealed that the PII T-loop residue Ser49 is neither essential for complex formation with NAGK nor for activation of the enzyme but necessary to form a stable complex and efficiently relieve NAGK from arginine inhibition; (iii It showed that arginine stabilizes the NAGK hexamer and stimulates PII - NAGK interaction.

  20. Phosphoproteomic profiling of in vivo signaling in liver by the mammalian target of rapamycin complex 1 (mTORC1.

    Directory of Open Access Journals (Sweden)

    Gokhan Demirkan

    Full Text Available Our understanding of signal transduction networks in the physiological context of an organism remains limited, partly due to the technical challenge of identifying serine/threonine phosphorylated peptides from complex tissue samples. In the present study, we focused on signaling through the mammalian target of rapamycin (mTOR complex 1 (mTORC1, which is at the center of a nutrient- and growth factor-responsive cell signaling network. Though studied extensively, the mechanisms involved in many mTORC1 biological functions remain poorly understood.We developed a phosphoproteomic strategy to purify, enrich and identify phosphopeptides from rat liver homogenates. Using the anticancer drug rapamycin, the only known target of which is mTORC1, we characterized signaling in liver from rats in which the complex was maximally activated by refeeding following 48 hr of starvation. Using protein and peptide fractionation methods, TiO(2 affinity purification of phosphopeptides and mass spectrometry, we reproducibly identified and quantified over four thousand phosphopeptides. Along with 5 known rapamycin-sensitive phosphorylation events, we identified 62 new rapamycin-responsive candidate phosphorylation sites. Among these were PRAS40, gephyrin, and AMP kinase 2. We observed similar proportions of increased and reduced phosphorylation in response to rapamycin. Gene ontology analysis revealed over-representation of mTOR pathway components among rapamycin-sensitive phosphopeptide candidates.In addition to identifying potential new mTORC1-mediated phosphorylation events, and providing information relevant to the biology of this signaling network, our experimental and analytical approaches indicate the feasibility of large-scale phosphoproteomic profiling of tissue samples to study physiological signaling events in vivo.

  1. ALGORITHM OF CARDIO COMPLEX DETECTION AND SORTING FOR PROCESSING THE DATA OF CONTINUOUS CARDIO SIGNAL MONITORING.

    Science.gov (United States)

    Krasichkov, A S; Grigoriev, E B; Nifontov, E M; Shapovalov, V V

    The paper presents an algorithm of cardio complex classification as part of processing the data of continuous cardiac monitoring. R-wave detection concurrently with cardio complex sorting is discussed. The core of this approach is the use of prior information about. cardio complex forms, segmental structure, and degree of kindness. Results of the sorting algorithm testing are provided.

  2. Loss of Signal, Aeromedical Lessons Learned for the STS-I07 Columbia Space Shuttle Mishap

    Science.gov (United States)

    Patlach, Robert; Stepaniak, Philip C.; Lane, Helen W.

    2014-01-01

    Loss of Signal, a NASA publication to be available in May 2014, presents the aeromedical lessons learned from the Columbia accident that will enhance crew safety and survival on human space flight missions. These lessons were presented to limited audiences at three separate Aerospace Medical Association (AsMA) conferences: in 2004 in Anchorage, Alaska, on the causes of the accident; in 2005 in Kansas City, Missouri, on the response, recovery, and identification aspects of the investigation; and in 2011, again in Anchorage, Alaska, on future implications for human space flight. As we embark on the development of new spacefaring vehicles through both government and commercial efforts, the NASA Johnson Space Center Human Health and Performance Directorate is continuing to make this information available to a wider audience engaged in the design and development of future space vehicles. Loss of Signal summarizes and consolidates the aeromedical impacts of the Columbia mishap process-the response, recovery, identification, investigative studies, medical and legal forensic analysis, and future preparation that are needed to respond to spacecraft mishaps. The goals of this book are to provide an account of the aeromedical aspects of the Columbia accident and the investigation that followed, and to encourage aerospace medical specialists to continue to capture information, learn from it, and improve procedures and spacecraft designs for the safety of future crews.

  3. Canonical TGF-β Signaling Negatively Regulates Neuronal Morphogenesis through TGIF/Smad Complex-Mediated CRMP2 Suppression.

    Science.gov (United States)

    Nakashima, Hideyuki; Tsujimura, Keita; Irie, Koichiro; Ishizu, Masataka; Pan, Miao; Kameda, Tomonori; Nakashima, Kinichi

    2018-05-16

    Functional neuronal connectivity requires proper neuronal morphogenesis and its dysregulation causes neurodevelopmental diseases. Transforming growth factor-β (TGF-β) family cytokines play pivotal roles in development, but little is known about their contribution to morphological development of neurons. Here we show that the Smad-dependent canonical signaling of TGF-β family cytokines negatively regulates neuronal morphogenesis during brain development. Mechanistically, activated Smads form a complex with transcriptional repressor TG-interacting factor (TGIF), and downregulate the expression of a neuronal polarity regulator, collapsin response mediator protein 2. We also demonstrate that TGF-β family signaling inhibits neurite elongation of human induced pluripotent stem cell-derived neurons. Furthermore, the expression of TGF-β receptor 1, Smad4, or TGIF, which have mutations found in patients with neurodevelopmental disorders, disrupted neuronal morphogenesis in both mouse (male and female) and human (female) neurons. Together, these findings suggest that the regulation of neuronal morphogenesis by an evolutionarily conserved function of TGF-β signaling is involved in the pathogenesis of neurodevelopmental diseases. SIGNIFICANCE STATEMENT Canonical transforming growth factor-β (TGF-β) signaling plays a crucial role in multiple organ development, including brain, and mutations in components of the signaling pathway associated with several human developmental disorders. In this study, we found that Smads/TG-interacting factor-dependent canonical TGF-β signaling regulates neuronal morphogenesis through the suppression of collapsin response mediator protein-2 (CRMP2) expression during brain development, and that function of this signaling is evolutionarily conserved in the mammalian brain. Mutations in canonical TGF-β signaling factors identified in patients with neurodevelopmental disorders disrupt the morphological development of neurons. Thus, our

  4. Correlations between the signal complexity of cerebral and cardiac electrical activity: a multiscale entropy analysis.

    Directory of Open Access Journals (Sweden)

    Pei-Feng Lin

    Full Text Available The heart begins to beat before the brain is formed. Whether conventional hierarchical central commands sent by the brain to the heart alone explain all the interplay between these two organs should be reconsidered. Here, we demonstrate correlations between the signal complexity of brain and cardiac activity. Eighty-seven geriatric outpatients with healthy hearts and varied cognitive abilities each provided a 24-hour electrocardiography (ECG and a 19-channel eye-closed routine electroencephalography (EEG. Multiscale entropy (MSE analysis was applied to three epochs (resting-awake state, photic stimulation of fast frequencies (fast-PS, and photic stimulation of slow frequencies (slow-PS of EEG in the 1-58 Hz frequency range, and three RR interval (RRI time series (awake-state, sleep and that concomitant with the EEG for each subject. The low-to-high frequency power (LF/HF ratio of RRI was calculated to represent sympatho-vagal balance. With statistics after Bonferroni corrections, we found that: (a the summed MSE value on coarse scales of the awake RRI (scales 11-20, RRI-MSE-coarse were inversely correlated with the summed MSE value on coarse scales of the resting-awake EEG (scales 6-20, EEG-MSE-coarse at Fp2, C4, T6 and T4; (b the awake RRI-MSE-coarse was inversely correlated with the fast-PS EEG-MSE-coarse at O1, O2 and C4; (c the sleep RRI-MSE-coarse was inversely correlated with the slow-PS EEG-MSE-coarse at Fp2; (d the RRI-MSE-coarse and LF/HF ratio of the awake RRI were correlated positively to each other; (e the EEG-MSE-coarse at F8 was proportional to the cognitive test score; (f the results conform to the cholinergic hypothesis which states that cognitive impairment causes reduction in vagal cardiac modulation; (g fast-PS significantly lowered the EEG-MSE-coarse globally. Whether these heart-brain correlations could be fully explained by the central autonomic network is unknown and needs further exploration.

  5. Persistent activation of DNA damage signaling in response to complex mixtures of PAHs in air particulate matter

    Energy Technology Data Exchange (ETDEWEB)

    Jarvis, Ian W.H., E-mail: Ian.Jarvis@ki.se [Institute of Environmental Medicine, Karolinska Institutet, Box 210, SE-171 77 Stockholm (Sweden); Bergvall, Christoffer, E-mail: Christoffer.Bergvall@anchem.su.se [Department of Analytical Chemistry, Stockholm University, Svante Arrhenius väg 16, SE-106 91 Stockholm (Sweden); Bottai, Matteo, E-mail: Matteo.Bottai@ki.se [Institute of Environmental Medicine, Karolinska Institutet, Box 210, SE-171 77 Stockholm (Sweden); Westerholm, Roger, E-mail: Roger.Westerholm@anchem.su.se [Department of Analytical Chemistry, Stockholm University, Svante Arrhenius väg 16, SE-106 91 Stockholm (Sweden); Stenius, Ulla, E-mail: Ulla.Stenius@ki.se [Institute of Environmental Medicine, Karolinska Institutet, Box 210, SE-171 77 Stockholm (Sweden); Dreij, Kristian, E-mail: Kristian.Dreij@ki.se [Institute of Environmental Medicine, Karolinska Institutet, Box 210, SE-171 77 Stockholm (Sweden)

    2013-02-01

    Complex mixtures of polycyclic aromatic hydrocarbons (PAHs) are present in air particulate matter (PM) and have been associated with many adverse human health effects including cancer and respiratory disease. However, due to their complexity, the risk of exposure to mixtures is difficult to estimate. In the present study the effects of binary mixtures of benzo[a]pyrene (BP) and dibenzo[a,l]pyrene (DBP) and complex mixtures of PAHs in urban air PM extracts on DNA damage signaling was investigated. Applying a statistical model to the data we observed a more than additive response for binary mixtures of BP and DBP on activation of DNA damage signaling. Persistent activation of checkpoint kinase 1 (Chk1) was observed at significantly lower BP equivalent concentrations in air PM extracts than BP alone. Activation of DNA damage signaling was also more persistent in air PM fractions containing PAHs with more than four aromatic rings suggesting larger PAHs contribute a greater risk to human health. Altogether our data suggests that human health risk assessment based on additivity such as toxicity equivalency factor scales may significantly underestimate the risk of exposure to complex mixtures of PAHs. The data confirms our previous findings with PAH-contaminated soil (Niziolek-Kierecka et al., 2012) and suggests a possible role for Chk1 Ser317 phosphorylation as a biological marker for future analyses of complex mixtures of PAHs. -- Highlights: ► Benzo[a]pyrene (BP), dibenzo[a,l]pyrene (DBP) and air PM PAH extracts were compared. ► Binary mixture of BP and DBP induced a more than additive DNA damage response. ► Air PM PAH extracts were more potent than toxicity equivalency factor estimates. ► Larger PAHs (> 4 rings) contribute more to the genotoxicity of PAHs in air PM. ► Chk1 is a sensitive marker for persistent activation of DNA damage signaling from PAH mixtures.

  6. Persistent activation of DNA damage signaling in response to complex mixtures of PAHs in air particulate matter

    International Nuclear Information System (INIS)

    Jarvis, Ian W.H.; Bergvall, Christoffer; Bottai, Matteo; Westerholm, Roger; Stenius, Ulla; Dreij, Kristian

    2013-01-01

    Complex mixtures of polycyclic aromatic hydrocarbons (PAHs) are present in air particulate matter (PM) and have been associated with many adverse human health effects including cancer and respiratory disease. However, due to their complexity, the risk of exposure to mixtures is difficult to estimate. In the present study the effects of binary mixtures of benzo[a]pyrene (BP) and dibenzo[a,l]pyrene (DBP) and complex mixtures of PAHs in urban air PM extracts on DNA damage signaling was investigated. Applying a statistical model to the data we observed a more than additive response for binary mixtures of BP and DBP on activation of DNA damage signaling. Persistent activation of checkpoint kinase 1 (Chk1) was observed at significantly lower BP equivalent concentrations in air PM extracts than BP alone. Activation of DNA damage signaling was also more persistent in air PM fractions containing PAHs with more than four aromatic rings suggesting larger PAHs contribute a greater risk to human health. Altogether our data suggests that human health risk assessment based on additivity such as toxicity equivalency factor scales may significantly underestimate the risk of exposure to complex mixtures of PAHs. The data confirms our previous findings with PAH-contaminated soil (Niziolek-Kierecka et al., 2012) and suggests a possible role for Chk1 Ser317 phosphorylation as a biological marker for future analyses of complex mixtures of PAHs. -- Highlights: ► Benzo[a]pyrene (BP), dibenzo[a,l]pyrene (DBP) and air PM PAH extracts were compared. ► Binary mixture of BP and DBP induced a more than additive DNA damage response. ► Air PM PAH extracts were more potent than toxicity equivalency factor estimates. ► Larger PAHs (> 4 rings) contribute more to the genotoxicity of PAHs in air PM. ► Chk1 is a sensitive marker for persistent activation of DNA damage signaling from PAH mixtures.

  7. Learning about Complex Multi-Stakeholder Issues: Assessing the Visual Problem Appraisal

    NARCIS (Netherlands)

    Witteveen, L.M.; Put, M.; Leeuwis, C.

    2010-01-01

    This paper presents an evaluation of the visual problem appraisal (VPA) learning environment in higher education. The VPA has been designed for the training of competences that are required in complex stakeholder settings in relation to sustainability issues. The design of VPA incorporates a

  8. Are Agile and Lean Manufacturing Systems Employing Sustainability, Complexity and Organizational Learning?

    Science.gov (United States)

    Flumerfelt, Shannon; Siriban-Manalang, Anna Bella; Kahlen, Franz-Josef

    2012-01-01

    Purpose: This paper aims to peruse theories and practices of agile and lean manufacturing systems to determine whether they employ sustainability, complexity and organizational learning. Design/methodology/approach: The critical review of the comparative operational similarities and difference of the two systems was conducted while the new views…

  9. Environmental Factors Affecting Computer Assisted Language Learning Success: A Complex Dynamic Systems Conceptual Model

    Science.gov (United States)

    Marek, Michael W.; Wu, Wen-Chi Vivian

    2014-01-01

    This conceptual, interdisciplinary inquiry explores Complex Dynamic Systems as the concept relates to the internal and external environmental factors affecting computer assisted language learning (CALL). Based on the results obtained by de Rosnay ["World Futures: The Journal of General Evolution", 67(4/5), 304-315 (2011)], who observed…

  10. Low Complexity Sparse Bayesian Learning for Channel Estimation Using Generalized Mean Field

    DEFF Research Database (Denmark)

    Pedersen, Niels Lovmand; Manchón, Carles Navarro; Fleury, Bernard Henri

    2014-01-01

    We derive low complexity versions of a wide range of algorithms for sparse Bayesian learning (SBL) in underdetermined linear systems. The proposed algorithms are obtained by applying the generalized mean field (GMF) inference framework to a generic SBL probabilistic model. In the GMF framework, we...

  11. The effects of practice schedule and critical thinking prompts on learning and transfer of complex judgment

    NARCIS (Netherlands)

    Helsdingen, Anne; Van Gog, Tamara; Van Merriënboer, Jeroen

    2010-01-01

    Helsdingen, A. S., Van Gog, T., & Van Merriënboer, J. J. G. (2011). The effects of practice schedule and critical thinking prompts on learning and transfer of complex judgment task. Journal of Educational Psychology, 103(2), 383-398. doi:10.1037/a0022370

  12. Self-Disclosure and Adults with Learning Disabilities: Practical Ideas about a Complex Process

    Science.gov (United States)

    Gerber, Paul J.; Price, Lynda A.

    2008-01-01

    Self-disclosure for adults with learning disabilities is very complex after the beyond-school years. The issues of invisibility, risk/benefit, and the multiple contexts of adult functioning create many challenges in the process of disclosure. Moreover, self-disclosure, one element of the larger issue of self-determination, is viewed as an entry…

  13. Optimizing the number of steps in learning tasks for complex skills.

    NARCIS (Netherlands)

    Nadolski, Rob; Kirschner, Paul A.; Van Merriënboer, Jeroen

    2007-01-01

    Background. Carrying out whole tasks is often too difficult for novice learners attempting to acquire complex skills. The common solution is to split up the tasks into a number of smaller steps. The number of steps must be optimised for efficient and effective learning. Aim. The aim of the study is

  14. Instructional Control of Cognitive Load in the Design of Complex Learning Environments

    NARCIS (Netherlands)

    Kester, Liesbeth; Paas, Fred; Van Merriënboer, Jeroen

    2010-01-01

    Kester, L., Paas, F., & Van Merriënboer, J. J. G. (2010). Instructional control of cognitive load in the design of complex learning environments. In J. L. Plass, R. Moreno, & Roland Brünken (Eds.), Cognitive Load Theory (pp. 109-130). New York: Cambridge University Press.

  15. Successfully carrying out complex learning-tasks through guiding teams' qualitative and quantitative reasoning

    NARCIS (Netherlands)

    Slof, B.; Erkens, G.; Kirschner, P. A.; Janssen, J.; Jaspers, J. G. M.

    This study investigated whether and how scripting learners' use of representational tools in a computer supported collaborative learning (CSCL)-environment fostered their collaborative performance on a complex business-economics task. Scripting the problem-solving process sequenced and made its

  16. Successfully Carrying out Complex Learning-Tasks through Guiding Teams' Qualitative and Quantitative Reasoning

    Science.gov (United States)

    Slof, B.; Erkens, G.; Kirschner, P. A.; Janssen, J.; Jaspers, J. G. M.

    2012-01-01

    This study investigated whether and how scripting learners' use of representational tools in a computer supported collaborative learning (CSCL)-environment fostered their collaborative performance on a complex business-economics task. Scripting the problem-solving process sequenced and made its phase-related part-task demands explicit, namely…

  17. Force and complexity of tongue task training influences behavioral measures of motor learning

    DEFF Research Database (Denmark)

    Kothari, Mohit; Svensson, Peter; Huo, Xueliang

    2012-01-01

    Relearning of motor skills is important in neurorehabilitation. We investigated the improvement of training success during simple tongue protrusion (two force levels) and a more complex tongue-training paradigm using the Tongue Drive System (TDS). We also compared subject-based reports of fun, pain...... training influences behavioral aspects of tongue motor learning....

  18. Learning and inference using complex generative models in a spatial localization task.

    Science.gov (United States)

    Bejjanki, Vikranth R; Knill, David C; Aslin, Richard N

    2016-01-01

    A large body of research has established that, under relatively simple task conditions, human observers integrate uncertain sensory information with learned prior knowledge in an approximately Bayes-optimal manner. However, in many natural tasks, observers must perform this sensory-plus-prior integration when the underlying generative model of the environment consists of multiple causes. Here we ask if the Bayes-optimal integration seen with simple tasks also applies to such natural tasks when the generative model is more complex, or whether observers rely instead on a less efficient set of heuristics that approximate ideal performance. Participants localized a "hidden" target whose position on a touch screen was sampled from a location-contingent bimodal generative model with different variances around each mode. Over repeated exposure to this task, participants learned the a priori locations of the target (i.e., the bimodal generative model), and integrated this learned knowledge with uncertain sensory information on a trial-by-trial basis in a manner consistent with the predictions of Bayes-optimal behavior. In particular, participants rapidly learned the locations of the two modes of the generative model, but the relative variances of the modes were learned much more slowly. Taken together, our results suggest that human performance in a more complex localization task, which requires the integration of sensory information with learned knowledge of a bimodal generative model, is consistent with the predictions of Bayes-optimal behavior, but involves a much longer time-course than in simpler tasks.

  19. Fast social-like learning of complex behaviors based on motor motifs

    Science.gov (United States)

    Calvo Tapia, Carlos; Tyukin, Ivan Y.; Makarov, Valeri A.

    2018-05-01

    Social learning is widely observed in many species. Less experienced agents copy successful behaviors exhibited by more experienced individuals. Nevertheless, the dynamical mechanisms behind this process remain largely unknown. Here we assume that a complex behavior can be decomposed into a sequence of n motor motifs. Then a neural network capable of activating motor motifs in a given sequence can drive an agent. To account for (n -1 )! possible sequences of motifs in a neural network, we employ the winnerless competition approach. We then consider a teacher-learner situation: one agent exhibits a complex movement, while another one aims at mimicking the teacher's behavior. Despite the huge variety of possible motif sequences we show that the learner, equipped with the provided learning model, can rewire "on the fly" its synaptic couplings in no more than (n -1 ) learning cycles and converge exponentially to the durations of the teacher's motifs. We validate the learning model on mobile robots. Experimental results show that the learner is indeed capable of copying the teacher's behavior composed of six motor motifs in a few learning cycles. The reported mechanism of learning is general and can be used for replicating different functions, including, for example, sound patterns or speech.

  20. Maize and Arabidopsis ARGOS Proteins Interact with Ethylene Receptor Signaling Complex, Supporting a Regulatory Role for ARGOS in Ethylene Signal Transduction[OPEN

    Science.gov (United States)

    Shi, Jinrui; Wang, Hongyu; Habben, Jeffrey E.

    2016-01-01

    The phytohormone ethylene regulates plant growth and development as well as plant response to environmental cues. ARGOS genes reduce plant sensitivity to ethylene when overexpressed in transgenic Arabidopsis (Arabidopsis thaliana) and maize (Zea mays). A previous genetic study suggested that the endoplasmic reticulum and Golgi-localized maize ARGOS1 targets the ethylene signal transduction components at or upstream of CONSTITUTIVE TRIPLE RESPONSE1, but the mechanism of ARGOS modulating ethylene signaling is unknown. Here, we demonstrate in Arabidopsis that ZmARGOS1, as well as the Arabidopsis ARGOS homolog ORGAN SIZE RELATED1, physically interacts with Arabidopsis REVERSION-TO-ETHYLENE SENSITIVITY1 (RTE1), an ethylene receptor interacting protein that regulates the activity of ETHYLENE RESPONSE1. The protein-protein interaction was also detected with the yeast split-ubiquitin two-hybrid system. Using the same yeast assay, we found that maize RTE1 homolog REVERSION-TO-ETHYLENE SENSITIVITY1 LIKE4 (ZmRTL4) and ZmRTL2 also interact with maize and Arabidopsis ARGOS proteins. Like AtRTE1 in Arabidopsis, ZmRTL4 and ZmRTL2 reduce ethylene responses when overexpressed in maize, indicating a similar mechanism for ARGOS regulating ethylene signaling in maize. A polypeptide fragment derived from ZmARGOS8, consisting of a Pro-rich motif flanked by two transmembrane helices that are conserved among members of the ARGOS family, can interact with AtRTE1 and maize RTL proteins in Arabidopsis. The conserved domain is necessary and sufficient to reduce ethylene sensitivity in Arabidopsis and maize. Overall, these results suggest a physical association between ARGOS and the ethylene receptor signaling complex via AtRTE1 and maize RTL proteins, supporting a role for ARGOS in regulating ethylene perception and the early steps of signal transduction in Arabidopsis and maize. PMID:27268962

  1. Deciphering complex dynamics of water counteraction around secondary structural elements of allosteric protein complex: Case study of SAP-SLAM system in signal transduction cascade.

    Science.gov (United States)

    Samanta, Sudipta; Mukherjee, Sanchita

    2018-01-28

    The first hydration shell of a protein exhibits heterogeneous behavior owing to several attributes, majorly local polarity and structural flexibility as revealed by solvation dynamics of secondary structural elements. We attempt to recognize the change in complex water counteraction generated due to substantial alteration in flexibility during protein complex formation. The investigation is carried out with the signaling lymphocytic activation molecule (SLAM) family of receptors, expressed by an array of immune cells, and interacting with SLAM-associated protein (SAP), composed of one SH2 domain. All atom molecular dynamics simulations are employed to the aqueous solutions of free SAP and SLAM-peptide bound SAP. We observed that water dynamics around different secondary structural elements became highly affected as well as nicely correlated with the SLAM-peptide induced change in structural rigidity obtained by thermodynamic quantification. A few instances of contradictory dynamic features of water to the change in structural flexibility are explained by means of occluded polar residues by the peptide. For βD, EFloop, and BGloop, both structural flexibility and solvent accessibility of the residues confirm the obvious contribution. Most importantly, we have quantified enhanced restriction in water dynamics around the second Fyn-binding site of the SAP due to SAP-SLAM complexation, even prior to the presence of Fyn. This observation leads to a novel argument that SLAM induced more restricted water molecules could offer more water entropic contribution during the subsequent Fyn binding and provide enhanced stability to the SAP-Fyn complex in the signaling cascade. Finally, SLAM induced water counteraction around the second binding site of the SAP sheds light on the allosteric property of the SAP, which becomes an integral part of the underlying signal transduction mechanism.

  2. Deciphering complex dynamics of water counteraction around secondary structural elements of allosteric protein complex: Case study of SAP-SLAM system in signal transduction cascade

    Science.gov (United States)

    Samanta, Sudipta; Mukherjee, Sanchita

    2018-01-01

    The first hydration shell of a protein exhibits heterogeneous behavior owing to several attributes, majorly local polarity and structural flexibility as revealed by solvation dynamics of secondary structural elements. We attempt to recognize the change in complex water counteraction generated due to substantial alteration in flexibility during protein complex formation. The investigation is carried out with the signaling lymphocytic activation molecule (SLAM) family of receptors, expressed by an array of immune cells, and interacting with SLAM-associated protein (SAP), composed of one SH2 domain. All atom molecular dynamics simulations are employed to the aqueous solutions of free SAP and SLAM-peptide bound SAP. We observed that water dynamics around different secondary structural elements became highly affected as well as nicely correlated with the SLAM-peptide induced change in structural rigidity obtained by thermodynamic quantification. A few instances of contradictory dynamic features of water to the change in structural flexibility are explained by means of occluded polar residues by the peptide. For βD, EFloop, and BGloop, both structural flexibility and solvent accessibility of the residues confirm the obvious contribution. Most importantly, we have quantified enhanced restriction in water dynamics around the second Fyn-binding site of the SAP due to SAP-SLAM complexation, even prior to the presence of Fyn. This observation leads to a novel argument that SLAM induced more restricted water molecules could offer more water entropic contribution during the subsequent Fyn binding and provide enhanced stability to the SAP-Fyn complex in the signaling cascade. Finally, SLAM induced water counteraction around the second binding site of the SAP sheds light on the allosteric property of the SAP, which becomes an integral part of the underlying signal transduction mechanism.

  3. Investigating complex patterns of blocked intestinal artery blood pressure signals by empirical mode decomposition and linguistic analysis

    International Nuclear Information System (INIS)

    Yeh, J-R; Lin, T-Y; Shieh, J-S; Chen, Y; Huang, N E; Wu, Z; Peng, C-K

    2008-01-01

    In this investigation, surgical operations of blocked intestinal artery have been conducted on pigs to simulate the condition of acute mesenteric arterial occlusion. The empirical mode decomposition method and the algorithm of linguistic analysis were applied to verify the blood pressure signals in simulated situation. We assumed that there was some information hidden in the high-frequency part of the blood pressure signal when an intestinal artery is blocked. The empirical mode decomposition method (EMD) has been applied to decompose the intrinsic mode functions (IMF) from a complex time series. But, the end effects and phenomenon of intermittence damage the consistence of each IMF. Thus, we proposed the complementary ensemble empirical mode decomposition method (CEEMD) to solve the problems of end effects and the phenomenon of intermittence. The main wave of blood pressure signals can be reconstructed by the main components, identified by Monte Carlo verification, and removed from the original signal to derive a riding wave. Furthermore, the concept of linguistic analysis was applied to design the blocking index to verify the pattern of riding wave of blood pressure using the measurements of dissimilarity. Blocking index works well to identify the situation in which the sampled time series of blood pressure signal was recorded. Here, these two totally different algorithms are successfully integrated and the existence of the existence of information hidden in high-frequency part of blood pressure signal has been proven

  4. Investigating complex patterns of blocked intestinal artery blood pressure signals by empirical mode decomposition and linguistic analysis

    Energy Technology Data Exchange (ETDEWEB)

    Yeh, J-R; Lin, T-Y; Shieh, J-S [Department of Mechanical Engineering, Yuan Ze University, 135 Far-East Road, Chung-Li, Taoyuan, Taiwan (China); Chen, Y [Far Eastern Memorial Hospital, Taiwan (China); Huang, N E [Research Center for Adaptive Data Analysis, National Central University, Taiwan (China); Wu, Z [Center for Ocean-Land-Atmosphere Studies (United States); Peng, C-K [Beth Israel Deaconess Medical Center, Harvard Medical School (United States)], E-mail: s939205@ mail.yzu.edu.tw

    2008-02-15

    In this investigation, surgical operations of blocked intestinal artery have been conducted on pigs to simulate the condition of acute mesenteric arterial occlusion. The empirical mode decomposition method and the algorithm of linguistic analysis were applied to verify the blood pressure signals in simulated situation. We assumed that there was some information hidden in the high-frequency part of the blood pressure signal when an intestinal artery is blocked. The empirical mode decomposition method (EMD) has been applied to decompose the intrinsic mode functions (IMF) from a complex time series. But, the end effects and phenomenon of intermittence damage the consistence of each IMF. Thus, we proposed the complementary ensemble empirical mode decomposition method (CEEMD) to solve the problems of end effects and the phenomenon of intermittence. The main wave of blood pressure signals can be reconstructed by the main components, identified by Monte Carlo verification, and removed from the original signal to derive a riding wave. Furthermore, the concept of linguistic analysis was applied to design the blocking index to verify the pattern of riding wave of blood pressure using the measurements of dissimilarity. Blocking index works well to identify the situation in which the sampled time series of blood pressure signal was recorded. Here, these two totally different algorithms are successfully integrated and the existence of the existence of information hidden in high-frequency part of blood pressure signal has been proven.

  5. The importance of cultivating a preference for complexity in veterinarians for effective lifelong learning.

    Science.gov (United States)

    Dale, Vicki H M; Pierce, Stephanie E; May, Stephen A

    2010-01-01

    Much attention has been paid to the link between students' approaches to study and the quality of their learning. Less attention has been paid to the lifelong learner. We conceptualized a tripartite relationship between three measures of learning preference: conceptions of knowledge (construction and use vs. intake), need for cognition (high vs. low), and approach to study (deep vs. surface) and hypothesized that an individual's profile on these three measures-reconceptualized as a preference for complexity versus simplicity-would affect their attitude toward continuing professional development (CPD). A questionnaire was mailed to 2,000 randomly selected, home-practicing UK veterinarians to quantify their learning preferences, motivation to engage in CPD, and perception of barriers to participation and to assess the relationships between these constructs. Analysis of 775 responses (a 38.8% response rate) confirmed our tripartite model of learning and showed that a preference for complexity was negatively correlated with barriers and positively correlated with intrinsic, social, and extrinsic motivating factors, suggesting that all play a role in the continuing education of this group of professionals. A preference for simplicity was negatively correlated with social motivation and positively correlated with barriers. This study demonstrates that approach not only affects the quality of learning but crucially affects motivation to engage in CPD and perception of barriers to lifelong learning. This should emphasize to veterinary educators the importance of fostering a preference for complexity from an early age, both in terms of its immediate benefits (better understanding) and longer-term benefits (continued engagement with learning).

  6. Examining Motivation in Online Distance Learning Environments: Complex, Multifaceted, and Situation-Dependent

    Directory of Open Access Journals (Sweden)

    Maggie Hartnett

    2011-10-01

    Full Text Available Existing research into motivation in online environments has tended to use one of two approaches. The first adopts a trait-like model that views motivation as a relatively stable, personal characteristic of the learner. Research from this perspective has contributed to the notion that online learners are, on the whole, intrinsically motivated. The alternative view concentrates on the design of online learning environments to encourage optimal learner motivation. Neither approach acknowledges a contemporary view of motivation that emphasises the situated, mutually constitutive relationship of the learner and the learning environment. Using self-determination theory (SDT as a framework, this paper explores the motivation to learn of preservice teachers in two online distance-learning contexts. In this study, learners were found to be not primarily intrinsically motivated. Instead, student motivation was found to be complex, multifaceted, and sensitive to situational conditions.

  7. Preprotein import into chloroplasts via the Toc and Tic complexes is regulated by redox signals in Pisum sativum.

    Science.gov (United States)

    Stengel, Anna; Benz, J Philipp; Buchanan, Bob B; Soll, Jürgen; Bölter, Bettina

    2009-11-01

    The import of nuclear-encoded preproteins is necessary to maintain chloroplast function. The recognition and transfer of most precursor proteins across the chloroplast envelopes are facilitated by two membrane-inserted protein complexes, the translocons of the chloroplast outer and inner envelope (Toc and Tic complexes, respectively). Several signals have been invoked to regulate the import of preproteins. In our study, we were interested in redox-based import regulation mediated by two signals: regulation based on thiols and on the metabolic NADP+/NADPH ratio. We sought to identify the proteins participating in the regulation of these transport pathways and to characterize the preprotein subgroups whose import is redox-dependent. Our results provide evidence that the formation and reduction of disulfide bridges in the Toc receptors and Toc translocation channel have a strong influence on import yield of all tested preproteins that depend on the Toc complex for translocation. Furthermore, the metabolic NADP+/NADPH ratio influences not only the composition of the Tic complex, but also the import efficiency of most, but not all, preproteins tested. Thus, several Tic subcomplexes appear to participate in the translocation of different preprotein subgroups, and the redox-active components of these complexes likely play a role in regulating transport.

  8. Reinforcement learning signals in the human striatum distinguish learners from nonlearners during reward-based decision making.

    Science.gov (United States)

    Schönberg, Tom; Daw, Nathaniel D; Joel, Daphna; O'Doherty, John P

    2007-11-21

    The computational framework of reinforcement learning has been used to forward our understanding of the neural mechanisms underlying reward learning and decision-making behavior. It is known that humans vary widely in their performance in decision-making tasks. Here, we used a simple four-armed bandit task in which subjects are almost evenly split into two groups on the basis of their performance: those who do learn to favor choice of the optimal action and those who do not. Using models of reinforcement learning we sought to determine the neural basis of these intrinsic differences in performance by scanning both groups with functional magnetic resonance imaging. We scanned 29 subjects while they performed the reward-based decision-making task. Our results suggest that these two groups differ markedly in the degree to which reinforcement learning signals in the striatum are engaged during task performance. While the learners showed robust prediction error signals in both the ventral and dorsal striatum during learning, the nonlearner group showed a marked absence of such signals. Moreover, the magnitude of prediction error signals in a region of dorsal striatum correlated significantly with a measure of behavioral performance across all subjects. These findings support a crucial role of prediction error signals, likely originating from dopaminergic midbrain neurons, in enabling learning of action selection preferences on the basis of obtained rewards. Thus, spontaneously observed individual differences in decision making performance demonstrate the suggested dependence of this type of learning on the functional integrity of the dopaminergic striatal system in humans.

  9. Effect of harmonicity on the detection of a signal in a complex masker and on spatial release from masking.

    Directory of Open Access Journals (Sweden)

    Astrid Klinge

    Full Text Available The amount of masking of sounds from one source (signals by sounds from a competing source (maskers heavily depends on the sound characteristics of the masker and the signal and on their relative spatial location. Numerous studies investigated the ability to detect a signal in a speech or a noise masker or the effect of spatial separation of signal and masker on the amount of masking, but there is a lack of studies investigating the combined effects of many cues on the masking as is typical for natural listening situations. The current study using free-field listening systematically evaluates the combined effects of harmonicity and inharmonicity cues in multi-tone maskers and cues resulting from spatial separation of target signal and masker on the detection of a pure tone in a multi-tone or a noise masker. A linear binaural processing model was implemented to predict the masked thresholds in order to estimate whether the observed thresholds can be accounted for by energetic masking in the auditory periphery or whether other effects are involved. Thresholds were determined for combinations of two target frequencies (1 and 8 kHz, two spatial configurations (masker and target either co-located or spatially separated by 90 degrees azimuth, and five different masker types (four complex multi-tone stimuli, one noise masker. A spatial separation of target and masker resulted in a release from masking for all masker types. The amount of masking significantly depended on the masker type and frequency range. The various harmonic and inharmonic relations between target and masker or between components of the masker resulted in a complex pattern of increased or decreased masked thresholds in comparison to the predicted energetic masking. The results indicate that harmonicity cues affect the detectability of a tonal target in a complex masker.

  10. Entamoeba histolytica: a beta 1 integrin-like fibronectin receptor assembles a signaling complex similar to those of mammalian cells.

    Science.gov (United States)

    Flores-Robles, Donaciano; Rosales, Carlos; Rosales-Encina, José Luis; Talamás-Rohana, Patricia

    2003-01-01

    During tissue invasion, Entamoeba histolytica trophozoites interact with endothelial cells and extracellular matrix (ECM) proteins such as fibronectin (FN), collagen, and laminin. It has been demonstrated that trophozoites interact with FN through a beta1 integrin-like FN receptor (beta 1EhFNR), activating tyrosine kinases. In order to characterize the signaling process triggered by the amoebic receptor, activation, and association of tyrosine kinases and structural proteins were determined. As a result of FN binding by the beta 1EhFNR, the receptor itself, FAK, and paxillin were phosphorylated in tyrosine. Co-immunoprecipitation experiments showed that a multimolecular signaling complex was formed by the amoebic FN receptor, FAK, paxillin, and vinculin. These results strongly suggest that a signaling pathway, similar to the one used in mammalian cells, is activated when E. histolytica trophozoites adhere to FN.

  11. Applying Statistical and Complex Network Methods to Explore the Key Signaling Molecules of Acupuncture Regulating Neuroendocrine-Immune Network

    Directory of Open Access Journals (Sweden)

    Kuo Zhang

    2018-01-01

    Full Text Available The mechanisms of acupuncture are still unclear. In order to reveal the regulatory effect of manual acupuncture (MA on the neuroendocrine-immune (NEI network and identify the key signaling molecules during MA modulating NEI network, we used a rat complete Freund’s adjuvant (CFA model to observe the analgesic and anti-inflammatory effect of MA, and, what is more, we used statistical and complex network methods to analyze the data about the expression of 55 common signaling molecules of NEI network in ST36 (Zusanli acupoint, and serum and hind foot pad tissue. The results indicate that MA had significant analgesic, anti-inflammatory effects on CFA rats; the key signaling molecules may play a key role during MA regulating NEI network, but further research is needed.

  12. CYLD Limits Lys63- and Met1-Linked Ubiquitin at Receptor Complexes to Regulate Innate Immune Signaling

    Directory of Open Access Journals (Sweden)

    Matous Hrdinka

    2016-03-01

    Full Text Available Innate immune signaling relies on the deposition of non-degradative polyubiquitin at receptor-signaling complexes, but how these ubiquitin modifications are regulated by deubiquitinases remains incompletely understood. Met1-linked ubiquitin (Met1-Ub is assembled by the linear ubiquitin assembly complex (LUBAC, and this is counteracted by the Met1-Ub-specific deubiquitinase OTULIN, which binds to the catalytic LUBAC subunit HOIP. In this study, we report that HOIP also interacts with the deubiquitinase CYLD but that CYLD does not regulate ubiquitination of LUBAC components. Instead, CYLD limits extension of Lys63-Ub and Met1-Ub conjugated to RIPK2 to restrict signaling and cytokine production. Accordingly, Met1-Ub and Lys63-Ub were individually required for productive NOD2 signaling. Our study thus suggests that LUBAC, through its associated deubiquitinases, coordinates the deposition of not only Met1-Ub but also Lys63-Ub to ensure an appropriate response to innate immune receptor activation.

  13. Conformational coupling between receptor and kinase binding sites through a conserved salt bridge in a signaling complex scaffold protein.

    Directory of Open Access Journals (Sweden)

    Davi R Ortega

    Full Text Available Bacterial chemotaxis is one of the best studied signal transduction pathways. CheW is a scaffold protein that mediates the association of the chemoreceptors and the CheA kinase in a ternary signaling complex. The effects of replacing conserved Arg62 of CheW with other residues suggested that the scaffold protein plays a more complex role than simply binding its partner proteins. Although R62A CheW had essentially the same affinity for chemoreceptors and CheA, cells expressing the mutant protein are impaired in chemotaxis. Using a combination of molecular dynamics simulations (MD, NMR spectroscopy, and circular dichroism (CD, we addressed the role of Arg62. Here we show that Arg62 forms a salt bridge with another highly conserved residue, Glu38. Although this interaction is unimportant for overall protein stability, it is essential to maintain the correct alignment of the chemoreceptor and kinase binding sites of CheW. Computational and experimental data suggest that the role of the salt bridge in maintaining the alignment of the two partner binding sites is fundamental to the function of the signaling complex but not to its assembly. We conclude that a key feature of CheW is to maintain the specific geometry between the two interaction sites required for its function as a scaffold.

  14. The Conceptual Mechanism for Viable Organizational Learning Based on Complex System Theory and the Viable System Model

    Science.gov (United States)

    Sung, Dia; You, Yeongmahn; Song, Ji Hoon

    2008-01-01

    The purpose of this research is to explore the possibility of viable learning organizations based on identifying viable organizational learning mechanisms. Two theoretical foundations, complex system theory and viable system theory, have been integrated to provide the rationale for building the sustainable organizational learning mechanism. The…

  15. Achieving Complex Learning Outcomes through Adoption of a Pedagogical Perspective: A Model for Computer Technology Delivered Instruction

    Science.gov (United States)

    Bellard, Breshanica

    2018-01-01

    Professionals responsible for the delivery of education and training using technology systems and platforms can facilitate complex learning through application of relevant strategies, principles and theories that support how learners learn and that support how curriculum should be designed in a technology based learning environment. Technological…

  16. CD163: a signal receptor scavenging haptoglobin-hemoglobin complexes from plasma

    DEFF Research Database (Denmark)

    Graversen, Jonas Heilskov; Madsen, Mette; Moestrup, Søren K

    2002-01-01

    as the endocytic receptor binding hemoglobin (Hb) in complex with the plasma protein haptoglobin (Hp). This specific receptor-ligand interaction leading to removal from plasma of the Hp-Hb complex-but not free Hp or Hb-now explains the depletion of circulating Hp in individuals with increased intravascular...

  17. Wing, tail, and vocal contributions to the complex acoustic signals of courting Calliope hummingbirds

    Directory of Open Access Journals (Sweden)

    Christopher James CLARK

    2011-04-01

    Full Text Available Multi-component signals contain multiple signal parts expressed in the same physical modality. One way to identify individual components is if they are produced by different physical mechanisms. Here, I studied the mechanisms generating acoustic signals in the courtship displays of the Calliope hummingbird Stellula calliope. Display dives consisted of three synchronized sound elements, a high-frequency tone (hft, a low frequency tone (lft, and atonal sound pulses (asp, which were then followed by a frequency-modulated fall. Manipulating any of the rectrices (tail-feathers of wild males impaired production of the lft and asp but not the hft or fall, which are apparently vocal. I tested the sound production capabilities of the rectrices in a wind tunnel. Single rectrices could generate the lft but not the asp, whereas multiple rectrices tested together produced sounds similar to the asp when they fluttered and collided with their neighbors percussively, representing a previously unknown mechanism of sound production. During the shuttle display, a trill is generated by the wings during pulses in which the wingbeat frequency is elevated to 95 Hz, 40% higher than the typical hovering wingbeat frequency. The Calliope hummingbird courtship displays include sounds produced by three independent mechanisms, and thus include a minimum of three acoustic signal components. These acoustic mechanisms have different constraints and thus potentially contain different messages. Producing multiple acoustic signals via multiple mechanisms may be a way to escape the constraints present in any single mechanism [Current Zoology 57 (2: 187–196, 2011].

  18. Thrive or overload? The effect of task complexity on novices' simulation-based learning.

    Science.gov (United States)

    Haji, Faizal A; Cheung, Jeffrey J H; Woods, Nicole; Regehr, Glenn; de Ribaupierre, Sandrine; Dubrowski, Adam

    2016-09-01

    Fidelity is widely viewed as an important element of simulation instructional design based on its purported relationship with transfer of learning. However, higher levels of fidelity may increase task complexity to a point at which novices' cognitive resources become overloaded. In this experiment, we investigate the effects of variations in task complexity on novices' cognitive load and learning during simulation-based procedural skills training. Thirty-eight medical students were randomly assigned to simulation training on a simple or complex lumbar puncture (LP) task. Participants completed four practice trials on this task (skill acquisition). After 10 days of rest, all participants completed one additional trial on their assigned task (retention) and one trial on a 'very complex' simulation designed to be similar to the complex task (transfer). We assessed LP performance and cognitive load on each trial using multiple measures. In both groups, LP performance improved significantly during skill acquisition (p ≤ 0.047, f = 0.29-0.96) and was maintained at retention. The simple task group demonstrated superior performance compared with the complex task group throughout these phases (p ≤ 0.002, d = 1.13-2.31). Cognitive load declined significantly in the simple task group (p Education.

  19. Precise Temporal Profiling of Signaling Complexes in Primary Cells Using SWATH Mass Spectrometry

    Directory of Open Access Journals (Sweden)

    Etienne Caron

    2017-03-01

    Full Text Available Spatiotemporal organization of protein interactions in cell signaling is a fundamental process that drives cellular functions. Given differential protein expression across tissues and developmental stages, the architecture and dynamics of signaling interaction proteomes is, likely, highly context dependent. However, current interaction information has been almost exclusively obtained from transformed cells. In this study, we applied an advanced and robust workflow combining mouse genetics and affinity purification (AP-SWATH mass spectrometry to profile the dynamics of 53 high-confidence protein interactions in primary T cells, using the scaffold protein GRB2 as a model. The workflow also provided a sufficient level of robustness to pinpoint differential interaction dynamics between two similar, but functionally distinct, primary T cell populations. Altogether, we demonstrated that precise and reproducible quantitative measurements of protein interaction dynamics can be achieved in primary cells isolated from mammalian tissues, allowing resolution of the tissue-specific context of cell-signaling events.

  20. The biology of skin wetness perception and its implications in manual function and for reproducing complex somatosensory signals in neuroprosthetics

    Science.gov (United States)

    Ackerley, Rochelle

    2017-01-01

    Our perception of skin wetness is generated readily, yet humans have no known receptor (hygroreceptor) to signal this directly. It is easy to imagine the sensation of water running over our hands or the feel of rain on our skin. The synthetic sensation of wetness is thought to be produced from a combination of specific skin thermal and tactile inputs, registered through thermoreceptors and mechanoreceptors, respectively. The present review explores how thermal and tactile afference from the periphery can generate the percept of wetness centrally. We propose that the main signals include information about skin cooling, signaled primarily by thinly myelinated thermoreceptors, and rapid changes in touch, through fast-conducting, myelinated mechanoreceptors. Potential central sites for integration of these signals, and thus the perception of skin wetness, include the primary and secondary somatosensory cortices and the insula cortex. The interactions underlying these processes can also be modeled to aid in understanding and engineering the mechanisms. Furthermore, we discuss the role that sensing wetness could play in precision grip and the dexterous manipulation of objects. We expand on these lines of inquiry to the application of the knowledge in designing and creating skin sensory feedback in prosthetics. The addition of real-time, complex sensory signals would mark a significant advance in the use and incorporation of prosthetic body parts for amputees in everyday life. PMID:28123008

  1. Stream computing for biomedical signal processing: A QRS complex detection case-study.

    Science.gov (United States)

    Murphy, B M; O'Driscoll, C; Boylan, G B; Lightbody, G; Marnane, W P

    2015-01-01

    Recent developments in "Big Data" have brought significant gains in the ability to process large amounts of data on commodity server hardware. Stream computing is a relatively new paradigm in this area, addressing the need to process data in real time with very low latency. While this approach has been developed for dealing with large scale data from the world of business, security and finance, there is a natural overlap with clinical needs for physiological signal processing. In this work we present a case study of streams processing applied to a typical physiological signal processing problem: QRS detection from ECG data.

  2. Simulation-Based Learning Environments to Teach Complexity: The Missing Link in Teaching Sustainable Public Management

    Directory of Open Access Journals (Sweden)

    Michael Deegan

    2014-05-01

    Full Text Available While public-sector management problems are steeped in positivistic and socially constructed complexity, public management education in the management of complexity lags behind that of business schools, particularly in the application of simulation-based learning. This paper describes a Simulation-Based Learning Environment for public management education that includes a coupled case study and System Dynamics simulation surrounding flood protection, a domain where stewardship decisions regarding public infrastructure and investment have direct and indirect effects on businesses and the public. The Pointe Claire case and CoastalProtectSIM simulation provide a platform for policy experimentation under conditions of exogenous uncertainty (weather and climate change as well as endogenous effects generated by structure. We discuss the model in some detail, and present teaching materials developed to date to support the use of our work in public administration curricula. Our experience with this case demonstrates the potential of this approach to motivate sustainable learning about complexity in public management settings and enhance learners’ competency to deal with complex dynamic problems.

  3. Statistical Challenges in Modeling Big Brain Signals

    KAUST Repository

    Yu, Zhaoxia

    2017-11-01

    Brain signal data are inherently big: massive in amount, complex in structure, and high in dimensions. These characteristics impose great challenges for statistical inference and learning. Here we review several key challenges, discuss possible solutions, and highlight future research directions.

  4. Statistical Challenges in Modeling Big Brain Signals

    KAUST Repository

    Yu, Zhaoxia; Pluta, Dustin; Shen, Tong; Chen, Chuansheng; Xue, Gui; Ombao, Hernando

    2017-01-01

    Brain signal data are inherently big: massive in amount, complex in structure, and high in dimensions. These characteristics impose great challenges for statistical inference and learning. Here we review several key challenges, discuss possible

  5. [Dinitrosyl iron complexes are endogenous signaling agents in animal and human cells and tissues (a hypothesis)].

    Science.gov (United States)

    Vanin, A F

    2004-01-01

    The hypothesis was advanced that dinitrosyl iron complexes generated in animal and human cells and tissues producing nitric oxide can function as endogenous universal regulators of biochemical and physiological processes. This function is realized by the ability of dinitrosyl iron complexes to act as donors of free nitric oxide molecules interacting with the heme groups of proteins, nitrosonium ions, or Fe+(NO+)2 interacting with the thiol groups of proteins. The effect of dinitrosyl iron complexes on the activity of some enzymes and the expression of the genome at the translation and transcription levels was considered.

  6. The quantitative evaluation of complex defect signals from eddy current testings with multi-frequency methods

    International Nuclear Information System (INIS)

    Naegele, W.

    1982-01-01

    The usual formulation of multi-frequency eddy current signals of large defects by linearized impedance diagrams may lead to misinterpretations. Here a developement of the linear superposition principle is proposed, which takes into account also the curvature of the impedance diagrams thus allowing to identify even large defects in an unambiguous way. (orig.) [de

  7. Temporal signal energy correction and low-complexity encoder feedback for lossy scalable video coding

    NARCIS (Netherlands)

    Loomans, M.J.H.; Koeleman, C.J.; With, de P.H.N.

    2010-01-01

    In this paper, we address two problems found in embedded implementations of Scalable Video Codecs (SVCs): the temporal signal energy distribution and frame-to-frame quality fluctuations. The unequal energy distribution between the low- and high-pass band with integer-based wavelets leads to

  8. Conceptualization of the Complex Outcomes of Sexual Abuse: A Signal Detection Analysis

    Science.gov (United States)

    Pechtel, Pia; Evans, Ian M.; Podd, John V.

    2011-01-01

    Eighty-five New Zealand based practitioners experienced in treating adults with a history of child sexual abuse participated in an online judgment study of child sexual abuse outcomes using signal detection theory methodology. Participants' level of sensitivity was assessed independent of their degree of response bias when discriminating (a) known…

  9. Signal-to-Noise ratio and design complexity based on Unified Loss ...

    African Journals Online (AJOL)

    Taguchi's quality loss function for larger-the-better performance characteristics uses a reciprocal transformation to compute quality loss. This paper suggests that reciprocal transformation unnecessarily complicates and may distort results. Examples of this distortion include the signal-to-noise ratio based on mean squared ...

  10. Cytoplasmic organelles determine complexity and specificity of calcium signalling in adrenal chromaffin cells

    Czech Academy of Sciences Publication Activity Database

    Garsia-Sancho, J.; Verkhratsky, Alexei

    2008-01-01

    Roč. 192, č. 2 (2008), s. 263-271 ISSN 1748-1708 Institutional research plan: CEZ:AV0Z50390512 Keywords : Ca2+ signalling * calcium microdomains * chromaffin cells Subject RIV: JE - Non-nuclear Energetics, Energy Consumption ; Use Impact factor: 2.455, year: 2008

  11. Orphan receptor GPR179 forms macromolecular complexes with components of metabotropic signaling cascade in retina ON-bipolar neurons.

    Science.gov (United States)

    Orlandi, Cesare; Cao, Yan; Martemyanov, Kirill A

    2013-10-29

    In the mammalian retina, synaptic transmission between light-excited rod photoreceptors and downstream ON-bipolar neurons is indispensable for dim vision, and disruption of this process leads to congenital stationary night blindness in human patients. The ON-bipolar neurons use the metabotropic signaling cascade, initiated by the mGluR6 receptor, to generate depolarizing responses to light-induced changes in neurotransmitter glutamate release from the photoreceptor axonal terminals. Evidence for the identity of the components involved in transducing these signals is growing rapidly. Recently, the orphan receptor, GPR179, a member of the G protein-coupled receptor (GPCR) superfamily, has been shown to be indispensable for the synaptic responses of ON-bipolar cells. In our study, we investigated the interaction of GPR179 with principle components of the signal transduction cascade. We used immunoprecipitation and proximity ligation assays in transfected cells and native retinas to characterize the protein-protein interactions involving GPR179. The influence of cascade components on GPR179 localization was examined through immunohistochemical staining of the retinas from genetic mouse models. We demonstrated that, in mouse retinas, GPR179 forms physical complexes with the main components of the metabotropic cascade, recruiting mGluR6, TRPM1, and the RGS proteins. Elimination of mGluR6 or RGS proteins, but not TRPM1, detrimentally affects postsynaptic targeting or GPR179 expression. These observations suggest that the mGluR6 signaling cascade is scaffolded as a macromolecular complex in which the interactions between the components ensure the optimal spatiotemporal characteristics of signal transduction.

  12. Multiobjective Reinforcement Learning for Traffic Signal Control Using Vehicular Ad Hoc Network

    Directory of Open Access Journals (Sweden)

    Houli Duan

    2010-01-01

    Full Text Available We propose a new multiobjective control algorithm based on reinforcement learning for urban traffic signal control, named multi-RL. A multiagent structure is used to describe the traffic system. A vehicular ad hoc network is used for the data exchange among agents. A reinforcement learning algorithm is applied to predict the overall value of the optimization objective given vehicles' states. The policy which minimizes the cumulative value of the optimization objective is regarded as the optimal one. In order to make the method adaptive to various traffic conditions, we also introduce a multiobjective control scheme in which the optimization objective is selected adaptively to real-time traffic states. The optimization objectives include the vehicle stops, the average waiting time, and the maximum queue length of the next intersection. In addition, we also accommodate a priority control to the buses and the emergency vehicles through our model. The simulation results indicated that our algorithm could perform more efficiently than traditional traffic light control methods.

  13. Separation of pulsar signals from noise using supervised machine learning algorithms

    Science.gov (United States)

    Bethapudi, S.; Desai, S.

    2018-04-01

    We evaluate the performance of four different machine learning (ML) algorithms: an Artificial Neural Network Multi-Layer Perceptron (ANN MLP), Adaboost, Gradient Boosting Classifier (GBC), and XGBoost, for the separation of pulsars from radio frequency interference (RFI) and other sources of noise, using a dataset obtained from the post-processing of a pulsar search pipeline. This dataset was previously used for the cross-validation of the SPINN-based machine learning engine, obtained from the reprocessing of the HTRU-S survey data (Morello et al., 2014). We have used the Synthetic Minority Over-sampling Technique (SMOTE) to deal with high-class imbalance in the dataset. We report a variety of quality scores from all four of these algorithms on both the non-SMOTE and SMOTE datasets. For all the above ML methods, we report high accuracy and G-mean for both the non-SMOTE and SMOTE cases. We study the feature importances using Adaboost, GBC, and XGBoost and also from the minimum Redundancy Maximum Relevance approach to report algorithm-agnostic feature ranking. From these methods, we find that the signal to noise of the folded profile to be the best feature. We find that all the ML algorithms report FPRs about an order of magnitude lower than the corresponding FPRs obtained in Morello et al. (2014), for the same recall value.

  14. Using Differential Evolution to Optimize Learning from Signals and Enhance Network Security

    Energy Technology Data Exchange (ETDEWEB)

    Harmer, Paul K [Air Force Institute of Technology; Temple, Michael A [Air Force Institute of Technology; Buckner, Mark A [ORNL; Farquhar, Ethan [ORNL

    2011-01-01

    Computer and communication network attacks are commonly orchestrated through Wireless Access Points (WAPs). This paper summarizes proof-of-concept research activity aimed at developing a physical layer Radio Frequency (RF) air monitoring capability to limit unauthorizedWAP access and mprove network security. This is done using Differential Evolution (DE) to optimize the performance of a Learning from Signals (LFS) classifier implemented with RF Distinct Native Attribute (RF-DNA) fingerprints. Performance of the resultant DE-optimized LFS classifier is demonstrated using 802.11a WiFi devices under the most challenging conditions of intra-manufacturer classification, i.e., using emissions of like-model devices that only differ in serial number. Using identical classifier input features, performance of the DE-optimized LFS classifier is assessed relative to a Multiple Discriminant Analysis / Maximum Likelihood (MDA/ML) classifier that has been used for previous demonstrations. The comparative assessment is made using both Time Domain (TD) and Spectral Domain (SD) fingerprint features. For all combinations of classifier type, feature type, and signal-to-noise ratio considered, results show that the DEoptimized LFS classifier with TD features is uperior and provides up to 20% improvement in classification accuracy with proper selection of DE parameters.

  15. Effect of Error Augmentation on Brain Activation and Motor Learning of a Complex Locomotor Task

    Directory of Open Access Journals (Sweden)

    Laura Marchal-Crespo

    2017-09-01

    Full Text Available Up to date, the functional gains obtained after robot-aided gait rehabilitation training are limited. Error augmenting strategies have a great potential to enhance motor learning of simple motor tasks. However, little is known about the effect of these error modulating strategies on complex tasks, such as relearning to walk after a neurologic accident. Additionally, neuroimaging evaluation of brain regions involved in learning processes could provide valuable information on behavioral outcomes. We investigated the effect of robotic training strategies that augment errors—error amplification and random force disturbance—and training without perturbations on brain activation and motor learning of a complex locomotor task. Thirty-four healthy subjects performed the experiment with a robotic stepper (MARCOS in a 1.5 T MR scanner. The task consisted in tracking a Lissajous figure presented on a display by coordinating the legs in a gait-like movement pattern. Behavioral results showed that training without perturbations enhanced motor learning in initially less skilled subjects, while error amplification benefited better-skilled subjects. Training with error amplification, however, hampered transfer of learning. Randomly disturbing forces induced learning and promoted transfer in all subjects, probably because the unexpected forces increased subjects' attention. Functional MRI revealed main effects of training strategy and skill level during training. A main effect of training strategy was seen in brain regions typically associated with motor control and learning, such as, the basal ganglia, cerebellum, intraparietal sulcus, and angular gyrus. Especially, random disturbance and no perturbation lead to stronger brain activation in similar brain regions than error amplification. Skill-level related effects were observed in the IPS, in parts of the superior parietal lobe (SPL, i.e., precuneus, and temporal cortex. These neuroimaging findings

  16. Switch I-dependent allosteric signaling in a G-protein chaperone-B12 enzyme complex.

    Science.gov (United States)

    Campanello, Gregory C; Lofgren, Michael; Yokom, Adam L; Southworth, Daniel R; Banerjee, Ruma

    2017-10-27

    G-proteins regulate various processes ranging from DNA replication and protein synthesis to cytoskeletal dynamics and cofactor assimilation and serve as models for uncovering strategies deployed for allosteric signal transduction. MeaB is a multifunctional G-protein chaperone, which gates loading of the active 5'-deoxyadenosylcobalamin cofactor onto methylmalonyl-CoA mutase (MCM) and precludes loading of inactive cofactor forms. MeaB also safeguards MCM, which uses radical chemistry, against inactivation and rescues MCM inactivated during catalytic turnover by using the GTP-binding energy to offload inactive cofactor. The conserved switch I and II signaling motifs used by G-proteins are predicted to mediate allosteric regulation in response to nucleotide binding and hydrolysis in MeaB. Herein, we targeted conserved residues in the MeaB switch I motif to interrogate the function of this loop. Unexpectedly, the switch I mutations had only modest effects on GTP binding and on GTPase activity and did not perturb stability of the MCM-MeaB complex. However, these mutations disrupted multiple MeaB chaperone functions, including cofactor editing, loading, and offloading. Hence, although residues in the switch I motif are not essential for catalysis, they are important for allosteric regulation. Furthermore, single-particle EM analysis revealed, for the first time, the overall architecture of the MCM-MeaB complex, which exhibits a 2:1 stoichiometry. These EM studies also demonstrate that the complex exhibits considerable conformational flexibility. In conclusion, the switch I element does not significantly stabilize the MCM-MeaB complex or influence the affinity of MeaB for GTP but is required for transducing signals between MeaB and MCM. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

  17. Improved Neural Signal Classification in a Rapid Serial Visual Presentation Task Using Active Learning.

    Science.gov (United States)

    Marathe, Amar R; Lawhern, Vernon J; Wu, Dongrui; Slayback, David; Lance, Brent J

    2016-03-01

    The application space for brain-computer interface (BCI) technologies is rapidly expanding with improvements in technology. However, most real-time BCIs require extensive individualized calibration prior to use, and systems often have to be recalibrated to account for changes in the neural signals due to a variety of factors including changes in human state, the surrounding environment, and task conditions. Novel approaches to reduce calibration time or effort will dramatically improve the usability of BCI systems. Active Learning (AL) is an iterative semi-supervised learning technique for learning in situations in which data may be abundant, but labels for the data are difficult or expensive to obtain. In this paper, we apply AL to a simulated BCI system for target identification using data from a rapid serial visual presentation (RSVP) paradigm to minimize the amount of training samples needed to initially calibrate a neural classifier. Our results show AL can produce similar overall classification accuracy with significantly less labeled data (in some cases less than 20%) when compared to alternative calibration approaches. In fact, AL classification performance matches performance of 10-fold cross-validation (CV) in over 70% of subjects when training with less than 50% of the data. To our knowledge, this is the first work to demonstrate the use of AL for offline electroencephalography (EEG) calibration in a simulated BCI paradigm. While AL itself is not often amenable for use in real-time systems, this work opens the door to alternative AL-like systems that are more amenable for BCI applications and thus enables future efforts for developing highly adaptive BCI systems.

  18. Inferior frontal gyrus preserves working memory and emotional learning under conditions of impaired noradrenergic signaling

    Directory of Open Access Journals (Sweden)

    Benjamin eBecker

    2013-12-01

    Full Text Available Compensation has been widely applied to explain neuroimaging findings in neuropsychiatric patients. Functional compensation is often invoked when patients display equal performance and increased neural activity in comparison to healthy controls. According to the compensatory hypothesis increased activity allows the brain to maintain cognitive performance despite underlying neuropathological changes. Due to methodological and pathology-related issues, however, the functional relevance of the increased activity and the specific brain regions involved in the compensatory response remain unclear. An experimental approach that allows a transient induction of compensatory responses in the healthy brain could help to overcome these issues. To this end we used the nonselective beta-blocker propranolol to pharmacologically induce sub-optimal noradrenergic signaling in healthy participants. In two independent fMRI experiments participants received either placebo or propranolol before they underwent a cognitive challenge (experiment 1: working memory; experiment 2: emotional learning: Pavlovian fear conditioning. In experiment 1 propranolol had no effects on working memory performance, but evoked stronger activity in the left inferior frontal gyrus (IFG. In experiment 2 propranolol produced no effects on emotional memory formation, but evoked stronger activity in the right IFG. The present finding that sub-optimal beta-adrenergic signaling did not disrupt performance and concomitantly increased IFG activity is consistent with, and extends, current perspectives on functional compensation. Together, our findings suggest that under conditions of impaired noradrenergic signaling, heightened activity in brain regions located within the cognitive control network, particularly the IFG, may reflect compensatory operations subserving the maintenance of behavioral performance.

  19. Synergistic target combination prediction from curated signaling networks: Machine learning meets systems biology and pharmacology.

    Science.gov (United States)

    Chua, Huey Eng; Bhowmick, Sourav S; Tucker-Kellogg, Lisa

    2017-10-01

    Given a signaling network, the target combination prediction problem aims to predict efficacious and safe target combinations for combination therapy. State-of-the-art in silico methods use Monte Carlo simulated annealing (mcsa) to modify a candidate solution stochastically, and use the Metropolis criterion to accept or reject the proposed modifications. However, such stochastic modifications ignore the impact of the choice of targets and their activities on the combination's therapeutic effect and off-target effects, which directly affect the solution quality. In this paper, we present mascot, a method that addresses this limitation by leveraging two additional heuristic criteria to minimize off-target effects and achieve synergy for candidate modification. Specifically, off-target effects measure the unintended response of a signaling network to the target combination and is often associated with toxicity. Synergy occurs when a pair of targets exerts effects that are greater than the sum of their individual effects, and is generally a beneficial strategy for maximizing effect while minimizing toxicity. mascot leverages on a machine learning-based target prioritization method which prioritizes potential targets in a given disease-associated network to select more effective targets (better therapeutic effect and/or lower off-target effects); and on Loewe additivity theory from pharmacology which assesses the non-additive effects in a combination drug treatment to select synergistic target activities. Our experimental study on two disease-related signaling networks demonstrates the superiority of mascot in comparison to existing approaches. Copyright © 2017 Elsevier Inc. All rights reserved.

  20. Estimating the complexity of 3D structural models using machine learning methods

    Science.gov (United States)

    Mejía-Herrera, Pablo; Kakurina, Maria; Royer, Jean-Jacques

    2016-04-01

    Quantifying the complexity of 3D geological structural models can play a major role in natural resources exploration surveys, for predicting environmental hazards or for forecasting fossil resources. This paper proposes a structural complexity index which can be used to help in defining the degree of effort necessary to build a 3D model for a given degree of confidence, and also to identify locations where addition efforts are required to meet a given acceptable risk of uncertainty. In this work, it is considered that the structural complexity index can be estimated using machine learning methods on raw geo-data. More precisely, the metrics for measuring the complexity can be approximated as the difficulty degree associated to the prediction of the geological objects distribution calculated based on partial information on the actual structural distribution of materials. The proposed methodology is tested on a set of 3D synthetic structural models for which the degree of effort during their building is assessed using various parameters (such as number of faults, number of part in a surface object, number of borders, ...), the rank of geological elements contained in each model, and, finally, their level of deformation (folding and faulting). The results show how the estimated complexity in a 3D model can be approximated by the quantity of partial data necessaries to simulated at a given precision the actual 3D model without error using machine learning algorithms.

  1. Community detection in complex networks using deep auto-encoded extreme learning machine

    Science.gov (United States)

    Wang, Feifan; Zhang, Baihai; Chai, Senchun; Xia, Yuanqing

    2018-06-01

    Community detection has long been a fascinating topic in complex networks since the community structure usually unveils valuable information of interest. The prevalence and evolution of deep learning and neural networks have been pushing forward the advancement in various research fields and also provide us numerous useful and off the shelf techniques. In this paper, we put the cascaded stacked autoencoders and the unsupervised extreme learning machine (ELM) together in a two-level embedding process and propose a novel community detection algorithm. Extensive comparison experiments in circumstances of both synthetic and real-world networks manifest the advantages of the proposed algorithm. On one hand, it outperforms the k-means clustering in terms of the accuracy and stability thus benefiting from the determinate dimensions of the ELM block and the integration of sparsity restrictions. On the other hand, it endures smaller complexity than the spectral clustering method on account of the shrinkage in time spent on the eigenvalue decomposition procedure.

  2. ASSESSMENT OF STUDENT LEARNING IN VIRTUAL SPACES, USING ORDERS OF COMPLEXITY IN LEVELS OF THINKING

    Directory of Open Access Journals (Sweden)

    Jose CAPACHO

    2017-04-01

    Full Text Available This paper aims at showing a new methodology to assess student learning in virtual spaces supported by Information and Communications Technology-ICT. The methodology is based on the Conceptual Pedagogy Theory, and is supported both on knowledge instruments (KI and intelectual operations (IO. KI are made up of teaching materials embedded in the virtual environment. The student carries out IO in his/her virtual formation process based on KI. Both instruments of knowledge and intellectual operations can be mathematically modelled by using functions of increasing complexity order. These functions represent the student’s learning change. This paper main contribution is to show that these functions let the student go from a concrete thinking to a formal one in his/her virtual learning process. The research showed that 47% of the students moved from a concrete thinking level to the formal thinking level.

  3. Complex signal-based optical coherence tomography angiography enables in vivo visualization of choriocapillaris in human choroid

    Science.gov (United States)

    Chu, Zhongdi; Chen, Chieh-Li; Zhang, Qinqin; Pepple, Kathryn; Durbin, Mary; Gregori, Giovanni; Wang, Ruikang K.

    2017-12-01

    The choriocapillaris (CC) plays an essential role in maintaining the normal functions of the human eye. There is increasing interest in the community to develop an imaging technique for visualizing the CC, yet this remains underexplored due to technical limitations. We propose an approach for the visualization of the CC in humans via a complex signal-based optical microangiography (OMAG) algorithm, based on commercially available spectral domain optical coherence tomography (SD-OCT). We show that the complex signal-based OMAG was superior to both the phase and amplitude signal-based approaches in detailing the vascular lobules previously seen with histological analysis. With this improved ability to visualize the lobular vascular networks, it is possible to identify the feeding arterioles and draining venules around the lobules, which is important in understanding the role of the CC in the pathogenesis of ocular diseases. With built-in FastTrac™ and montage scanning capabilities, we also demonstrate wide-field SD-OCT angiograms of the CC with a field of view at 9×11 mm2.

  4. Influence of multi-microphone signal enhancement algorithms on auditory movement detection in acoustically complex situations

    DEFF Research Database (Denmark)

    Lundbeck, Micha; Hartog, Laura; Grimm, Giso

    2017-01-01

    The influence of hearing aid (HA) signal processing on the perception of spatially dynamic sounds has not been systematically investigated so far. Previously, we observed that interfering sounds impaired the detectability of left-right source movements and reverberation that of near-far source...... movements for elderly hearing-impaired (EHI) listeners (Lundbeck et al., 2017). Here, we explored potential ways of improving these deficits with HAs. To that end, we carried out acoustic analyses to examine the impact of two beamforming algorithms and a binaural coherence-based noise reduction scheme...... on the cues underlying movement perception. While binaural cues remained mostly unchanged, there were greater monaural spectral changes and increases in signal-to-noise ratio and direct-to-reverberant sound ratio as a result of the applied processing. Based on these findings, we conducted a listening test...

  5. The Azteca Chess experience: learning how to share concepts of ecological complexity with small coffee farmers

    Directory of Open Access Journals (Sweden)

    Luís García-Barrios

    2017-06-01

    Full Text Available Small-scale coffee farmers understand certain complex ecological processes, and successfully navigate some of the challenges emerging from the ecological complexity on their farms. It is generally thought that scientific knowledge is able to complement farmers' knowledge. However, for this collaboration to be fruitful, the gap between the knowledge frameworks of both farmers and scientists will need to be closed. We report on the learning results of 14 workshops held in Chiapas, Mexico during 2015 in which 117 small-scale coffee farmers of all genders (30% women and ages who had little schooling were exposed by researchers to a natural history narrative, a multispecies network representation, a board game, and a series of graphical quizzes, all related to a nine-species complex ecological network with potential for autonomous control of the ongoing and devastating coffee rust epidemic that was affecting them. Farmers' retention and understanding of direct and indirect bilateral interactions among organisms was assessed with different methods to elucidate the effect of adding Azteca Chess gaming sessions to a detailed and very graphical lecture. Evaluation methods that were better adapted to farmers' conditions improved learning scores and showed statistically significant age effect (players older than 40 had lower retention scores and gaming effect (lower retention of interactions included in the lecture but not in the game. The combination of lecture and game sessions helped participants better understand cascades of trait-mediated interactions. Participants' debriefings confirmed qualitatively that they learned that beneficial organisms and interactions occur on their farms, and that gaming was enjoyable, motivating, and critical to grasp complex interactions. Many of the farmers concluded that the outcome of these interactions is not unique and not always in favor of rust control but is context dependent. Many concluded that there are

  6. Using mLearning and MOOCs to Understand Chaos, Emergence, and Complexity in Education

    Science.gov (United States)

    deWaard, Inge; Abajian, Sean; Gallagher, Michael Sean; Hogue, Rebecca; Keskin, Nilgun; Koutropoulos, Apostolos; Rodriguez, Osvaldo C.

    2011-01-01

    In this paper, we look at how the massive open online course (MOOC) format developed by connectivist researchers and enthusiasts can help analyze the complexity, emergence, and chaos at work in the field of education today. We do this through the prism of a MobiMOOC, a six-week course focusing on mLearning that ran from April to May 2011. MobiMOOC…

  7. Acoustic wave focusing in complex media using Nonlinear Time Reversal coded signal processing

    Czech Academy of Sciences Publication Activity Database

    Dos Santos, S.; Dvořáková, Zuzana; Lints, M.; Kůs, V.; Salupere, A.; Převorovský, Zdeněk

    2014-01-01

    Roč. 19, č. 12 (2014) ISSN 1435-4934. [European Conference on Non-Destructive Testing (ECNDT 2014) /11./. Praha, 06.10.2014-10.10.2014] Institutional support: RVO:61388998 Keywords : ultrasonic testing (UT) * signal processing * TR- NEWS * nonlinear time reversal * NDT * nonlinear acoustics Subject RIV: BI - Acoustics http://www.ndt.net/events/ECNDT2014/app/content/Slides/590_DosSantos_Rev1.pdf

  8. Sonification and haptic feedback in addition to visual feedback enhances complex motor task learning.

    Science.gov (United States)

    Sigrist, Roland; Rauter, Georg; Marchal-Crespo, Laura; Riener, Robert; Wolf, Peter

    2015-03-01

    Concurrent augmented feedback has been shown to be less effective for learning simple motor tasks than for complex tasks. However, as mostly artificial tasks have been investigated, transfer of results to tasks in sports and rehabilitation remains unknown. Therefore, in this study, the effect of different concurrent feedback was evaluated in trunk-arm rowing. It was then investigated whether multimodal audiovisual and visuohaptic feedback are more effective for learning than visual feedback only. Naïve subjects (N = 24) trained in three groups on a highly realistic virtual reality-based rowing simulator. In the visual feedback group, the subject's oar was superimposed to the target oar, which continuously became more transparent when the deviation between the oars decreased. Moreover, a trace of the subject's trajectory emerged if deviations exceeded a threshold. The audiovisual feedback group trained with oar movement sonification in addition to visual feedback to facilitate learning of the velocity profile. In the visuohaptic group, the oar movement was inhibited by path deviation-dependent braking forces to enhance learning of spatial aspects. All groups significantly decreased the spatial error (tendency in visual group) and velocity error from baseline to the retention tests. Audiovisual feedback fostered learning of the velocity profile significantly more than visuohaptic feedback. The study revealed that well-designed concurrent feedback fosters complex task learning, especially if the advantages of different modalities are exploited. Further studies should analyze the impact of within-feedback design parameters and the transferability of the results to other tasks in sports and rehabilitation.

  9. Targeted learning in data science causal inference for complex longitudinal studies

    CERN Document Server

    van der Laan, Mark J

    2018-01-01

    This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generatio...

  10. Down-Regulation of Neuregulin1/ErbB4 Signaling in the Hippocampus Is Critical for Learning and Memory.

    Science.gov (United States)

    Tian, Jia; Geng, Fei; Gao, Feng; Chen, Yi-Hua; Liu, Ji-Hong; Wu, Jian-Lin; Lan, Yu-Jie; Zeng, Yuan-Ning; Li, Xiao-Wen; Yang, Jian-Ming; Gao, Tian-Ming

    2017-08-01

    Hippocampal function is important for learning and memory, and dysfunction of the hippocampus has been linked to the pathophysiology of neuropsychiatric diseases such as schizophrenia. Neuregulin1 (NRG1) and ErbB4, two susceptibility genes for schizophrenia, reportedly modulate long-term potentiation (LTP) at hippocampal Schaffer collateral (SC)-CA1 synapses. However, little is known regarding the contribution of hippocampal NRG1/ErbB4 signaling to learning and memory function. Here, quantitative real-time PCR and Western blotting were used to assess the mRNA and protein levels of NRG1 and ErbB4. Pharmacological and genetic approaches were used to manipulate NRG1/ErbB4 signaling, following which learning and memory behaviors were evaluated using the Morris water maze, Y-maze test, and the novel object recognition test. Spatial learning was found to reduce hippocampal NRG1 and ErbB4 expression. The blockade of NRG1/ErbB4 signaling in hippocampal CA1, either by neutralizing endogenous NRG1 or inhibiting/ablating ErbB4 receptor activity, enhanced hippocampus-dependent spatial learning, spatial working memory, and novel object recognition memory. Accordingly, administration of exogenous NRG1 impaired those functions. More importantly, the specific ablation of ErbB4 in parvalbumin interneurons also improved learning and memory performance. The manipulation of NRG1/ErbB4 signaling in the present study revealed that NRG1/ErbB4 activity in the hippocampus is critical for learning and memory. These findings might provide novel insights on the pathophysiological mechanisms of schizophrenia and a new target for the treatment of Alzheimer's disease, which is characterized by a progressive decline in cognitive function.

  11. Biogenesis of the mitochondrial TOM complex: Mim1 promotes insertion and assembly of signal-anchored receptors.

    Science.gov (United States)

    Becker, Thomas; Pfannschmidt, Sylvia; Guiard, Bernard; Stojanovski, Diana; Milenkovic, Dusanka; Kutik, Stephan; Pfanner, Nikolaus; Meisinger, Chris; Wiedemann, Nils

    2008-01-04

    The translocase of the outer membrane (TOM complex) is the central entry gate for nuclear-encoded mitochondrial precursor proteins. All Tom proteins are also encoded by nuclear genes and synthesized as precursors in the cytosol. The channel-forming beta-barrel protein Tom40 is targeted to mitochondria via Tom receptors and inserted into the outer membrane by the sorting and assembly machinery (SAM complex). A further outer membrane protein, Mim1, plays a less defined role in assembly of Tom40 into the TOM complex. The three receptors Tom20, Tom22, and Tom70 are anchored in the outer membrane by a single transmembrane alpha-helix, located at the N terminus in the case of Tom20 and Tom70 (signal-anchored) or in the C-terminal portion in the case of Tom22 (tail-anchored). Insertion of the precursor of Tom22 into the outer membrane requires pre-existing Tom receptors while the import pathway of the precursors of Tom20 and Tom70 is only poorly understood. We report that Mim1 is required for efficient membrane insertion and assembly of Tom20 and Tom70, but not Tom22. We show that Mim1 associates with SAM(core) components to a large SAM complex, explaining its role in late steps of the assembly pathway of Tom40. We conclude that Mim1 is not only required for biogenesis of the beta-barrel protein Tom40 but also for membrane insertion and assembly of signal-anchored Tom receptors. Thus, Mim1 plays an important role in the efficient assembly of the mitochondrial TOM complex.

  12. Abnormal Cell Properties and Down-Regulated FAK-Src Complex Signaling in B Lymphoblasts of Autistic Subjects

    Science.gov (United States)

    Wei, Hongen; Malik, Mazhar; Sheikh, Ashfaq M.; Merz, George; Ted Brown, W.; Li, Xiaohong

    2011-01-01

    Recent studies suggest that one of the major pathways to the pathogenesis of autism is reduced cell migration. Focal adhesion kinase (FAK) has an important role in neural migration, dendritic morphological characteristics, axonal branching, and synapse formation. The FAK-Src complex, activated by upstream reelin and integrin β1, can initiate a cascade of phosphorylation events to trigger multiple intracellular pathways, including mitogen-activated protein kinase–extracellular signal–regulated kinase and phosphatidylinositol 3-kinase–Akt signaling. In this study, by using B lymphoblasts as a model, we tested whether integrin β1 and FAK-Src signaling are abnormally regulated in autism and whether abnormal FAK-Src signaling leads to defects in B-lymphoblast adhesion, migration, proliferation, and IgG production. To our knowledge, for the first time, we show that protein expression levels of both integrin β1 and FAK are significantly decreased in autistic lymphoblasts and that Src protein expression and the phosphorylation of an active site (Y416) are also significantly decreased. We also found that lymphoblasts from autistic subjects exhibit significantly decreased migration, increased adhesion properties, and an impaired capacity for IgG production. The overexpression of FAK in autistic lymphoblasts countered the adhesion and migration defects. In addition, we demonstrate that FAK mediates its effect through the activation of Src, phosphatidylinositol 3-kinase–Akt, and mitogen-activated protein kinase signaling cascades and that paxillin is also likely involved in the regulation of adhesion and migration in autistic lymphoblasts. PMID:21703394

  13. Improving the arterial input function in dynamic contrast enhanced MRI by fitting the signal in the complex plane.

    Science.gov (United States)

    Simonis, Frank F J; Sbrizzi, Alessandro; Beld, Ellis; Lagendijk, Jan J W; van den Berg, Cornelis A T

    2016-10-01

    Dynamic contrast enhanced (DCE) imaging is a widely used technique in oncologic imaging. An essential prerequisite for obtaining quantitative values from DCE-MRI is the determination of the arterial input function (AIF). However, it is very challenging to accurately estimate the AIF using MR. A comprehensive model, which uses complex data instead of either magnitude or phase, was developed to improve AIF estimation. The model was first applied to simulated data. Subsequently, the accuracy of the estimated contrast agent concentration was validated in a phantom. Finally the method was applied to existing DCE scans of 13 prostate cancer patients. The complex signal method combines the complementary strengths of the magnitude and phase method, increasing the precision and accuracy of concentration estimation in simulated and phantom data. The in vivo AIFs show a good agreement between arterial voxels (standard deviation in the peak and tail equal 0.4 mM and 0.12 mM, respectively). Furthermore, the dynamic behavior closely followed the AIF obtained with DCE-CT in the same patients (mean correlation coefficient: 0.92). By using the complex signal, the AIF estimation becomes more accurate and precise. This might enable patient specific AIFs, thereby improving the quantitative values obtained from DCE-MRI. Magn Reson Med 76:1236-1245, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

  14. Effects of cerebellar nuclear inactivation on the learning of a complex forelimb movement in cats.

    Science.gov (United States)

    Wang, J J; Shimansky, Y; Bracha, V; Bloedel, J R

    1998-05-01

    The purpose of this study was to determine the effects of inactivating concurrently the cerebellar interposed and dentate nuclei on the capacity of cats to acquire and retain a complex, goal-directed forelimb movement. To assess the effects on acquisition, cats were required to learn to move a vertical manipulandum bar through a two-segment template with a shape approximating an inverted "L" after the injection of muscimol (saline for the control group) in the interposed and dentate cerebellar nuclei. During training periods, they were exposed progressively to more difficult templates, which were created by decreasing the angle between the two segments of the template. After determining the most difficult template the injected animals could learn within the specified time and performance constraints, the retraining phase of the experiment was initiated in which the cats were required to execute the same sequence of templates in the absence of any injection. This stage of the experiment assessed retention and determined the extent of any relearning required to execute the task at criterion levels. Next, the animals were overtrained without any injection on the most difficult template they could perform. Finally, to determine the effects of nuclear inactivation on retention after extensive retraining, their capacity to perform the same template was determined after muscimol injection in the interposed and dentate nuclei. The findings show that during the inactivation of the dentate and interposed nuclei the animals could learn to execute the more difficult templates. However, when required to execute the most difficult template learned under muscimol on the day after injections were discontinued, the cats had to "relearn" (reacquire) the movement. Finally, when the cerebellar nuclei were inactivated after the animals learned the task in the absence of any injections during the retraining phase, retention was not blocked. The data indicate that the intermediate and

  15. Research on Healthy Anomaly Detection Model Based on Deep Learning from Multiple Time-Series Physiological Signals

    Directory of Open Access Journals (Sweden)

    Kai Wang

    2016-01-01

    Full Text Available Health is vital to every human being. To further improve its already respectable medical technology, the medical community is transitioning towards a proactive approach which anticipates and mitigates risks before getting ill. This approach requires measuring the physiological signals of human and analyzes these data at regular intervals. In this paper, we present a novel approach to apply deep learning in physiological signals analysis that allows doctor to identify latent risks. However, extracting high level information from physiological time-series data is a hard problem faced by the machine learning communities. Therefore, in this approach, we apply model based on convolutional neural network that can automatically learn features from raw physiological signals in an unsupervised manner and then based on the learned features use multivariate Gauss distribution anomaly detection method to detect anomaly data. Our experiment is shown to have a significant performance in physiological signals anomaly detection. So it is a promising tool for doctor to identify early signs of illness even if the criteria are unknown a priori.

  16. Requirement of Dopamine Signaling in the Amygdala and Striatum for Learning and Maintenance of a Conditioned Avoidance Response

    Science.gov (United States)

    Darvas, Martin; Fadok, Jonathan P.; Palmiter, Richard D.

    2011-01-01

    Two-way active avoidance (2WAA) involves learning Pavlovian (association of a sound cue with a foot shock) and instrumental (shock avoidance) contingencies. To identify regions where dopamine (DA) is involved in mediating 2WAA, we restored DA signaling in specific brain areas of dopamine-deficient (DD) mice by local reactivation of conditionally…

  17. Price computation in electricity auctions with complex rules: An analysis of investment signals

    International Nuclear Information System (INIS)

    Vazquez, Carlos; Hallack, Michelle; Vazquez, Miguel

    2017-01-01

    This paper discusses the problem of defining marginal costs when integer variables are present, in the context of short-term power auctions. Most of the proposals for price computation existing in the literature are concerned with short-term competitive equilibrium (generators should not be willing to change the dispatch assigned to them by the auctioneer), which implies operational-cost recovery for all of the generators accepted in the auction. However, this is in general not enough to choose between the different pricing schemes. We propose to include an additional criterion in order to discriminate among different pricing schemes: prices have to be also signals for generation expansion. Using this condition, we arrive to a single solution to the problem of defining prices, where they are computed as the shadow prices of the balance equations in a linear version of the unit commitment problem. Importantly, not every linearization of the unit commitment is valid; we develop the conditions for this linear model to provide adequate investment signals. Compared to other proposals in the literature, our results provide a strong motivation for the pricing scheme and a simple method for price computation. - Highlights: • Pricing proposals in power markets often deal with just accounting-cost recovery. • Including opportunity costs is an additional property required for efficient pricing. • We develop a framework to analyze the pricing proposals found in the literature. • We propose a pricing mechanism to include the costs of short-run integer decisions. • As it includes short-run opportunity costs, it provides efficient long-term signals.

  18. Mislocalization of the MRN complex prevents ATR signaling during adenovirus infection

    DEFF Research Database (Denmark)

    Carson, Christian T; Orazio, Nicole I; Lee, Darwin V

    2009-01-01

    The protein kinases ataxia-telangiectasia mutated (ATM) and ATM-Rad3 related (ATR) are activated in response to DNA damage, genotoxic stress and virus infections. Here we show that during infection with wild-type adenovirus, ATR and its cofactors RPA32, ATRIP and TopBP1 accumulate at viral...... during virus infection, which is independent of Mre11 nuclease activity and recruitment of RPA/ATR/ATRIP/TopBP1. Unlike other damage scenarios, we found that ATM and ATR signaling are not dependent on each other during infection. We identify a region of the viral E4orf3 protein responsible...

  19. CRTC1 Nuclear Translocation Following Learning Modulates Memory Strength via Exchange of Chromatin Remodeling Complexes on the Fgf1 Gene.

    Science.gov (United States)

    Uchida, Shusaku; Teubner, Brett J W; Hevi, Charles; Hara, Kumiko; Kobayashi, Ayumi; Dave, Rutu M; Shintaku, Tatsushi; Jaikhan, Pattaporn; Yamagata, Hirotaka; Suzuki, Takayoshi; Watanabe, Yoshifumi; Zakharenko, Stanislav S; Shumyatsky, Gleb P

    2017-01-10

    Memory is formed by synapse-to-nucleus communication that leads to regulation of gene transcription, but the identity and organizational logic of signaling pathways involved in this communication remain unclear. Here we find that the transcription cofactor CRTC1 is a critical determinant of sustained gene transcription and memory strength in the hippocampus. Following associative learning, synaptically localized CRTC1 is translocated to the nucleus and regulates Fgf1b transcription in an activity-dependent manner. After both weak and strong training, the HDAC3-N-CoR corepressor complex leaves the Fgf1b promoter and a complex involving the translocated CRTC1, phosphorylated CREB, and histone acetyltransferase CBP induces transient transcription. Strong training later substitutes KAT5 for CBP, a process that is dependent on CRTC1, but not on CREB phosphorylation. This in turn leads to long-lasting Fgf1b transcription and memory enhancement. Thus, memory strength relies on activity-dependent changes in chromatin and temporal regulation of gene transcription on specific CREB/CRTC1 gene targets. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

  20. Low-Complexity Iterative Receiver for Space-Time Coded Signals over Frequency Selective Channels

    Directory of Open Access Journals (Sweden)

    Mohamed Siala

    2002-05-01

    Full Text Available We propose a low-complexity turbo-detector scheme for frequency selective multiple-input multiple-output channels. The detection part of the receiver is based on a List-type MAP equalizer which is a state-reduction algorithm of the MAP algorithm using per-survivor technique. This alternative achieves a good tradeoff between performance and complexity provided a small amount of the channel is neglected. In order to induce the good performance of this equalizer, we propose to use a whitened matched filter (WMF which leads to a white-noise “minimum phase” channel model. Simulation results show that the use of the WMF yields significant improvement, particularly over severe channels. Thanks to the iterative turbo processing (detection and decoding are iterated several times, the performance loss due to the use of the suboptimum List-type equalizer is recovered.

  1. Broad-Complex acts downstream of Met in juvenile hormone signaling to coordinate primitive holometabolan metamorphosis

    Czech Academy of Sciences Publication Activity Database

    Konopová, Barbora; Jindra, Marek

    2008-01-01

    Roč. 135, č. 3 (2008), s. 559-568 ISSN 0950-1991 R&D Projects: GA ČR(CZ) GA204/07/1032; GA AV ČR IAA5007305; GA MŠk LC07032 Institutional research plan: CEZ:AV0Z50070508 Keywords : metamorphosis * juvenile hormone * broad-complex Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 6.812, year: 2008

  2. Gold nanoparticle-mediated laser stimulation causes a complex stress signal in neuronal cells

    Science.gov (United States)

    Johannsmeier, Sonja; Heeger, Patrick; Terakawa, Mitsuhiro; Kalies, Stefan; Heisterkamp, Alexander; Ripken, Tammo; Heinemann, Dag

    2017-07-01

    Gold nanoparticle mediated laser stimulation of neuronal cells allows for cell activation on a single-cell level. It could therefore be considered an alternative to classical electric neurostimulation. The physiological impact of this new approach has not been intensively studied so far. Here, we investigate the targeted cell's reaction to a laser stimulus based on its calcium response. A complex cellular reaction involving multiple sources has been revealed.

  3. The biology of skin wetness perception and its implications in manual function and for reproducing complex somatosensory signals in neuroprosthetics.

    Science.gov (United States)

    Filingeri, Davide; Ackerley, Rochelle

    2017-04-01

    Our perception of skin wetness is generated readily, yet humans have no known receptor (hygroreceptor) to signal this directly. It is easy to imagine the sensation of water running over our hands or the feel of rain on our skin. The synthetic sensation of wetness is thought to be produced from a combination of specific skin thermal and tactile inputs, registered through thermoreceptors and mechanoreceptors, respectively. The present review explores how thermal and tactile afference from the periphery can generate the percept of wetness centrally. We propose that the main signals include information about skin cooling, signaled primarily by thinly myelinated thermoreceptors, and rapid changes in touch, through fast-conducting, myelinated mechanoreceptors. Potential central sites for integration of these signals, and thus the perception of skin wetness, include the primary and secondary somatosensory cortices and the insula cortex. The interactions underlying these processes can also be modeled to aid in understanding and engineering the mechanisms. Furthermore, we discuss the role that sensing wetness could play in precision grip and the dexterous manipulation of objects. We expand on these lines of inquiry to the application of the knowledge in designing and creating skin sensory feedback in prosthetics. The addition of real-time, complex sensory signals would mark a significant advance in the use and incorporation of prosthetic body parts for amputees in everyday life. NEW & NOTEWORTHY Little is known about the underlying mechanisms that generate the perception of skin wetness. Humans have no specific hygroreceptor, and thus temperature and touch information combine to produce wetness sensations. The present review covers the potential mechanisms leading to the perception of wetness, both peripherally and centrally, along with their implications for manual function. These insights are relevant to inform the design of neuroengineering interfaces, such as sensory

  4. Organisational simplification and secondary complexity in health services for adults with learning disabilities.

    Science.gov (United States)

    Heyman, Bob; Swain, John; Gillman, Maureen

    2004-01-01

    This paper explores the role of complexity and simplification in the delivery of health care for adults with learning disabilities, drawing upon qualitative data obtained in a study carried out in NE England. It is argued that the requirement to manage complex health needs with limited resources causes service providers to simplify, standardise and routinise care. Simplified service models may work well enough for the majority of clients, but can impede recognition of the needs of those whose characteristics are not congruent with an adopted model. The data were analysed in relation to the core category, identified through thematic analysis, of secondary complexity arising from organisational simplification. Organisational simplification generates secondary complexity when operational routines designed to make health complexity manageable cannot accommodate the needs of non-standard service users. Associated themes, namely the social context of services, power and control, communication skills, expertise and service inclusiveness and evaluation are explored in relation to the core category. The concept of secondary complexity resulting from organisational simplification may partly explain seemingly irrational health service provider behaviour.

  5. Task Complexity Modulates Sleep-Related Offline Learning in Sequential Motor Skills

    Directory of Open Access Journals (Sweden)

    Klaus Blischke

    2017-07-01

    Full Text Available Recently, a number of authors have advocated the introduction of gross motor tasks into research on sleep-related motor offline learning. Such tasks are often designed to be more complex than traditional key-pressing tasks. However, until now, little effort has been undertaken to scrutinize the role of task complexity in any systematic way. Therefore, the effect of task complexity on the consolidation of gross motor sequence memory was examined by our group in a series of three experiments. Criterion tasks always required participants to produce unrestrained arm movement sequences by successively fitting a small peg into target holes on a pegboard. The sequences always followed a certain spatial pattern in the horizontal plane. The targets were visualized prior to each transport movement on a computer screen. The tasks differed with respect to sequence length and structural complexity. In each experiment, half of the participants initially learned the task in the morning and were retested 12 h later following a wake retention interval. The other half of the subjects underwent practice in the evening and was retested 12 h later following a night of sleep. The dependent variables were the error rate and total sequence execution time (inverse to the sequence execution speed. Performance generally improved during acquisition. The error rate was always low and remained stable during retention. The sequence execution time significantly decreased again following sleep but not after waking when the sequence length was long and structural complexity was high. However, sleep-related offline improvements were absent when the sequence length was short or when subjects performed a highly regular movement pattern. It is assumed that the occurrence of sleep-related offline performance improvements in sequential motor tasks is associated with a sufficient amount of motor task complexity.

  6. Elevation of endogenous anandamide impairs LTP, learning, and memory through CB1 receptor signaling in mice.

    Science.gov (United States)

    Basavarajappa, Balapal S; Nagre, Nagaraja N; Xie, Shan; Subbanna, Shivakumar

    2014-07-01

    In rodents, many exogenous and endogenous cannabinoids, such as anandamide (AEA) and 2-arachidonyl glycerol (2-AG), have been shown to play an important role in certain hippocampal memory processes. However, the mechanisms by which endogenous AEA regulate this processes are not well understood. Here the effects of AEA on long-term potentiation (LTP), hippocampal-dependent learning and memory tasks, pERK1/2, pCaMKIV, and pCREB signaling events in both cannabinoid receptor type 1 (CB1R) wild-type (WT) and knockout (KO) mice were assessed following administration of URB597, an inhibitor of the fatty acid amide hydrolase (FAAH). Acute administration of URB597 enhanced AEA levels without affecting the levels of 2-AG or CB1R in the hippocampus and neocortex as compared to vehicle. In hippocampal slices, URB597 impaired LTP in CB1R WT but not in KO littermates. URB597 impaired object recognition, spontaneous alternation and spatial memory in the Y-maze test in CB1R WT mice but not in KO mice. Furthermore, URB597 enhanced ERK phosphorylation in WT without affecting total ERK levels in WT or KO mice. URB597 impaired CaMKIV and CREB phosphorylation in WT but not in KO mice. CB1R KO mice have a lower pCaMKIV/CaMKIV ratio and higher pCREB/CREB ratio as compared to WT littermates. Our results indicate that pharmacologically elevated AEA impair LTP, learning and memory and inhibit CaMKIV and CREB phosphorylation, via the activation of CB1Rs. Collectively, these findings also suggest that pharmacological elevation of AEA beyond normal concentrations is also detrimental for the underlying physiological responses. © 2014 Wiley Periodicals, Inc.

  7. Reward prediction error signal enhanced by striatum-amygdala interaction explains the acceleration of probabilistic reward learning by emotion.

    Science.gov (United States)

    Watanabe, Noriya; Sakagami, Masamichi; Haruno, Masahiko

    2013-03-06

    Learning does not only depend on rationality, because real-life learning cannot be isolated from emotion or social factors. Therefore, it is intriguing to determine how emotion changes learning, and to identify which neural substrates underlie this interaction. Here, we show that the task-independent presentation of an emotional face before a reward-predicting cue increases the speed of cue-reward association learning in human subjects compared with trials in which a neutral face is presented. This phenomenon was attributable to an increase in the learning rate, which regulates reward prediction errors. Parallel to these behavioral findings, functional magnetic resonance imaging demonstrated that presentation of an emotional face enhanced reward prediction error (RPE) signal in the ventral striatum. In addition, we also found a functional link between this enhanced RPE signal and increased activity in the amygdala following presentation of an emotional face. Thus, this study revealed an acceleration of cue-reward association learning by emotion, and underscored a role of striatum-amygdala interactions in the modulation of the reward prediction errors by emotion.

  8. Low-complexity R-peak detection in ECG signals: a preliminary step towards ambulatory fetal monitoring.

    Science.gov (United States)

    Rooijakkers, Michiel; Rabotti, Chiara; Bennebroek, Martijn; van Meerbergen, Jef; Mischi, Massimo

    2011-01-01

    Non-invasive fetal health monitoring during pregnancy has become increasingly important. Recent advances in signal processing technology have enabled fetal monitoring during pregnancy, using abdominal ECG recordings. Ubiquitous ambulatory monitoring for continuous fetal health measurement is however still unfeasible due to the computational complexity of noise robust solutions. In this paper an ECG R-peak detection algorithm for ambulatory R-peak detection is proposed, as part of a fetal ECG detection algorithm. The proposed algorithm is optimized to reduce computational complexity, while increasing the R-peak detection quality compared to existing R-peak detection schemes. Validation of the algorithm is performed on two manually annotated datasets, the MIT/BIH Arrhythmia database and an in-house abdominal database. Both R-peak detection quality and computational complexity are compared to state-of-the-art algorithms as described in the literature. With a detection error rate of 0.22% and 0.12% on the MIT/BIH Arrhythmia and in-house databases, respectively, the quality of the proposed algorithm is comparable to the best state-of-the-art algorithms, at a reduced computational complexity.

  9. Beyond negative valence: 2-week administration of a serotonergic antidepressant enhances both reward and effort learning signals.

    Directory of Open Access Journals (Sweden)

    Jacqueline Scholl

    2017-02-01

    Full Text Available To make good decisions, humans need to learn about and integrate different sources of appetitive and aversive information. While serotonin has been linked to value-based decision-making, its role in learning is less clear, with acute manipulations often producing inconsistent results. Here, we show that when the effects of a selective serotonin reuptake inhibitor (SSRI, citalopram are studied over longer timescales, learning is robustly improved. We measured brain activity with functional magnetic resonance imaging (fMRI in volunteers as they performed a concurrent appetitive (money and aversive (effort learning task. We found that 2 weeks of citalopram enhanced reward and effort learning signals in a widespread network of brain regions, including ventromedial prefrontal and anterior cingulate cortex. At a behavioral level, this was accompanied by more robust reward learning. This suggests that serotonin can modulate the ability to learn via a mechanism that is independent of stimulus valence. Such effects may partly underlie SSRIs' impact in treating psychological illnesses. Our results highlight both a specific function in learning for serotonin and the importance of studying its role across longer timescales.

  10. Design of complete software GPS signal simulator with low complexity and precise multipath channel model

    Directory of Open Access Journals (Sweden)

    G. Arul Elango

    2016-09-01

    Full Text Available The need for GPS data simulators have become important due to the tremendous growth in the design of versatile GPS receivers. Commercial hardware and software based GPS simulators are expensive and time consuming. In this work, a low cost simple novel GPS L1 signal simulator is designed for testing and evaluating the performance of software GPS receiver in a laboratory environment. A typical real time paradigm, similar to actual satellite derived GPS signal is created on a computer generated scenario. In this paper, a GPS software simulator is proposed that may offer a lot of analysis and testing flexibility to the researchers and developers as it is totally software based primarily running on a laptop/personal computer without the requirement of any hardware. The proposed GPS simulator allows provision for re-configurability and test repeatability and is developed in VC++ platform to minimize the simulation time. It also incorporates Rayleigh multipath channel fading model under non-line of sight (NLOS conditions. In this work, to efficiently design the simulator, several Rayleigh fading models viz. Inverse Discrete Fourier Transform (IDFT, Filtering White Gaussian Noise (FWFN and modified Sum of Sinusoidal (SOS simulators are tested and compared in terms of accuracy of its first and second order statistical metrics, execution time and the later one is found to be as the best appropriate Rayleigh multipath model suitable for incorporating with GPS simulator. The fading model written in ‘MATLAB’ engine has been linked with software GPS simulator module enable to test GPS receiver’s functionality in different fading environments.

  11. Integrating Emotion and Cognition in Successful Service Learning: A Complex System Approach (Invited)

    Science.gov (United States)

    Raia, F.

    2010-12-01

    Service-learning (S-L) has evolved as valuable pedagogic concept during the last two decades, based on the hypothesis that learning can best be accomplished when placed in the context of real-life social settings, e.g. schools, production, research, healthcare etc. What students learn in the academic course/context must be elaborated in the context of the S-L experience. In return for the authentic learning experience, the learner provides the service-provider with a "free" service. This reciprocality makes service-learning an appealing concept. Because of its attractive "win-win" design, the field of service-learning is continuously expanding. At a major public university CCNY with a very diverse student population, we were interested in developing and participating in S-L experience in the field of Earth System Science. We designed an upper level undergraduate course - Environmental Soil Science for Urban Sustainability - specifically targeted to students of Earth Science, Engineering, Economics and, Political Sciences to support environmental entrepreneurship. Specifically, we integrated S-L activities in the exploration of soil studies and urban agriculture. Students worked together in small groups both in class and for their S-L experience (30 hours) with urban garden and agriculture organizations. Students were required to apply the content learned in the academic course providing soil testing and soil evaluation to the partners, generate reports through a series of homework assignments and journal entries connecting three major components: Community Service, Personal Experience and Course Content. Our experience with this course shows the following results: S-L must be considered a complex system characterized by the continually changing interactions among the above mentioned three major components and three social and academic diverse groups of people involved: Students, Service-Providers and Academic Instructors. Because experience alone does not produce

  12. IL-7 Induces an Epitope Masking of γc Protein in IL-7 Receptor Signaling Complex

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    Tae Sik Goh

    2017-01-01

    Full Text Available IL-7 signaling via IL-7Rα and common γ-chain (γc is necessary for the development and homeostasis of T cells. Although the delicate mechanism in which IL-7Rα downregulation allows the homeostasis of T cell with limited IL-7 has been well known, the exact mechanism behind the interaction between IL-7Rα and γc in the absence or presence of IL-7 remains unclear. Additionally, we are still uncertain as to how only IL-7Rα is separately downregulated by the binding of IL-7 from the IL-7Rα/γc complex. We demonstrate here that 4G3, TUGm2, and 3E12 epitope masking of γc protein are induced in the presence of IL-7, indicating that the epitope alteration is induced by IL-7 binding to the preassembled receptor core. Moreover, the epitope masking of γc protein is inversely correlated with the expression of IL-7Rα upon IL-7 binding, implying that the structural alteration of γc might be involved in the regulation of IL-7Rα expression. The conformational change in γc upon IL-7 binding may contribute not only to forming the functional IL-7 signaling complex but also to optimally regulating the expression of IL-7Rα.

  13. IL-7 Induces an Epitope Masking of γc Protein in IL-7 Receptor Signaling Complex

    Science.gov (United States)

    Goh, Tae Sik; Jo, Yuna; Lee, Byunghyuk; Kim, Geona; Hwang, Hyunju; Ko, Eunhee; Kang, Seung Wan; Oh, Sae-Ock; Baek, Sun-Yong; Yoon, Sik; Lee, Jung Sub

    2017-01-01

    IL-7 signaling via IL-7Rα and common γ-chain (γc) is necessary for the development and homeostasis of T cells. Although the delicate mechanism in which IL-7Rα downregulation allows the homeostasis of T cell with limited IL-7 has been well known, the exact mechanism behind the interaction between IL-7Rα and γc in the absence or presence of IL-7 remains unclear. Additionally, we are still uncertain as to how only IL-7Rα is separately downregulated by the binding of IL-7 from the IL-7Rα/γc complex. We demonstrate here that 4G3, TUGm2, and 3E12 epitope masking of γc protein are induced in the presence of IL-7, indicating that the epitope alteration is induced by IL-7 binding to the preassembled receptor core. Moreover, the epitope masking of γc protein is inversely correlated with the expression of IL-7Rα upon IL-7 binding, implying that the structural alteration of γc might be involved in the regulation of IL-7Rα expression. The conformational change in γc upon IL-7 binding may contribute not only to forming the functional IL-7 signaling complex but also to optimally regulating the expression of IL-7Rα. PMID:28127156

  14. Effect of tDCS on task relevant and irrelevant perceptual learning of complex objects.

    Science.gov (United States)

    Van Meel, Chayenne; Daniels, Nicky; de Beeck, Hans Op; Baeck, Annelies

    2016-01-01

    During perceptual learning the visual representations in the brain are altered, but these changes' causal role has not yet been fully characterized. We used transcranial direct current stimulation (tDCS) to investigate the role of higher visual regions in lateral occipital cortex (LO) in perceptual learning with complex objects. We also investigated whether object learning is dependent on the relevance of the objects for the learning task. Participants were trained in two tasks: object recognition using a backward masking paradigm and an orientation judgment task. During both tasks, an object with a red line on top of it were presented in each trial. The crucial difference between both tasks was the relevance of the object: the object was relevant for the object recognition task, but not for the orientation judgment task. During training, half of the participants received anodal tDCS stimulation targeted at the lateral occipital cortex (LO). Afterwards, participants were tested on how well they recognized the trained objects, the irrelevant objects presented during the orientation judgment task and a set of completely new objects. Participants stimulated with tDCS during training showed larger improvements of performance compared to participants in the sham condition. No learning effect was found for the objects presented during the orientation judgment task. To conclude, this study suggests a causal role of LO in relevant object learning, but given the rather low spatial resolution of tDCS, more research on the specificity of this effect is needed. Further, mere exposure is not sufficient to train object recognition in our paradigm.

  15. Changes in Cerebral Hemodynamics during Complex Motor Learning by Character Entry into Touch-Screen Terminals.

    Directory of Open Access Journals (Sweden)

    Akira Sagari

    Full Text Available Studies of cerebral hemodynamics during motor learning have mostly focused on neurorehabilitation interventions and their effectiveness. However, only a few imaging studies of motor learning and the underlying complex cognitive processes have been performed.We measured cerebral hemodynamics using near-infrared spectroscopy (NIRS in relation to acquisition patterns of motor skills in healthy subjects using character entry into a touch-screen terminal. Twenty healthy, right-handed subjects who had no previous experience with character entry using a touch-screen terminal participated in this study. They were asked to enter the characters of a randomly formed Japanese syllabary into the touch-screen terminal. All subjects performed the task with their right thumb for 15 s alternating with 25 s of rest for 30 repetitions. Performance was calculated by subtracting the number of incorrect answers from the number of correct answers, and gains in motor skills were evaluated according to the changes in performance across cycles. Behavioral and oxygenated hemoglobin concentration changes across task cycles were analyzed using Spearman's rank correlations.Performance correlated positively with task cycle, thus confirming motor learning. Hemodynamic activation over the left sensorimotor cortex (SMC showed a positive correlation with task cycle, whereas activations over the right prefrontal cortex (PFC and supplementary motor area (SMA showed negative correlations.We suggest that increases in finger momentum with motor learning are reflected in the activity of the left SMC. We further speculate that the right PFC and SMA were activated during the early phases of motor learning, and that this activity was attenuated with learning progress.

  16. Protein complex detection in PPI networks based on data integration and supervised learning method.

    Science.gov (United States)

    Yu, Feng; Yang, Zhi; Hu, Xiao; Sun, Yuan; Lin, Hong; Wang, Jian

    2015-01-01

    Revealing protein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein interactions, which makes it possible to predict protein complexes from protein-protein interaction (PPI) networks. However, the small amount of known physical interactions may limit protein complex detection. The new PPI networks are constructed by integrating PPI datasets with the large and readily available PPI data from biomedical literature, and then the less reliable PPI between two proteins are filtered out based on semantic similarity and topological similarity of the two proteins. Finally, the supervised learning protein complex detection (SLPC), which can make full use of the information of available known complexes, is applied to detect protein complex on the new PPI networks. The experimental results of SLPC on two different categories yeast PPI networks demonstrate effectiveness of the approach: compared with the original PPI networks, the best average improvements of 4.76, 6.81 and 15.75 percentage units in the F-score, accuracy and maximum matching ratio (MMR) are achieved respectively; compared with the denoising PPI networks, the best average improvements of 3.91, 4.61 and 12.10 percentage units in the F-score, accuracy and MMR are achieved respectively; compared with ClusterONE, the start-of the-art complex detection method, on the denoising extended PPI networks, the average improvements of 26.02 and 22.40 percentage units in the F-score and MMR are achieved respectively. The experimental results show that the performances of SLPC have a large improvement through integration of new receivable PPI data from biomedical literature into original PPI networks and denoising PPI networks. In addition, our protein complexes detection method can achieve better performance than ClusterONE.

  17. A new inhibitor of the β-arrestin/AP2 endocytic complex reveals interplay between GPCR internalization and signalling

    Science.gov (United States)

    Beautrait, Alexandre; Paradis, Justine S.; Zimmerman, Brandon; Giubilaro, Jenna; Nikolajev, Ljiljana; Armando, Sylvain; Kobayashi, Hiroyuki; Yamani, Lama; Namkung, Yoon; Heydenreich, Franziska M.; Khoury, Etienne; Audet, Martin; Roux, Philippe P.; Veprintsev, Dmitry B.; Laporte, Stéphane A.; Bouvier, Michel

    2017-04-01

    In addition to G protein-coupled receptor (GPCR) desensitization and endocytosis, β-arrestin recruitment to ligand-stimulated GPCRs promotes non-canonical signalling cascades. Distinguishing the respective contributions of β-arrestin recruitment to the receptor and β-arrestin-promoted endocytosis in propagating receptor signalling has been limited by the lack of selective analytical tools. Here, using a combination of virtual screening and cell-based assays, we have identified a small molecule that selectively inhibits the interaction between β-arrestin and the β2-adaptin subunit of the clathrin adaptor protein AP2 without interfering with the formation of receptor/β-arrestin complexes. This selective β-arrestin/β2-adaptin inhibitor (Barbadin) blocks agonist-promoted endocytosis of the prototypical β2-adrenergic (β2AR), V2-vasopressin (V2R) and angiotensin-II type-1 (AT1R) receptors, but does not affect β-arrestin-independent (transferrin) or AP2-independent (endothelin-A) receptor internalization. Interestingly, Barbadin fully blocks V2R-stimulated ERK1/2 activation and blunts cAMP accumulation promoted by both V2R and β2AR, supporting the concept of β-arrestin/AP2-dependent signalling for both G protein-dependent and -independent pathways.

  18. Hypothalamic roles of mTOR complex I: integration of nutrient and hormone signals to regulate energy homeostasis.

    Science.gov (United States)

    Hu, Fang; Xu, Yong; Liu, Feng

    2016-06-01

    Mammalian or mechanistic target of rapamycin (mTOR) senses nutrient, energy, and hormone signals to regulate metabolism and energy homeostasis. mTOR activity in the hypothalamus, which is associated with changes in energy status, plays a critical role in the regulation of food intake and body weight. mTOR integrates signals from a variety of "energy balancing" hormones such as leptin, insulin, and ghrelin, although its action varies in response to these distinct hormonal stimuli as well as across different neuronal populations. In this review, we summarize and highlight recent findings regarding the functional roles of mTOR complex 1 (mTORC1) in the hypothalamus specifically in its regulation of body weight, energy expenditure, and glucose/lipid homeostasis. Understanding the role and underlying mechanisms behind mTOR-related signaling in the brain will undoubtedly pave new avenues for future therapeutics and interventions that can combat obesity, insulin resistance, and diabetes. Copyright © 2016 the American Physiological Society.

  19. Use of Signaling to Integrate Desktop Virtual Reality and Online Learning Management Systems

    Science.gov (United States)

    Dodd, Bucky J.; Antonenko, Pavlo D.

    2012-01-01

    Desktop virtual reality is an emerging educational technology that offers many potential benefits for learners in online learning contexts; however, a limited body of research is available that connects current multimedia learning techniques with these new forms of media. Because most formal online learning is delivered using learning management…

  20. QUALITY ASSURANCE IN RWANDAN HIGHER LEARNING EDUCATION: IS THE SYSTEM ADAPTIVE OR COMPLEX?

    Directory of Open Access Journals (Sweden)

    Nathan Kanuma Taremwa

    2014-01-01

    Full Text Available Developing knowledge infrastructure by massive investments in education and training are taken as a benchmark in facilitating the acceleration and possible increases in skills, capacities and competences of Rwandan people has become apriority issue in the recent years. This notion is relevant to vision 2020 where human resource development and building of a knowledge based economy are fundamental pillars. In the past years, several policy reforms have taken place in education sector. However, the overarching question is if such reforms are becoming adaptive or complex and if such reforms will not compromise the quality of education in higher learning education in Rwanda? The main objective of the study was to investigate the impact of changes in Higher Learning Institutions on the quality of education in Rwanda. This research had three hypotheses, namely; there is an impact of changes in Higher Learning Institutions on quality of education in Rwanda; the current complexity in Rwandan education system is affecting the quality of education in HLIs; Tailoring education system to the regional reforms and implementation strategies is affecting the quality of education in Rwanda. This study was carried out in 10 higher learning institutions (5 public, 5 private and 2 Ministry of Education directorates (HEC and REB. Key informants were the senior management/head of institutions, experienced academic staff, and students. The parameters considered included; the learning methods, assessment styles, workloads, language of instruction, merging of public HLIs, curriculum, and the transformation of some private higher learning institutions into company forms. Main research instruments were questionnaires and interview guides. Both qualitative and quantitative research was collected. Analyses were done using SPSS and excel packages. Major findings indicate that the system is still in transition with indicative gaps. Ample time would therefore be necessary for

  1. Learned stressor resistance requires extracellular signal-regulated kinase in the prefrontal cortex

    Directory of Open Access Journals (Sweden)

    John Paul Christianson

    2014-10-01

    Full Text Available Behaviorally controllable stressors confer protection from the neurochemical and behavioral consequences of future uncontrollable stressors, a phenomenon termed behavioral immunization. Recent data implicate neuroplasticity within the ventromedial prefrontal cortex (mPFC as critical to behavioral immunization. Adult, male Sprague-Dawley rats were exposed to a series of controllable tailshocks and one week later to uncontrollable tailshocks, followed 24h later by social exploration and shuttlebox escape tests. To test the involvement of N-methyl-D-aspartate receptors (NMDAR and the extracellular signal-regulated kinase (ERK cascade in behavioral immunization, either D-AP5 or the MEK inhibitor U0126 was injected to the prelimbic (PL or infralimbic (IL mPFC prior to controllable stress exposure. Phosphorylated ERK and P70S6K, regulators of transcription and translation, were quantified by Western blot or immunohistochemistry after controllable or uncontrollable tailshocks. Prior controllable stress prevented the social exploration and shuttlebox performance deficits caused by the later uncontrollable stressor, and this effect was blocked by injections of D-AP5 into mPFC. A significant increase in phosphorylated ERK1 and ERK2, but not P70S6K, occurred within the PL and IL in rats exposed to controllable stress, but not to uncontrollable stress. However, U0126 only prevented behavioral immunization when injected to the PL. We provide evidence that NMDAR and ERK dependent plasticity within the PL region is required for behavioral immunization, a learned form of stressor resistance.

  2. Learning contrast-invariant cancellation of redundant signals in neural systems.

    Directory of Open Access Journals (Sweden)

    Jorge F Mejias

    Full Text Available Cancellation of redundant information is a highly desirable feature of sensory systems, since it would potentially lead to a more efficient detection of novel information. However, biologically plausible mechanisms responsible for such selective cancellation, and especially those robust to realistic variations in the intensity of the redundant signals, are mostly unknown. In this work, we study, via in vivo experimental recordings and computational models, the behavior of a cerebellar-like circuit in the weakly electric fish which is known to perform cancellation of redundant stimuli. We experimentally observe contrast invariance in the cancellation of spatially and temporally redundant stimuli in such a system. Our model, which incorporates heterogeneously-delayed feedback, bursting dynamics and burst-induced STDP, is in agreement with our in vivo observations. In addition, the model gives insight on the activity of granule cells and parallel fibers involved in the feedback pathway, and provides a strong prediction on the parallel fiber potentiation time scale. Finally, our model predicts the existence of an optimal learning contrast around 15% contrast levels, which are commonly experienced by interacting fish.

  3. Recognition of emotions using multimodal physiological signals and an ensemble deep learning model.

    Science.gov (United States)

    Yin, Zhong; Zhao, Mengyuan; Wang, Yongxiong; Yang, Jingdong; Zhang, Jianhua

    2017-03-01

    Using deep-learning methodologies to analyze multimodal physiological signals becomes increasingly attractive for recognizing human emotions. However, the conventional deep emotion classifiers may suffer from the drawback of the lack of the expertise for determining model structure and the oversimplification of combining multimodal feature abstractions. In this study, a multiple-fusion-layer based ensemble classifier of stacked autoencoder (MESAE) is proposed for recognizing emotions, in which the deep structure is identified based on a physiological-data-driven approach. Each SAE consists of three hidden layers to filter the unwanted noise in the physiological features and derives the stable feature representations. An additional deep model is used to achieve the SAE ensembles. The physiological features are split into several subsets according to different feature extraction approaches with each subset separately encoded by a SAE. The derived SAE abstractions are combined according to the physiological modality to create six sets of encodings, which are then fed to a three-layer, adjacent-graph-based network for feature fusion. The fused features are used to recognize binary arousal or valence states. DEAP multimodal database was employed to validate the performance of the MESAE. By comparing with the best existing emotion classifier, the mean of classification rate and F-score improves by 5.26%. The superiority of the MESAE against the state-of-the-art shallow and deep emotion classifiers has been demonstrated under different sizes of the available physiological instances. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  4. The emergence of learning-teaching trajectories in education: a complex dynamic systems approach.

    Science.gov (United States)

    Steenbeek, Henderien; van Geert, Paul

    2013-04-01

    In this article we shall focus on learning-teaching trajectories ='successful' as well as 'unsuccessful' ones - as emergent and dynamic phenomena resulting from the interactions in the entire educational context, in particular the interaction between students and teachers viewed as processes of intertwining self-, other- and co-regulation. The article provides a review of the educational research literature on action regulation in learning and teaching, and interprets this literature in light of the theory of complex dynamic systems. Based on this reinterpretation of the literature, two dynamic models are proposed, one focusing on the short-term dynamics of learning-teaching interactions as they take place in classrooms, the other focusing on the long-term dynamics of interactions in a network of variables encompassing concerns, evaluations, actions and action effects (such as learning) students and teachers. The aim of presenting these models is to demonstrate, first, the possibility of transforming existing educational theory into dynamic models and, second, to provide some suggestions as to how such models can be used to further educational theory and practice.

  5. The effectiveness of signaling principle in virtual reality courseware towards achievement of transfer learning among students with different spatial ability

    Science.gov (United States)

    Yahaya, Wan Ahmad Jaafar Wan; Ahmad, Awaatif

    2017-10-01

    Past research revealed that students and society, in general, are relatively under-skilled in performing the practice of Islamic funeral management which is one of the "ibadah fardu kifayah" (a legal obligation that must be discharged by the Muslim community as a whole) in Islam. Participation among youth in managing funerals is relatively low, partly due to the ineffectiveness of the instructional approach. This paper aims to examine the effectiveness of the signaling principle in virtual reality courseware pertaining to the topic of Islamic Funeral Management in the Islamic Education subject to ensure the accomplishment of transfer learning among students with different spatial abilities. The study comprises of two phases namely the courseware development phase and treatment phase. The courseware development employs the Instructional Design Model by Alessi and Trollip. Besides that, the courseware is integrated with components of CLE, principles in Theory of CATLM and signaling principle in multimedia learning. The sample consisted of 130 Form Two students who were selected randomly from four Malaysian secondary schools. They were divided into two experimental groups with 63 students in group one and 67 students in group two. The experimental group one used VR courseware without the signaling principle (VRTI) while experimental group two used the VR courseware with the signaling principle (VRDI). The experiment lasted for three weeks. ANOVA was utilised to analyse the data from this research. The findings showed significant differences between students who used VRDI in the transfer of learning compared to students who used VRTI.

  6. Examining the Potential of Web-Based Multimedia to Support Complex Fine Motor Skill Learning: An Empirical Study

    Science.gov (United States)

    Papastergiou, Marina; Pollatou, Elisana; Theofylaktou, Ioannis; Karadimou, Konstantina

    2014-01-01

    Research on the utilization of the Web for complex fine motor skill learning that involves whole body movements is still scarce. The aim of this study was to evaluate the impact of the introduction of a multimedia web-based learning environment, which was targeted at a rhythmic gymnastics routine consisting of eight fine motor skills, into an…

  7. Designing for Discovery Learning of Complexity Principles of Congestion by Driving Together in the TrafficJams Simulation

    Science.gov (United States)

    Levy, Sharona T.; Peleg, Ran; Ofeck, Eyal; Tabor, Naamit; Dubovi, Ilana; Bluestein, Shiri; Ben-Zur, Hadar

    2018-01-01

    We propose and evaluate a framework supporting collaborative discovery learning of complex systems. The framework blends five design principles: (1) individual action: amidst (2) social interactions; challenged with (3) multiple tasks; set in (4) a constrained interactive learning environment that draws attention to (5) highlighted target…

  8. Self-Efficacy, Task Complexity and Task Performance: Exploring Interactions in Two Versions of Vocabulary Learning Tasks

    Science.gov (United States)

    Wu, Xiaoli; Lowyck, Joost; Sercu, Lies; Elen, Jan

    2012-01-01

    The present study aimed for better understanding of the interactions between task complexity and students' self-efficacy beliefs and students' use of learning strategies, and finally their interacting effects on task performance. This investigation was carried out in the context of Chinese students learning English as a foreign language in a…

  9. CRTC1 Nuclear Translocation Following Learning Modulates Memory Strength via Exchange of Chromatin Remodeling Complexes on the Fgf1 Gene

    Directory of Open Access Journals (Sweden)

    Shusaku Uchida

    2017-01-01

    Full Text Available Summary: Memory is formed by synapse-to-nucleus communication that leads to regulation of gene transcription, but the identity and organizational logic of signaling pathways involved in this communication remain unclear. Here we find that the transcription cofactor CRTC1 is a critical determinant of sustained gene transcription and memory strength in the hippocampus. Following associative learning, synaptically localized CRTC1 is translocated to the nucleus and regulates Fgf1b transcription in an activity-dependent manner. After both weak and strong training, the HDAC3-N-CoR corepressor complex leaves the Fgf1b promoter and a complex involving the translocated CRTC1, phosphorylated CREB, and histone acetyltransferase CBP induces transient transcription. Strong training later substitutes KAT5 for CBP, a process that is dependent on CRTC1, but not on CREB phosphorylation. This in turn leads to long-lasting Fgf1b transcription and memory enhancement. Thus, memory strength relies on activity-dependent changes in chromatin and temporal regulation of gene transcription on specific CREB/CRTC1 gene targets. : Uchida et al. link CRTC1 synapse-to-nucleus shuttling in memory. Weak and strong training induce CRTC1 nuclear transport and transient Fgf1b transcription by a complex including CRTC1, CREB, and histone acetyltransferase CBP, whereas strong training alone maintains Fgf1b transcription through CRTC1-dependent substitution of KAT5 for CBP, leading to memory enhancement. Keywords: memory enhancement, long-term potentiation, hippocampus, nuclear transport, epigenetics, FGF1, CRTC1, KAT5/Tip60, HDAC3, CREB

  10. Using complexity theory to develop a student-directed interprofessional learning activity for 1220 healthcare students.

    Science.gov (United States)

    Jorm, Christine; Nisbet, Gillian; Roberts, Chris; Gordon, Christopher; Gentilcore, Stacey; Chen, Timothy F

    2016-08-08

    More and better interprofessional practice is predicated to be necessary to deliver good care to the patients of the future. However, universities struggle to create authentic learning activities that enable students to experience the dynamic interprofessional interactions common in healthcare and that can accommodate large interprofessional student cohorts. We investigated a large-scale mandatory interprofessional learning (IPL) activity for health professional students designed to promote social learning. A mixed methods research approach determined feasibility, acceptability and the extent to which student IPL outcomes were met. We developed an IPL activity founded in complexity theory to prepare students for future practice by engaging them in a self-directed (self-organised) learning activity with a diverse team, whose assessable products would be emergent creations. Complicated but authentic clinical cases (n = 12) were developed to challenge student teams (n = 5 or 6). Assessment consisted of a written management plan (academically marked) and a five-minute video (peer marked) designed to assess creative collaboration as well as provide evidence of integrated collective knowledge; the cohesive patient-centred management plan. All students (including the disciplines of diagnostic radiology, exercise physiology, medicine, nursing, occupational therapy, pharmacy, physiotherapy and speech pathology), completed all tasks successfully. Of the 26 % of students who completed the evaluation survey, 70 % agreed or strongly agreed that the IPL activity was worthwhile, and 87 % agreed or strongly agreed that their case study was relevant. Thematic analysis found overarching themes of engagement and collaboration-in-action suggesting that the IPL activity enabled students to achieve the intended learning objectives. Students recognised the contribution of others and described negotiation, collaboration and creation of new collective knowledge after working

  11. Machine learning and complex-network for personalized and systems biomedicine

    KAUST Repository

    Cannistraci, Carlo Vittorio

    2016-01-27

    The talk will begin with an introduction on using machine learning to discover hidden information and unexpected patterns in large biomedical datasets. Then, recent results on the use of complex network theory in biomedicine and neuroscience will be discussed. In particular, metagenomics and metabolomics data, approaches for drug-target repositioning, functional/structural MR connectomes and gut-brain axis data will be presented. The conclusion will outline the novel and exciting perspectives offered by the translation of these methods from systems biology to systems medicine.

  12. Metal Dependence of Signal Transmission through MolecularQuantum-Dot Cellular Automata (QCA: A Theoretical Studyon Fe, Ru, and Os Mixed-Valence Complexes

    Directory of Open Access Journals (Sweden)

    Ken Tokunaga

    2010-08-01

    Full Text Available Dynamic behavior of signal transmission through metal complexes [L5M-BL-ML5]5+ (M=Fe, Ru, Os, BL=pyrazine (py, 4,4’-bipyridine (bpy, L=NH3, which are simplified models of the molecular quantum-dot cellular automata (molecular QCA, is discussed from the viewpoint of one-electron theory, density functional theory. It is found that for py complexes, the signal transmission time (tst is Fe(0.6 fs < Os(0.7 fs < Ru(1.1 fs and the signal amplitude (A is Fe(0.05 e < Os(0.06 e < Ru(0.10 e. For bpy complexes, tst and A are Fe(1.4 fs < Os(1.7 fs < Ru(2.5 fs and Os(0.11 e < Ru(0.12 e complexes generally have stronger signal amplitude, but waste longer time for signal transmission than py complexes. Among all complexes, Fe complex with bpy BL shows the best result. These results are discussed from overlap integral and energy gap of molecular orbitals.

  13. TOR complex 2-Ypk1 signaling is an essential positive regulator of the general amino acid control response and autophagy.

    Science.gov (United States)

    Vlahakis, Ariadne; Graef, Martin; Nunnari, Jodi; Powers, Ted

    2014-07-22

    The highly conserved Target of Rapamycin (TOR) kinase is a central regulator of cell growth and metabolism in response to nutrient availability. TOR functions in two structurally and functionally distinct complexes, TOR Complex 1 (TORC1) and TOR Complex 2 (TORC2). Through TORC1, TOR negatively regulates autophagy, a conserved process that functions in quality control and cellular homeostasis and, in this capacity, is part of an adaptive nutrient deprivation response. Here we demonstrate that during amino acid starvation TOR also operates independently as a positive regulator of autophagy through the conserved TORC2 and its downstream target protein kinase, Ypk1. Under these conditions, TORC2-Ypk1 signaling negatively regulates the Ca(2+)/calmodulin-dependent phosphatase, calcineurin, to enable the activation of the amino acid-sensing eIF2α kinase, Gcn2, and to promote autophagy. Our work reveals that the TORC2 pathway regulates autophagy in an opposing manner to TORC1 to provide a tunable response to cellular metabolic status.

  14. Close relationship between fMRI signals and transient heart rate changes accompanying K-complex. Simultaneous EEG/fMRI study

    International Nuclear Information System (INIS)

    Kan, Shigeyuki; Koike, Takahiko; Miyauchi, Satoru; Misaki, Masaya

    2009-01-01

    Combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) allows the investigation of spontaneous activities in the human brain. Recently, by using this technique, increases in fMRI signal accompanying transient EEG activities such as sleep spindles and slow waves were reported. Although these fMRI signal increases appear to arise as a result of the neural activities being reflected in the EEG, when the influence of physiological activities upon fMRI signals are taken into consideration, it is highly controversial that fMRI signal increases accompanying transient EEG activities reflect actual neural activities. In the present study, we conducted simultaneous fMRI and polysomnograph recording of 18 normal adults, to study the effect of transient heart rate changes after a K-complex on fMRI signals. Significant fMRI signal increase was observed in the cerebellum, the ventral thalamus, the dorsal part of the brainstem, the periventricular white matter and the ventricle (quadrigeminal cistern). On the other hand, significant fMRI signal decrease was observed only in the right insula. Moreover, intensities of fMRI signal increase that was accompanied by a K-complex correlated positively with the magnitude of heart rate changes after a K-complex. Previous studies have reported that K-complex is closely related with sympathetic nervous activity and that the attributes of perfusion regulation in the brain differ during wakefulness and sleep. By taking these findings into consideration, our present results indicate that a close relationship exists between a K-complex and the changes in cardio- and neurovascular regulations that are mediated by the autonomic nervous system during sleep; further, these results indicate that transient heart rate changes after a K-complex can affect the fMRI signal generated in certain brain regions. (author)

  15. Striatal and Tegmental Neurons Code Critical Signals for Temporal-Difference Learning of State Value in Domestic Chicks

    Directory of Open Access Journals (Sweden)

    Chentao Wen

    2016-11-01

    Full Text Available To ensure survival, animals must update the internal representations of their environment in a trial-and-error fashion. Psychological studies of associative learning and neurophysiological analyses of dopaminergic neurons have suggested that this updating process involves the temporal-difference (TD method in the basal ganglia network. However, the way in which the component variables of the TD method are implemented at the neuronal level is unclear. To investigate the underlying neural mechanisms, we trained domestic chicks to associate color cues with food rewards. We recorded neuronal activities from the medial striatum or tegmentum in a freely behaving condition and examined how reward omission changed neuronal firing. To compare neuronal activities with the signals assumed in the TD method, we simulated the behavioral task in the form of a finite sequence composed of discrete steps of time. The three signals assumed in the simulated task were the prediction signal, the target signal for updating, and the TD-error signal. In both the medial striatum and tegmentum, the majority of recorded neurons were categorized into three types according to their fitness for three models, though these neurons tended to form a continuum spectrum without distinct differences in the firing rate. Specifically, two types of striatal neurons successfully mimicked the target signal and the prediction signal. A linear summation of these two types of striatum neurons was a good fit for the activity of one type of tegmental neurons mimicking the TD-error signal. The present study thus demonstrates that the striatum and tegmentum can convey the signals critically required for the TD method. Based on the theoretical and neurophysiological studies, together with tract-tracing data, we propose a novel model to explain how the convergence of signals represented in the striatum could lead to the computation of TD error in tegmental dopaminergic neurons.

  16. Pharmacological rescue of Ras signaling, GluA1-dependent synaptic plasticity, and learning deficits in a fragile X model

    OpenAIRE

    Lim, Chae-Seok; Hoang, Elizabeth T.; Viar, Kenneth E.; Stornetta, Ruth L.; Scott, Michael M.; Zhu, J. Julius

    2014-01-01

    Fragile X syndrome, caused by the loss of Fmr1 gene function, is the most common form of inherited mental retardation. Lim et al. find that compounds activating serotonin (5HT) subtype 2B receptors or dopamine (DA) subtype 1-like receptors and those inhibiting 5HT2A-Rs or D2-Rs enhance Ras signaling, GluA1-dependent synaptic plasticity, and learning in Fmr1 knockout mice. Combining 5HT and DA compounds at low doses synergistically restored normal learning. This suggests that properly dosed an...

  17. Trafficking of plant plasma membrane aquaporins: multiple regulation levels and complex sorting signals.

    Science.gov (United States)

    Chevalier, Adrien S; Chaumont, François

    2015-05-01

    Aquaporins are small channel proteins which facilitate the diffusion of water and small neutral molecules across biological membranes. Compared with animals, plant genomes encode numerous aquaporins, which display a large variety of subcellular localization patterns. More specifically, plant aquaporins of the plasma membrane intrinsic protein (PIP) subfamily were first described as plasma membrane (PM)-resident proteins, but recent research has demonstrated that the trafficking and subcellular localization of these proteins are complex and highly regulated. In the past few years, PIPs emerged as new model proteins to study subcellular sorting and membrane dynamics in plant cells. At least two distinct sorting motifs (one cytosolic, the other buried in the membrane) are required to direct PIPs to the PM. Hetero-oligomerization and interaction with SNAREs (soluble N-ethylmaleimide-sensitive factor protein attachment protein receptors) also influence the subcellular trafficking of PIPs. In addition to these constitutive processes, both the progression of PIPs through the secretory pathway and their dynamics at the PM are responsive to changing environmental conditions. © The Author 2014. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.

  18. Better decision making in complex, dynamic tasks training with human-facilitated interactive learning environments

    CERN Document Server

    Qudrat-Ullah, Hassan

    2015-01-01

    This book describes interactive learning environments (ILEs) and their underlying concepts. It explains how ILEs can be used to improve the decision-making process and how these improvements can be empirically verified. The objective of this book is to enhance our understanding of and to gain insights into the process by which human facilitated ILEs are effectively designed and used in improving users’ decision making in complex, dynamic tasks. This book is divided into four major parts. Part I serves as an introduction to the importance and complexity of decision making in dynamic tasks. Part II provides background material, drawing upon relevant literature, for the development of an integrated process model on the effectiveness of human facilitated ILEs in improving decision making in dynamic tasks. Part III focuses on the design, development, and application of FishBankILE in laboratory experiments to gather empirical evidence for the validity of the process model. Finally, part IV presents a comprehensi...

  19. Striatal and Hippocampal Entropy and Recognition Signals in Category Learning: Simultaneous Processes Revealed by Model-Based fMRI

    Science.gov (United States)

    Davis, Tyler; Love, Bradley C.; Preston, Alison R.

    2012-01-01

    Category learning is a complex phenomenon that engages multiple cognitive processes, many of which occur simultaneously and unfold dynamically over time. For example, as people encounter objects in the world, they simultaneously engage processes to determine their fit with current knowledge structures, gather new information about the objects, and…

  20. Brainstem auditory evoked potentials with the use of acoustic clicks and complex verbal sounds in young adults with learning disabilities.

    Science.gov (United States)

    Kouni, Sophia N; Giannopoulos, Sotirios; Ziavra, Nausika; Koutsojannis, Constantinos

    2013-01-01

    Acoustic signals are transmitted through the external and middle ear mechanically to the cochlea where they are transduced into electrical impulse for further transmission via the auditory nerve. The auditory nerve encodes the acoustic sounds that are conveyed to the auditory brainstem. Multiple brainstem nuclei, the cochlea, the midbrain, the thalamus, and the cortex constitute the central auditory system. In clinical practice, auditory brainstem responses (ABRs) to simple stimuli such as click or tones are widely used. Recently, complex stimuli or complex auditory brain responses (cABRs), such as monosyllabic speech stimuli and music, are being used as a tool to study the brainstem processing of speech sounds. We have used the classic 'click' as well as, for the first time, the artificial successive complex stimuli 'ba', which constitutes the Greek word 'baba' corresponding to the English 'daddy'. Twenty young adults institutionally diagnosed as dyslexic (10 subjects) or light dyslexic (10 subjects) comprised the diseased group. Twenty sex-, age-, education-, hearing sensitivity-, and IQ-matched normal subjects comprised the control group. Measurements included the absolute latencies of waves I through V, the interpeak latencies elicited by the classical acoustic click, the negative peak latencies of A and C waves, as well as the interpeak latencies of A-C elicited by the verbal stimulus 'baba' created on a digital speech synthesizer. The absolute peak latencies of waves I, III, and V in response to monoaural rarefaction clicks as well as the interpeak latencies I-III, III-V, and I-V in the dyslexic subjects, although increased in comparison with normal subjects, did not reach the level of a significant difference (pwave C and the interpeak latencies of A-C elicited by verbal stimuli were found to be increased in the dyslexic group in comparison with the control group (p=0.0004 and p=0.045, respectively). In the subgroup consisting of 10 patients suffering from

  1. Interference in ballistic motor learning: specificity and role of sensory error signals

    DEFF Research Database (Denmark)

    Lundbye-Jensen, Jesper; Petersen, Tue Hvass; Rothwell, John C

    2011-01-01

    Humans are capable of learning numerous motor skills, but newly acquired skills may be abolished by subsequent learning. Here we ask what factors determine whether interference occurs in motor learning. We speculated that interference requires competing processes of synaptic plasticity in overlap......Humans are capable of learning numerous motor skills, but newly acquired skills may be abolished by subsequent learning. Here we ask what factors determine whether interference occurs in motor learning. We speculated that interference requires competing processes of synaptic plasticity...... in overlapping circuits and predicted specificity. To test this, subjects learned a ballistic motor task. Interference was observed following subsequent learning of an accuracy-tracking task, but only if the competing task involved the same muscles and movement direction. Interference was not observed from a non......-learning task suggesting that interference requires competing learning. Subsequent learning of the competing task 4 h after initial learning did not cause interference suggesting disruption of early motor memory consolidation as one possible mechanism underlying interference. Repeated transcranial magnetic...

  2. Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Network

    OpenAIRE

    Gao, Juntao; Shen, Yulong; Liu, Jia; Ito, Minoru; Shiratori, Norio

    2017-01-01

    Adaptive traffic signal control, which adjusts traffic signal timing according to real-time traffic, has been shown to be an effective method to reduce traffic congestion. Available works on adaptive traffic signal control make responsive traffic signal control decisions based on human-crafted features (e.g. vehicle queue length). However, human-crafted features are abstractions of raw traffic data (e.g., position and speed of vehicles), which ignore some useful traffic information and lead t...

  3. Machine-Learning-Based Future Received Signal Strength Prediction Using Depth Images for mmWave Communications

    OpenAIRE

    Okamoto, Hironao; Nishio, Takayuki; Nakashima, Kota; Koda, Yusuke; Yamamoto, Koji; Morikura, Masahiro; Asai, Yusuke; Miyatake, Ryo

    2018-01-01

    This paper discusses a machine-learning (ML)-based future received signal strength (RSS) prediction scheme using depth camera images for millimeter-wave (mmWave) networks. The scheme provides the future RSS prediction of any mmWave links within the camera's view, including links where nodes are not transmitting frames. This enables network controllers to conduct network operations before line-of-sight path blockages degrade the RSS. Using the ML techniques, the prediction scheme automatically...

  4. Hsp27 regulates Akt activation and polymorphonuclear leukocyte apoptosis by scaffolding MK2 to Akt signal complex.

    Science.gov (United States)

    Wu, Rui; Kausar, Hina; Johnson, Paul; Montoya-Durango, Diego E; Merchant, Michael; Rane, Madhavi J

    2007-07-27

    We have shown previously that Akt exists in a signal complex with p38 MAPK, MAPK-activated protein kinase-2 (MK2), and heat shock protein 27 (Hsp27) and MK2 phosphorylates Akt on Ser-473. Additionally, dissociation of Hsp27 from Akt, prior to Akt activation, induced polymorphonuclear leukocyte (PMN) apoptosis. However, the role of Hsp27 in regulating Akt activation was not examined. This study tested the hypothesis that Hsp27 regulates Akt activation and promotes cell survival by scaffolding MK2 to the Akt signal complex. Here we show that loss of Akt/Hsp27 interaction by anti-Hsp27 antibody treatment resulted in loss of Akt/MK2 interaction, loss of Akt-Ser-473 phosphorylation, and induced PMN apoptosis. Transfection of myristoylated Akt (AktCA) in HK-11 cells induced Akt-Ser-473 phosphorylation, activation, and Hsp27-Ser-82 phosphorylation. Cotransfection of AktCA with Hsp27 short interfering RNA, but not scrambled short interfering RNA, silenced Hsp27 expression, without altering Akt expression in HK-11 cells. Silencing Hsp27 expression inhibited Akt/MK2 interaction, inhibited Akt phosphorylation and Akt activation, and induced HK-11 cell death. Deletion mutagenesis studies identified acidic linker region (amino acids 117-128) on Akt as an Hsp27 binding region. Deletion of amino acids 117-128 on Akt resulted in loss of its interaction with Hsp27 and MK2 but not with Hsp90 as demonstrated by immunoprecipitation and glutathione S-transferase pulldown studies. Co-transfection studies demonstrated that constitutively active MK2 (MK2EE) phosphorylated Aktwt (wild type) on Ser-473 but failed to phosphorylate Akt(Delta117-128) mutant in transfixed cells. These studies collectively define a novel role of Hsp27 in regulating Akt activation and cellular apoptosis by mediating interaction between Akt and its upstream activator MK2.

  5. Testing complex animal cognition: Concept learning, proactive interference, and list memory.

    Science.gov (United States)

    Wright, Anthony A

    2018-01-01

    This article describes an approach for assessing and comparing complex cognition in rhesus monkeys and pigeons by training them in a sequence of synergistic tasks, each yielding a whole function for enhanced comparisons. These species were trained in similar same/different tasks with expanding training sets (8, 16, 32, 64, 128 … 1024 pictures) followed by novel-stimulus transfer eventually resulting in full abstract-concept learning. Concept-learning functions revealed better rhesus transfer throughout and full concept learning at the 128 set, versus pigeons at the 256 set. They were then tested in delayed same/different tasks for proactive interference by inserting occasional tests within trial-unique sessions where the test stimulus matched a previous sample stimulus (1, 2, 4, 8, 16 trials prior). Proactive-interference functions revealed time-based interference for pigeons (1, 10 s delays), but event-based interference for rhesus (no effect of 1, 10, 20 s delays). They were then tested in list-memory tasks by expanding the sample to four samples in trial-unique sessions (minimizing proactive interference). The four-item, list-memory functions revealed strong recency memory at short delays, gradually changing to strong primacy memory at long delays over 30 s for rhesus, and 10 s for pigeons. Other species comparisons and future directions are discussed. © 2018 Society for the Experimental Analysis of Behavior.

  6. Disrupting neural activity related to awake-state sharp wave-ripple complexes prevents hippocampal learning.

    Science.gov (United States)

    Nokia, Miriam S; Mikkonen, Jarno E; Penttonen, Markku; Wikgren, Jan

    2012-01-01

    Oscillations in hippocampal local-field potentials (LFPs) reflect the crucial involvement of the hippocampus in memory trace formation: theta (4-8 Hz) oscillations and ripples (~200 Hz) occurring during sharp waves are thought to mediate encoding and consolidation, respectively. During sharp wave-ripple complexes (SPW-Rs), hippocampal cell firing closely follows the pattern that took place during the initial experience, most likely reflecting replay of that event. Disrupting hippocampal ripples using electrical stimulation either during training in awake animals or during sleep after training retards spatial learning. Here, adult rabbits were trained in trace eyeblink conditioning, a hippocampus-dependent associative learning task. A bright light was presented to the animals during the inter-trial interval (ITI), when awake, either during SPW-Rs or irrespective of their neural state. Learning was particularly poor when the light was presented following SPW-Rs. While the light did not disrupt the ripple itself, it elicited a theta-band oscillation, a state that does not usually coincide with SPW-Rs. Thus, it seems that consolidation depends on neuronal activity within and beyond the hippocampus taking place immediately after, but by no means limited to, hippocampal SPW-Rs.

  7. Replicating the Ice-Volume Signal of the Early Pleistocene with a Complex Earth System Model

    Science.gov (United States)

    Tabor, C. R.; Poulsen, C. J.; Pollard, D.

    2013-12-01

    Milankovitch theory proposes high-latitude summer insolation intensity paces the ice ages by controlling perennial snow cover amounts (Milankovitch, 1941). According to theory, the ~21 kyr cycle of precession should dominate the ice-volume records since it has the greatest influence on high-latitude summer insolation. Modeling experiments frequently support Milankovitch theory by attributing the majority of Northern Hemisphere high-latitude summer snowmelt to changes in the cycle of precession (e.g. Jackson and Broccoli, 2003). However, ice-volume proxy records, especially those of the Early Pleistocene (2.6-0.8 Ma), display variability with a period of ~41 kyr (Raymo and Lisiecki, 2005), indicative of insolation forcing from obliquity, which has a much smaller influence on summer insolation intensity than precession. Several hypotheses attempt to explain the discrepancies between Milkankovitch theory and the proxy records by invoking phenomena such as insolation gradients (Raymo and Nisancioglu, 2003), hemispheric offset (Raymo et al., 2006; Lee and Poulsen, 2009), and integrated summer energy (Huybers, 2006); however, all of these hypotheses contain caveats (Ruddiman, 2006) and have yet to be supported by modeling studies that use a complex GCM. To explore potential solutions to this '41 kyr problem,' we use an Earth system model composed of the GENESIS GCM and Land Surface model, the BIOME4 vegetation model, and the Pennsylvania State ice-sheet model. Using an asynchronous coupling technique, we run four idealized transient combinations of obliquity and precession, representing the orbital extremes of the Pleistocene (Berger and Loutre, 1991). Each experiment is run through several complete orbital cycles with a dynamic ice domain spanning North America and Greenland, and fixed preindustrial greenhouse-gas concentrations. For all orbital configurations, model results produce greater ice-volume spectral power at the frequency of obliquity despite significantly

  8. Resveratrol upregulates Egr-1 expression and activity involving extracellular signal-regulated protein kinase and ternary complex factors

    Energy Technology Data Exchange (ETDEWEB)

    Rössler, Oliver G.; Glatzel, Daniel; Thiel, Gerald, E-mail: gerald.thiel@uks.eu

    2015-03-01

    Many intracellular functions have been attributed to resveratrol, a polyphenolic phytoalexin found in grapes and in other plants. Here, we show that resveratrol induces the expression of the transcription factor Egr-1 in human embryonic kidney cells. Using a chromosomally embedded Egr-1-responsive reporter gene, we show that the Egr-1 activity was significantly elevated in resveratrol-treated cells, indicating that the newly synthesized Egr-1 protein was biologically active. Stimulus-transcription coupling leading to the resveratrol-induced upregulation of Egr-1 expression and activity requires the protein kinases Raf and extracellular signal-regulated protein kinase ERK, while MAP kinase phosphatase-1 functions as a nuclear shut-off device that interrupts the signaling cascade connecting resveratrol stimulation with enhanced Egr-1 expression. On the transcriptional level, Elk-1, a key transcriptional regulator of serum response element-driven gene transcription, connects the intracellular signaling cascade elicited by resveratrol with transcription of the Egr-1 gene. These data were corroborated by the observation that stimulation of the cells with resveratrol increased the transcriptional activation potential of Elk-1. The SRE as well as the GC-rich DNA binding site of Egr-1 function as resveratrol-responsive elements. Thus, resveratrol regulates gene transcription via activation of the stimulus-regulated protein kinases Raf and ERK and the stimulus-responsive transcription factors TCF and Egr-1. - Highlights: • The plant polyphenol resveratrol upregulates Egr-1 expression and activity. • The stimulation of Egr-1 requires the protein kinases ERK and Raf. • Resveratrol treatment upregulates the transcriptional activation potential of Elk-1. • Resveratrol-induced stimulation of Egr-1 requires ternary complex factors. • Two distinct resveratrol-responsive elements were identified.

  9. Resveratrol upregulates Egr-1 expression and activity involving extracellular signal-regulated protein kinase and ternary complex factors

    International Nuclear Information System (INIS)

    Rössler, Oliver G.; Glatzel, Daniel; Thiel, Gerald

    2015-01-01

    Many intracellular functions have been attributed to resveratrol, a polyphenolic phytoalexin found in grapes and in other plants. Here, we show that resveratrol induces the expression of the transcription factor Egr-1 in human embryonic kidney cells. Using a chromosomally embedded Egr-1-responsive reporter gene, we show that the Egr-1 activity was significantly elevated in resveratrol-treated cells, indicating that the newly synthesized Egr-1 protein was biologically active. Stimulus-transcription coupling leading to the resveratrol-induced upregulation of Egr-1 expression and activity requires the protein kinases Raf and extracellular signal-regulated protein kinase ERK, while MAP kinase phosphatase-1 functions as a nuclear shut-off device that interrupts the signaling cascade connecting resveratrol stimulation with enhanced Egr-1 expression. On the transcriptional level, Elk-1, a key transcriptional regulator of serum response element-driven gene transcription, connects the intracellular signaling cascade elicited by resveratrol with transcription of the Egr-1 gene. These data were corroborated by the observation that stimulation of the cells with resveratrol increased the transcriptional activation potential of Elk-1. The SRE as well as the GC-rich DNA binding site of Egr-1 function as resveratrol-responsive elements. Thus, resveratrol regulates gene transcription via activation of the stimulus-regulated protein kinases Raf and ERK and the stimulus-responsive transcription factors TCF and Egr-1. - Highlights: • The plant polyphenol resveratrol upregulates Egr-1 expression and activity. • The stimulation of Egr-1 requires the protein kinases ERK and Raf. • Resveratrol treatment upregulates the transcriptional activation potential of Elk-1. • Resveratrol-induced stimulation of Egr-1 requires ternary complex factors. • Two distinct resveratrol-responsive elements were identified

  10. Unilateral vestibular deafferentation-induced changes in calcium signaling-related molecules in the rat vestibular nuclear complex.

    Science.gov (United States)

    Masumura, Chisako; Horii, Arata; Mitani, Kenji; Kitahara, Tadashi; Uno, Atsuhiko; Kubo, Takeshi

    2007-03-23

    Inquiries into the neurochemical mechanisms of vestibular compensation, a model of lesion-induced neuronal plasticity, reveal the involvement of both voltage-gated Ca(2+) channels (VGCC) and intracellular Ca(2+) signaling. Indeed, our previous microarray analysis showed an up-regulation of some calcium signaling-related genes such as the alpha2 subunit of L-type calcium channels, calcineurin, and plasma membrane Ca(2+) ATPase 1 (PMCA1) in the ipsilateral vestibular nuclear complex (VNC) following unilateral vestibular deafferentation (UVD). To further elucidate the role of calcium signaling-related molecules in vestibular compensation, we used a quantitative real-time polymerase chain reaction (PCR) method to confirm the microarray results and investigated changes in expression of these molecules at various stages of compensation (6 h to 2 weeks after UVD). We also investigated the changes in gene expression during Bechterew's phenomenon and the effects of a calcineurin inhibitor on vestibular compensation. Real-time PCR showed that genes for the alpha2 subunit of VGCC, PMCA2, and calcineurin were transiently up-regulated 6 h after UVD in ipsilateral VNC. A subsequent UVD, which induced Bechterew's phenomenon, reproduced a complete mirror image of the changes in gene expressions of PMCA2 and calcineurin seen in the initial UVD, while the alpha2 subunit of VGCC gene had a trend to increase in VNC ipsilateral to the second lesion. Pre-treatment by FK506, a calcineurin inhibitor, decelerated the vestibular compensation in a dose-dependent manner. Although it is still uncertain whether these changes in gene expression are causally related to the molecular mechanisms of vestibular compensation, this observation suggests that after increasing the Ca(2+) influx into the ipsilateral VNC neurons via up-regulated VGCC, calcineurin may be involved in their synaptic plasticity. Conversely, an up-regulation of PMCA2, a brain-specific Ca(2+) pump, would increase an efflux of Ca

  11. Complex network inference from P300 signals: Decoding brain state under visual stimulus for able-bodied and disabled subjects

    Science.gov (United States)

    Gao, Zhong-Ke; Cai, Qing; Dong, Na; Zhang, Shan-Shan; Bo, Yun; Zhang, Jie

    2016-10-01

    Distinguishing brain cognitive behavior underlying disabled and able-bodied subjects constitutes a challenging problem of significant importance. Complex network has established itself as a powerful tool for exploring functional brain networks, which sheds light on the inner workings of the human brain. Most existing works in constructing brain network focus on phase-synchronization measures between regional neural activities. In contrast, we propose a novel approach for inferring functional networks from P300 event-related potentials by integrating time and frequency domain information extracted from each channel signal, which we show to be efficient in subsequent pattern recognition. In particular, we construct brain network by regarding each channel signal as a node and determining the edges in terms of correlation of the extracted feature vectors. A six-choice P300 paradigm with six different images is used in testing our new approach, involving one able-bodied subject and three disabled subjects suffering from multiple sclerosis, cerebral palsy, traumatic brain and spinal-cord injury, respectively. We then exploit global efficiency, local efficiency and small-world indices from the derived brain networks to assess the network topological structure associated with different target images. The findings suggest that our method allows identifying brain cognitive behaviors related to visual stimulus between able-bodied and disabled subjects.

  12. CDIP1-BAP31 Complex Transduces Apoptotic Signals from Endoplasmic Reticulum to Mitochondria under Endoplasmic Reticulum Stress

    Directory of Open Access Journals (Sweden)

    Takushi Namba

    2013-10-01

    Full Text Available Resolved endoplasmic reticulum (ER stress response is essential for intracellular homeostatic balance, but unsettled ER stress can lead to apoptosis. Here, we show that a proapoptotic p53 target, CDIP1, acts as a key signal transducer of ER-stress-mediated apoptosis. We identify B-cell-receptor-associated protein 31 (BAP31 as an interacting partner of CDIP1. Upon ER stress, CDIP1 is induced and enhances an association with BAP31 at the ER membrane. We also show that CDIP1 binding to BAP31 is required for BAP31 cleavage upon ER stress and for BAP31-Bcl-2 association. The recruitment of Bcl-2 to the BAP31-CDIP1 complex, as well as CDIP1-dependent truncated Bid (tBid and caspase-8 activation, contributes to BAX oligomerization. Genetic knockout of CDIP1 in mice leads to impaired response to ER-stress-mediated apoptosis. Altogether, our data demonstrate that the CDIP1/BAP31-mediated regulation of mitochondrial apoptosis pathway represents a mechanism for establishing an ER-mitochondrial crosstalk for ER-stress-mediated apoptosis signaling.

  13. Multiscale Signal Analysis and Modeling

    CERN Document Server

    Zayed, Ahmed

    2013-01-01

    Multiscale Signal Analysis and Modeling presents recent advances in multiscale analysis and modeling using wavelets and other systems. This book also presents applications in digital signal processing using sampling theory and techniques from various function spaces, filter design, feature extraction and classification, signal and image representation/transmission, coding, nonparametric statistical signal processing, and statistical learning theory. This book also: Discusses recently developed signal modeling techniques, such as the multiscale method for complex time series modeling, multiscale positive density estimations, Bayesian Shrinkage Strategies, and algorithms for data adaptive statistics Introduces new sampling algorithms for multidimensional signal processing Provides comprehensive coverage of wavelets with presentations on waveform design and modeling, wavelet analysis of ECG signals and wavelet filters Reviews features extraction and classification algorithms for multiscale signal and image proce...

  14. Chronic alcohol intake abolishes the relationship between dopamine synthesis capacity and learning signals in the ventral striatum

    DEFF Research Database (Denmark)

    Deserno, Lorenz; Beck, Anne; Huys, Quentin J. M.

    2015-01-01

    Drugs of abuse elicit dopamine release in the ventral striatum, possibly biasing dopamine-driven reinforcement learning towards drug-related reward at the expense of non-drug-related reward. Indeed, in alcohol-dependent patients, reactivity in dopaminergic target areas is shifted from non-drug......-related stimuli towards drug-related stimuli. Such ‘hijacked’ dopamine signals may impair flexible learning from non-drug-related rewards, and thus promote craving for the drug of abuse. Here, we used functional magnetic resonance imaging to measure ventral striatal activation by reward prediction errors (RPEs......) during a probabilistic reversal learning task in recently detoxified alcohol-dependent patients and healthy controls (N = 27). All participants also underwent 6-[18F]fluoro-DOPA positron emission tomography to assess ventral striatal dopamine synthesis capacity. Neither ventral striatal activation...

  15. Process signal selection method to improve the impact mitigation of sensor broken for diagnosis using machine learning

    International Nuclear Information System (INIS)

    Minowa, Hirotsugu; Gofuku, Akio

    2014-01-01

    Accidents of industrial plants cause large loss on human, economic, social credibility. In recent, studies of diagnostic methods using techniques of machine learning are expected to detect early and correctly abnormality occurred in a plant. However, the general diagnostic machines are generated generally to require all process signals (hereafter, signals) for plant diagnosis. Thus if trouble occurs such as process sensor is broken, the diagnostic machine cannot diagnose or may decrease diagnostic performance. Therefore, we propose an important process signal selection method to improve impact mitigation without reducing the diagnostic performance by reducing the adverse effect of noises on multi-agent diagnostic system. The advantage of our method is the general-purpose property that allows to be applied to various supervised machine learning and to set the various parameters to decide termination of search. The experiment evaluation revealed that diagnostic machines generated by our method using SVM improved the impact mitigation and did not reduce performance about the diagnostic accuracy, the velocity of diagnosis, predictions of plant state near accident occurrence, in comparison with the basic diagnostic machine which diagnoses by using all signals. This paper reports our proposed method and the results evaluated which our method was applied to the simulated abnormal of the fast-breeder reactor Monju. (author)

  16. Klotho Regulates 14-3-3ζ Monomerization and Binding to the ASK1 Signaling Complex in Response to Oxidative Stress.

    Directory of Open Access Journals (Sweden)

    Reynolds K Brobey

    Full Text Available The reactive oxygen species (ROS-sensitive apoptosis signal-regulating kinase 1 (ASK1 signaling complex is a key regulator of p38 MAPK activity, a major modulator of stress-associated with aging disorders. We recently reported that the ratio of free ASK1 to the complex-bound ASK1 is significantly decreased in Klotho-responsive manner and that Klotho-deficient tissues have elevated levels of free ASK1 which coincides with increased oxidative stress. Here, we tested the hypothesis that: 1 covalent interactions exist among three identified proteins constituting the ASK1 signaling complex; 2 in normal unstressed cells the ASK1, 14-3-3ζ and thioredoxin (Trx proteins simultaneously engage in a tripartite complex formation; 3 Klotho's stabilizing effect on the complex relied solely on 14-3-3ζ expression and its apparent phosphorylation and dimerization changes. To verify the hypothesis, we performed 14-3-3ζ siRNA knock-down experiments in conjunction with cell-based assays to measure ASK1-client protein interactions in the presence and absence of Klotho, and with or without an oxidant such as rotenone. Our results show that Klotho activity induces posttranslational modifications in the complex targeting 14-3-3ζ monomer/dimer changes to effectively protect against ASK1 oxidation and dissociation. This is the first observation implicating all three proteins constituting the ASK1 signaling complex in close proximity.

  17. Visualizing complex processes using a cognitive-mapping tool to support the learning of clinical reasoning.

    Science.gov (United States)

    Wu, Bian; Wang, Minhong; Grotzer, Tina A; Liu, Jun; Johnson, Janice M

    2016-08-22

    Practical experience with clinical cases has played an important role in supporting the learning of clinical reasoning. However, learning through practical experience involves complex processes difficult to be captured by students. This study aimed to examine the effects of a computer-based cognitive-mapping approach that helps students to externalize the reasoning process and the knowledge underlying the reasoning process when they work with clinical cases. A comparison between the cognitive-mapping approach and the verbal-text approach was made by analyzing their effects on learning outcomes. Fifty-two third-year or higher students from two medical schools participated in the study. Students in the experimental group used the computer-base cognitive-mapping approach, while the control group used the verbal-text approach, to make sense of their thinking and actions when they worked with four simulated cases over 4 weeks. For each case, students in both groups reported their reasoning process (involving data capture, hypotheses formulation, and reasoning with justifications) and the underlying knowledge (involving identified concepts and the relationships between the concepts) using the given approach. The learning products (cognitive maps or verbal text) revealed that students in the cognitive-mapping group outperformed those in the verbal-text group in the reasoning process, but not in making sense of the knowledge underlying the reasoning process. No significant differences were found in a knowledge posttest between the two groups. The computer-based cognitive-mapping approach has shown a promising advantage over the verbal-text approach in improving students' reasoning performance. Further studies are needed to examine the effects of the cognitive-mapping approach in improving the construction of subject-matter knowledge on the basis of practical experience.

  18. Impact of a Modified Jigsaw Method for Learning an Unfamiliar, Complex Topic

    Directory of Open Access Journals (Sweden)

    Denise Kolanczyk

    2017-09-01

    Full Text Available Objective: The aim of this study was to use the jigsaw method with an unfamiliar, complex topic and to evaluate the effectiveness of the jigsaw teaching method on student learning of assigned material (“jigsaw expert” versus non-assigned material (“jigsaw learner”. Innovation: The innovation was implemented in an advanced cardiology elective. Forty students were assigned a pre-reading and one of four valvular heart disorders, a topic not previously taught in the curriculum. A pre-test and post-test evaluated overall student learning. Student performance on pre/post tests as the “jigsaw expert” and “jigsaw learner” was also compared. Critical Analysis: Overall, the post-test mean score of 85.75% was significantly higher than that of the pre-test score of 56.75% (p<0.05. There was significant improvement in scores regardless of whether the material was assigned (“jigsaw experts” pre=58.8% and post=82.5%; p<0.05 or not assigned (“jigsaw learners” pre= 56.25% and post= 86.56%, p<0.05 for pre-study. Next Steps: The use of the jigsaw method to teach unfamiliar, complex content helps students to become both teachers and active listeners, which are essential to the skills and professionalism of a health care provider. Further studies are needed to evaluate use of the jigsaw method to teach unfamiliar, complex content on long-term retention and to further examine the effects of expert vs. non-expert roles. Conflict of Interest We declare no conflicts of interest or financial interests that the authors or members of their immediate families have in any product or service discussed in the manuscript, including grants (pending or received, employment, gifts, stock holdings or options, honoraria, consultancies, expert testimony, patents and royalties.   Type: Note

  19. Action observation versus motor imagery in learning a complex motor task: a short review of literature and a kinematics study.

    Science.gov (United States)

    Gatti, R; Tettamanti, A; Gough, P M; Riboldi, E; Marinoni, L; Buccino, G

    2013-04-12

    Both motor imagery and action observation have been shown to play a role in learning or re-learning complex motor tasks. According to a well accepted view they share a common neurophysiological basis in the mirror neuron system. Neurons within this system discharge when individuals perform a specific action and when they look at another individual performing the same or a motorically related action. In the present paper, after a short review of literature on the role of action observation and motor imagery in motor learning, we report the results of a kinematics study where we directly compared motor imagery and action observation in learning a novel complex motor task. This involved movement of the right hand and foot in the same angular direction (in-phase movement), while at the same time moving the left hand and foot in an opposite angular direction (anti-phase movement), all at a frequency of 1Hz. Motor learning was assessed through kinematics recording of wrists and ankles. The results showed that action observation is better than motor imagery as a strategy for learning a novel complex motor task, at least in the fast early phase of motor learning. We forward that these results may have important implications in educational activities, sport training and neurorehabilitation. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  20. A heuristic method for simulating open-data of arbitrary complexity that can be used to compare and evaluate machine learning methods.

    Science.gov (United States)

    Moore, Jason H; Shestov, Maksim; Schmitt, Peter; Olson, Randal S

    2018-01-01

    A central challenge of developing and evaluating artificial intelligence and machine learning methods for regression and classification is access to data that illuminates the strengths and weaknesses of different methods. Open data plays an important role in this process by making it easy for computational researchers to easily access real data for this purpose. Genomics has in some examples taken a leading role in the open data effort starting with DNA microarrays. While real data from experimental and observational studies is necessary for developing computational methods it is not sufficient. This is because it is not possible to know what the ground truth is in real data. This must be accompanied by simulated data where that balance between signal and noise is known and can be directly evaluated. Unfortunately, there is a lack of methods and software for simulating data with the kind of complexity found in real biological and biomedical systems. We present here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating complex biological and biomedical data. Further, we introduce new methods for developing simulation models that generate data that specifically allows discrimination between different machine learning methods.

  1. CERN Technical Training 2003: Learning for the LHC ! DISP-2003 - Digital Signal Processing

    CERN Multimedia

    2003-01-01

    DISP-2003 - Digital Signal Processing DISP-2003 is a two-term course given by CERN and University of Lausanne (UNIL) experts within the framework of the Technical Training Programme. The course will review the current techniques dealing with Digital Signal Processing, and it is intended for an audience who work or will work on digital signal processing aspects, and who need an introductory or refresher/update course. The course will be in English, with question and answers also in French. Spring 2 Term: DISP-2003: Advanced Digital Signal Processing 30 April 2003 - 21 May 2003, 4 lectures, Wednesdays afternoon (attendance cost: 40.- CHF, registration required) Lecturers: Léonard Studer, UNIL; Laurent Deniau, AT-MTM; Elena Wildner, AT-MAS Programme: Intelligent signal processing (ISP). Non-linear time series analysis. Image processing. Wavelets. (Basic concepts and definitions have been introduced during the previous Spring 1 Term: DISP-2003: Introduction to Digital Signal Processing). DISP-2003 is open...

  2. CERN Technical Training 2003: Learning for the LHC ! DISP-2003  -  Digital Signal Processing

    CERN Multimedia

    2003-01-01

    DISP-2003 is a two-term course given by CERN and University of Lausanne (UNIL) experts within the framework of the Technical Training Programme. The course will review the current techniques dealing with Digital Signal Processing. The DISP-2003 lecture series is composed of two Terms, and it is intended for an audience who work or will work on digital signal processing aspects, and who need an introductory or refresher/update course. The course will be in English, with questions and answers also in French. Spring 1 Term: DISP-2003: Introduction to Digital Signal Processing 20 February 2003 - 3 April 2003, 7 lectures, Thursdays (attendance cost: 70.- CHF, registration required) Lecturers: Maria Elena Angoletta, AB-BDI; Guy Baribaud, AB-BDI; Philippe Baudrenghien, AB-RF; Laurent Deniau, AT-MTM Programme: 'Classical' digital signal processing. Fourier analysis. The Laplace transform. The z-transform. Digital filters. Statistics for Signal Processing. Signal Estimation and Spectral Analysis. Spring 2 T...

  3. Deep ECGNet: An Optimal Deep Learning Framework for Monitoring Mental Stress Using Ultra Short-Term ECG Signals.

    Science.gov (United States)

    Hwang, Bosun; You, Jiwoo; Vaessen, Thomas; Myin-Germeys, Inez; Park, Cheolsoo; Zhang, Byoung-Tak

    2018-02-08

    Stress recognition using electrocardiogram (ECG) signals requires the intractable long-term heart rate variability (HRV) parameter extraction process. This study proposes a novel deep learning framework to recognize the stressful states, the Deep ECGNet, using ultra short-term raw ECG signals without any feature engineering methods. The Deep ECGNet was developed through various experiments and analysis of ECG waveforms. We proposed the optimal recurrent and convolutional neural networks architecture, and also the optimal convolution filter length (related to the P, Q, R, S, and T wave durations of ECG) and pooling length (related to the heart beat period) based on the optimization experiments and analysis on the waveform characteristics of ECG signals. The experiments were also conducted with conventional methods using HRV parameters and frequency features as a benchmark test. The data used in this study were obtained from Kwangwoon University in Korea (13 subjects, Case 1) and KU Leuven University in Belgium (9 subjects, Case 2). Experiments were designed according to various experimental protocols to elicit stressful conditions. The proposed framework to recognize stress conditions, the Deep ECGNet, outperformed the conventional approaches with the highest accuracy of 87.39% for Case 1 and 73.96% for Case 2, respectively, that is, 16.22% and 10.98% improvements compared with those of the conventional HRV method. We proposed an optimal deep learning architecture and its parameters for stress recognition, and the theoretical consideration on how to design the deep learning structure based on the periodic patterns of the raw ECG data. Experimental results in this study have proved that the proposed deep learning model, the Deep ECGNet, is an optimal structure to recognize the stress conditions using ultra short-term ECG data.

  4. Visual artificial grammar learning by rhesus macaques (Macaca mulatta): exploring the role of grammar complexity and sequence length.

    Science.gov (United States)

    Heimbauer, Lisa A; Conway, Christopher M; Christiansen, Morten H; Beran, Michael J; Owren, Michael J

    2018-03-01

    Humans and nonhuman primates can learn about the organization of stimuli in the environment using implicit sequential pattern learning capabilities. However, most previous artificial grammar learning studies with nonhuman primates have involved relatively simple grammars and short input sequences. The goal in the current experiments was to assess the learning capabilities of monkeys on an artificial grammar-learning task that was more complex than most others previously used with nonhumans. Three experiments were conducted using a joystick-based, symmetrical-response serial reaction time task in which two monkeys were exposed to grammar-generated sequences at sequence lengths of four in Experiment 1, six in Experiment 2, and eight in Experiment 3. Over time, the monkeys came to respond faster to the sequences generated from the artificial grammar compared to random versions. In a subsequent generalization phase, subjects generalized their knowledge to novel sequences, responding significantly faster to novel instances of sequences produced using the familiar grammar compared to those constructed using an unfamiliar grammar. These results reveal that rhesus monkeys can learn and generalize the statistical structure inherent in an artificial grammar that is as complex as some used with humans, for sequences up to eight items long. These findings are discussed in relation to whether or not rhesus macaques and other primate species possess implicit sequence learning abilities that are similar to those that humans draw upon to learn natural language grammar.

  5. A Complex Signal

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    The removal of Taiwan from the common strategic objectives of the U.S.-Japan alliance could mean much more than a change of wording While U.S.and Japanese top diplomats and defense officials celebrated closer cooperation forged by their annual meeting in

  6. Understanding valve program complexity in a refurbishment environment - learning from the past

    International Nuclear Information System (INIS)

    Roth, H.E.

    2012-01-01

    The complexity of Valve Program development, planning, execution and management in a refurbishment environment is an enormous undertaking requiring the proper coordination and integration of many moving parts. As such, lack of attention and understanding of this complexity has led to significant cost and schedule overruns in past refurbishment projects in the province. OPEX indicates the challenges in completing valve scope during refurbishments are related but not limited to; lack of detailed condition assessments, improper scope development, insignificant strategic approach to work task planning, scheduling and procurement, absence of contingency planning for common ‘as found’ conditions during execution, lack of proper training requirements, etc. In addition, past contracting strategies to employ numerous companies in collaboration to complete such a complex and specialized program, has resulted in further complications surrounding the management and integration of multiple quality programs and internal company processes. Finally, the aftermath of such fragmented projects results in an absolute closeout nightmare, often times taking years to locate, sift through and re-integrate pertinent information back into customer systems. Valve Program complexity cannot be understood by just anyone, only those that have lived through a refurbishment project and experienced the challenges mentioned above have the knowledge, skill, and ability to appreciate how to tactically apply past learning to realize future improvements. Furthermore, effective contractor-customer collaboration is crucial; true and in-depth knowledge and understanding of the customer quality programs, engineering and work management processes, configuration management requirements, and most importantly the imperative significance of nuclear safety, are all essential components to ensure overall alignment and program success. (author)

  7. Building flexibility and managing complexity in community mental health: lessons learned in a large urban centre.

    Science.gov (United States)

    Stergiopoulos, Vicky; Saab, Dima; Francombe Pridham, Kate; Aery, Anjana; Nakhost, Arash

    2018-01-24

    Across many jurisdictions, adults with complex mental health and social needs face challenges accessing appropriate supports due to system fragmentation and strict eligibility criteria of existing services. To support this underserviced population, Toronto's local health authority launched two novel community mental health models in 2014, inspired by Flexible Assertive Community Team principles. This study explores service user and provider perspectives on the acceptability of these services, and lessons learned during early implementation. We purposively sampled 49 stakeholders (staff, physicians, service users, health systems stakeholders) and conducted 17 semi-structured qualitative interviews and 5 focus groups between October 23, 2014 and March 2, 2015, exploring stakeholder perspectives on the newly launched team based models, as well as activities and strategies employed to support early implementation. Interviews and focus groups were audio recorded, transcribed verbatim and analyzed using thematic analysis. Findings revealed wide-ranging endorsement for the two team-based models' success in engaging the target population of adults with complex service needs. Implementation strengths included the broad recognition of existing service gaps, the use of interdisciplinary teams and experienced service providers, broad partnerships and collaboration among various service sectors, training and team building activities. Emerging challenges included lack of complementary support services such as suitable housing, organizational contexts reluctant to embrace change and risk associated with complexity, as well as limited service provider and organizational capacity to deliver evidence-based interventions. Findings identified implementation drivers at the practitioner, program, and system levels, specific to the implementation of community mental health interventions for adults with complex health and social needs. These can inform future efforts to address the health

  8. Understanding valve program complexity in a refurbishment environment - learning from the past

    Energy Technology Data Exchange (ETDEWEB)

    Roth, H.E. [Babcock & Wilcox Canada Ltd., Cambridge, Ontario (Canada)

    2012-07-01

    The complexity of Valve Program development, planning, execution and management in a refurbishment environment is an enormous undertaking requiring the proper coordination and integration of many moving parts. As such, lack of attention and understanding of this complexity has led to significant cost and schedule overruns in past refurbishment projects in the province. OPEX indicates the challenges in completing valve scope during refurbishments are related but not limited to; lack of detailed condition assessments, improper scope development, insignificant strategic approach to work task planning, scheduling and procurement, absence of contingency planning for common ‘as found’ conditions during execution, lack of proper training requirements, etc. In addition, past contracting strategies to employ numerous companies in collaboration to complete such a complex and specialized program, has resulted in further complications surrounding the management and integration of multiple quality programs and internal company processes. Finally, the aftermath of such fragmented projects results in an absolute closeout nightmare, often times taking years to locate, sift through and re-integrate pertinent information back into customer systems. Valve Program complexity cannot be understood by just anyone, only those that have lived through a refurbishment project and experienced the challenges mentioned above have the knowledge, skill, and ability to appreciate how to tactically apply past learning to realize future improvements. Furthermore, effective contractor-customer collaboration is crucial; true and in-depth knowledge and understanding of the customer quality programs, engineering and work management processes, configuration management requirements, and most importantly the imperative significance of nuclear safety, are all essential components to ensure overall alignment and program success. (author)

  9. Classification of caesarean section and normal vaginal deliveries using foetal heart rate signals and advanced machine learning algorithms.

    Science.gov (United States)

    Fergus, Paul; Hussain, Abir; Al-Jumeily, Dhiya; Huang, De-Shuang; Bouguila, Nizar

    2017-07-06

    Visual inspection of cardiotocography traces by obstetricians and midwives is the gold standard for monitoring the wellbeing of the foetus during antenatal care. However, inter- and intra-observer variability is high with only a 30% positive predictive value for the classification of pathological outcomes. This has a significant negative impact on the perinatal foetus and often results in cardio-pulmonary arrest, brain and vital organ damage, cerebral palsy, hearing, visual and cognitive defects and in severe cases, death. This paper shows that using machine learning and foetal heart rate signals provides direct information about the foetal state and helps to filter the subjective opinions of medical practitioners when used as a decision support tool. The primary aim is to provide a proof-of-concept that demonstrates how machine learning can be used to objectively determine when medical intervention, such as caesarean section, is required and help avoid preventable perinatal deaths. This is evidenced using an open dataset that comprises 506 controls (normal virginal deliveries) and 46 cases (caesarean due to pH ≤ 7.20-acidosis, n = 18; pH > 7.20 and pH machine-learning algorithms are trained, and validated, using binary classifier performance measures. The findings show that deep learning classification achieves sensitivity = 94%, specificity = 91%, Area under the curve = 99%, F-score = 100%, and mean square error = 1%. The results demonstrate that machine learning significantly improves the efficiency for the detection of caesarean section and normal vaginal deliveries using foetal heart rate signals compared with obstetrician and midwife predictions and systems reported in previous studies.

  10. Prior stress promotes the generalization of contextual fear memories: Involvement of the gabaergic signaling within the basolateral amygdala complex.

    Science.gov (United States)

    Bender, C L; Otamendi, A; Calfa, G D; Molina, V A

    2018-04-20

    Fear generalization occurs when a response, previously acquired with a threatening stimulus, is transferred to a similar one. However, it could be maladaptive when stimuli that do not represent a real threat are appraised as dangerous, which is a hallmark of several anxiety disorders. Stress exposure is a major risk factor for the occurrence of anxiety disorders and it is well established that it influences different phases of fear memory; nevertheless, its impact on the generalization of contextual fear memories has been less studied. In the present work, we have characterized the impact of acute restraint stress prior to contextual fear conditioning on the generalization of this fear memory, and the role of the GABAergic signaling within the basolateral amygdala complex (BLA) on the stress modulatory effects. We have found that a single stress exposure promoted the generalization of this memory trace to a different context that was well discriminated in unstressed conditioned animals. Moreover, this effect was dependent on the formation of a contextual associative memory and on the testing order (i.e., conditioning context first vs generalization context first). Furthermore, we observed that increasing GABA-A signaling by intra-BLA midazolam administration prior to the stressful session exposure prevented the generalization of fear memory, whereas intra-BLA administration of the GABA-A antagonist (Bicuculline), prior to fear conditioning, induced the generalization of fear memory in unstressed rats. We concluded that stress exposure, prior to contextual fear conditioning, promotes the generalization of fear memory and that the GABAergic transmission within the BLA has a critical role in this phenomenon. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. Coping with complexity: machine learning optimization of cell-free protein synthesis.

    Science.gov (United States)

    Caschera, Filippo; Bedau, Mark A; Buchanan, Andrew; Cawse, James; de Lucrezia, Davide; Gazzola, Gianluca; Hanczyc, Martin M; Packard, Norman H

    2011-09-01

    Biological systems contain complex metabolic pathways with many nonlinearities and synergies that make them difficult to predict from first principles. Protein synthesis is a canonical example of such a pathway. Here we show how cell-free protein synthesis may be improved through a series of iterated high-throughput experiments guided by a machine-learning algorithm implementing a form of evolutionary design of experiments (Evo-DoE). The algorithm predicts fruitful experiments from statistical models of the previous experimental results, combined with stochastic exploration of the experimental space. The desired experimental response, or evolutionary fitness, was defined as the yield of the target product, and new experimental conditions were discovered to have ∼ 350% greater yield than the standard. An analysis of the best experimental conditions discovered indicates that there are two distinct classes of kinetics, thus showing how our evolutionary design of experiments is capable of significant innovation, as well as gradual improvement. Copyright © 2011 Wiley Periodicals, Inc.

  12. Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks

    Science.gov (United States)

    Ubbens, Jordan R.; Stavness, Ian

    2017-01-01

    Plant phenomics has received increasing interest in recent years in an attempt to bridge the genotype-to-phenotype knowledge gap. There is a need for expanded high-throughput phenotyping capabilities to keep up with an increasing amount of data from high-dimensional imaging sensors and the desire to measure more complex phenotypic traits (Knecht et al., 2016). In this paper, we introduce an open-source deep learning tool called Deep Plant Phenomics. This tool provides pre-trained neural networks for several common plant phenotyping tasks, as well as an easy platform that can be used by plant scientists to train models for their own phenotyping applications. We report performance results on three plant phenotyping benchmarks from the literature, including state of the art performance on leaf counting, as well as the first published results for the mutant classification and age regression tasks for Arabidopsis thaliana. PMID:28736569

  13. EGFR Signaling in the Brain Is Necessary for Olfactory Learning in "Drosophila" Larvae

    Science.gov (United States)

    Rahn, Tasja; Leippe, Matthias; Roeder, Thomas; Fedders, Henning

    2013-01-01

    Signaling via the epidermal growth factor receptor (EGFR) pathway has emerged as one of the key mechanisms in the development of the central nervous system in "Drosophila melanogaster." By contrast, little is known about the functions of EGFR signaling in the differentiated larval brain. Here, promoter-reporter lines of EGFR and its most prominent…

  14. Factors affecting learning of vector math from computer-based practice: Feedback complexity and prior knowledge

    Directory of Open Access Journals (Sweden)

    Andrew F. Heckler

    2016-06-01

    Full Text Available In experiments including over 450 university-level students, we studied the effectiveness and time efficiency of several levels of feedback complexity in simple, computer-based training utilizing static question sequences. The learning domain was simple vector math, an essential skill in introductory physics. In a unique full factorial design, we studied the relative effects of “knowledge of correct response” feedback and “elaborated feedback” (i.e., a general explanation both separately and together. A number of other factors were analyzed, including training time, physics course grade, prior knowledge of vector math, and student beliefs about both their proficiency in and the importance of vector math. We hypothesize a simple model predicting how the effectiveness of feedback depends on prior knowledge, and the results confirm this knowledge-by-treatment interaction. Most notably, elaborated feedback is the most effective feedback, especially for students with low prior knowledge and low course grade. In contrast, knowledge of correct response feedback was less effective for low-performing students, and including both kinds of feedback did not significantly improve performance compared to elaborated feedback alone. Further, while elaborated feedback resulted in higher scores, the learning rate was at best only marginally higher because the training time was slightly longer. Training time data revealed that students spent significantly more time on the elaborated feedback after answering a training question incorrectly. Finally, we found that training improved student self-reported proficiency and that belief in the importance of the learned domain improved the effectiveness of training. Overall, we found that computer based training with static question sequences and immediate elaborated feedback in the form of simple and general explanations can be an effective way to improve student performance on a physics essential skill

  15. On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions.

    Science.gov (United States)

    Schmitt, Michael

    2004-09-01

    We study networks of spiking neurons that use the timing of pulses to encode information. Nonlinear interactions model the spatial groupings of synapses on the neural dendrites and describe the computations performed at local branches. Within a theoretical framework of learning we analyze the question of how many training examples these networks must receive to be able to generalize well. Bounds for this sample complexity of learning can be obtained in terms of a combinatorial parameter known as the pseudodimension. This dimension characterizes the computational richness of a neural network and is given in terms of the number of network parameters. Two types of feedforward architectures are considered: constant-depth networks and networks of unconstrained depth. We derive asymptotically tight bounds for each of these network types. Constant depth networks are shown to have an almost linear pseudodimension, whereas the pseudodimension of general networks is quadratic. Networks of spiking neurons that use temporal coding are becoming increasingly more important in practical tasks such as computer vision, speech recognition, and motor control. The question of how well these networks generalize from a given set of training examples is a central issue for their successful application as adaptive systems. The results show that, although coding and computation in these networks is quite different and in many cases more powerful, their generalization capabilities are at least as good as those of traditional neural network models.

  16. Coupling Visualization, Simulation, and Deep Learning for Ensemble Steering of Complex Energy Models: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Potter, Kristin C [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Brunhart-Lupo, Nicholas J [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Bush, Brian W [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Gruchalla, Kenny M [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Bugbee, Bruce [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Krishnan, Venkat K [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2017-10-09

    We have developed a framework for the exploration, design, and planning of energy systems that combines interactive visualization with machine-learning based approximations of simulations through a general purpose dataflow API. Our system provides a visual inter- face allowing users to explore an ensemble of energy simulations representing a subset of the complex input parameter space, and spawn new simulations to 'fill in' input regions corresponding to new enegery system scenarios. Unfortunately, many energy simula- tions are far too slow to provide interactive responses. To support interactive feedback, we are developing reduced-form models via machine learning techniques, which provide statistically sound esti- mates of the full simulations at a fraction of the computational cost and which are used as proxies for the full-form models. Fast com- putation and an agile dataflow enhance the engagement with energy simulations, and allow researchers to better allocate computational resources to capture informative relationships within the system and provide a low-cost method for validating and quality-checking large-scale modeling efforts.

  17. CERN Technical Training 2003: Learning for the LHC! DISP-2003 - Digital Signal Processing

    CERN Multimedia

    2003-01-01

    DISP-2003 is a two-term course given by CERN and University of Lausanne (UNIL) experts within the framework of the Technical Training Programme. The course will review the current techniques dealing with Digital Signal Processing, and it is intended for an audience who work or will work on digital signal processing aspects, and who need an introductory or refresher/update course. The course will be in English, with question and answers also in French. Spring 2 Term: DISP-2003: Advanced Digital Signal Processing 30 April 2003 - 21 May 2003, 4 lectures, Wednesdays afternoon. Attendance cost: 40.- CHF, registration required. Lecturers: Léonard Studer, UNIL; Laurent Deniau, AT-MTM; Elena Wildner, AT-MAS. Programme: Intelligent signal processing (ISP). Non-linear time series analysis. Image processing. Wavelets. Basic concepts and definitions have been introduced during the previous Spring 1 Term: DISP-2003: Introduction to Digital Signal Processing. DISP-2003 is open to all people interested, but registrat...

  18. Pilot Skill Development with Implicit and Explicit Learning: Considerations for Task Complexity

    National Research Council Canada - National Science Library

    Sullivan, Ryan

    2000-01-01

    .... Research in learning strategies has recently focused on implicit and explicit learning to determine if it is more important to focus on conscious facts or unconscious procedural performance during the learning process...

  19. Estrogen/ERα signaling axis participates in osteoblast maturation via upregulating chromosomal and mitochondrial complex gene expressions

    Science.gov (United States)

    Lin, Pei-I; Tai, Yu-Ting; Chan, Wing P.; Lin, Yi-Ling; Liao, Mei-Hsiu; Chen, Ruei-Ming

    2018-01-01

    Estrogen deficiency usually leads to bone loss and osteoporosis in postmenopausal women. Osteoblasts play crucial roles in bone formation. However, osteoblast functions are influenced by mitochondrial bioenergetic conditions. In this study, we investigated the roles of the estrogen and estrogen receptor alpha (ERα) axis in mitochondrial energy metabolism and subsequent osteoblast mineralization. Exposure of rat calvarial osteoblasts to estradiol caused substantial improvements in alkaline phosphatase activities and cell calcification. In parallel, treatment of human osteoblast-like U2OS cells, derived from a female osteosarcoma patient, with estradiol specifically augmented ERα levels. Sequentially, estradiol stimulated translocation of ERα to nuclei in human osteoblasts and induced expressions of genomic respiratory chain complex NDUFA10, UQCRC1, cytochrome c oxidase (COX)8A, COX6A2, COX8C, COX6C, COX6B2, COX412, and ATP12A genes. Concurrently, estradiol stimulated translocation of ERα to mitochondria from the cytoplasm. A bioinformatic search found the existence of four estrogen response elements in the 5’-promoter region of the mitochondrial cox i gene. Interestingly, estradiol induced COX I mRNA and protein expressions in human osteoblasts or rat calvarial osteoblasts. Knocking-down ERα translation concurrently downregulated estradiol-induced COX I mRNA expression. Consequently, exposure to estradiol led to successive increases in the mitochondrial membrane potential, the mitochondrial enzyme activity, and cellular adenosine triphosphate levels. Taken together, this study showed the roles of the estradiol/ERα signaling axis in improving osteoblast maturation through upregulating the mitochondrial bioenergetic system due to induction of definite chromosomal and mitochondrial complex gene expressions. Our results provide novel insights elucidating the roles of the estrogen/ERα alliance in regulating bone formation. PMID:29416685

  20. Structure of the Regulator of G Protein Signaling 8 (RGS8)-Gαq Complex: MOLECULAR BASIS FOR Gα SELECTIVITY.

    Science.gov (United States)

    Taylor, Veronica G; Bommarito, Paige A; Tesmer, John J G

    2016-03-04

    Regulator of G protein signaling (RGS) proteins interact with activated Gα subunits via their RGS domains and accelerate the hydrolysis of GTP. Although the R4 subfamily of RGS proteins generally accepts both Gαi/o and Gαq/11 subunits as substrates, the R7 and R12 subfamilies select against Gαq/11. In contrast, only one RGS protein, RGS2, is known to be selective for Gαq/11. The molecular basis for this selectivity is not clear. Previously, the crystal structure of RGS2 in complex with Gαq revealed a non-canonical interaction that could be due to interfacial differences imposed by RGS2, the Gα subunit, or both. To resolve this ambiguity, the 2.6 Å crystal structure of RGS8, an R4 subfamily member, was determined in complex with Gαq. RGS8 adopts the same pose on Gαq as it does when bound to Gαi3, indicating that the non-canonical interaction of RGS2 with Gαq is due to unique features of RGS2. Based on the RGS8-Gαq structure, residues in RGS8 that contact a unique α-helical domain loop of Gαq were converted to those typically found in R12 subfamily members, and the reverse substitutions were introduced into RGS10, an R12 subfamily member. Although these substitutions perturbed their ability to stimulate GTP hydrolysis, they did not reverse selectivity. Instead, selectivity for Gαq seems more likely determined by whether strong contacts can be maintained between α6 of the RGS domain and Switch III of Gαq, regions of high sequence and conformational diversity in both protein families. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

  1. Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal.

    Science.gov (United States)

    Satija, Udit; Ramkumar, Barathram; Sabarimalai Manikandan, M

    2017-02-01

    Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal.

  2. Analyzing discourse and text complexity for learning and collaborating a cognitive approach based on natural language processing

    CERN Document Server

    Dascălu, Mihai

    2014-01-01

    With the advent and increasing popularity of Computer Supported Collaborative Learning (CSCL) and e-learning technologies, the need of automatic assessment and of teacher/tutor support for the two tightly intertwined activities of comprehension of reading materials and of collaboration among peers has grown significantly. In this context, a polyphonic model of discourse derived from Bakhtin’s work as a paradigm is used for analyzing both general texts and CSCL conversations in a unique framework focused on different facets of textual cohesion. As specificity of our analysis, the individual learning perspective is focused on the identification of reading strategies and on providing a multi-dimensional textual complexity model, whereas the collaborative learning dimension is centered on the evaluation of participants’ involvement, as well as on collaboration assessment. Our approach based on advanced Natural Language Processing techniques provides a qualitative estimation of the learning process and enhance...

  3. How and when Does Complex Reasoning Occur? Empirically Driven Development of a Learning Progression Focused on Complex Reasoning about Biodiversity

    Science.gov (United States)

    Songer, Nancy Butler; Kelcey, Ben; Gotwals, Amelia Wenk

    2009-01-01

    In order to compete in a global economy, students are going to need resources and curricula focusing on critical thinking and reasoning in science. Despite awareness for the need for complex reasoning, American students perform poorly relative to peers on international standardized tests measuring complex thinking in science. Research focusing on…

  4. Dissecting children's observational learning of complex actions through selective video displays.

    Science.gov (United States)

    Flynn, Emma; Whiten, Andrew

    2013-10-01

    Children can learn how to use complex objects by watching others, yet the relative importance of different elements they may observe, such as the interactions of the individual parts of the apparatus, a model's movements, and desirable outcomes, remains unclear. In total, 140 3-year-olds and 140 5-year-olds participated in a study where they observed a video showing tools being used to extract a reward item from a complex puzzle box. Conditions varied according to the elements that could be seen in the video: (a) the whole display, including the model's hands, the tools, and the box; (b) the tools and the box but not the model's hands; (c) the model's hands and the tools but not the box; (d) only the end state with the box opened; and (e) no demonstration. Children's later attempts at the task were coded to establish whether they imitated the hierarchically organized sequence of the model's actions, the action details, and/or the outcome. Children's successful retrieval of the reward from the box and the replication of hierarchical sequence information were reduced in all but the whole display condition. Only once children had attempted the task and witnessed a second demonstration did the display focused on the tools and box prove to be better for hierarchical sequence information than the display focused on the tools and hands only. Copyright © 2013 Elsevier Inc. All rights reserved.

  5. Signal one and two blockade are both critical for non-myeloablative murine HSCT across a major histocompatibility complex barrier.

    Directory of Open Access Journals (Sweden)

    Kia J Langford-Smith

    Full Text Available Non-myeloablative allogeneic haematopoietic stem cell transplantation (HSCT is rarely achievable clinically, except where donor cells have selective advantages. Murine non-myeloablative conditioning regimens have limited clinical success, partly through use of clinically unachievable cell doses or strain combinations permitting allograft acceptance using immunosuppression alone. We found that reducing busulfan conditioning in murine syngeneic HSCT, increases bone marrow (BM:blood SDF-1 ratio and total donor cells homing to BM, but reduces the proportion of donor cells engrafting. Despite this, syngeneic engraftment is achievable with non-myeloablative busulfan (25 mg/kg and higher cell doses induce increased chimerism. Therefore we investigated regimens promoting initial donor cell engraftment in the major histocompatibility complex barrier mismatched CBA to C57BL/6 allo-transplant model. This requires full myeloablation and immunosuppression with non-depleting anti-CD4/CD8 blocking antibodies to achieve engraftment of low cell doses, and rejects with reduced intensity conditioning (≤75 mg/kg busulfan. We compared increased antibody treatment, G-CSF, niche disruption and high cell dose, using reduced intensity busulfan and CD4/8 blockade in this model. Most treatments increased initial donor engraftment, but only addition of co-stimulatory blockade permitted long-term engraftment with reduced intensity or non-myeloablative conditioning, suggesting that signal 1 and 2 T-cell blockade is more important than early BM niche engraftment for transplant success.

  6. Signal one and two blockade are both critical for non-myeloablative murine HSCT across a major histocompatibility complex barrier.

    Science.gov (United States)

    Langford-Smith, Kia J; Sandiford, Zara; Langford-Smith, Alex; Wilkinson, Fiona L; Jones, Simon A; Wraith, J Ed; Wynn, Robert F; Bigger, Brian W

    2013-01-01

    Non-myeloablative allogeneic haematopoietic stem cell transplantation (HSCT) is rarely achievable clinically, except where donor cells have selective advantages. Murine non-myeloablative conditioning regimens have limited clinical success, partly through use of clinically unachievable cell doses or strain combinations permitting allograft acceptance using immunosuppression alone. We found that reducing busulfan conditioning in murine syngeneic HSCT, increases bone marrow (BM):blood SDF-1 ratio and total donor cells homing to BM, but reduces the proportion of donor cells engrafting. Despite this, syngeneic engraftment is achievable with non-myeloablative busulfan (25 mg/kg) and higher cell doses induce increased chimerism. Therefore we investigated regimens promoting initial donor cell engraftment in the major histocompatibility complex barrier mismatched CBA to C57BL/6 allo-transplant model. This requires full myeloablation and immunosuppression with non-depleting anti-CD4/CD8 blocking antibodies to achieve engraftment of low cell doses, and rejects with reduced intensity conditioning (≤75 mg/kg busulfan). We compared increased antibody treatment, G-CSF, niche disruption and high cell dose, using reduced intensity busulfan and CD4/8 blockade in this model. Most treatments increased initial donor engraftment, but only addition of co-stimulatory blockade permitted long-term engraftment with reduced intensity or non-myeloablative conditioning, suggesting that signal 1 and 2 T-cell blockade is more important than early BM niche engraftment for transplant success.

  7. Wnt-5a/Frizzled9 Receptor Signaling through the Gαo-Gβγ Complex Regulates Dendritic Spine Formation*

    Science.gov (United States)

    Ramírez, Valerie T.; Ramos-Fernández, Eva; Henríquez, Juan Pablo; Lorenzo, Alfredo; Inestrosa, Nibaldo C.

    2016-01-01

    Wnt ligands play crucial roles in the development and regulation of synapse structure and function. Specifically, Wnt-5a acts as a secreted growth factor that regulates dendritic spine formation in rodent hippocampal neurons, resulting in postsynaptic development that promotes the clustering of the PSD-95 (postsynaptic density protein 95). Here, we focused on the early events occurring after the interaction between Wnt-5a and its Frizzled receptor at the neuronal cell surface. Additionally, we studied the role of heterotrimeric G proteins in Wnt-5a-dependent synaptic development. We report that FZD9 (Frizzled9), a Wnt receptor related to Williams syndrome, is localized in the postsynaptic region, where it interacts with Wnt-5a. Functionally, FZD9 is required for the Wnt-5a-mediated increase in dendritic spine density. FZD9 forms a precoupled complex with Gαo under basal conditions that dissociates after Wnt-5a stimulation. Accordingly, we found that G protein inhibition abrogates the Wnt-5a-dependent pathway in hippocampal neurons. In particular, the activation of Gαo appears to be a key factor controlling the Wnt-5a-induced dendritic spine density. In addition, we found that Gβγ is required for the Wnt-5a-mediated increase in cytosolic calcium levels and spinogenesis. Our findings reveal that FZD9 and heterotrimeric G proteins regulate Wnt-5a signaling and dendritic spines in cultured hippocampal neurons. PMID:27402827

  8. Redefining the functional roles of the gastrointestinal migrating motor complex and motilin in small bacterial overgrowth and hunger signaling.

    Science.gov (United States)

    Deloose, Eveline; Tack, Jan

    2016-02-15

    During the fasting state the upper gastrointestinal tract exhibits a specific periodic migrating contraction pattern that is known as the migrating motor complex (MMC). Three different phases can be distinguished during the MMC. Phase III of the MMC is the most active of the three and can start either in the stomach or small intestine. Historically this pattern was designated to be the housekeeper of the gut since disturbances in the pattern were associated with small intestinal bacterial overgrowth; however, its role in the involvement of hunger sensations was already hinted in the beginning of the 20th century by both Cannon (Cannon W, Washburn A. Am J Physiol 29: 441-454, 1912) and Carlson (Carlson A. The Control of Hunger in Health and Disease. Chicago, IL: Univ. of Chicago Press, 1916). The discovery of motilin in 1973 shed more light on the control mechanisms of the MMC. Motilin plasma levels fluctuate together with the phases of the MMC and induce phase III contractions with a gastric onset. Recent research suggests that these motilin-induced phase III contractions signal hunger in healthy subjects and that this system is disturbed in morbidly obese patients. This minireview describes the functions of the MMC in the gut and its regulatory role in controlling hunger sensations. Copyright © 2016 the American Physiological Society.

  9. Electroacupuncture Ameliorates Learning and Memory via Activation of the CREB Signaling Pathway in the Hippocampus to Attenuate Apoptosis after Cerebral Hypoperfusion

    OpenAIRE

    Han, Xiaohua; Zhao, Xiuxiu; Lu, Min; Liu, Fang; Guo, Feng; Zhang, Jinghui; Huang, Xiaolin

    2013-01-01

    Studies have shown that electroacupuncture (EA) ameliorates learning and memory after ischemic injury. However, there have been few studies elucidating the mechanisms of EA on learning and memory in cerebral hypoperfusion. In this study, we explored the cAMP response element-binding protein (CREB) signaling pathway-mediated antiapoptotic action involved in EA-induced improvement of learning and memory. EA at GV20 and GV14 acupoints was applied in cerebral hypoperfusion rats. A Morris water ma...

  10. Basic roles of key molecules connected with NMDAR signaling pathway on regulating learning and memory and synaptic plasticity

    Institute of Scientific and Technical Information of China (English)

    Hui Wang; Rui-Yun Peng

    2016-01-01

    With key roles in essential brain functions ranging from the long-term potentiation (LTP) to synaptic plasticity,the N-methyl-D-aspartic acid receptor (NMDAR) can be considered as one of the fundamental glutamate receptors in the central nervous system.The role of NMDA R was first identified in synaptic plasticity and has been extensively studied.Some molecules,such as Ca2+,postsynaptic density 95 (PSD-95),calcium/calmodulin-dependent protein kinase Ⅱ (CaMK Ⅱ),protein kinase A (PKA),mitogen-activated protein kinase (MAPK) and cyclic adenosine monophosphate (cAMP) responsive element binding protein (CREB),are of special importance in learning and memory.This review mainly focused on the new research of key molecules connected with learning and memory,which played important roles in the NMDAR signaling pathway.

  11. Gaze-contingent reinforcement learning reveals incentive value of social signals in young children and adults.

    Science.gov (United States)

    Vernetti, Angélina; Smith, Tim J; Senju, Atsushi

    2017-03-15

    While numerous studies have demonstrated that infants and adults preferentially orient to social stimuli, it remains unclear as to what drives such preferential orienting. It has been suggested that the learned association between social cues and subsequent reward delivery might shape such social orienting. Using a novel, spontaneous indication of reinforcement learning (with the use of a gaze contingent reward-learning task), we investigated whether children and adults' orienting towards social and non-social visual cues can be elicited by the association between participants' visual attention and a rewarding outcome. Critically, we assessed whether the engaging nature of the social cues influences the process of reinforcement learning. Both children and adults learned to orient more often to the visual cues associated with reward delivery, demonstrating that cue-reward association reinforced visual orienting. More importantly, when the reward-predictive cue was social and engaging, both children and adults learned the cue-reward association faster and more efficiently than when the reward-predictive cue was social but non-engaging. These new findings indicate that social engaging cues have a positive incentive value. This could possibly be because they usually coincide with positive outcomes in real life, which could partly drive the development of social orienting. © 2017 The Authors.

  12. Spatial Navigation in Complex and Radial Mazes in APP23 Animals and Neurotrophin Signaling as a Biological Marker of Early Impairment

    Science.gov (United States)

    Hellweg, Rainer; Huber, Roman; Kuhl, Alexander; Riepe, Matthias W.; Lohmann, Peter

    2006-01-01

    Impairment of hippocampal function precedes frontal and parietal cortex impairment in human Alzheimer's disease(AD). Neurotrophins are critical for behavioral performance and neuronal survival in AD. We used complex and radial mazes to assess spatial orientation and learning in wild-type and B6-Tg(ThylAPP)23Sdz (APP23) animals, a transgenic mouse…

  13. Analysis of Maneuvering Targets with Complex Motions by Two-Dimensional Product Modified Lv’s Distribution for Quadratic Frequency Modulation Signals

    Directory of Open Access Journals (Sweden)

    Fulong Jing

    2017-06-01

    Full Text Available For targets with complex motion, such as ships fluctuating with oceanic waves and high maneuvering airplanes, azimuth echo signals can be modeled as multicomponent quadratic frequency modulation (QFM signals after migration compensation and phase adjustment. For the QFM signal model, the chirp rate (CR and the quadratic chirp rate (QCR are two important physical quantities, which need to be estimated. For multicomponent QFM signals, the cross terms create a challenge for detection, which needs to be addressed. In this paper, by employing a novel multi-scale parametric symmetric self-correlation function (PSSF and modified scaled Fourier transform (mSFT, an effective parameter estimation algorithm is proposed—referred to as the Two-Dimensional product modified Lv’s distribution (2D-PMLVD—for QFM signals. The 2D-PMLVD is simple and can be easily implemented by using fast Fourier transform (FFT and complex multiplication. These measures are analyzed in the paper, including the principle, the cross term, anti-noise performance, and computational complexity. Compared to the other three representative methods, the 2D-PMLVD can achieve better anti-noise performance. The 2D-PMLVD, which is free of searching and has no identifiability problems, is more suitable for multicomponent situations. Through several simulations and analyses, the effectiveness of the proposed estimation algorithm is verified.

  14. Nursing students learning the pharmacology of diabetes mellitus with complexity-based computerized models: A quasi-experimental study.

    Science.gov (United States)

    Dubovi, Ilana; Dagan, Efrat; Sader Mazbar, Ola; Nassar, Laila; Levy, Sharona T

    2018-02-01

    Pharmacology is a crucial component of medications administration in nursing, yet nursing students generally find it difficult and self-rate their pharmacology skills as low. To evaluate nursing students learning pharmacology with the Pharmacology Inter-Leaved Learning-Cells environment, a novel approach to modeling biochemical interactions using a multiscale, computer-based model with a complexity perspective based on a small set of entities and simple rules. This environment represents molecules, organelles and cells to enhance the understanding of cellular processes, and combines these cells at a higher scale to obtain whole-body interactions. Sophomore nursing students who learned the pharmacology of diabetes mellitus with the Pharmacology Inter-Leaved Learning-Cells environment (experimental group; n=94) or via a lecture-based curriculum (comparison group; n=54). A quasi-experimental pre- and post-test design was conducted. The Pharmacology-Diabetes-Mellitus questionnaire and the course's final exam were used to evaluate students' knowledge of the pharmacology of diabetes mellitus. Conceptual learning was significantly higher for the experimental than for the comparison group for the course final exam scores (unpaired t=-3.8, pLearning with complexity-based computerized models is highly effective and enhances the understanding of moving between micro and macro levels of the biochemical phenomena, this is then related to better understanding of medication actions. Moreover, the Pharmacology Inter-Leaved Learning-Cells approach provides a more general reasoning scheme for biochemical processes, which enhances pharmacology learning beyond the specific topic learned. The present study implies that deeper understanding of pharmacology will support nursing students' clinical decisions and empower their proficiency in medications administration. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics.

    Science.gov (United States)

    Yang, Qian; Sing-Long, Carlos A; Reed, Evan J

    2017-08-01

    We propose a novel statistical learning framework for automatically and efficiently building reduced kinetic Monte Carlo (KMC) models of large-scale elementary reaction networks from data generated by a single or few molecular dynamics simulations (MD). Existing approaches for identifying species and reactions from molecular dynamics typically use bond length and duration criteria, where bond duration is a fixed parameter motivated by an understanding of bond vibrational frequencies. In contrast, we show that for highly reactive systems, bond duration should be a model parameter that is chosen to maximize the predictive power of the resulting statistical model. We demonstrate our method on a high temperature, high pressure system of reacting liquid methane, and show that the learned KMC model is able to extrapolate more than an order of magnitude in time for key molecules. Additionally, our KMC model of elementary reactions enables us to isolate the most important set of reactions governing the behavior of key molecules found in the MD simulation. We develop a new data-driven algorithm to reduce the chemical reaction network which can be solved either as an integer program or efficiently using L1 regularization, and compare our results with simple count-based reduction. For our liquid methane system, we discover that rare reactions do not play a significant role in the system, and find that less than 7% of the approximately 2000 reactions observed from molecular dynamics are necessary to reproduce the molecular concentration over time of methane. The framework described in this work paves the way towards a genomic approach to studying complex chemical systems, where expensive MD simulation data can be reused to contribute to an increasingly large and accurate genome of elementary reactions and rates.

  16. ReactionPredictor: prediction of complex chemical reactions at the mechanistic level using machine learning.

    Science.gov (United States)

    Kayala, Matthew A; Baldi, Pierre

    2012-10-22

    Proposing reasonable mechanisms and predicting the course of chemical reactions is important to the practice of organic chemistry. Approaches to reaction prediction have historically used obfuscating representations and manually encoded patterns or rules. Here we present ReactionPredictor, a machine learning approach to reaction prediction that models elementary, mechanistic reactions as interactions between approximate molecular orbitals (MOs). A training data set of productive reactions known to occur at reasonable rates and yields and verified by inclusion in the literature or textbooks is derived from an existing rule-based system and expanded upon with manual curation from graduate level textbooks. Using this training data set of complex polar, hypervalent, radical, and pericyclic reactions, a two-stage machine learning prediction framework is trained and validated. In the first stage, filtering models trained at the level of individual MOs are used to reduce the space of possible reactions to consider. In the second stage, ranking models over the filtered space of possible reactions are used to order the reactions such that the productive reactions are the top ranked. The resulting model, ReactionPredictor, perfectly ranks polar reactions 78.1% of the time and recovers all productive reactions 95.7% of the time when allowing for small numbers of errors. Pericyclic and radical reactions are perfectly ranked 85.8% and 77.0% of the time, respectively, rising to >93% recovery for both reaction types with a small number of allowed errors. Decisions about which of the polar, pericyclic, or radical reaction type ranking models to use can be made with >99% accuracy. Finally, for multistep reaction pathways, we implement the first mechanistic pathway predictor using constrained tree-search to discover a set of reasonable mechanistic steps from given reactants to given products. Webserver implementations of both the single step and pathway versions of Reaction

  17. Scripted collaboration in serious gaming for complex learning: Effects of multiple perspectives when acquiring water management skills

    NARCIS (Netherlands)

    Hummel, Hans; Van Houcke, Jasper; Nadolski, Rob; Van der Hiele, Tony; Kurvers, Hub; Löhr, Ansje

    2010-01-01

    Hummel, H. G. K., Van Houcke, J., Nadolski, R. J., Van der Hiele, T., Kurvers, H., & Löhr, A. (2011). Scripted collaboration in gaming for complex learning: Effects of multiple perspectives when acquiring water management skills. British Journal of Educational Technology, 42(6),

  18. The Dream About the Magic Silver Bullet – the Complexity of Designing for Tablet-Mediated Learning

    DEFF Research Database (Denmark)

    Jahnke, Isa; Svendsen, Niels Vandel; Johansen, Simon Kristoffer

    2014-01-01

    learning. We report the gaps and interrelations between the dreams and the practice of the teachers. They dream about an interconnected praxis – the magic silver bullet – and establish their visions of inter- connectivity because of their breakdown experiences of media tablets aiding complexity instead...

  19. Preliminary Report Regarding State Allocation Board Funding of the Los Angeles Unified School District's Belmont Learning Complex.

    Science.gov (United States)

    Armoudian, Maria; Carman, Georgann; Havan, Artineh; Heron, Frank

    A preliminary report of the California Legislature's Joint Legislative Audit Committee presents findings on the construction team selection process for the Los Angeles Unified School District's (LAUSD's) Belmont Learning Complex. Evidence reveals a seriously flawed process that directly conflicted with existing law and practice. The report…

  20. Foreign language learning as a complex dynamic process: A microgenetic case study of a Chinese child's English learning trajectory

    NARCIS (Netherlands)

    Sun, He; Steinkrauss, Rasmus; van der Steen, Steffie; Cox, Ralf; de Bot, Kees

    2016-01-01

    The current study focuses on one child's (male, 3 years old) learning behaviors in an English as a Foreign Language classroom, and explores the coordination and developmental patterns of his nonverbal (gestures and body language) and verbal (verbal repetition and verbal responses) learning behaviors

  1. Pharmacological rescue of Ras signaling, GluA1-dependent synaptic plasticity, and learning deficits in a fragile X model.

    Science.gov (United States)

    Lim, Chae-Seok; Hoang, Elizabeth T; Viar, Kenneth E; Stornetta, Ruth L; Scott, Michael M; Zhu, J Julius

    2014-02-01

    Fragile X syndrome, caused by the loss of Fmr1 gene function, is the most common form of inherited mental retardation, with no effective treatment. Using a tractable animal model, we investigated mechanisms of action of a few FDA-approved psychoactive drugs that modestly benefit the cognitive performance in fragile X patients. Here we report that compounds activating serotonin (5HT) subtype 2B receptors (5HT2B-Rs) or dopamine (DA) subtype 1-like receptors (D1-Rs) and/or those inhibiting 5HT2A-Rs or D2-Rs moderately enhance Ras-PI3K/PKB signaling input, GluA1-dependent synaptic plasticity, and learning in Fmr1 knockout mice. Unexpectedly, combinations of these 5HT and DA compounds at low doses synergistically stimulate Ras-PI3K/PKB signal transduction and GluA1-dependent synaptic plasticity and remarkably restore normal learning in Fmr1 knockout mice without causing anxiety-related side effects. These findings suggest that properly dosed and combined FDA-approved psychoactive drugs may effectively treat the cognitive impairment associated with fragile X syndrome.

  2. Identification of Auditory Object-Specific Attention from Single-Trial Electroencephalogram Signals via Entropy Measures and Machine Learning

    Directory of Open Access Journals (Sweden)

    Yun Lu

    2018-05-01

    Full Text Available Existing research has revealed that auditory attention can be tracked from ongoing electroencephalography (EEG signals. The aim of this novel study was to investigate the identification of peoples’ attention to a specific auditory object from single-trial EEG signals via entropy measures and machine learning. Approximate entropy (ApEn, sample entropy (SampEn, composite multiscale entropy (CmpMSE and fuzzy entropy (FuzzyEn were used to extract the informative features of EEG signals under three kinds of auditory object-specific attention (Rest, Auditory Object1 Attention (AOA1 and Auditory Object2 Attention (AOA2. The linear discriminant analysis and support vector machine (SVM, were used to construct two auditory attention classifiers. The statistical results of entropy measures indicated that there were significant differences in the values of ApEn, SampEn, CmpMSE and FuzzyEn between Rest, AOA1 and AOA2. For the SVM-based auditory attention classifier, the auditory object-specific attention of Rest, AOA1 and AOA2 could be identified from EEG signals using ApEn, SampEn, CmpMSE and FuzzyEn as features and the identification rates were significantly different from chance level. The optimal identification was achieved by the SVM-based auditory attention classifier using CmpMSE with the scale factor τ = 10. This study demonstrated a novel solution to identify the auditory object-specific attention from single-trial EEG signals without the need to access the auditory stimulus.

  3. Formation and biochemical characterization of tube/pelle death domain complexes: critical regulators of postreceptor signaling by the Drosophila toll receptor.

    Science.gov (United States)

    Schiffmann, D A; White, J H; Cooper, A; Nutley, M A; Harding, S E; Jumel, K; Solari, R; Ray, K P; Gay, N J

    1999-09-07

    In Drosophila, the Toll receptor signaling pathway is required for embryonic dorso-ventral patterning and at later developmental stages for innate immune responses. It is thought that dimerization of the receptor by binding of the ligand spätzle causes the formation of a postreceptor activation complex at the cytoplasmic surface of the membrane. Two components of this complex are the adaptor tube and protein kinase pelle. These proteins both have "death domains", protein interaction motifs found in a number of signaling pathways, particularly those involved in apoptotic cell death. It is thought that pelle is bound by tube during formation of the activation complexes, and that this interaction is mediated by the death domains. In this paper, we show using the yeast two-hybrid system that the wild-type tube and pelle death domains bind together. Mutant tube proteins which do not support signaling in the embryo are also unable to bind pelle in the 2-hybrid assay. We have purified proteins corresponding to the death domains of tube and pelle and show that these form corresponding heterodimeric complexes in vitro. Partial proteolysis reveals a smaller core consisting of the minimal death domain sequences. We have studied the tube/pelle interaction with the techniques of surface plasmon resonance, analytical ultracentrifugation and isothermal titration calorimetry. These measurements produce a value of K(d) for the complex of about 0.5 microM.

  4. Complexity explained

    CERN Document Server

    Erdi, Peter

    2008-01-01

    This book explains why complex systems research is important in understanding the structure, function and dynamics of complex natural and social phenomena. Readers will learn the basic concepts and methods of complex system research.

  5. Insulin signaling and dietary restriction differentially influence the decline of learning and memory with age.

    Directory of Open Access Journals (Sweden)

    Amanda L Kauffman

    2010-05-01

    Full Text Available Of all the age-related declines, memory loss is one of the most devastating. While conditions that increase longevity have been identified, the effects of these longevity-promoting factors on learning and memory are unknown. Here we show that the C. elegans Insulin/IGF-1 receptor mutant daf-2 improves memory performance early in adulthood and maintains learning ability better with age but, surprisingly, demonstrates no extension in long-term memory with age. By contrast, eat-2 mutants, a model of Dietary Restriction (DR, exhibit impaired long-term memory in young adulthood but maintain this level of memory longer with age. We find that crh-1, the C. elegans homolog of the CREB transcription factor, is required for long-term associative memory, but not for learning or short-term memory. The expression of crh-1 declines with age and differs in the longevity mutants, and CREB expression and activity correlate with memory performance. Our results suggest that specific longevity treatments have acute and long-term effects on cognitive functions that decline with age through their regulation of rate-limiting genes required for learning and memory.

  6. Proceedings of IEEE Workshop on Machine Learning for Signal Processing XIV

    DEFF Research Database (Denmark)

    Larsen, Jan

    of machine learning. We would like to express our appreciation and gratitude to UFMA, EMAP, ELETROBRÁS, ELETRONORTE, ALUMAR and BASA, who contributed to the workshop by providing technical and financial support in various forms. Our warmest, special thanks go to our plenary speakers: Prof. Petar M. Djuric...

  7. Employer Learning and the Signaling Value of Education. National Longitudinal Surveys Discussion Paper.

    Science.gov (United States)

    Altonji, Joseph G.; Pierret, Charles R.

    A statistical analysis was performed to test the hypothesis that, if profit-maximizing firms have limited information about the general productivity of new workers, they may choose to use easily observable characteristics such as years of education to discriminate statistically among workers. Information about employer learning was obtained by…

  8. Identifying and responding to weak signals to improve learning from experiences in high-risk industry

    NARCIS (Netherlands)

    Guillaume, E.G.

    2011-01-01

    1. Context This thesis forms part of an extended study funded by FonCSI (Fondation pour une Culture de Sécurité Industrielle) about learning systems of major hazard companies. All French industrial sites running a risky activity – e.g. petrochemicals, steel making plants – must put a Safety

  9. Development of an Advanced, Automatic, Ultrasonic NDE Imaging System via Adaptive Learning Network Signal Processing Techniques

    Science.gov (United States)

    1981-03-13

    UNCLASSIFIED SECURITY CLAS,:FtfC ’i OF TH*!’ AGC W~ct P- A* 7~9r1) 0. ABSTRACT (continued) onuing in concert with a sophisticated detector has...and New York, 1969. Whalen, M.F., L.J. O’Brien, and A.N. Mucciardi, "Application of Adaptive Learning Netowrks for the Characterization of Two

  10. How to detect a cuckoo egg : A signal-detection theory model for recognition and learning

    NARCIS (Netherlands)

    Rodriguez-Girones, MA; Lotem, A

    This article presents a model of egg rejection in cases of brood parasitism. The model is developed in three stages in the framework of signal-detection theory. We first assume that the behavior of host females is adapted to the relevant parameters concerning the appearance of the eggs they lay. In

  11. Machine learning approaches to evaluate correlation patterns in allosteric signaling: A case study of the PDZ2 domain

    Science.gov (United States)

    Botlani, Mohsen; Siddiqui, Ahnaf; Varma, Sameer

    2018-06-01

    Many proteins are regulated by dynamic allostery wherein regulator-induced changes in structure are comparable with thermal fluctuations. Consequently, understanding their mechanisms requires assessment of relationships between and within conformational ensembles of different states. Here we show how machine learning based approaches can be used to simplify this high-dimensional data mining task and also obtain mechanistic insight. In particular, we use these approaches to investigate two fundamental questions in dynamic allostery. First, how do regulators modify inter-site correlations in conformational fluctuations (Cij)? Second, how are regulator-induced shifts in conformational ensembles at two different sites in a protein related to each other? We address these questions in the context of the human protein tyrosine phosphatase 1E's PDZ2 domain, which is a model protein for studying dynamic allostery. We use molecular dynamics to generate conformational ensembles of the PDZ2 domain in both the regulator-bound and regulator-free states. The employed protocol reproduces methyl deuterium order parameters from NMR. Results from unsupervised clustering of Cij combined with flow analyses of weighted graphs of Cij show that regulator binding significantly alters the global signaling network in the protein; however, not by altering the spatial arrangement of strongly interacting amino acid clusters but by modifying the connectivity between clusters. Additionally, we find that regulator-induced shifts in conformational ensembles, which we evaluate by repartitioning ensembles using supervised learning, are, in fact, correlated. This correlation Δij is less extensive compared to Cij, but in contrast to Cij, Δij depends inversely on the distance from the regulator binding site. Assuming that Δij is an indicator of the transduction of the regulatory signal leads to the conclusion that the regulatory signal weakens with distance from the regulatory site. Overall, this

  12. Non-Technical Skills Bingo-a game to facilitate the learning of complex concepts.

    Science.gov (United States)

    Dieckmann, Peter; Glavin, Ronnie; Hartvigsen Grønholm Jepsen, Rikke Malene; Krage, Ralf

    2016-01-01

    Acquiring the concepts of non-technical skills (NTS) beyond a superficial level is a challenge for healthcare professionals and simulation faculty. Current simulation-based approaches to teach NTS are challenged when learners have to master NTS concepts, clinically challenging situations, and simulation as a complex technique. The combination of all three aspects might overwhelm learners. To facilitate the deeper comprehension of NTS concepts, we describe an innovative video-based game, the Non-Technical Skills (NTS) Bingo. Participants get NTS Bingo cards that show five NTS elements each. While observing (non-medical) video clips, they try to find examples for the elements on their cards, typically observable behaviours that match a given element. After the video, participants "defend" their solution in a discussion with the game leader and other players. This discussion and the reflection aim to deepen the processing of the NTS concepts. We provide practical guidance for the conduct of NTS Bingo, including a selection of usable video clips and tips for the facilitated discussion after a clip. We use NTS in anaesthesia as example and provide guidance on how to adapt NTS Bingo to other disciplines. NTS Bingo is based on theoretical considerations on concept learning, which we describe to support the rationale for its conduct.

  13. Detecting outliers and learning complex structures with large spectroscopic surveys - a case study with APOGEE stars

    Science.gov (United States)

    Reis, Itamar; Poznanski, Dovi; Baron, Dalya; Zasowski, Gail; Shahaf, Sahar

    2018-05-01

    In this work, we apply and expand on a recently introduced outlier detection algorithm that is based on an unsupervised random forest. We use the algorithm to calculate a similarity measure for stellar spectra from the Apache Point Observatory Galactic Evolution Experiment (APOGEE). We show that the similarity measure traces non-trivial physical properties and contains information about complex structures in the data. We use it for visualization and clustering of the data set, and discuss its ability to find groups of highly similar objects, including spectroscopic twins. Using the similarity matrix to search the data set for objects allows us to find objects that are impossible to find using their best-fitting model parameters. This includes extreme objects for which the models fail, and rare objects that are outside the scope of the model. We use the similarity measure to detect outliers in the data set, and find a number of previously unknown Be-type stars, spectroscopic binaries, carbon rich stars, young stars, and a few that we cannot interpret. Our work further demonstrates the potential for scientific discovery when combining machine learning methods with modern survey data.

  14. Signal information available for plume source tracking with and without surface waves and learning by undergraduates assisting with the research

    Science.gov (United States)

    Wiley, Megan Beth

    Autonomous vehicles have had limited success in locating point sources of pollutants, chemicals, and other passive scalars. However, animals such as stomatopods, a mantis shrimp, track odor plumes easily for food, mates, and habitat. Laboratory experiments using Planar Laser Induced Fluorescence measured odor concentration downstream of a diffusive source with and without live stomatopods to investigate their source-tracking strategies in unidirectional and "wave-affected" (surface waves with a mean current) flows. Despite the dearth of signal, extreme temporal variation, and meandering plume centerline, the stomatopods were able to locate the source, especially in the wave-affected flow. Differences in the two plumes far from the source (>160 cm) appeared to help the animals in the wave-affected flow position themselves closer to the source (fluid mechanics, and there was little evidence of learning by participation in the RAship. One RA's conceptions of turbulence did change, but a group workshop seemed to support this learning more than the RAship. The documented conceptions could aid in curriculum design, since situating new information within current knowledge seems to deepen learning outcomes. The RAs' conceptions varied widely with some overlap of ideas. The interviews also showed that most RAs did not discuss molecular diffusion as part of the mixing process and some remembered information from course demonstrations, but applied them inappropriately to the interview questions.

  15. Evaluation of different time domain peak models using extreme learning machine-based peak detection for EEG signal.

    Science.gov (United States)

    Adam, Asrul; Ibrahim, Zuwairie; Mokhtar, Norrima; Shapiai, Mohd Ibrahim; Cumming, Paul; Mubin, Marizan

    2016-01-01

    Various peak models have been introduced to detect and analyze peaks in the time domain analysis of electroencephalogram (EEG) signals. In general, peak model in the time domain analysis consists of a set of signal parameters, such as amplitude, width, and slope. Models including those proposed by Dumpala, Acir, Liu, and Dingle are routinely used to detect peaks in EEG signals acquired in clinical studies of epilepsy or eye blink. The optimal peak model is the most reliable peak detection performance in a particular application. A fair measure of performance of different models requires a common and unbiased platform. In this study, we evaluate the performance of the four different peak models using the extreme learning machine (ELM)-based peak detection algorithm. We found that the Dingle model gave the best performance, with 72 % accuracy in the analysis of real EEG data. Statistical analysis conferred that the Dingle model afforded significantly better mean testing accuracy than did the Acir and Liu models, which were in the range 37-52 %. Meanwhile, the Dingle model has no significant difference compared to Dumpala model.

  16. Learning from simple ebooks, online cases or classroom teaching when acquiring complex knowledge. A randomized controlled trial in respiratory physiology and pulmonology

    DEFF Research Database (Denmark)

    Worm, Bjarne Skjødt

    2013-01-01

    E-learning is developing fast because of the rapid increased use of smartphones, tablets and portable computers. We might not think of it as e-learning, but today many new e-books are in fact very complex electronic teaching platforms. It is generally accepted that e-learning is as effective...... as classroom teaching methods, but little is known about its value in relaying contents of different levels of complexity to students. We set out to investigate e-learning effects on simple recall and complex problem-solving compared to classroom teaching....

  17. Managing the Complexity of Design Problems through Studio-Based Learning

    Science.gov (United States)

    Cennamo, Katherine; Brandt, Carol; Scott, Brigitte; Douglas, Sarah; McGrath, Margarita; Reimer, Yolanda; Vernon, Mitzi

    2011-01-01

    The ill-structured nature of design problems makes them particularly challenging for problem-based learning. Studio-based learning (SBL), however, has much in common with problem-based learning and indeed has a long history of use in teaching students to solve design problems. The purpose of this ethnographic study of an industrial design class,…

  18. Revisiting the Blended Learning Literature: Using a Complex Adaptive Systems Framework

    Science.gov (United States)

    Wang, Yuping; Han, Xibin; Yang, Juan

    2015-01-01

    This research has two aims: (1) to bridge a gap in blended learning research--the lack of a systems approach to the understanding of blended learning research and practice, and (2) to promote a more comprehensive understanding of what has been achieved and what needs to be achieved in blended learning research and practice. To achieve these aims,…

  19. Students' Learning with the Connected Chemistry (CC1) Curriculum: Navigating the Complexities of the Particulate World

    Science.gov (United States)

    Levy, Sharona T.; Wilensky, Uri

    2009-01-01

    The focus of this study is students' learning with a Connected Chemistry unit, CC1 (denotes Connected Chemistry, chapter 1), a computer-based environment for learning the topics of gas laws and kinetic molecular theory in chemistry (Levy and Wilensky 2009). An investigation was conducted into high-school students' learning with Connected…

  20. Embracing Big Data in Complex Educational Systems: The Learning Analytics Imperative and the Policy Challenge

    Science.gov (United States)

    Macfadyen, Leah P.; Dawson, Shane; Pardo, Abelardo; Gaševic, Dragan

    2014-01-01

    In the new era of big educational data, learning analytics (LA) offer the possibility of implementing real-time assessment and feedback systems and processes at scale that are focused on improvement of learning, development of self-regulated learning skills, and student success. However, to realize this promise, the necessary shifts in the…