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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. 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...

  3. 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.

  4. 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.

  5. 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...

  6. 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.

  7. 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.

  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. 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.

  10. 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.

  11. 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.

  12. 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...

  13. 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.

  14. 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

  15. 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...

  16. 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...

  17. 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.

  18. 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...

  19. 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...

  20. 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.

  1. 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.

  2. 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

  3. 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...

  4. 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...

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. 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.

  12. 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

  13. 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)

  14. 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

  15. 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.

  16. 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

  17. 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...

  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. 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...

  20. 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

  1. 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.

  2. 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

  3. 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.

  4. 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

  5. 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.

  6. 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

  7. 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

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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

  19. 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...

  20. 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.

  1. 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

  2. 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

  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. 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

  5. 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,…

  6. 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.

  7. Signals, processes, and systems an interactive multimedia introduction to signal processing

    CERN Document Server

    Karrenberg, Ulrich

    2013-01-01

    This is a very new concept for learning Signal Processing, not only from the physically-based scientific fundamentals, but also from the didactic perspective, based on modern results of brain research. The textbook together with the DVD form a learning system that provides investigative studies and enables the reader to interactively visualize even complex processes. The unique didactic concept is built on visualizing signals and processes on the one hand, and on graphical programming of signal processing systems on the other. The concept has been designed especially for microelectronics, computer technology and communication. The book allows to develop, modify, and optimize useful applications using DasyLab - a professional and globally supported software for metrology and control engineering. With the 3rd edition, the software is also suitable for 64 bit systems running on Windows 7. Real signals can be acquired, processed and played on the sound card of your computer. The book provides more than 200 pre-pr...

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  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. 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...

  15. 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.

  16. 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.

  17. 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...

  18. 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.

  19. 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.

  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. 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.

  2. 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.

  3. 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.

  4. 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

  5. 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.

  6. 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

  7. 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

  8. 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.

  9. 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.

  10. 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

  11. 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

  12. 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.

  13. 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

  14. 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.

  15. Slow feature analysis: unsupervised learning of invariances.

    Science.gov (United States)

    Wiskott, Laurenz; Sejnowski, Terrence J

    2002-04-01

    Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decorrelated features, which are ordered by their degree of invariance. SFA can be applied hierarchically to process high-dimensional input signals and extract complex features. SFA is applied first to complex cell tuning properties based on simple cell output, including disparity and motion. Then more complicated input-output functions are learned by repeated application of SFA. Finally, a hierarchical network of SFA modules is presented as a simple model of the visual system. The same unstructured network can learn translation, size, rotation, contrast, or, to a lesser degree, illumination invariance for one-dimensional objects, depending on only the training stimulus. Surprisingly, only a few training objects suffice to achieve good generalization to new objects. The generated representation is suitable for object recognition. Performance degrades if the network is trained to learn multiple invariances simultaneously.

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. 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

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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...

  8. 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

  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. 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.

  11. 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.

  12. 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

  13. 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.

  14. 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.

  15. 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

  16. 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

  17. 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.

  18. 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.…

  19. 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…

  20. 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.

  1. Sparse Estimation Using Bayesian Hierarchical Prior Modeling for Real and Complex Linear Models

    DEFF Research Database (Denmark)

    Pedersen, Niels Lovmand; Manchón, Carles Navarro; Badiu, Mihai Alin

    2015-01-01

    In sparse Bayesian learning (SBL), Gaussian scale mixtures (GSMs) have been used to model sparsity-inducing priors that realize a class of concave penalty functions for the regression task in real-valued signal models. Motivated by the relative scarcity of formal tools for SBL in complex-valued m......In sparse Bayesian learning (SBL), Gaussian scale mixtures (GSMs) have been used to model sparsity-inducing priors that realize a class of concave penalty functions for the regression task in real-valued signal models. Motivated by the relative scarcity of formal tools for SBL in complex...... error, and robustness in low and medium signal-to-noise ratio regimes....

  2. 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…

  3. 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.

  4. 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.

  5. 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.

  6. Fetal QRS extraction from abdominal recordings via model-based signal processing and intelligent signal merging

    International Nuclear Information System (INIS)

    Haghpanahi, Masoumeh; Borkholder, David A

    2014-01-01

    Noninvasive fetal ECG (fECG) monitoring has potential applications in diagnosing congenital heart diseases in a timely manner and assisting clinicians to make more appropriate decisions during labor. However, despite advances in signal processing and machine learning techniques, the analysis of fECG signals has still remained in its preliminary stages. In this work, we describe an algorithm to automatically locate QRS complexes in noninvasive fECG signals obtained from a set of four electrodes placed on the mother’s abdomen. The algorithm is based on an iterative decomposition of the maternal and fetal subspaces and filtering of the maternal ECG (mECG) components from the fECG recordings. Once the maternal components are removed, a novel merging technique is applied to merge the signals and detect the fetal QRS (fQRS) complexes. The algorithm was trained and tested on the fECG datasets provided by the PhysioNet/CinC challenge 2013. The final results indicate that the algorithm is able to detect fetal peaks for a variety of signals with different morphologies and strength levels encountered in clinical practice. (paper)

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. 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

  12. 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

  13. 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.

  14. 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

  15. 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.

  16. 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...

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. Study of the convergence behavior of the complex kernel least mean square algorithm.

    Science.gov (United States)

    Paul, Thomas K; Ogunfunmi, Tokunbo

    2013-09-01

    The complex kernel least mean square (CKLMS) algorithm is recently derived and allows for online kernel adaptive learning for complex data. Kernel adaptive methods can be used in finding solutions for neural network and machine learning applications. The derivation of CKLMS involved the development of a modified Wirtinger calculus for Hilbert spaces to obtain the cost function gradient. We analyze the convergence of the CKLMS with different kernel forms for complex data. The expressions obtained enable us to generate theory-predicted mean-square error curves considering the circularity of the complex input signals and their effect on nonlinear learning. Simulations are used for verifying the analysis results.

  3. Complexity and competition in appetitive and aversive neural circuits

    Directory of Open Access Journals (Sweden)

    Crista L. Barberini

    2012-11-01

    Full Text Available Decision-making often involves using sensory cues to predict possible rewarding or punishing reinforcement outcomes before selecting a course of action. Recent work has revealed complexity in how the brain learns to predict rewards and punishments. Analysis of neural signaling during and after learning in the amygdala and orbitofrontal cortex, two brain areas that process appetitive and aversive stimuli, reveals a dynamic relationship between appetitive and aversive circuits. Specifically, the relationship between signaling in appetitive and aversive circuits in these areas shifts as a function of learning. Furthermore, although appetitive and aversive circuits may often drive opposite behaviors – approaching or avoiding reinforcement depending upon its valence – these circuits can also drive similar behaviors, such as enhanced arousal or attention; these processes also may influence choice behavior. These data highlight the formidable challenges ahead in dissecting how appetitive and aversive neural circuits interact to produce a complex and nuanced range of behaviors.

  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. 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

  6. 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.

  7. 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....

  8. 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 ...

  9. 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.

  10. 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...

  11. Knee joint vibroarthrographic signal processing and analysis

    CERN Document Server

    Wu, Yunfeng

    2015-01-01

    This book presents the cutting-edge technologies of knee joint vibroarthrographic signal analysis for the screening and detection of knee joint injuries. It describes a number of effective computer-aided methods for analysis of the nonlinear and nonstationary biomedical signals generated by complex physiological mechanics. This book also introduces several popular machine learning and pattern recognition algorithms for biomedical signal classifications. The book is well-suited for all researchers looking to better understand knee joint biomechanics and the advanced technology for vibration arthrometry. Dr. Yunfeng Wu is an Associate Professor at the School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China.

  12. 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.

  13. 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.

  14. Learning multimodal dictionaries.

    Science.gov (United States)

    Monaci, Gianluca; Jost, Philippe; Vandergheynst, Pierre; Mailhé, Boris; Lesage, Sylvain; Gribonval, Rémi

    2007-09-01

    Real-world phenomena involve complex interactions between multiple signal modalities. As a consequence, humans are used to integrate at each instant perceptions from all their senses in order to enrich their understanding of the surrounding world. This paradigm can be also extremely useful in many signal processing and computer vision problems involving mutually related signals. The simultaneous processing of multimodal data can, in fact, reveal information that is otherwise hidden when considering the signals independently. However, in natural multimodal signals, the statistical dependencies between modalities are in general not obvious. Learning fundamental multimodal patterns could offer deep insight into the structure of such signals. In this paper, we present a novel model of multimodal signals based on their sparse decomposition over a dictionary of multimodal structures. An algorithm for iteratively learning multimodal generating functions that can be shifted at all positions in the signal is proposed, as well. The learning is defined in such a way that it can be accomplished by iteratively solving a generalized eigenvector problem, which makes the algorithm fast, flexible, and free of user-defined parameters. The proposed algorithm is applied to audiovisual sequences and it is able to discover underlying structures in the data. The detection of such audio-video patterns in audiovisual clips allows to effectively localize the sound source on the video in presence of substantial acoustic and visual distractors, outperforming state-of-the-art audiovisual localization algorithms.

  15. 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.

  16. 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.

  17. 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

  18. 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…

  19. 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)

  20. 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.

  1. 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.

  2. 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…

  3. 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

  4. 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.

  5. 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.

  6. 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.

  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. Machine intelligence and signal processing

    CERN Document Server

    Vatsa, Mayank; Majumdar, Angshul; Kumar, Ajay

    2016-01-01

    This book comprises chapters on key problems in machine learning and signal processing arenas. The contents of the book are a result of a 2014 Workshop on Machine Intelligence and Signal Processing held at the Indraprastha Institute of Information Technology. Traditionally, signal processing and machine learning were considered to be separate areas of research. However in recent times the two communities are getting closer. In a very abstract fashion, signal processing is the study of operator design. The contributions of signal processing had been to device operators for restoration, compression, etc. Applied Mathematicians were more interested in operator analysis. Nowadays signal processing research is gravitating towards operator learning – instead of designing operators based on heuristics (for example wavelets), the trend is to learn these operators (for example dictionary learning). And thus, the gap between signal processing and machine learning is fast converging. The 2014 Workshop on Machine Intel...

  9. 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...

  10. 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

  11. 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.

  12. 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

  13. 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

  14. 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)

  15. 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…

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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

  1. 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.

  2. 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)

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. 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 ...

  10. 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.

  11. 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.

  12. 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.…

  13. 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.

  14. 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.

  15. 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.

  16. 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…

  17. 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.

  18. 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

  19. 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…

  20. 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).

  1. 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.

  2. 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.

  3. 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.

  4. 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

  5. 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.

  6. 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.

  7. Wires in the soup: quantitative models of cell signaling

    Science.gov (United States)

    Cheong, Raymond; Levchenko, Andre

    2014-01-01

    Living cells are capable of extracting information from their environments and mounting appropriate responses to a variety of associated challenges. The underlying signal transduction networks enabling this can be quite complex, necessitating for their unraveling by sophisticated computational modeling coupled with precise experimentation. Although we are still at the beginning of this process, some recent examples of integrative analysis of cell signaling are very encouraging. This review highlights the case of the NF-κB pathway in order to illustrate how a quantitative model of a signaling pathway can be gradually constructed through continuous experimental validation, and what lessons one might learn from such exercises. PMID:18291655

  8. 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.

  9. 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.

  10. Road Artery Traffic Light Optimization with Use of the Reinforcement Learning

    Directory of Open Access Journals (Sweden)

    Rok Marsetič

    2014-04-01

    Full Text Available The basic principle of optimal traffic control is the appropriate real-time response to dynamic traffic flow changes. Signal plan efficiency depends on a large number of input parameters. An actuated signal system can adjust very well to traffic conditions, but cannot fully adjust to stochastic traffic volume oscillation. Due to the complexity of the problem analytical methods are not applicable for use in real time, therefore the purpose of this paper is to introduce heuristic method suitable for traffic light optimization in real time. With the evolution of artificial intelligence new possibilities for solving complex problems have been introduced. The goal of this paper is to demonstrate that the use of the Q learning algorithm for traffic lights optimization is suitable. The Q learning algorithm was verified on a road artery with three intersections. For estimation of the effectiveness and efficiency of the proposed algorithm comparison with an actuated signal plan was carried out. The results (average delay per vehicle and the number of vehicles that left road network show that Q learning algorithm outperforms the actuated signal controllers. The proposed algorithm converges to the minimal delay per vehicle regardless of the stochastic nature of traffic. In this research the impact of the model parameters (learning rate, exploration rate, influence of communication between agents and reward type on algorithm effectiveness were analysed as well.

  11. 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.

  12. The electrocardiogram signal of Seba's short-tailed bat, Carollia perspicillata.

    Science.gov (United States)

    Mihova, Diana; Hechavarría, Julio C

    2016-07-01

    A number of studies have successfully used electrocardiogram (ECG) signals to characterize complex physiological phenomena such as associative learning in bats. However, at present, no thorough characterization of the structure of ECG signals is available for these animals. The aim of the present study was to quantitatively characterize features of the ECG signals in the bat species Carollia perspicillata, a species that is commonly used in neuroethology studies. Our results show that the ECG signals of C. perspicillata follow the typical mammalian pattern, in that they are composed by a P wave, QRS complex and a T wave. Peak-to-peak amplitudes in the bats' ECG signals were larger in measuring configurations in which one of the electrodes was attached to the right thumb. In addition, large differences in the instantaneous heart rate (HR) distributions were observed between ketamine/xylazine anesthetized and awake bats. Ketamine/xylazine might target the neural circuits that control HR, therefore, instantaneous HR measurements should only be used as physiological marker in awake animals.

  13. 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...

  14. 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.

  15. 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,…

  16. β-Adrenergic receptor signaling and modulation of long-term potentiation in the mammalian hippocampus

    OpenAIRE

    O'Dell, Thomas J.; Connor, Steven A.; Guglietta, Ryan; Nguyen, Peter V.

    2015-01-01

    Encoding new information in the brain requires changes in synaptic strength. Neuromodulatory transmitters can facilitate synaptic plasticity by modifying the actions and expression of specific signaling cascades, transmitter receptors and their associated signaling complexes, genes, and effector proteins. One critical neuromodulator in the mammalian brain is norepinephrine (NE), which regulates multiple brain functions such as attention, perception, arousal, sleep, learning, and memory. The m...

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. DOLPHIn—Dictionary Learning for Phase Retrieval

    Science.gov (United States)

    Tillmann, Andreas M.; Eldar, Yonina C.; Mairal, Julien

    2016-12-01

    We propose a new algorithm to learn a dictionary for reconstructing and sparsely encoding signals from measurements without phase. Specifically, we consider the task of estimating a two-dimensional image from squared-magnitude measurements of a complex-valued linear transformation of the original image. Several recent phase retrieval algorithms exploit underlying sparsity of the unknown signal in order to improve recovery performance. In this work, we consider such a sparse signal prior in the context of phase retrieval, when the sparsifying dictionary is not known in advance. Our algorithm jointly reconstructs the unknown signal - possibly corrupted by noise - and learns a dictionary such that each patch of the estimated image can be sparsely represented. Numerical experiments demonstrate that our approach can obtain significantly better reconstructions for phase retrieval problems with noise than methods that cannot exploit such "hidden" sparsity. Moreover, on the theoretical side, we provide a convergence result for our method.

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. Functionally distinct dopamine signals in nucleus accumbens core and shell in the freely moving rat

    DEFF Research Database (Denmark)

    Dreyer, Jakob K.; Vander Weele, Caitlin M.; Lovic, Vedran

    2016-01-01

    Dynamic signaling of mesolimbic dopamine (DA) neurons has been implicated in reward learning, drug abuse, and motivation. However, this system is complex because firing patterns of these neurons are heterogeneous; subpopulations receive distinct synaptic inputs, and project to anatomically...

  10. 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

  11. 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.

  12. Signals structural analysis and processing: application to acoustic signals recorded during sodium boiling in a nuclear reactor

    International Nuclear Information System (INIS)

    Rodriguez, J.

    1986-02-01

    An acoustic system that uses examples to learn the structure of specific signals linked to a corresponding class of physical phenomena, and classify an unknown signal (possibly with noise present) into one of the learned classes is presented. The first stage consists of smoothing the data. The signal is represented as a trace according to background and event. To learn the structures in each class, smoothed, segmented signals are used. For classification, three operations to modify the signal so that it perfectly verifies the model description are available [fr

  13. 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.

  14. Role of a transductional-transcriptional processor complex involving MyD88 and IRF-7 in Toll-like receptor signaling

    Science.gov (United States)

    Honda, Kenya; Yanai, Hideyuki; Mizutani, Tatsuaki; Negishi, Hideo; Shimada, Naoya; Suzuki, Nobutaka; Ohba, Yusuke; Takaoka, Akinori; Yeh, Wen-Chen; Taniguchi, Tadatsugu

    2004-01-01

    Toll-like receptor (TLR) activation is central to immunity, wherein the activation of the TLR9 subfamily members TLR9 and TLR7 results in the robust induction of type I IFNs (IFN-α/β) by means of the MyD88 adaptor protein. However, it remains unknown how the TLR signal “input” can be processed through MyD88 to “output” the induction of the IFN genes. Here, we demonstrate that the transcription factor IRF-7 interacts with MyD88 to form a complex in the cytoplasm. We provide evidence that this complex also involves IRAK4 and TRAF6 and provides the foundation for the TLR9-dependent activation of the IFN genes. The complex defined in this study represents an example of how the coupling of the signaling adaptor and effector kinase molecules together with the transcription factor regulate the processing of an extracellular signal to evoke its versatile downstream transcriptional events in a cell. Thus, we propose that this molecular complex may function as a cytoplasmic transductional-transcriptional processor. PMID:15492225

  15. 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.

  16. 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…

  17. 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...

  18. 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…

  19. Signal Sampling for Efficient Sparse Representation of Resting State FMRI Data

    Science.gov (United States)

    Ge, Bao; Makkie, Milad; Wang, Jin; Zhao, Shijie; Jiang, Xi; Li, Xiang; Lv, Jinglei; Zhang, Shu; Zhang, Wei; Han, Junwei; Guo, Lei; Liu, Tianming

    2015-01-01

    As the size of brain imaging data such as fMRI grows explosively, it provides us with unprecedented and abundant information about the brain. How to reduce the size of fMRI data but not lose much information becomes a more and more pressing issue. Recent literature studies tried to deal with it by dictionary learning and sparse representation methods, however, their computation complexities are still high, which hampers the wider application of sparse representation method to large scale fMRI datasets. To effectively address this problem, this work proposes to represent resting state fMRI (rs-fMRI) signals of a whole brain via a statistical sampling based sparse representation. First we sampled the whole brain’s signals via different sampling methods, then the sampled signals were aggregate into an input data matrix to learn a dictionary, finally this dictionary was used to sparsely represent the whole brain’s signals and identify the resting state networks. Comparative experiments demonstrate that the proposed signal sampling framework can speed-up by ten times in reconstructing concurrent brain networks without losing much information. The experiments on the 1000 Functional Connectomes Project further demonstrate its effectiveness and superiority. PMID:26646924

  20. 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.

  1. 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.

  2. 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

  3. 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)

  4. 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.

  5. Incremental Learning of Skill Collections based on Intrinsic Motivation

    Directory of Open Access Journals (Sweden)

    Jan Hendrik Metzen

    2013-07-01

    Full Text Available Life-long learning of reusable, versatile skills is a key prerequisite forembodied agents that act in a complex, dynamic environment and are faced withdifferent tasks over their lifetime. We address the question of how an agentcan learn useful skills efficiently during a developmental period,i.e., when no task is imposed on him and no external reward signal is provided.Learning of skills in a developmental period needs to be incremental andself-motivated. We propose a new incremental, task-independent skill discoveryapproach that is suited for continuous domains. Furthermore, the agent learnsspecific skills based on intrinsic motivation mechanisms thatdetermine on which skills learning is focused at a given point in time. Weevaluate the approach in a reinforcement learning setup in two continuousdomains with complex dynamics. We show that an intrinsically motivated, skilllearning agent outperforms an agent which learns task solutions from scratch.Furthermore, we compare different intrinsic motivation mechanisms and howefficiently they make use of the agent's developmental period.

  6. 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

  7. 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

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. Incremental learning of skill collections based on intrinsic motivation

    Science.gov (United States)

    Metzen, Jan H.; Kirchner, Frank

    2013-01-01

    Life-long learning of reusable, versatile skills is a key prerequisite for embodied agents that act in a complex, dynamic environment and are faced with different tasks over their lifetime. We address the question of how an agent can learn useful skills efficiently during a developmental period, i.e., when no task is imposed on him and no external reward signal is provided. Learning of skills in a developmental period needs to be incremental and self-motivated. We propose a new incremental, task-independent skill discovery approach that is suited for continuous domains. Furthermore, the agent learns specific skills based on intrinsic motivation mechanisms that determine on which skills learning is focused at a given point in time. We evaluate the approach in a reinforcement learning setup in two continuous domains with complex dynamics. We show that an intrinsically motivated, skill learning agent outperforms an agent which learns task solutions from scratch. Furthermore, we compare different intrinsic motivation mechanisms and how efficiently they make use of the agent's developmental period. PMID:23898265

  13. 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.

  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. 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

  17. CLEANing the Reward: Counterfactual Actions to Remove Exploratory Action Noise in Multiagent Learning

    Science.gov (United States)

    HolmesParker, Chris; Taylor, Mathew E.; Tumer, Kagan; Agogino, Adrian

    2014-01-01

    Learning in multiagent systems can be slow because agents must learn both how to behave in a complex environment and how to account for the actions of other agents. The inability of an agent to distinguish between the true environmental dynamics and those caused by the stochastic exploratory actions of other agents creates noise in each agent's reward signal. This learning noise can have unforeseen and often undesirable effects on the resultant system performance. We define such noise as exploratory action noise, demonstrate the critical impact it can have on the learning process in multiagent settings, and introduce a reward structure to effectively remove such noise from each agent's reward signal. In particular, we introduce Coordinated Learning without Exploratory Action Noise (CLEAN) rewards and empirically demonstrate their benefits

  18. 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.

  19. 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.

  20. 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...

  1. Impact of pedagogical approaches on cognitive complexity and motivation to learn: Comparing nursing and engineering undergraduate students.

    Science.gov (United States)

    McComb, Sara A; Kirkpatrick, Jane M

    2016-01-01

    The changing higher education landscape is prompting nurses to rethink educational strategies. Looking beyond traditional professional boundaries may be beneficial. We compare nursing to engineering because engineering has similar accreditation outcome goals and different pedagogical approaches. We compare students' cognitive complexity and motivation to learn to identify opportunities to share pedagogical approaches between nursing and engineering. Cross-sectional data were collected from 1,167 freshmen through super senior students. Comparisons were made across years and between majors. Overall nursing and engineering students advance in cognitive complexity while maintaining motivation for learning. Sophomores reported the lowest scores on many dimensions indicating that their experiences need review. The strong influence of the National Council Licensure Examination on nursing students may drive their classroom preferences. Increased intrinsic motivation, coupled with decreased extrinsic motivation, suggests that we are graduating burgeoning life-long learners equipped to maintain currency. The disciplines' strategies for incorporating real-world learning opportunities differ, yet the students similarly advance in cognitive complexity and maintain motivation to learn. Lessons can be exchanged across professional boundaries. Copyright © 2016 Elsevier Inc. All rights reserved.

  2. 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....

  3. 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.

  4. 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.

  5. 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.

  6. 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)

  7. Cross-Modal Associative Mnemonic Signals in Crow Endbrain Neurons.

    Science.gov (United States)

    Moll, Felix W; Nieder, Andreas

    2015-08-17

    The ability to associate stimuli across time and sensory modalities endows animals and humans with many of the complex, learned behaviors. For successful performance, associations need to be retrieved from long-term memory and maintained active in working memory. We investigated how this is accomplished in the avian brain. We trained carrion crows (Corvus corone) to perform a bimodal delayed paired associate task in which the crows had to match auditory stimuli to delayed visual items. Single-unit recordings from the association area nidopallium caudolaterale (NCL) revealed sustained memory signals that selectively correlated with the learned audio-visual associations across time and modality, and sustained activity prospectively encoded the crows' choices. NCL neurons carried an internal, stimulus-independent signal that was predictive of error and type of error. These results underscore the role of corvid NCL in synthesizing external multisensory information and internal mnemonic data needed for executive control of behavior. Copyright © 2015 Elsevier Ltd. All rights reserved.

  8. 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.

  9. A reinforcement learning model of joy, distress, hope and fear

    Science.gov (United States)

    Broekens, Joost; Jacobs, Elmer; Jonker, Catholijn M.

    2015-07-01

    In this paper we computationally study the relation between adaptive behaviour and emotion. Using the reinforcement learning framework, we propose that learned state utility, ?, models fear (negative) and hope (positive) based on the fact that both signals are about anticipation of loss or gain. Further, we propose that joy/distress is a signal similar to the error signal. We present agent-based simulation experiments that show that this model replicates psychological and behavioural dynamics of emotion. This work distinguishes itself by assessing the dynamics of emotion in an adaptive agent framework - coupling it to the literature on habituation, development, extinction and hope theory. Our results support the idea that the function of emotion is to provide a complex feedback signal for an organism to adapt its behaviour. Our work is relevant for understanding the relation between emotion and adaptation in animals, as well as for human-robot interaction, in particular how emotional signals can be used to communicate between adaptive agents and humans.

  10. 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.

  11. Propofol exposure during late stages of pregnancy impairs learning and memory in rat offspring via the BDNF-TrkB signalling pathway.

    Science.gov (United States)

    Zhong, Liang; Luo, Foquan; Zhao, Weilu; Feng, Yunlin; Wu, Liuqin; Lin, Jiamei; Liu, Tianyin; Wang, Shengqiang; You, Xuexue; Zhang, Wei

    2016-10-01

    The brain-derived neurotrophic factor (BDNF)-tyrosine kinase B (TrkB) (BDNF-TrkB) signalling pathway plays a crucial role in regulating learning and memory. Synaptophysin provides the structural basis for synaptic plasticity and depends on BDNF processing and subsequent TrkB signalling. Our previous studies demonstrated that maternal exposure to propofol during late stages of pregnancy impaired learning and memory in rat offspring. The purpose of this study is to investigate whether the BDNF-TrkB signalling pathway is involved in propofol-induced learning and memory impairments. Propofol was intravenously infused into pregnant rats for 4 hrs on gestational day 18 (E18). Thirty days after birth, learning and memory of offspring was assessed by the Morris water maze (MWM) test. After the MWM test, BDNF and TrkB transcript and protein levels were measured in rat offspring hippocampus tissues using real-time PCR (RT-PCR) and immunohistochemistry (IHC), respectively. The levels of phosphorylated-TrkB (phospho-TrkB) and synaptophysin were measured by western blot. It was discovered that maternal exposure to propofol on day E18 impaired spatial learning and memory of rat offspring, decreased mRNA and protein levels of BDNF and TrkB, and decreased the levels of both phospho-TrkB and synaptophysin in the hippocampus. Furthermore, the TrkB agonist 7,8-dihydroxyflavone (7,8-DHF) reversed all of the observed changes. Treatment with 7,8-DHF had no significant effects on the offspring that were not exposed to propofol. The results herein indicate that maternal exposure to propofol during the late stages of pregnancy impairs spatial learning and memory of offspring by disturbing the BDNF-TrkB signalling pathway. The TrkB agonist 7,8-DHF might be a potential therapy for learning and memory impairments induced by maternal propofol exposure. © 2016 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular

  12. 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.

  13. 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

  14. 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.

  15. 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.

  16. 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

  17. 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…

  18. 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.

  19. Reward processing in the value-driven attention network: reward signals tracking cue identity and location.

    Science.gov (United States)

    Anderson, Brian A

    2017-03-01

    Through associative reward learning, arbitrary cues acquire the ability to automatically capture visual attention. Previous studies have examined the neural correlates of value-driven attentional orienting, revealing elevated activity within a network of brain regions encompassing the visual corticostriatal loop [caudate tail, lateral occipital complex (LOC) and early visual cortex] and intraparietal sulcus (IPS). Such attentional priority signals raise a broader question concerning how visual signals are combined with reward signals during learning to create a representation that is sensitive to the confluence of the two. This study examines reward signals during the cued reward training phase commonly used to generate value-driven attentional biases. High, compared with low, reward feedback preferentially activated the value-driven attention network, in addition to regions typically implicated in reward processing. Further examination of these reward signals within the visual system revealed information about the identity of the preceding cue in the caudate tail and LOC, and information about the location of the preceding cue in IPS, while early visual cortex represented both location and identity. The results reveal teaching signals within the value-driven attention network during associative reward learning, and further suggest functional specialization within different regions of this network during the acquisition of an integrated representation of stimulus value. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  20. 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.

  1. 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.

  2. 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…

  3. 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...

  4. Digital signal processing with kernel methods

    CERN Document Server

    Rojo-Alvarez, José Luis; Muñoz-Marí, Jordi; Camps-Valls, Gustavo

    2018-01-01

    A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors. * Presents the necess...

  5. 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

  6. Hierarchical learning induces two simultaneous, but separable, prediction errors in human basal ganglia.

    Science.gov (United States)

    Diuk, Carlos; Tsai, Karin; Wallis, Jonathan; Botvinick, Matthew; Niv, Yael

    2013-03-27

    Studies suggest that dopaminergic neurons report a unitary, global reward prediction error signal. However, learning in complex real-life tasks, in particular tasks that show hierarchical structure, requires multiple prediction errors that may coincide in time. We used functional neuroimaging to measure prediction error signals in humans performing such a hierarchical task involving simultaneous, uncorrelated prediction errors. Analysis of signals in a priori anatomical regions of interest in the ventral striatum and the ventral tegmental area indeed evidenced two simultaneous, but separable, prediction error signals corresponding to the two levels of hierarchy in the task. This result suggests that suitably designed tasks may reveal a more intricate pattern of firing in dopaminergic neurons. Moreover, the need for downstream separation of these signals implies possible limitations on the number of different task levels that we can learn about simultaneously.

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  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. Early motor learning changes in upper-limb dynamics and shoulder complex loading during handrim wheelchair propulsion.

    Science.gov (United States)

    Vegter, Riemer J K; Hartog, Johanneke; de Groot, Sonja; Lamoth, Claudine J; Bekker, Michel J; van der Scheer, Jan W; van der Woude, Lucas H V; Veeger, Dirkjan H E J

    2015-03-10

    To propel in an energy-efficient manner, handrim wheelchair users must learn to control the bimanually applied forces onto the rims, preserving both speed and direction of locomotion. Previous studies have found an increase in mechanical efficiency due to motor learning associated with changes in propulsion technique, but it is unclear in what way the propulsion technique impacts the load on the shoulder complex. The purpose of this study was to evaluate mechanical efficiency, propulsion technique and load on the shoulder complex during the initial stage of motor learning. 15 naive able-bodied participants received 12-minutes uninstructed wheelchair practice on a motor driven treadmill, consisting of three 4-minute blocks separated by two minutes rest. Practice was performed at a fixed belt speed (v = 1.1 m/s) and constant low-intensity power output (0.2 W/kg). Energy consumption, kinematics and kinetics of propulsion technique were continuously measured. The Delft Shoulder Model was used to calculate net joint moments, muscle activity and glenohumeral reaction force. With practice mechanical efficiency increased and propulsion technique changed, reflected by a reduced push frequency and increased work per push, performed over a larger contact angle, with more tangentially applied force and reduced power losses before and after each push. Contrary to our expectations, the above mentioned propulsion technique changes were found together with an increased load on the shoulder complex reflected by higher net moments, a higher total muscle power and higher peak and mean glenohumeral reaction forces. It appears that the early stages of motor learning in handrim wheelchair propulsion are indeed associated with improved technique and efficiency due to optimization of the kinematics and dynamics of the upper extremity. This process goes at the cost of an increased muscular effort and mechanical loading of the shoulder complex. This seems to be associated with an

  15. 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.

  16. The effects of cholesterol on learning and memory.

    Science.gov (United States)

    Schreurs, Bernard G

    2010-07-01

    Cholesterol is vital to normal brain function including learning and memory but that involvement is as complex as the synthesis, metabolism and excretion of cholesterol itself. Dietary cholesterol influences learning tasks from water maze to fear conditioning even though cholesterol does not cross the blood brain barrier. Excess cholesterol has many consequences including peripheral pathology that can signal brain via cholesterol metabolites, pro-inflammatory mediators and antioxidant processes. Manipulations of cholesterol within the central nervous system through genetic, pharmacological, or metabolic means circumvent the blood brain barrier and affect learning and memory but often in animals already otherwise compromised. The human literature is no less complex. Cholesterol reduction using statins improves memory in some cases but not others. There is also controversy over statin use to alleviate memory problems in Alzheimer's disease. Correlations of cholesterol and cognitive function are mixed and association studies find some genetic polymorphisms are related to cognitive function but others are not. In sum, the field is in flux with a number of seemingly contradictory results and many complexities. Nevertheless, understanding cholesterol effects on learning and memory is too important to ignore.

  17. Altered ERK1/2 Signaling in the Brain of Learned Helpless Rats: Relevance in Vulnerability to Developing Stress-Induced Depression

    Directory of Open Access Journals (Sweden)

    Yogesh Dwivedi

    2016-01-01

    Full Text Available Extracellular signal-regulated kinase 1/2- (ERK1/2- mediated cellular signaling plays a major role in synaptic and structural plasticity. Although ERK1/2 signaling has been shown to be involved in stress and depression, whether vulnerability to develop depression is associated with abnormalities in ERK1/2 signaling is not clearly known. The present study examined ERK1/2 signaling in frontal cortex and hippocampus of rats that showed vulnerability (learned helplessness, (LH or resiliency (non-learned helplessness, (non-LH to developing stress-induced depression. In frontal cortex and hippocampus of LH rats, we found that mRNA and protein expressions of ERK1 and ERK2 were significantly reduced, which was associated with their reduced activation and phosphorylation in cytosolic and nuclear fractions, where ERK1 and ERK2 target their substrates. In addition, ERK1/2-mediated catalytic activities and phosphorylation of downstream substrates RSK1 (cytosolic and nuclear and MSK1 (nuclear were also lower in the frontal cortex and hippocampus of LH rats without any change in their mRNA or protein expression. None of these changes were evident in non-LH rats. Our study indicates that ERK1/2 signaling is differentially regulated in LH and non-LH rats and suggests that abnormalities in ERK1/2 signaling may be crucial in the vulnerability to developing depression.

  18. 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

  19. 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…

  20. The Errors of Our Ways: Understanding Error Representations in Cerebellar-Dependent Motor Learning.

    Science.gov (United States)

    Popa, Laurentiu S; Streng, Martha L; Hewitt, Angela L; Ebner, Timothy J

    2016-04-01

    The cerebellum is essential for error-driven motor learning and is strongly implicated in detecting and correcting for motor errors. Therefore, elucidating how motor errors are represented in the cerebellum is essential in understanding cerebellar function, in general, and its role in motor learning, in particular. This review examines how motor errors are encoded in the cerebellar cortex in the context of a forward internal model that generates predictions about the upcoming movement and drives learning and adaptation. In this framework, sensory prediction errors, defined as the discrepancy between the predicted consequences of motor commands and the sensory feedback, are crucial for both on-line movement control and motor learning. While many studies support the dominant view that motor errors are encoded in the complex spike discharge of Purkinje cells, others have failed to relate complex spike activity with errors. Given these limitations, we review recent findings in the monkey showing that complex spike modulation is not necessarily required for motor learning or for simple spike adaptation. Also, new results demonstrate that the simple spike discharge provides continuous error signals that both lead and lag the actual movements in time, suggesting errors are encoded as both an internal prediction of motor commands and the actual sensory feedback. These dual error representations have opposing effects on simple spike discharge, consistent with the signals needed to generate sensory prediction errors used to update a forward internal model.

  1. 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...

  2. 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.

  3. Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing

    Science.gov (United States)

    Shao, Haidong; Jiang, Hongkai; Zhang, Haizhou; Duan, Wenjing; Liang, Tianchen; Wu, Shuaipeng

    2018-02-01

    The vibration signals collected from rolling bearing are usually complex and non-stationary with heavy background noise. Therefore, it is a great challenge to efficiently learn the representative fault features of the collected vibration signals. In this paper, a novel method called improved convolutional deep belief network (CDBN) with compressed sensing (CS) is developed for feature learning and fault diagnosis of rolling bearing. Firstly, CS is adopted for reducing the vibration data amount to improve analysis efficiency. Secondly, a new CDBN model is constructed with Gaussian visible units to enhance the feature learning ability for the compressed data. Finally, exponential moving average (EMA) technique is employed to improve the generalization performance of the constructed deep model. The developed method is applied to analyze the experimental rolling bearing vibration signals. The results confirm that the developed method is more effective than the traditional methods.

  4. 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.

  5. 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.

  6. Music Learning with Long Short Term Memory Networks

    OpenAIRE

    Colombo, Florian François

    2015-01-01

    Humans are able to learn and compose complex, yet beautiful, pieces of music as seen in e.g. the highly complicated works of J.S. Bach. However, how our brain is able to store and produce these very long temporal sequences is still an open question. Long short-term memory (LSTM) artificial neural networks have been shown to be efficient in sequence learning tasks thanks to their inherent ability to bridge long time lags between input events and their target signals. Here, I investigate the po...

  7. 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....

  8. Learning invariance from natural images inspired by observations in the primary visual cortex.

    Science.gov (United States)

    Teichmann, Michael; Wiltschut, Jan; Hamker, Fred

    2012-05-01

    The human visual system has the remarkable ability to largely recognize objects invariant of their position, rotation, and scale. A good interpretation of neurobiological findings involves a computational model that simulates signal processing of the visual cortex. In part, this is likely achieved step by step from early to late areas of visual perception. While several algorithms have been proposed for learning feature detectors, only few studies at hand cover the issue of biologically plausible learning of such invariance. In this study, a set of Hebbian learning rules based on calcium dynamics and homeostatic regulations of single neurons is proposed. Their performance is verified within a simple model of the primary visual cortex to learn so-called complex cells, based on a sequence of static images. As a result, the learned complex-cell responses are largely invariant to phase and position.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. Dynamic complexity: plant receptor complexes at the plasma membrane.

    Science.gov (United States)

    Burkart, Rebecca C; Stahl, Yvonne

    2017-12-01

    Plant receptor complexes at the cell surface perceive many different external and internal signalling molecules and relay these signals into the cell to regulate development, growth and immunity. Recent progress in the analyses of receptor complexes using different live cell imaging approaches have shown that receptor complex formation and composition are dynamic and take place at specific microdomains at the plasma membrane. In this review we focus on three prominent examples of Arabidopsis thaliana receptor complexes and how their dynamic spatio-temporal distribution at the PM has been studied recently. We will elaborate on the newly emerging concept of plasma membrane microdomains as potential hubs for specific receptor complex assembly and signalling outputs. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. 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…

  15. 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…

  16. 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.

  17. 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.

  18. Audio-based, unsupervised machine learning reveals cyclic changes in earthquake mechanisms in the Geysers geothermal field, California

    Science.gov (United States)

    Holtzman, B. K.; Paté, A.; Paisley, J.; Waldhauser, F.; Repetto, D.; Boschi, L.

    2017-12-01

    The earthquake process reflects complex interactions of stress, fracture and frictional properties. New machine learning methods reveal patterns in time-dependent spectral properties of seismic signals and enable identification of changes in faulting processes. Our methods are based closely on those developed for music information retrieval and voice recognition, using the spectrogram instead of the waveform directly. Unsupervised learning involves identification of patterns based on differences among signals without any additional information provided to the algorithm. Clustering of 46,000 earthquakes of $0.3

  19. 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...

  20. Pattern-recognition software detecting the onset of failures in complex systems

    International Nuclear Information System (INIS)

    Mott, J.; King, R.

    1987-01-01

    A very general mathematical framework for embodying learned data from a complex system and combining it with a current observation to estimate the true current state of the system has been implemented using nearly universal pattern-recognition algorithms and applied to surveillance of the EBR-II power plant. In this application the methodology can provide signal validation and replacement of faulty signals on a near-real-time basis for hundreds of plant parameters. The mathematical framework, the pattern-recognition algorithms, examples of the learning and estimating process, and plant operating decisions made using this methodology are discussed. The entire methodology has been reduced to a set of FORTRAN subroutines which are small, fast, robust and executable on a personal computer with a serial link to the system's data acquisition computer, or on the data acquisition computer itself

  1. 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.

  2. 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.

  3. Analysis Sparse Representation for Nonnegative Signals Based on Determinant Measure by DC Programming

    Directory of Open Access Journals (Sweden)

    Yujie Li

    2018-01-01

    Full Text Available Analysis sparse representation has recently emerged as an alternative approach to the synthesis sparse model. Most existing algorithms typically employ the l0-norm, which is generally NP-hard. Other existing algorithms employ the l1-norm to relax the l0-norm, which sometimes cannot promote adequate sparsity. Most of these existing algorithms focus on general signals and are not suitable for nonnegative signals. However, many signals are necessarily nonnegative such as spectral data. In this paper, we present a novel and efficient analysis dictionary learning algorithm for nonnegative signals with the determinant-type sparsity measure which is convex and differentiable. The analysis sparse representation can be cast in three subproblems, sparse coding, dictionary update, and signal update, because the determinant-type sparsity measure would result in a complex nonconvex optimization problem, which cannot be easily solved by standard convex optimization methods. Therefore, in the proposed algorithms, we use a difference of convex (DC programming scheme for solving the nonconvex problem. According to our theoretical analysis and simulation study, the main advantage of the proposed algorithm is its greater dictionary learning efficiency, particularly compared with state-of-the-art algorithms. In addition, our proposed algorithm performs well in image denoising.

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. 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.

  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. Problem-based learning in communication systems using MATLAB and Simulink

    CERN Document Server

    Choi, Kwonhue

    2016-01-01

    Designed to help teach and understand communication systems using a classroom-tested, active learning approach. This book covers the basic concepts of signals, and analog and digital communications, to more complex simulations in communication systems. Problem-Based Learning in Communication Systems Using MATLAB and Simulink begins by introducing MATLAB and Simulink to prepare readers who are unfamiliar with these environments in order to tackle projects and exercises included in this book. Discussions on simulation of signals, filter design, sampling and reconstruction, and analog communications are covered next. The book concludes by covering advanced topics such as Viterbi decoding, OFDM and MIMO. In addition, this book contains examples of how to convert waveforms, constructed in simulation, into electric signals. It also includes problems illustrating how to complete actual wireless communications in the band near ultrasonic frequencies. A content-m pping table is included in this book to help instruc...

  11. Biosignals learning and synthesis using deep neural networks.

    Science.gov (United States)

    Belo, David; Rodrigues, João; Vaz, João R; Pezarat-Correia, Pedro; Gamboa, Hugo

    2017-09-25

    Modeling physiological signals is a complex task both for understanding and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the original ones. This research could lead the creation of novel algorithms for signal reconstruction in heavily noisy data and source detection in biomedical engineering field. The present work explores the gated recurrent units (GRU) employed in the training of respiration (RESP), electromyograms (EMG) and electrocardiograms (ECG). Each signal is pre-processed, segmented and quantized in a specific number of classes, corresponding to the amplitude of each sample and fed to the model, which is composed by an embedded matrix, three GRU blocks and a softmax function. This network is trained by adjusting its internal parameters, acquiring the representation of the abstract notion of the next value based on the previous ones. The simulated signal was generated by forecasting a random value and re-feeding itself. The resulting generated signals are similar with the morphological expression of the originals. During the learning process, after a set of iterations, the model starts to grasp the basic morphological characteristics of the signal and later their cyclic characteristics. After training, these models' prediction are closer to the signals that trained them, specially the RESP and ECG. This synthesis mechanism has shown relevant results that inspire the use to characterize signals from other physiological sources.

  12. 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.

  13. 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

  14. Embedded interruptions and task complexity influence schema-related cognitive load progression in an abstract learning task.

    Science.gov (United States)

    Wirzberger, Maria; Esmaeili Bijarsari, Shirin; Rey, Günter Daniel

    2017-09-01

    Cognitive processes related to schema acquisition comprise an essential source of demands in learning situations. Since the related amount of cognitive load is supposed to change over time, plausible temporal models of load progression based on different theoretical backgrounds are inspected in this study. A total of 116 student participants completed a basal symbol sequence learning task, which provided insights into underlying cognitive dynamics. Two levels of task complexity were determined by the amount of elements within the symbol sequence. In addition, interruptions due to an embedded secondary task occurred at five predefined stages over the task. Within the resulting 2x5-factorial mixed between-within design, the continuous monitoring of efficiency in learning performance enabled assumptions on relevant resource investment. From the obtained results, a nonlinear change of learning efficiency over time seems most plausible in terms of cognitive load progression. Moreover, different effects of the induced interruptions show up in conditions of task complexity, which indicate the activation of distinct cognitive mechanisms related to structural aspects of the task. Findings are discussed in the light of evidence from research on memory and information processing. Copyright © 2017 Elsevier B.V. All rights reserved.

  15. 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.

  16. 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…

  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. 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.

  19. 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.

  20. A Graphical Evolutionary Game Approach to Social Learning

    Science.gov (United States)

    Cao, Xuanyu; Liu, K. J. Ray

    2017-06-01

    In this work, we study the social learning problem, in which agents of a networked system collaborate to detect the state of the nature based on their private signals. A novel distributed graphical evolutionary game theoretic learning method is proposed. In the proposed game-theoretic method, agents only need to communicate their binary decisions rather than the real-valued beliefs with their neighbors, which endows the method with low communication complexity. Under mean field approximations, we theoretically analyze the steady state equilibria of the game and show that the evolutionarily stable states (ESSs) coincide with the decisions of the benchmark centralized detector. Numerical experiments are implemented to confirm the effectiveness of the proposed game-theoretic learning method.

  1. 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.

  2. 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

  3. Ancient Origin of the CARD–Coiled Coil/Bcl10/MALT1-Like Paracaspase Signaling Complex Indicates Unknown Critical Functions

    Directory of Open Access Journals (Sweden)

    Jens Staal

    2018-05-01

    Full Text Available The CARD–coiled coil (CC/Bcl10/MALT1-like paracaspase (CBM signaling complexes composed of a CARD–CC family member (CARD-9, -10, -11, or -14, Bcl10, and the type 1 paracaspase MALT1 (PCASP1 play a pivotal role in immunity, inflammation, and cancer. Targeting MALT1 proteolytic activity is of potential therapeutic interest. However, little is known about the evolutionary origin and the original functions of the CBM complex. Type 1 paracaspases originated before the last common ancestor of planulozoa (bilaterians and cnidarians. Notably in bilaterians, Ecdysozoa (e.g., nematodes and insects lacks Bcl10, whereas other lineages have a Bcl10 homolog. A survey of invertebrate CARD–CC homologs revealed such homologs only in species with Bcl10, indicating an ancient common origin of the entire CBM complex. Furthermore, vertebrate-like Syk/Zap70 tyrosine kinase homologs with the ITAM-binding SH2 domain were only found in invertebrate organisms with CARD–CC/Bcl10, indicating that this pathway might be related to the original function of the CBM complex. Moreover, the type 1 paracaspase sequences from invertebrate organisms that have CARD–CC/Bcl10 are more similar to vertebrate paracaspases. Functional analysis of protein–protein interactions, NF-κB signaling, and CYLD cleavage for selected invertebrate type 1 paracaspase and Bcl10 homologs supports this scenario and indicates an ancient origin of the CARD–CC/Bcl10/paracaspase signaling complex. By contrast, many of the known MALT1-associated activities evolved fairly recently, indicating that unknown functions are at the basis of the protein conservation. As a proof-of-concept, we provide initial evidence for a CBM- and NF-κB-independent neuronal function of the Caenorhabditis elegans type 1 paracaspase malt-1. In conclusion, this study shows how evolutionary insights may point at alternative functions of MALT1.

  4. 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

  5. Complex-enhanced chaotic signals with time-delay signature suppression based on vertical-cavity surface-emitting lasers subject to chaotic optical injection

    Science.gov (United States)

    Chen, Jianjun; Duan, Yingni; Zhong, Zhuqiang

    2018-03-01

    A chaotic system is constructed on the basis of vertical-cavity surface-emitting lasers (VCSELs), where a slave VCSEL subject to chaotic optical injection (COI) from a master VCSEL with the external feedback. The complex degree (CD) and time-delay signature (TDS) of chaotic signals generated by this chaotic system are investigated numerically via permutation entropy (PE) and self-correlation function (SF) methods, respectively. The results show that, compared with master VCSEL subject to optical feedback, complex-enhanced chaotic signals with TDS suppression can be achieved for S-VCSEL subject to COI. Meanwhile, the influences of several controllable parameters on the evolution maps of CD of chaotic signals are carefully considered. It is shown that the CD of chaotic signals for S-VCSEL is always higher than that for M-VCSEL due to the CIO effect. The TDS of chaotic signals can be significantly suppressed by choosing the reasonable parameters in this system. Furthermore, TDS suppression and high CD chaos can be obtained simultaneously in the specific parameter ranges. The results confirm that this chaotic system may effectively improve the security of a chaos-based communication scheme.

  6. Machine learning in jet physics

    CERN Multimedia

    CERN. Geneva

    2018-01-01

    High energy collider experiments produce several petabytes of data every year. Given the magnitude and complexity of the raw data, machine learning algorithms provide the best available platform to transform and analyse these data to obtain valuable insights to understand Standard Model and Beyond Standard Model theories. These collider experiments produce both quark and gluon initiated hadronic jets as the core components. Deep learning techniques enable us to classify quark/gluon jets through image recognition and help us to differentiate signals and backgrounds in Beyond Standard Model searches at LHC. We are currently working on quark/gluon jet classification and progressing in our studies to find the bias between event generators using domain adversarial neural networks (DANN). We also plan to investigate top tagging, weak supervision on mixed samples in high energy physics, utilizing transfer learning from simulated data to real experimental data.

  7. 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.

  8. Learning from simple ebooks, online cases or classroom teaching when acquiring complex knowledge. A randomized controlled trial in respiratory physiology and pulmonology.

    Science.gov (United States)

    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. 63 nurses specializing in anesthesiology were evenly randomized into three groups. They were given internet-based knowledge tests before and after attending a teaching module about respiratory physiology and pulmonology. The three groups was either an e-learning group with eBook teaching material, an e-learning group with case-based teaching or a group with face-to-face case-based classroom teaching. After the module the students were required to answer a post-test. Time spent and the number of logged into the system was also measured. For simple recall, all methods were equally effective. For problem-solving, the eCase group achieved a comparable knowledge level to classroom teaching, while textbook learning was inferior to both (p<0.01). The textbook group also spent the least amount of time on acquiring knowledge (33 minutes, p<0.001), while the eCase group spent significantly more time on the subject (53 minutes, p<0.001) and logged into the system significantly more (2.8 vs 1.6, p<0.001). E-learning based cases are an effective tool for teaching complex knowledge and problem-solving ability, but future studies using higher-level e-learning are encouraged.Simple recall skills, however, do not require any particular learning method.

  9. Learning from simple ebooks, online cases or classroom teaching when acquiring complex knowledge. A randomized controlled trial in respiratory physiology and pulmonology.

    Directory of Open Access Journals (Sweden)

    Bjarne Skjødt Worm

    Full Text Available BACKGROUND AND AIMS: 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. METHODS: 63 nurses specializing in anesthesiology were evenly randomized into three groups. They were given internet-based knowledge tests before and after attending a teaching module about respiratory physiology and pulmonology. The three groups was either an e-learning group with eBook teaching material, an e-learning group with case-based teaching or a group with face-to-face case-based classroom teaching. After the module the students were required to answer a post-test. Time spent and the number of logged into the system was also measured. RESULTS: For simple recall, all methods were equally effective. For problem-solving, the eCase group achieved a comparable knowledge level to classroom teaching, while textbook learning was inferior to both (p<0.01. The textbook group also spent the least amount of time on acquiring knowledge (33 minutes, p<0.001, while the eCase group spent significantly more time on the subject (53 minutes, p<0.001 and logged into the system significantly more (2.8 vs 1.6, p<0.001. CONCLUSIONS: E-learning based cases are an effective tool for teaching complex knowledge and problem-solving ability, but future studies using higher-level e-learning are encouraged.Simple recall skills, however, do not require any particular learning method.

  10. Deep learning for automated drivetrain fault detection

    DEFF Research Database (Denmark)

    Bach-Andersen, Martin; Rømer-Odgaard, Bo; Winther, Ole

    2018-01-01

    A novel data-driven deep-learning system for large-scale wind turbine drivetrain monitoring applications is presented. It uses convolutional neural network processing on complex vibration signal inputs. The system is demonstrated to learn successfully from the actions of human diagnostic experts...... the fleet-wide diagnostic model performance. The analysis also explores the time dependence of the diagnostic performance, providing a detailed view of the timeliness and accuracy of the diagnostic outputs across the different architectures. Deep architectures are shown to outperform the human analyst...... as well as shallow-learning architectures, and the results demonstrate that when applied in a large-scale monitoring system, machine intelligence is now able to handle some of the most challenging diagnostic tasks related to wind turbines....

  11. 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…

  12. 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.

  13. 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.

  14. 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.

  15. 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...

  16. 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.

  17. 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.

  18. 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...

  19. Managing Complexity

    DEFF Research Database (Denmark)

    Maylath, Bruce; Vandepitte, Sonia; Minacori, Patricia

    2013-01-01

    and into French. The complexity of the undertaking proved to be a central element in the students' learning, as the collaboration closely resembles the complexity of international documentation workplaces of language service providers. © Association of Teachers of Technical Writing.......This article discusses the largest and most complex international learning-by-doing project to date- a project involving translation from Danish and Dutch into English and editing into American English alongside a project involving writing, usability testing, and translation from English into Dutch...

  20. 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…

  1. Fundamentals of adaptive signal processing

    CERN Document Server

    Uncini, Aurelio

    2015-01-01

    This book is an accessible guide to adaptive signal processing methods that equips the reader with advanced theoretical and practical tools for the study and development of circuit structures and provides robust algorithms relevant to a wide variety of application scenarios. Examples include multimodal and multimedia communications, the biological and biomedical fields, economic models, environmental sciences, acoustics, telecommunications, remote sensing, monitoring, and, in general, the modeling and prediction of complex physical phenomena. The reader will learn not only how to design and implement the algorithms but also how to evaluate their performance for specific applications utilizing the tools provided. While using a simple mathematical language, the employed approach is very rigorous. The text will be of value both for research purposes and for courses of study.

  2. 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.

  3. 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.

  4. 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

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. 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…

  10. 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

  11. Automated design of complex dynamic systems.

    Directory of Open Access Journals (Sweden)

    Michiel Hermans

    Full Text Available Several fields of study are concerned with uniting the concept of computation with that of the design of physical systems. For example, a recent trend in robotics is to design robots in such a way that they require a minimal control effort. Another example is found in the domain of photonics, where recent efforts try to benefit directly from the complex nonlinear dynamics to achieve more efficient signal processing. The underlying goal of these and similar research efforts is to internalize a large part of the necessary computations within the physical system itself by exploiting its inherent non-linear dynamics. This, however, often requires the optimization of large numbers of system parameters, related to both the system's structure as well as its material properties. In addition, many of these parameters are subject to fabrication variability or to variations through time. In this paper we apply a machine learning algorithm to optimize physical dynamic systems. We show that such algorithms, which are normally applied on abstract computational entities, can be extended to the field of differential equations and used to optimize an associated set of parameters which determine their behavior. We show that machine learning training methodologies are highly useful in designing robust systems, and we provide a set of both simple and complex examples using models of physical dynamical systems. Interestingly, the derived optimization method is intimately related to direct collocation a method known in the field of optimal control. Our work suggests that the application domains of both machine learning and optimal control have a largely unexplored overlapping area which envelopes a novel design methodology of smart and highly complex physical systems.

  12. A signal-flow-graph approach to on-line gradient calculation.

    Science.gov (United States)

    Campolucci, P; Uncini, A; Piazza, F

    2000-08-01

    A large class of nonlinear dynamic adaptive systems such as dynamic recurrent neural networks can be effectively represented by signal flow graphs (SFGs). By this method, complex systems are described as a general connection of many simple components, each of them implementing a simple one-input, one-output transformation, as in an electrical circuit. Even if graph representations are popular in the neural network community, they are often used for qualitative description rather than for rigorous representation and computational purposes. In this article, a method for both on-line and batch-backward gradient computation of a system output or cost function with respect to system parameters is derived by the SFG representation theory and its known properties. The system can be any causal, in general nonlinear and time-variant, dynamic system represented by an SFG, in particular any feedforward, time-delay, or recurrent neural network. In this work, we use discrete-time notation, but the same theory holds for the continuous-time case. The gradient is obtained in a straightforward way by the analysis of two SFGs, the original one and its adjoint (obtained from the first by simple transformations), without the complex chain rule expansions of derivatives usually employed. This method can be used for sensitivity analysis and for learning both off-line and on-line. On-line learning is particularly important since it is required by many real applications, such as digital signal processing, system identification and control, channel equalization, and predistortion.

  13. 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.

  14. Disruption of the ErbB signaling in adolescence increases striatal dopamine levels and affects learning and hedonic-like behavior in the adult mouse.

    Science.gov (United States)

    Golani, Idit; Tadmor, Hagar; Buonanno, Andres; Kremer, Ilana; Shamir, Alon

    2014-11-01

    The ErbB signaling pathway has been genetically and functionally implicated in schizophrenia. Numerous findings support the dysregulation of Neuregulin (NRG) and epidermal growth factor (EGF) signaling in schizophrenia. However, it is unclear whether alterations of these pathways in the adult brain or during development are involved in the pathophysiology of schizophrenia. Herein we characterized the behavioral profile and molecular changes resulting from pharmacologically blocking the ErbB signaling pathway during a critical period in the development of decision making, planning, judgments, emotions, social cognition and cognitive skills, namely adolescence. We demonstrate that chronic administration of the pan-ErbB kinase inhibitor JNJ-28871063 (JNJ) to adolescent mice elevated striatal dopamine levels and reduced preference for sucrose without affecting locomotor activity and exploratory behavior. In adulthood, adolescent JNJ-treated mice continue to consume less sucrose and needed significantly more correct-response trials to reach the learning criterion during the discrimination phase of the T-maze reversal learning task than their saline-injected controls. In addition, JNJ mice exhibited deficit in reference memory but not in working memory as measured in the radial arm maze. Inhibition of the pathway during adolescence did not affect exploratory behavior and locomotor activity in the open field, social interaction, social memory, and reversal learning in adult mice. Our data suggest that alteration of ErbB signaling during adolescence resulted in changes in the dopaminergic systems that emerge in pathological learning and hedonic behavior in adulthood, and pinpoints the possible role of the pathway in the development of cognitive skills and motivated behavior. Copyright © 2014 Elsevier B.V. and ECNP. All rights reserved.

  15. PKA-CREB-BDNF signaling pathway mediates propofol-induced long-term learning and memory impairment in hippocampus of rats.

    Science.gov (United States)

    Zhong, Yu; Chen, Jing; Li, Li; Qin, Yi; Wei, Yi; Pan, Shining; Jiang, Yage; Chen, Jialin; Xie, Yubo

    2018-04-20

    Studies have found that propofol can induce widespread neuroapoptosis in developing brains, which leads to cause long-term learning and memory abnormalities. However, the specific cellular and molecular mechanisms underlying propofol-induced neuroapoptosis remain elusive. The aim of the present study was to explore the role of PKA-CREB-BDNF signaling pathway in propofol-induced long-term learning and memory impairment during brain development. Seven-day-old rats were randomly assigned to control, intralipid and three treatment groups (n = 5). Rats in control group received no treatment. Intralipid (10%, 10 mL/kg) for vehicle control and different dosage of propofol for three treatment groups (50, 100 and 200 mg/kg) were administered intraperitoneally. FJB staining, immunohistochemistry analysis for neuronal nuclei antigen and transmission electron microscopy were used to detect neuronal apoptosis and structure changes. MWM test examines the long-term spatial learning and memory impairment. The expression of PKA, pCREB and BDNF was quantified using western blots. Propofol induced significant increase of FJB-positive cells and decrease of PKA, pCREB and BDNF protein levels in the immature brain of P7 rats. Using the MWM test, propofol-treated rats demonstrated long-term spatial learning and memory impairment. Moreover, hippocampal NeuN-positive cell loss, long-lasting ultrastructural abnormalities of the neurons and synapses, and long-term down-regulation of PKA, pCREB and BDNF protein expression in adult hippocampus were also found. Our results indicated that neonatal propofol exposure can significantly result in long-term learning and memory impairment in adulthood. The possible mechanism involved in the propofol-induced neuroapoptosis was related to down-regulation of PKA-CREB-BDNF signaling pathway. Copyright © 2018. Published by Elsevier B.V.

  16. A Study of the Effect of Dyad Practice Versus That of Individual Practice on Simulation-Based Complex Skills Learning and of Students’ Perceptions of How and Why Dyad Practice Contributes to Learning

    DEFF Research Database (Denmark)

    Räder, Sune Bernd Emil Werner; Henriksen, Ann-Helen; Butrymovich, Vitalij

    2014-01-01

    PURPOSE: The aims of this study were (1) to explore the effectiveness of dyad practice compared with individual practice on a simulator for learning a complex clinical skill and (2) to explore medical students' perceptions of how and why dyad practice on a simulator contributes to learning...... a complex skill. METHOD: In 2011, the authors randomly assigned 84 medical students to either the dyad or the individual practice group to learn coronary angiography skills using instruction videos and a simulator. Two weeks later, participants each performed two video-recorded coronary angiographies...... of the two groups (mean±standard deviation, 68%±13% for individual versus 63%±16% for dyad practice; P=.18). Dyad practice participants noted that several key factors contributed to their learning: being equal-level novices, the quality of the cooperation between partners, observational learning and overt...

  17. 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.

  18. 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.

  19. 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

  20. 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

  1. 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…

  2. Zebrafish and relational memory: Could a simple fish be useful for the analysis of biological mechanisms of complex vertebrate learning?

    Science.gov (United States)

    Gerlai, Robert

    2017-08-01

    Analysis of the zebrafish allows one to combine two distinct scientific approaches, comparative ethology and neurobehavioral genetics. Furthermore, this species arguably represents an optimal compromise between system complexity and practical simplicity. This mini-review focuses on a complex form of learning, relational learning and memory, in zebrafish. It argues that zebrafish are capable of this type of learning, and it attempts to show how this species may be useful in the analysis of the mechanisms and the evolution of this complex brain function. The review is not intended to be comprehensive. It is a short opinion piece that reflects the author's own biases, and it draws some of its examples from the work coming from his own laboratory. Nevertheless, it is written in the hope that it will persuade those who have not utilized zebrafish and who may be interested in opening their research horizon to this relatively novel but powerful vertebrate research tool. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. 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.

  4. LUBAC-Recruited CYLD and A20 Regulate Gene Activation and Cell Death by Exerting Opposing Effects on Linear Ubiquitin in Signaling Complexes

    Directory of Open Access Journals (Sweden)

    Peter Draber

    2015-12-01

    Full Text Available Ubiquitination and deubiquitination are crucial for assembly and disassembly of signaling complexes. LUBAC-generated linear (M1 ubiquitin is important for signaling via various immune receptors. We show here that the deubiquitinases CYLD and A20, but not OTULIN, are recruited to the TNFR1- and NOD2-associated signaling complexes (TNF-RSC and NOD2-SC, at which they cooperate to limit gene activation. Whereas CYLD recruitment depends on its interaction with LUBAC, but not on LUBAC’s M1-chain-forming capacity, A20 recruitment requires this activity. Intriguingly, CYLD and A20 exert opposing effects on M1 chain stability in the TNF-RSC and NOD2-SC. While CYLD cleaves M1 chains, and thereby sensitizes cells to TNF-induced death, A20 binding to them prevents their removal and, consequently, inhibits cell death. Thus, CYLD and A20 cooperatively restrict gene activation and regulate cell death via their respective activities on M1 chains. Hence, the interplay between LUBAC, M1-ubiquitin, CYLD, and A20 is central for physiological signaling through innate immune receptors.

  5. 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...

  6. Weakly Supervised Dictionary Learning

    Science.gov (United States)

    You, Zeyu; Raich, Raviv; Fern, Xiaoli Z.; Kim, Jinsub

    2018-05-01

    We present a probabilistic modeling and inference framework for discriminative analysis dictionary learning under a weak supervision setting. Dictionary learning approaches have been widely used for tasks such as low-level signal denoising and restoration as well as high-level classification tasks, which can be applied to audio and image analysis. Synthesis dictionary learning aims at jointly learning a dictionary and corresponding sparse coefficients to provide accurate data representation. This approach is useful for denoising and signal restoration, but may lead to sub-optimal classification performance. By contrast, analysis dictionary learning provides a transform that maps data to a sparse discriminative representation suitable for classification. We consider the problem of analysis dictionary learning for time-series data under a weak supervision setting in which signals are assigned with a global label instead of an instantaneous label signal. We propose a discriminative probabilistic model that incorporates both label information and sparsity constraints on the underlying latent instantaneous label signal using cardinality control. We present the expectation maximization (EM) procedure for maximum likelihood estimation (MLE) of the proposed model. To facilitate a computationally efficient E-step, we propose both a chain and a novel tree graph reformulation of the graphical model. The performance of the proposed model is demonstrated on both synthetic and real-world data.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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

  12. Neuron class-specific requirements for Fragile X Mental Retardation Protein in critical period development of calcium signaling in learning and memory circuitry.

    Science.gov (United States)

    Doll, Caleb A; Broadie, Kendal

    2016-05-01

    Neural circuit optimization occurs through sensory activity-dependent mechanisms that refine synaptic connectivity and information processing during early-use developmental critical periods. Fragile X Mental Retardation Protein (FMRP), the gene product lost in Fragile X syndrome (FXS), acts as an activity sensor during critical period development, both as an RNA-binding translation regulator and channel-binding excitability regulator. Here, we employ a Drosophila FXS disease model to assay calcium signaling dynamics with a targeted transgenic GCaMP reporter during critical period development of the mushroom body (MB) learning/memory circuit. We find FMRP regulates depolarization-induced calcium signaling in a neuron-specific manner within this circuit, suppressing activity-dependent calcium transients in excitatory cholinergic MB input projection neurons and enhancing calcium signals in inhibitory GABAergic MB output neurons. Both changes are restricted to the developmental critical period and rectified at maturity. Importantly, conditional genetic (dfmr1) rescue of null mutants during the critical period corrects calcium signaling defects in both neuron classes, indicating a temporally restricted FMRP requirement. Likewise, conditional dfmr1 knockdown (RNAi) during the critical period replicates constitutive null mutant defects in both neuron classes, confirming cell-autonomous requirements for FMRP in developmental regulation of calcium signaling dynamics. Optogenetic stimulation during the critical period enhances depolarization-induced calcium signaling in both neuron classes, but this developmental change is eliminated in dfmr1 null mutants, indicating the activity-dependent regulation requires FMRP. These results show FMRP shapes neuron class-specific calcium signaling in excitatory vs. inhibitory neurons in developing learning/memory circuitry, and that FMRP mediates activity-dependent regulation of calcium signaling specifically during the early

  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. DDX3 directly regulates TRAF3 ubiquitination and acts as a scaffold to co-ordinate assembly of signalling complexes downstream from MAVS.

    Science.gov (United States)

    Gu, Lili; Fullam, Anthony; McCormack, Niamh; Höhn, Yvette; Schröder, Martina

    2017-02-15

    The human DEAD-box helicase 3 (DDX3) has been shown to contribute to type I interferon (IFN) induction downstream from antiviral pattern recognition receptors. It binds to TANK-binding kinase 1 and IκB-kinase-ε (IKKε), the two key kinases mediating activation of IFN regulatory factor (IRF) 3 and IRF7. We previously demonstrated that DDX3 facilitates IKKε activation downstream from RIG-I and then links the activated kinase to IRF3. In the present study, we probed the interactions between DDX3 and other key signalling molecules in the RIG-I pathway and identified a novel direct interaction between DDX3 and TNF receptor-associated factor 3 (TRAF3) mediated by a TRAF-interaction motif in the N-terminus of DDX3, which was required for TRAF3 ubiquitination. Interestingly, we observed two waves of K63-linked TRAF3 ubiquitination following RIG-I activation by Sendai virus (SeV) infection, both of which were suppressed by DDX3 knockdown. We also investigated the spatiotemporal formation of endogenous downstream signalling complexes containing the mitochondrial antiviral signalling (MAVS) adaptor, DDX3, IκB-kinase-ε (IKKε), TRAF3 and IRF3. DDX3 was recruited to MAVS early after SeV infection, suggesting that it might mediate subsequent recruitment of other molecules. Indeed, knockdown of DDX3 prevented the formation of TRAF3-MAVS and TRAF3-IKKε complexes. Based on our data, we propose that early TRAF3 ubiquitination is required for the formation of a stable MAVS-TRAF3 complex, while the second wave of TRAF3 ubiquitination mediates IRF3 recruitment and activation. Our study characterises DDX3 as a multifunctional adaptor molecule that co-ordinates assembly of different TRAF3, IKKε and IRF3-containing signalling complexes downstream from MAVS. Additionally, it provides novel insights into the role of TRAF3 in RIG-I signalling. © 2017 The Author(s); published by Portland Press Limited on behalf of the Biochemical Society.

  15. Signals and Circuits in the Purkinje Neuron

    Directory of Open Access Journals (Sweden)

    Ze'ev R Abrams

    2011-09-01

    Full Text Available Purkinje neurons in the cerebellum have over 100,000 inputs organized in an orthogonal geometry, and a single output channel. As the sole output of the cerebellar cortex layer, their complex firing pattern has been associated with motor control and learning. As such they have been extensively modeled and measured using tools ranging from electrophysiology and neuroanatomy, to dynamic systems and artificial intelligence methods. However, there is an alternative approach to analyze and describe the neuronal output of these cells using concepts from Electrical Engineering, particularly signal processing and digital/analog circuits. By viewing the Purkinje neuron as an unknown circuit to be reverse-engineered, we can use the tools that provide the foundations of today’s integrated circuits and communication systems to analyze the Purkinje system at the circuit level. We use Fourier transforms to analyze and isolate the inherent frequency modes in the Purkinje neuron and define 3 unique frequency ranges associated with the cells’ output. Comparing the Purkinje neuron to a signal generator that can be externally modulated adds an entire level of complexity to the functional role of these neurons both in terms of data analysis and information processing, relying on Fourier analysis methods in place of statistical ones. We also re-describe some of the recent literature in the field, using the nomenclature of signal processing. Furthermore, by comparing the experimental data of the past decade with basic electronic circuitry, we can resolve the outstanding controversy in the field, by recognizing that the Purkinje neuron can act as a multivibrator circuit.

  16. Regulation of neurite morphogenesis by interaction between R7 regulator of G protein signaling complexes and G protein subunit Gα13.

    Science.gov (United States)

    Scherer, Stephanie L; Cain, Matthew D; Kanai, Stanley M; Kaltenbronn, Kevin M; Blumer, Kendall J

    2017-06-16

    The R7 regulator of G protein signaling family (R7-RGS) critically regulates nervous system development and function. Mice lacking all R7-RGS subtypes exhibit diverse neurological phenotypes, and humans bearing mutations in the retinal R7-RGS isoform RGS9-1 have vision deficits. Although each R7-RGS subtype forms heterotrimeric complexes with Gβ 5 and R7-RGS-binding protein (R7BP) that regulate G protein-coupled receptor signaling by accelerating deactivation of G i/o α-subunits, several neurological phenotypes of R7-RGS knock-out mice are not readily explained by dysregulated G i/o signaling. Accordingly, we used tandem affinity purification and LC-MS/MS to search for novel proteins that interact with R7-RGS heterotrimers in the mouse brain. Among several proteins detected, we focused on Gα 13 because it had not been linked to R7-RGS complexes before. Split-luciferase complementation assays indicated that Gα 13 in its active or inactive state interacts with R7-RGS heterotrimers containing any R7-RGS isoform. LARG (leukemia-associated Rho guanine nucleotide exchange factor (GEF)), PDZ-RhoGEF, and p115RhoGEF augmented interaction between activated Gα 13 and R7-RGS heterotrimers, indicating that these effector RhoGEFs can engage Gα 13 ·R7-RGS complexes. Because Gα 13 /R7-RGS interaction required R7BP, we analyzed phenotypes of neuronal cell lines expressing RGS7 and Gβ 5 with or without R7BP. We found that neurite retraction evoked by Gα 12/13 -dependent lysophosphatidic acid receptors was augmented in R7BP-expressing cells. R7BP expression blunted neurite formation evoked by serum starvation by signaling mechanisms involving Gα 12/13 but not Gα i/o These findings provide the first evidence that R7-RGS heterotrimers interact with Gα 13 to augment signaling pathways that regulate neurite morphogenesis. This mechanism expands the diversity of functions whereby R7-RGS complexes regulate critical aspects of nervous system development and function. © 2017 by

  17. Role of PKA signaling in D2 receptor-expressing neurons in the core of the nucleus accumbens in aversive learning.

    Science.gov (United States)

    Yamaguchi, Takashi; Goto, Akihiro; Nakahara, Ichiro; Yawata, Satoshi; Hikida, Takatoshi; Matsuda, Michiyuki; Funabiki, Kazuo; Nakanishi, Shigetada

    2015-09-08

    The nucleus accumbens (NAc) serves as a key neural substrate for aversive learning and consists of two distinct subpopulations of medium-sized spiny neurons (MSNs). The MSNs of the direct pathway (dMSNs) and the indirect pathway (iMSNs) predominantly express dopamine (DA) D1 and D2 receptors, respectively, and are positively and negatively modulated by DA transmitters via Gs- and Gi-coupled cAMP-dependent protein kinase A (PKA) signaling cascades, respectively. In this investigation, we addressed how intracellular PKA signaling is involved in aversive learning in a cell type-specific manner. When the transmission of either dMSNs or iMSNs was unilaterally blocked by pathway-specific expression of transmission-blocking tetanus toxin, infusion of PKA inhibitors into the intact side of the NAc core abolished passive avoidance learning toward an electric shock in the indirect pathway-blocked mice, but not in the direct pathway-blocked mice. We then examined temporal changes in PKA activity in dMSNs and iMSNs in behaving mice by monitoring Förster resonance energy transfer responses of the PKA biosensor with the aid of microendoscopy. PKA activity was increased in iMSNs and decreased in dMSNs in both aversive memory formation and retrieval. Importantly, the increased PKA activity in iMSNs disappeared when aversive memory was prevented by keeping mice in the conditioning apparatus. Furthermore, the increase in PKA activity in iMSNs by aversive stimuli reflected facilitation of aversive memory retention. These results indicate that PKA signaling in iMSNs plays a critical role in both aversive memory formation and retention.

  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. 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.

  20. 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

  1. 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

  2. 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.

  3. Human algorithmic stability and human Rademacher complexity

    NARCIS (Netherlands)

    Vahdat, Mehrnoosh; Oneto, L.; Ghio, A; Anguita, D.; Funk, M.; Rauterberg, G.W.M.

    2015-01-01

    In Machine Learning (ML), the learning process of an algo- rithm given a set of evidences is studied via complexity measures. The way towards using ML complexity measures in the Human Learning (HL) domain has been paved by a previous study, which introduced Human Rademacher Complexity (HRC): in this

  4. 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

  5. Learning Predictive Statistics: Strategies and Brain Mechanisms.

    Science.gov (United States)

    Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E; Kourtzi, Zoe

    2017-08-30

    When immersed in a new environment, we are challenged to decipher initially incomprehensible streams of sensory information. However, quite rapidly, the brain finds structure and meaning in these incoming signals, helping us to predict and prepare ourselves for future actions. This skill relies on extracting the statistics of event streams in the environment that contain regularities of variable complexity from simple repetitive patterns to complex probabilistic combinations. Here, we test the brain mechanisms that mediate our ability to adapt to the environment's statistics and predict upcoming events. By combining behavioral training and multisession fMRI in human participants (male and female), we track the corticostriatal mechanisms that mediate learning of temporal sequences as they change in structure complexity. We show that learning of predictive structures relates to individual decision strategy; that is, selecting the most probable outcome in a given context (maximizing) versus matching the exact sequence statistics. These strategies engage distinct human brain regions: maximizing engages dorsolateral prefrontal, cingulate, sensory-motor regions, and basal ganglia (dorsal caudate, putamen), whereas matching engages occipitotemporal regions (including the hippocampus) and basal ganglia (ventral caudate). Our findings provide evidence for distinct corticostriatal mechanisms that facilitate our ability to extract behaviorally relevant statistics to make predictions. SIGNIFICANCE STATEMENT Making predictions about future events relies on interpreting streams of information that may initially appear incomprehensible. Past work has studied how humans identify repetitive patterns and associative pairings. However, the natural environment contains regularities that vary in complexity from simple repetition to complex probabilistic combinations. Here, we combine behavior and multisession fMRI to track the brain mechanisms that mediate our ability to adapt to

  6. Signal Conditioning An Introduction to Continuous Wave Communication and Signal Processing

    CERN Document Server

    Das, Apurba

    2012-01-01

    "Signal Conditioning” is a comprehensive introduction to electronic signal processing. The book presents the mathematical basics including the implications of various transformed domain representations in signal synthesis and analysis in an understandable and lucid fashion and illustrates the theory through many applications and examples from communication systems. The ease to learn is supported by well-chosen exercises which give readers the flavor of the subject. Supplementary electronic materials available on http://extras.springer.com including MATLAB codes illuminating applications in the domain of one dimensional electrical signal processing, image processing and speech processing. The book is an introduction for students with a basic understanding in engineering or natural sciences.

  7. Asymmetric cell division and Notch signaling specify dopaminergic neurons in Drosophila.

    Directory of Open Access Journals (Sweden)

    Murni Tio

    Full Text Available In Drosophila, dopaminergic (DA neurons can be found from mid embryonic stages of development till adulthood. Despite their functional involvement in learning and memory, not much is known about the developmental as well as molecular mechanisms involved in the events of DA neuronal specification, differentiation and maturation. In this report we demonstrate that most larval DA neurons are generated during embryonic development. Furthermore, we show that loss of function (l-o-f mutations of genes of the apical complex proteins in the asymmetric cell division (ACD machinery, such as inscuteable and bazooka result in supernumerary DA neurons, whereas l-o-f mutations of genes of the basal complex proteins such as numb result in loss or reduction of DA neurons. In addition, when Notch signaling is reduced or abolished, additional DA neurons are formed and conversely, when Notch signaling is activated, less DA neurons are generated. Our data demonstrate that both ACD and Notch signaling are crucial mechanisms for DA neuronal specification. We propose a model in which ACD results in differential Notch activation in direct siblings and in this context Notch acts as a repressor for DA neuronal specification in the sibling that receives active Notch signaling. Our study provides the first link of ACD and Notch signaling in the specification of a neurotransmitter phenotype in Drosophila. Given the high degree of conservation between Drosophila and vertebrate systems, this study could be of significance to mechanisms of DA neuronal differentiation not limited to flies.

  8. Rac1 augments Wnt signaling by stimulating β-catenin–lymphoid enhancer factor-1 complex assembly independent of β-catenin nuclear import

    Science.gov (United States)

    Jamieson, Cara; Lui, Christina; Brocardo, Mariana G.; Martino-Echarri, Estefania; Henderson, Beric R.

    2015-01-01

    ABSTRACT β-Catenin transduces the Wnt signaling pathway and its nuclear accumulation leads to gene transactivation and cancer. Rac1 GTPase is known to stimulate β-catenin-dependent transcription of Wnt target genes and we confirmed this activity. Here we tested the recent hypothesis that Rac1 augments Wnt signaling by enhancing β-catenin nuclear import; however, we found that silencing/inhibition or up-regulation of Rac1 had no influence on nuclear accumulation of β-catenin. To better define the role of Rac1, we employed proximity ligation assays (PLA) and discovered that a significant pool of Rac1–β-catenin protein complexes redistribute from the plasma membrane to the nucleus upon Wnt or Rac1 activation. More importantly, active Rac1 was shown to stimulate the formation of nuclear β-catenin–lymphoid enhancer factor 1 (LEF-1) complexes. This regulation required Rac1-dependent phosphorylation of β-catenin at specific serines, which when mutated (S191A and S605A) reduced β-catenin binding to LEF-1 by up to 50%, as revealed by PLA and immunoprecipitation experiments. We propose that Rac1-mediated phosphorylation of β-catenin stimulates Wnt-dependent gene transactivation by enhancing β-catenin–LEF-1 complex assembly, providing new insight into the mechanism of cross-talk between Rac1 and canonical Wnt/β-catenin signaling. PMID:26403202

  9. 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.

  10. 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.

  11. 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...

  12. 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

  13. Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.

    Directory of Open Access Journals (Sweden)

    Masanao Sato

    Full Text Available Biological signaling processes may be mediated by complex networks in which network components and network sectors interact with each other in complex ways. Studies of complex networks benefit from approaches in which the roles of individual components are considered in the context of the network. The plant immune signaling network, which controls inducible responses to pathogen attack, is such a complex network. We studied the Arabidopsis immune signaling network upon challenge with a strain of the bacterial pathogen Pseudomonas syringae expressing the effector protein AvrRpt2 (Pto DC3000 AvrRpt2. This bacterial strain feeds multiple inputs into the signaling network, allowing many parts of the network to be activated at once. mRNA profiles for 571 immune response genes of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with Pto DC3000 AvrRpt2. The mRNA profiles were analyzed as detailed descriptions of changes in the network state resulting from the genetic perturbations. Regulatory relationships among the genes corresponding to the mutations were inferred by recursively applying a non-linear dimensionality reduction procedure to the mRNA profile data. The resulting static network model accurately predicted 23 of 25 regulatory relationships reported in the literature, suggesting that predictions of novel regulatory relationships are also accurate. The network model revealed two striking features: (i the components of the network are highly interconnected; and (ii negative regulatory relationships are common between signaling sectors. Complex regulatory relationships, including a novel negative regulatory relationship between the early microbe-associated molecular pattern-triggered signaling sectors and the salicylic acid sector, were further validated. We propose that prevalent negative regulatory relationships among the signaling sectors make the plant immune signaling network a "sector

  14. Simplification of neural network model for predicting local power distributions of BWR fuel bundle using learning algorithm with forgetting

    International Nuclear Information System (INIS)

    Tanabe, Akira; Yamamoto, Toru; Shinfuku, Kimihiro; Nakamae, Takuji; Nishide, Fusayo.

    1995-01-01

    Previously a two-layered neural network model was developed to predict the relation between fissile enrichment of each fuel rod and local power distribution in a BWR fuel bundle. This model was obtained intuitively based on 33 patterns of training signals after an intensive survey of the models. Recently, a learning algorithm with forgetting was reported to simplify neural network models. It is an interesting subject what kind of model will be obtained if this algorithm is applied to the complex three-layered model which learns the same training signals. A three-layered model which is expanded to have direct connections between the 1st and the 3rd layer elements has been constructed and the learning method of normal back propagation was applied first to this model. The forgetting algorithm was then added to this learning process. The connections concerned with the 2nd layer elements disappeared and the 2nd layer has become unnecessary. It took a longer computing time by an order to learn the same training signals than the simple back propagation, but the two-layered model was obtained autonomously from the expanded three-layered model. (author)

  15. 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.

  16. Second-hand signals

    DEFF Research Database (Denmark)

    Bergenholtz, Carsten

    2014-01-01

    Studies of signaling theory have traditionally focused on the dyadic link between the sender and receiver of the signal. Within a science‐based perspective this framing has led scholars to investigate how patents and publications of firms function as signals. I explore another important type...... used by various agents in their search for and assessment of products and firms. I conclude by arguing how this second‐hand nature of signals goes beyond a simple dyadic focus on senders and receivers of signals, and thus elucidates the more complex interrelations of the various types of agents...

  17. 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.

  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. TERMA Framework for Biomedical Signal Analysis: An Economic-Inspired Approach

    Directory of Open Access Journals (Sweden)

    Mohamed Elgendi

    2016-11-01

    Full Text Available Biomedical signals contain features that represent physiological events, and each of these events has peaks. The analysis of biomedical signals for monitoring or diagnosing diseases requires the detection of these peaks, making event detection a crucial step in biomedical signal processing. Many researchers have difficulty detecting these peaks to investigate, interpret and analyze their corresponding events. To date, there is no generic framework that captures these events in a robust, efficient and consistent manner. A new method referred to for the first time as two event-related moving averages (“TERMA” involves event-related moving averages and detects events in biomedical signals. The TERMA framework is flexible and universal and consists of six independent LEGO building bricks to achieve high accuracy detection of biomedical events. Results recommend that the window sizes for the two moving averages ( W 1 and W 2 have to follow the inequality ( 8 × W 1 ≥ W 2 ≥ ( 2 × W 1 . Moreover, TERMA is a simple yet efficient event detector that is suitable for wearable devices, point-of-care devices, fitness trackers and smart watches, compared to more complex machine learning solutions.

  20. 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.

  1. 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...

  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. Learning and coding in biological neural networks

    Science.gov (United States)

    Fiete, Ila Rani

    How can large groups of neurons that locally modify their activities learn to collectively perform a desired task? Do studies of learning in small networks tell us anything about learning in the fantastically large collection of neurons that make up a vertebrate brain? What factors do neurons optimize by encoding sensory inputs or motor commands in the way they do? In this thesis I present a collection of four theoretical works: each of the projects was motivated by specific constraints and complexities of biological neural networks, as revealed by experimental studies; together, they aim to partially address some of the central questions of neuroscience posed above. We first study the role of sparse neural activity, as seen in the coding of sequential commands in a premotor area responsible for birdsong. We show that the sparse coding of temporal sequences in the songbird brain can, in a network where the feedforward plastic weights must translate the sparse sequential code into a time-varying muscle code, facilitate learning by minimizing synaptic interference. Next, we propose a biologically plausible synaptic plasticity rule that can perform goal-directed learning in recurrent networks of voltage-based spiking neurons that interact through conductances. Learning is based on the correlation of noisy local activity with a global reward signal; we prove that this rule performs stochastic gradient ascent on the reward. Thus, if the reward signal quantifies network performance on some desired task, the plasticity rule provably drives goal-directed learning in the network. To assess the convergence properties of the learning rule, we compare it with a known example of learning in the brain. Song-learning in finches is a clear example of a learned behavior, with detailed available neurophysiological data. With our learning rule, we train an anatomically accurate model birdsong network that drives a sound source to mimic an actual zebrafinch song. Simulation and

  4. IL-7 Induces an Epitope Masking of γc Protein in IL-7 Receptor Signaling Complex

    Directory of Open Access Journals (Sweden)

    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.

  5. 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

  6. 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.

  7. 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.

  8. Threshold Learning Dynamics in Social Networks

    Science.gov (United States)

    González-Avella, Juan Carlos; Eguíluz, Victor M.; Marsili, Matteo; Vega-Redondo, Fernado; San Miguel, Maxi

    2011-01-01

    Social learning is defined as the ability of a population to aggregate information, a process which must crucially depend on the mechanisms of social interaction. Consumers choosing which product to buy, or voters deciding which option to take with respect to an important issue, typically confront external signals to the information gathered from their contacts. Economic models typically predict that correct social learning occurs in large populations unless some individuals display unbounded influence. We challenge this conclusion by showing that an intuitive threshold process of individual adjustment does not always lead to such social learning. We find, specifically, that three generic regimes exist separated by sharp discontinuous transitions. And only in one of them, where the threshold is within a suitable intermediate range, the population learns the correct information. In the other two, where the threshold is either too high or too low, the system either freezes or enters into persistent flux, respectively. These regimes are generally observed in different social networks (both complex or regular), but limited interaction is found to promote correct learning by enlarging the parameter region where it occurs. PMID:21637714

  9. 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.

  10. Critical nodes in signalling pathways

    DEFF Research Database (Denmark)

    Taniguchi, Cullen M; Emanuelli, Brice; Kahn, C Ronald

    2006-01-01

    Physiologically important cell-signalling networks are complex, and contain several points of regulation, signal divergence and crosstalk with other signalling cascades. Here, we use the concept of 'critical nodes' to define the important junctions in these pathways and illustrate their unique role...... using insulin signalling as a model system....

  11. 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.

  12. 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....

  13. 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.

  14. 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.

  15. Emergence: Complexity Pedagogy in Action

    Science.gov (United States)

    Jonas-Simpson, Christine

    2015-01-01

    Many educators are looking for new ways to engage students and each other in order to enrich curriculum and the teaching-learning process. We describe an example of how we enacted teaching-learning approaches through the insights of complexity thinking, an approach that supports the emergence of new possibilities for teaching-learning in the classroom and online. Our story begins with an occasion to meet with 10 nursing colleagues in a three-hour workshop using four activities that engaged learning about complexity thinking and pedagogy. Guiding concepts for the collaborative workshop were nonlinearity, distributed decision-making, divergent thinking, self-organization, emergence, and creative exploration. The workshop approach considered critical questions to spark our collective inquiry. We asked, “What is emergent learning?” and “How do we, as educators and learners, engage a community so that new learning surfaces?” We integrated the arts, creative play, and perturbations within a complexity approach. PMID:25838945

  16. 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.

  17. 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.

  18. Sensitive and fast detection of fructose in complex media via symmetry breaking and signal amplification using surface-enhanced Raman spectroscopy.

    Science.gov (United States)

    Sun, Fang; Bai, Tao; Zhang, Lei; Ella-Menye, Jean-Rene; Liu, Sijun; Nowinski, Ann K; Jiang, Shaoyi; Yu, Qiuming

    2014-03-04

    A new strategy is proposed to sensitively and rapidly detect analytes with weak Raman signals in complex media using surface-enhanced Raman spectroscopy (SERS) via detecting the SERS signal changes of the immobilized probe molecules on SERS-active substrates upon binding of the analytes. In this work, 4-mercaptophenylboronic acid (4-MPBA) was selected as the probe molecule which was immobilized on the gold surface of a quasi-three-dimensional plasmonic nanostructure array (Q3D-PNA) SERS substrate to detect fructose. The molecule of 4-MPBA possesses three key functions: molecule recognition and reversible binding of the analyte via the boronic acid group, amplification of SERS signals by the phenyl group and thus shielding of the background noise of complex media, and immobilization on the surface of SERS-active substrates via the thiol group. Most importantly, the symmetry breaking of the 4-MPBA molecule upon fructose binding leads to the change of area ratio between totally symmetric 8a ring mode and nontotally symmetric 8b ring mode, which enables the detection. The detection curves were obtained in phosphate-buffered saline (PBS) and in undiluted artificial urine at clinically relevant concentrations, and the limit of detection of 0.05 mM was achieved.

  19. 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.

  20. 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…

  1. 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),

  2. Signals and systems laboratory with Matlab

    CERN Document Server

    Palamides, Alex

    2010-01-01

    Introduction to MATLAB®Working EnvironmentGetting StartedMemory ManagementVectorsMatricesPlotting with MATLABComplex NumbersM-FilesInput-Output CommandsFile ManagementLogical-Relational OperatorsControl FlowSymbolic VariablesPolynomials(Pseudo)Random NumbersSignalsCategorization by the Variable TypeBasic Continuous-Time SignalsDiscrete-Time SignalsProperties of SignalsTransformations of the Time Variable for Continuous-Time SignalsTransformations of the Time Variable for Discrete-Time SignalsSystemsSystems ClassificationProperties of SystemsTime Domain System AnalysisImpulse ResponseContinuous Time Convolution Convolution PropertiesInterconnections of SystemsStabilityDiscrete-Time ConvolutionSystems Described by Difference EquationsFiltersStability Criterion for Discrete-Time SystemsSystems Described by Differential EquationsStep Response of a SystemFourier SeriesOrthogonality of Complex Exponential SignalsComplex Exponential Fourier SeriesTrigonometric Fourier SeriesFourier Series in the Cosine with Phase F...

  3. HACE1 Negatively Regulates Virus-Triggered Type I IFN Signaling by Impeding the Formation of the MAVS-TRAF3 Complex

    Directory of Open Access Journals (Sweden)

    He-Ting Mao

    2016-05-01

    Full Text Available During virus infection, the cascade signaling pathway that leads to the production of proinflammatory cytokines is controlled at multiple levels to avoid detrimental overreaction. HACE1 has been characterized as an important tumor suppressor. Here, we identified HACE1 as an important negative regulator of virus-triggered type I IFN signaling. Overexpression of HACE1 inhibited Sendai virus- or poly (I:C-induced signaling and resulted in reduced IFNB1 production and enhanced virus replication. Knockdown of HACE1 expression exhibited the opposite effects. Ubiquitin E3 ligase activity of the dead mutant HACE1/C876A had a comparable inhibitory function as WT HACE1, suggesting that the suppressive function of HACE1 on virus-induced signaling is independent of its E3 ligase activity. Further study indicated that HACE1 acted downstream of MAVS and upstream of TBK1. Mechanistic studies showed that HACE1 exerts its inhibitory role on virus-induced signaling by disrupting the MAVS-TRAF3 complex. Therefore, we uncovered a novel function of HACE1 in innate immunity regulation.

  4. Multivariate Analysis for the Processing of Signals

    Directory of Open Access Journals (Sweden)

    Beattie J.R.

    2014-01-01

    Full Text Available Real-world experiments are becoming increasingly more complex, needing techniques capable of tracking this complexity. Signal based measurements are often used to capture this complexity, where a signal is a record of a sample’s response to a parameter (e.g. time, displacement, voltage, wavelength that is varied over a range of values. In signals the responses at each value of the varied parameter are related to each other, depending on the composition or state sample being measured. Since signals contain multiple information points, they have rich information content but are generally complex to comprehend. Multivariate Analysis (MA has profoundly transformed their analysis by allowing gross simplification of the tangled web of variation. In addition MA has also provided the advantage of being much more robust to the influence of noise than univariate methods of analysis. In recent years, there has been a growing awareness that the nature of the multivariate methods allows exploitation of its benefits for purposes other than data analysis, such as pre-processing of signals with the aim of eliminating irrelevant variations prior to analysis of the signal of interest. It has been shown that exploiting multivariate data reduction in an appropriate way can allow high fidelity denoising (removal of irreproducible non-signals, consistent and reproducible noise-insensitive correction of baseline distortions (removal of reproducible non-signals, accurate elimination of interfering signals (removal of reproducible but unwanted signals and the standardisation of signal amplitude fluctuations. At present, the field is relatively small but the possibilities for much wider application are considerable. Where signal properties are suitable for MA (such as the signal being stationary along the x-axis, these signal based corrections have the potential to be highly reproducible, and highly adaptable and are applicable in situations where the data is noisy or

  5. 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.

  6. 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

  7. Calcium signals can freely cross the nuclear envelope in hippocampal neurons: somatic calcium increases generate nuclear calcium transients

    Directory of Open Access Journals (Sweden)

    Bading Hilmar

    2007-07-01

    Full Text Available Abstract Background In hippocampal neurons, nuclear calcium signaling is important for learning- and neuronal survival-associated gene expression. However, it is unknown whether calcium signals generated by neuronal activity at the cell membrane and propagated to the soma can unrestrictedly cross the nuclear envelope to invade the nucleus. The nuclear envelope, which allows ion transit via the nuclear pore complex, may represent a barrier for calcium and has been suggested to insulate the nucleus from activity-induced cytoplasmic calcium transients in some cell types. Results Using laser-assisted uncaging of caged calcium compounds in defined sub-cellular domains, we show here that the nuclear compartment border does not represent a barrier for calcium signals in hippocampal neurons. Although passive diffusion of molecules between the cytosol and the nucleoplasm may be modulated through changes in conformational state of the nuclear pore complex, we found no evidence for a gating mechanism for calcium movement across the nuclear border. Conclusion Thus, the nuclear envelope does not spatially restrict calcium transients to the somatic cytosol but allows calcium signals to freely enter the cell nucleus to trigger genomic events.

  8. Baby Cry Detection in Domestic Environment using Deep Learning

    OpenAIRE

    Ijzerman, Hans

    2017-01-01

    Automatic detection of a baby cry in audio signals is an essential step in applications such as remote baby monitoring. It is also important for researchers, who study the relation between baby cry patterns and various health or developmental parameters. In this paper, we propose two machine-learning algorithms for automatic detection of baby cry in audio recordings. The first algorithm is a low-complexity logistic regression classifier, used as a reference. To train this classifier, we extra...

  9. Managing Contextual Complexity in an Experiential Learning Course: A Dynamic Systems Approach through the Identification of Turning Points in Students' Emotional Trajectories

    Directory of Open Access Journals (Sweden)

    Gloria Nogueiras

    2017-05-01

    Full Text Available This study adopts a dynamic systems approach to investigate how individuals successfully manage contextual complexity. To that end, we tracked individuals' emotional trajectories during a challenging training course, seeking qualitative changes–turning points—and we tested their relationship with the perceived complexity of the training. The research context was a 5-day higher education course based on process-oriented experiential learning, and the sample consisted of 17 students. The students used a five-point Likert scale to rate the intensity of 16 emotions and the complexity of the training on 8 measurement points. Monte Carlo permutation tests enabled to identify 30 turning points in the 272 emotional trajectories analyzed (17 students * 16 emotions each. 83% of the turning points indicated a change of pattern in the emotional trajectories that consisted of: (a increasingly intense positive emotions or (b decreasingly intense negative emotions. These turning points also coincided with particularly complex periods in the training as perceived by the participants (p = 0.003, and p = 0.001 respectively. The relationship between positively-trended turning points in the students' emotional trajectories and the complexity of the training may be interpreted as evidence of a successful management of the cognitive conflict arising from the clash between the students' prior ways of meaning-making and the challenging demands of the training. One of the strengths of this study is that it provides a relatively simple procedure for identifying turning points in developmental trajectories, which can be applied to various longitudinal experiences that are very common in educational and developmental contexts. Additionally, the findings contribute to sustaining that the assumption that complex contextual demands lead unfailingly to individuals' learning is incomplete. Instead, it is how individuals manage complexity which may or may not lead to

  10. 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 ...

  11. Learning-by-Concordance (LbC): introducing undergraduate students to the complexity and uncertainty of clinical practice.

    Science.gov (United States)

    Fernandez, Nicolas; Foucault, Amélie; Dubé, Serge; Robert, Diane; Lafond, Chantal; Vincent, Anne-Marie; Kassis, Jeannine; Kazitani, Driss; Charlin, Bernard

    2016-10-01

    A current challenge in medical education is the steep exposure to the complexity and uncertainty of clinical practice in early clerkship. The gap between pre-clinical courses and the reality of clinical decision-making can be overwhelming for undergraduate students. The Learning-by-Concordance (LbC) approach aims to bridge this gap by embedding complexity and uncertainty by relying on real-life situations and exposure to expert reasoning processes to support learning. LbC provides three forms of support: 1) expert responses that students compare with their own, 2) expert explanations and 3) recognized scholars' key-messages. Three different LbC inspired learning tools were used by 900 undergraduate medical students in three courses: Concordance-of-Reasoning in a 1 st -year hematology course; Concordance-of-Perception in a 2nd-year pulmonary physio-pathology course, and; Concordance-of-Professional-Judgment with 3rd-year clerkship students. Thematic analysis was conducted on freely volunteered qualitative comments provided by 404 students. Absence of a right answer was challenging for 1 st year concordance-of-reasoning group; the 2 nd year visual concordance group found radiology images initially difficult and unnerving and the 3 rd year concordance-of-judgment group recognized the importance of divergent expert opinion. Expert panel answers and explanations constitute an example of "cognitive apprenticeship" that could contribute to the development of appropriate professional reasoning processes.

  12. Non-Smad signaling pathways.

    Science.gov (United States)

    Mu, Yabing; Gudey, Shyam Kumar; Landström, Maréne

    2012-01-01

    Transforming growth factor-beta (TGFβ) is a key regulator of cell fate during embryogenesis and has also emerged as a potent driver of the epithelial-mesenchymal transition during tumor progression. TGFβ signals are transduced by transmembrane type I and type II serine/threonine kinase receptors (TβRI and TβRII, respectively). The activated TβR complex phosphorylates Smad2 and Smad3, converting them into transcriptional regulators that complex with Smad4. TGFβ also uses non-Smad signaling pathways such as the p38 and Jun N-terminal kinase (JNK) mitogen-activated protein kinase (MAPK) pathways to convey its signals. Ubiquitin ligase tumor necrosis factor (TNF)-receptor-associated factor 6 (TRAF6) and TGFβ-associated kinase 1 (TAK1) have recently been shown to be crucial for the activation of the p38 and JNK MAPK pathways. Other TGFβ-induced non-Smad signaling pathways include the phosphoinositide 3-kinase-Akt-mTOR pathway, the small GTPases Rho, Rac, and Cdc42, and the Ras-Erk-MAPK pathway. Signals induced by TGFβ are tightly regulated and specified by post-translational modifications of the signaling components, since they dictate the subcellular localization, activity, and duration of the signal. In this review, we discuss recent findings in the field of TGFβ-induced responses by non-Smad signaling pathways.

  13. 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

  14. 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…

  15. 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…

  16. Song learning and cognitive ability are not consistently related in a songbird.

    Science.gov (United States)

    Anderson, Rindy C; Searcy, William A; Peters, Susan; Hughes, Melissa; DuBois, Adrienne L; Nowicki, Stephen

    2017-03-01

    Learned aspects of song have been hypothesized to signal cognitive ability in songbirds. We tested this hypothesis in hand-reared song sparrows (Melospiza melodia) that were tutored with playback of adult songs during the critical period for song learning. The songs developed by the 19 male subjects were compared to the model songs to produce two measures of song learning: the proportion of notes copied from models and the average spectrogram cross-correlation between copied notes and model notes. Song repertoire size, which reflects song complexity, was also measured. At 1 year of age, subjects were given a battery of five cognitive tests that measured speed of learning in the context of a novel foraging task, color association, color reversal, detour-reaching, and spatial learning. Bivariate correlations between the three song measures and the five cognitive measures revealed no significant associations. As in other studies of avian cognition, different cognitive measures were for the most part not correlated with each other, and this result remained true when 22 hand-reared female song sparrows were added to the analysis. General linear mixed models controlling for effects of neophobia and nest of origin indicated that all three song measures were associated with better performance on color reversal and spatial learning but were associated with worse performance on novel foraging and detour-reaching. Overall, the results do not support the hypothesis that learned aspects of song signal cognitive ability.

  17. 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.

  18. Learning Analytics for Networked Learning Models

    Science.gov (United States)

    Joksimovic, Srecko; Hatala, Marek; Gaševic, Dragan

    2014-01-01

    Teaching and learning in networked settings has attracted significant attention recently. The central topic of networked learning research is human-human and human-information interactions occurring within a networked learning environment. The nature of these interactions is highly complex and usually requires a multi-dimensional approach to…

  19. Complexity Thinking in PE: Game-Centred Approaches, Games as Complex Adaptive Systems, and Ecological Values

    Science.gov (United States)

    Storey, Brian; Butler, Joy

    2013-01-01

    Background: This article draws on the literature relating to game-centred approaches (GCAs), such as Teaching Games for Understanding, and dynamical systems views of motor learning to demonstrate a convergence of ideas around games as complex adaptive learning systems. This convergence is organized under the title "complexity thinking"…

  20. 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.

  1. Can motto-goals outperform learning and performance goals? Influence of goal setting on performance and affect in a complex problem solving task

    Directory of Open Access Journals (Sweden)

    Miriam S. Rohe

    2016-09-01

    Full Text Available In this paper, we bring together research on complex problem solving with that on motivational psychology about goal setting. Complex problems require motivational effort because of their inherent difficulties. Goal Setting Theory has shown with simple tasks that high, specific performance goals lead to better performance outcome than do-your-best goals. However, in complex tasks, learning goals have proven more effective than performance goals. Based on the Zurich Resource Model (Storch & Krause, 2014, so-called motto-goals (e.g., "I breathe happiness" should activate a person’s resources through positive affect. It was found that motto-goals are effective with unpleasant duties. Therefore, we tested the hypothesis that motto-goals outperform learning and performance goals in the case of complex problems. A total of N = 123 subjects participated in the experiment. In dependence of their goal condition, subjects developed a personal motto, learning, or performance goal. This goal was adapted for the computer-simulated complex scenario Tailorshop, where subjects worked as managers in a small fictional company. Other than expected, there was no main effect of goal condition for the management performance. As hypothesized, motto goals led to higher positive and lower negative affect than the other two goal types. Even though positive affect decreased and negative affect increased in all three groups during Tailorshop completion, participants with motto goals reported the lowest rates of negative affect over time. Exploratory analyses investigated the role of affect in complex problem solving via mediational analyses and the influence of goal type on perceived goal attainment.

  2. TGF-β Signaling in Dopaminergic Neurons Regulates Dendritic Growth, Excitatory-Inhibitory Synaptic Balance, and Reversal Learning

    Directory of Open Access Journals (Sweden)

    Sarah X. Luo

    2016-12-01

    Full Text Available Neural circuits involving midbrain dopaminergic (DA neurons regulate reward and goal-directed behaviors. Although local GABAergic input is known to modulate DA circuits, the mechanism that controls excitatory/inhibitory synaptic balance in DA neurons remains unclear. Here, we show that DA neurons use autocrine transforming growth factor β (TGF-β signaling to promote the growth of axons and dendrites. Surprisingly, removing TGF-β type II receptor in DA neurons also disrupts the balance in TGF-β1 expression in DA neurons and neighboring GABAergic neurons, which increases inhibitory input, reduces excitatory synaptic input, and alters phasic firing patterns in DA neurons. Mice lacking TGF-β signaling in DA neurons are hyperactive and exhibit inflexibility in relinquishing learned behaviors and re-establishing new stimulus-reward associations. These results support a role for TGF-β in regulating the delicate balance of excitatory/inhibitory synaptic input in local microcircuits involving DA and GABAergic neurons and its potential contributions to neuropsychiatric disorders.

  3. Complex scenes and situations visualization in hierarchical learning algorithm with dynamic 3D NeoAxis engine

    Science.gov (United States)

    Graham, James; Ternovskiy, Igor V.

    2013-06-01

    We applied a two stage unsupervised hierarchical learning system to model complex dynamic surveillance and cyber space monitoring systems using a non-commercial version of the NeoAxis visualization software. The hierarchical scene learning and recognition approach is based on hierarchical expectation maximization, and was linked to a 3D graphics engine for validation of learning and classification results and understanding the human - autonomous system relationship. Scene recognition is performed by taking synthetically generated data and feeding it to a dynamic logic algorithm. The algorithm performs hierarchical recognition of the scene by first examining the features of the objects to determine which objects are present, and then determines the scene based on the objects present. This paper presents a framework within which low level data linked to higher-level visualization can provide support to a human operator and be evaluated in a detailed and systematic way.

  4. Neuronal RING finger protein 11 (RNF11 regulates canonical NF-κB signaling

    Directory of Open Access Journals (Sweden)

    Pranski Elaine L

    2012-04-01

    Full Text Available Abstract Background The RING domain-containing protein RING finger protein 11 (RNF11 is a member of the A20 ubiquitin-editing protein complex and modulates peripheral NF-κB signaling. RNF11 is robustly expressed in neurons and colocalizes with a population of α-synuclein-positive Lewy bodies and neurites in Parkinson disease patients. The NF-κB pathway has an important role in the vertebrate nervous system, where the absence of NF-κB activity during development can result in learning and memory deficits, whereas chronic NF-κB activation is associated with persistent neuroinflammation. We examined the functional role of RNF11 with respect to canonical NF-κB signaling in neurons to gain understanding of the tight association of inflammatory pathways, including NF-κB, with the pathogenesis of neurodegenerative diseases. Methods and results Luciferase assays were employed to assess NF-κB activity under targeted short hairpin RNA (shRNA knockdown of RNF11 in human neuroblastoma cells and murine primary neurons, which suggested that RNF11 acts as a negative regulator of canonical neuronal NF-κB signaling. These results were further supported by analyses of p65 translocation to the nucleus following depletion of RNF11. Coimmunoprecipitation experiments indicated that RNF11 associates with members of the A20 ubiquitin-editing protein complex in neurons. Site-directed mutagenesis of the myristoylation domain, which is necessary for endosomal targeting of RNF11, altered the impact of RNF11 on NF-κB signaling and abrogated RNF11’s association with the A20 ubiquitin-editing protein complex. A partial effect on canonical NF-κB signaling and an association with the A20 ubiquitin-editing protein complex was observed with mutagenesis of the PPxY motif, a proline-rich region involved in Nedd4-like protein interactions. Last, shRNA-mediated reduction of RNF11 in neurons and neuronal cell lines elevated levels of monocyte chemoattractant protein 1 and

  5. 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.

  6. 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

  7. 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.

  8. Plasticity-related genes in brain development and amygdala-dependent learning.

    Science.gov (United States)

    Ehrlich, D E; Josselyn, S A

    2016-01-01

    Learning about motivationally important stimuli involves plasticity in the amygdala, a temporal lobe structure. Amygdala-dependent learning involves a growing number of plasticity-related signaling pathways also implicated in brain development, suggesting that learning-related signaling in juveniles may simultaneously influence development. Here, we review the pleiotropic functions in nervous system development and amygdala-dependent learning of a signaling pathway that includes brain-derived neurotrophic factor (BDNF), extracellular signaling-related kinases (ERKs) and cyclic AMP-response element binding protein (CREB). Using these canonical, plasticity-related genes as an example, we discuss the intersection of learning-related and developmental plasticity in the immature amygdala, when aversive and appetitive learning may influence the developmental trajectory of amygdala function. We propose that learning-dependent activation of BDNF, ERK and CREB signaling in the immature amygdala exaggerates and accelerates neural development, promoting amygdala excitability and environmental sensitivity later in life. © 2015 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

  9. Complex Constructivism: A Theoretical Model of Complexity and Cognition

    Science.gov (United States)

    Doolittle, Peter E.

    2014-01-01

    Education has long been driven by its metaphors for teaching and learning. These metaphors have influenced both educational research and educational practice. Complexity and constructivism are two theories that provide functional and robust metaphors. Complexity provides a metaphor for the structure of myriad phenomena, while constructivism…

  10. 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.

  11. Modeling high dimensional multichannel brain signals

    KAUST Repository

    Hu, Lechuan

    2017-03-27

    In this paper, our goal is to model functional and effective (directional) connectivity in network of multichannel brain physiological signals (e.g., electroencephalograms, local field potentials). The primary challenges here are twofold: first, there are major statistical and computational difficulties for modeling and analyzing high dimensional multichannel brain signals; second, there is no set of universally-agreed measures for characterizing connectivity. To model multichannel brain signals, our approach is to fit a vector autoregressive (VAR) model with sufficiently high order so that complex lead-lag temporal dynamics between the channels can be accurately characterized. However, such a model contains a large number of parameters. Thus, we will estimate the high dimensional VAR parameter space by our proposed hybrid LASSLE method (LASSO+LSE) which is imposes regularization on the first step (to control for sparsity) and constrained least squares estimation on the second step (to improve bias and mean-squared error of the estimator). Then to characterize connectivity between channels in a brain network, we will use various measures but put an emphasis on partial directed coherence (PDC) in order to capture directional connectivity between channels. PDC is a directed frequency-specific measure that explains the extent to which the present oscillatory activity in a sender channel influences the future oscillatory activity in a specific receiver channel relative all possible receivers in the network. Using the proposed modeling approach, we have achieved some insights on learning in a rat engaged in a non-spatial memory task.

  12. Modeling high dimensional multichannel brain signals

    KAUST Repository

    Hu, Lechuan; Fortin, Norbert; Ombao, Hernando

    2017-01-01

    In this paper, our goal is to model functional and effective (directional) connectivity in network of multichannel brain physiological signals (e.g., electroencephalograms, local field potentials). The primary challenges here are twofold: first, there are major statistical and computational difficulties for modeling and analyzing high dimensional multichannel brain signals; second, there is no set of universally-agreed measures for characterizing connectivity. To model multichannel brain signals, our approach is to fit a vector autoregressive (VAR) model with sufficiently high order so that complex lead-lag temporal dynamics between the channels can be accurately characterized. However, such a model contains a large number of parameters. Thus, we will estimate the high dimensional VAR parameter space by our proposed hybrid LASSLE method (LASSO+LSE) which is imposes regularization on the first step (to control for sparsity) and constrained least squares estimation on the second step (to improve bias and mean-squared error of the estimator). Then to characterize connectivity between channels in a brain network, we will use various measures but put an emphasis on partial directed coherence (PDC) in order to capture directional connectivity between channels. PDC is a directed frequency-specific measure that explains the extent to which the present oscillatory activity in a sender channel influences the future oscillatory activity in a specific receiver channel relative all possible receivers in the network. Using the proposed modeling approach, we have achieved some insights on learning in a rat engaged in a non-spatial memory task.

  13. Cooperative ethylene receptor signaling

    OpenAIRE

    Liu, Qian; Wen, Chi-Kuang

    2012-01-01

    The gaseous plant hormone ethylene is perceived by a family of five ethylene receptor members in the dicotyledonous model plant Arabidopsis. Genetic and biochemical studies suggest that the ethylene response is suppressed by ethylene receptor complexes, but the biochemical nature of the receptor signal is unknown. Without appropriate biochemical measures to trace the ethylene receptor signal and quantify the signal strength, the biological significance of the modulation of ethylene responses ...

  14. Coordinate Activation of Redox-Dependent ASK1/TGF-β Signaling by a Multiprotein Complex (MPK38, ASK1, SMADs, ZPR9, and TRX) Improves Glucose and Lipid Metabolism in Mice.

    Science.gov (United States)

    Seong, Hyun-A; Manoharan, Ravi; Ha, Hyunjung

    2016-03-10

    To explore the molecular connections between redox-dependent apoptosis signal-regulating kinase 1 (ASK1) and transforming growth factor-β (TGF-β) signaling pathways and to examine the physiological processes in which coordinated regulation of these two signaling pathways plays a critical role. We provide evidence that the ASK1 and TGF-β signaling pathways are interconnected by a multiprotein complex harboring murine protein serine-threonine kinase 38 (MPK38), ASK1, Sma- and Mad-related proteins (SMADs), zinc-finger-like protein 9 (ZPR9), and thioredoxin (TRX) and demonstrate that the activation of either ASK1 or TGF-β activity is sufficient to activate both the redox-dependent ASK1 and TGF-β signaling pathways. Physiologically, the restoration of the downregulated activation levels of ASK1 and TGF-β signaling in genetically and diet-induced obese mice by adenoviral delivery of SMAD3 or ZPR9 results in the amelioration of adiposity, hyperglycemia, hyperlipidemia, and impaired ketogenesis. Our data suggest that the multiprotein complex linking ASK1 and TGF-β signaling pathways may be a potential target for redox-mediated metabolic complications.

  15. 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)

  16. 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)

  17. Framework for robot skill learning using reinforcement learning

    Science.gov (United States)

    Wei, Yingzi; Zhao, Mingyang

    2003-09-01

    Robot acquiring skill is a process similar to human skill learning. Reinforcement learning (RL) is an on-line actor critic method for a robot to develop its skill. The reinforcement function has become the critical component for its effect of evaluating the action and guiding the learning process. We present an augmented reward function that provides a new way for RL controller to incorporate prior knowledge and experience into the RL controller. Also, the difference form of augmented reward function is considered carefully. The additional reward beyond conventional reward will provide more heuristic information for RL. In this paper, we present a strategy for the task of complex skill learning. Automatic robot shaping policy is to dissolve the complex skill into a hierarchical learning process. The new form of value function is introduced to attain smooth motion switching swiftly. We present a formal, but practical, framework for robot skill learning and also illustrate with an example the utility of method for learning skilled robot control on line.

  18. 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...

  19. 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...

  20. United States Support Programme (USSP): Lessons Learned from the Management of Complex, Multi-Stakeholder Projects for International Safeguards

    International Nuclear Information System (INIS)

    Diaz, R.; Tackentien, J.

    2015-01-01

    This paper will review USSP experiences, lessons learned, and proposed future strategies on the management of complex projects including the Universal Non-Destructive Assay Data Acquisition Platform (UNAP) instrument development task. The focus will be on identifying lessons learned to formulate strategies to minimize risk and maximize the potential of commercial success for future complex projects. Topics planned for inclusion are: 1. Initial agreement amongst all stakeholders on the justification of the need of the development including market studies of existing/near term future COTS technology capabilities; 2. Initial confirmation that there is a market for the product other than the IAEA to reduce investment risk; 3. Agreement on an accelerated initial project schedule from request acceptance to commercial unit production including per unit cost and quantities; 4. During product development, obtaining periodic customer reaffirmation of the need and quantities for the product per the existing schedule and per unit price. (author)

  1. Acquiring organizational learning norms: a contingency approach for understanding deutero learning

    NARCIS (Netherlands)

    Wijnhoven, Alphonsus B.J.M.

    2001-01-01

    'The Learning Organization' is a configuration of learning norms (called a learning prototype here), which is seldom related to varying levels of learning needs. This article assumes that organizational environmental complexity and dynamics define four learning needs levels. Consequently, four

  2. Stabilization of dendritic spine clusters and hyperactive Ras-MAPK signaling predict enhanced motor learning in an autistic savant mouse model

    Directory of Open Access Journals (Sweden)

    Ryan Thomas Ash

    2014-03-01

    Full Text Available That both prominent behavioral inflexibility and exceptional learning abilities are seen occasionally in autistic patients is a mystery. We hypothesize that these altered patterns of learning and memory can arise from a pathological imbalance between the stability and plasticity of internal neural representations. We evaluated this hypothesis in the mouse model of MECP2 duplication syndrome, which demonstrates enhanced motor learning, stereotyped behaviors, and social avoidance. Learning-associated structural plasticity was measured in the motor cortex of MECP2 duplication mice by 2-photon imaging (Fig. 1A. An increased stabilization rate of learning-associated dendritic spines was observed in mutants, and this correlated with rotarod performance. Analysis of the spatial distribution of stabilized spines revealed that the mutant’s increased spine stabilization was due to a specific increase in the stability of spines jointly formed in ~9-micron clusters. Clustered spine stabilization but not isolated spine stabilization predicted enhanced motor performance in MECP2 duplication mice (Fig. 1B. Biochemical assays of Ras-MAPK and mTOR pathway activation demonstrated profound hyperphosphorylation of MAPK in the motor cortex of MECP2 duplication mice with motor training (Fig. 1C. Taken together these data suggest that a pathological bias towards hyperstability of learning-associated dendritic spine clusters driven by hyperactive Ras-MAPK signaling could contribute to neurobehavioral phenotypes in this form of syndromic autism.

  3. 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.

  4. Analog and mixed-signal electronics

    CERN Document Server

    Stephan, Karl

    2015-01-01

    A practical guide to analog and mixed-signal electronics, with an emphasis on design problems and applications This book provides an in-depth coverage of essential analog and mixed-signal topics such as power amplifiers, active filters, noise and dynamic range, analog-to-digital and digital-to-analog conversion techniques, phase-locked loops, and switching power supplies. Readers will learn the basics of linear systems, types of nonlinearities and their effects, op-amp circuits, the high-gain analog filter-amplifier, and signal generation. The author uses system design examples to motivate

  5. Does supporting multiple student strategies lead to greater learning and motivation? Investigating a source of complexity in the architecture of intelligent tutoring systems

    NARCIS (Netherlands)

    Waalkens, Maaike; Aleven, Vincent; Taatgen, Niels

    Intelligent tutoring systems (ITS) support students in learning a complex problem-solving skill. One feature that makes an ITS architecturally complex, and hard to build, is support for strategy freedom, that is, the ability to let students pursue multiple solution strategies within a given problem.

  6. 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.

  7. Metric Learning Method Aided Data-Driven Design of Fault Detection Systems

    Directory of Open Access Journals (Sweden)

    Guoyang Yan

    2014-01-01

    Full Text Available Fault detection is fundamental to many industrial applications. With the development of system complexity, the number of sensors is increasing, which makes traditional fault detection methods lose efficiency. Metric learning is an efficient way to build the relationship between feature vectors with the categories of instances. In this paper, we firstly propose a metric learning-based fault detection framework in fault detection. Meanwhile, a novel feature extraction method based on wavelet transform is used to obtain the feature vector from detection signals. Experiments on Tennessee Eastman (TE chemical process datasets demonstrate that the proposed method has a better performance when comparing with existing methods, for example, principal component analysis (PCA and fisher discriminate analysis (FDA.

  8. 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…

  9. The ShcA SH2 domain engages a 14-3-3/PI3'K signaling complex and promotes breast cancer cell survival.

    Science.gov (United States)

    Ursini-Siegel, J; Hardy, W R; Zheng, Y; Ling, C; Zuo, D; Zhang, C; Podmore, L; Pawson, T; Muller, W J

    2012-11-29

    The ShcA adapter protein transmits activating signals downstream of receptor and cytoplasmic tyrosine kinases through the establishment of phosphotyrosine-dependent complexes. In this regard, ShcA possesses both a phosphotyrosine-binding domain (PTB) and Src homology 2 domain (SH2), which bind phosphotyrosine residues in a sequence-specific manner. Although the majority of receptor tyrosine kinases expressed in breast cancer cells bind the PTB domain, very little is known regarding the biological importance of SH2-driven ShcA signaling during mammary tumorigenesis. To address this, we employed transgenic mice expressing a mutant ShcA allele harboring a non-functional SH2 domain (ShcR397K) under the transcriptional control of the endogenous ShcA promoter. Using transplantation approaches, we demonstrate that SH2-dependent ShcA signaling within the mammary epithelial compartment is essential for breast tumor outgrowth, survival and the development of lung metastases. We further show that the ShcA SH2 domain activates the AKT pathway, potentially through a novel SH2-mediated complex between ShcA, 14-3-3ζ and the p85 regulatory subunit of phosphatidylinositol 3 (PI3') kinase. This study is the first to demonstrate that the SH2 domain of ShcA is critical for tumor survival during mammary tumorigenesis.

  10. [Effects of Biejiajian Pills on Wnt signal pathway signal molecules β-catenin/TCF4 complex activities and downstream proteins cyclin D1 and MMP-2 in hepatocellular carcinoma cells].

    Science.gov (United States)

    Wen, Bin; Sun, Haitao; He, Songqi; Cheng, Yang; Jia, Wenyan; Fan, Eryan; Pang, Jie

    2014-12-01

    To study the effect of Biejiajian Pills on Wnt signal pathway and the mechanisms underlying its action to suppress the invasiveness of hepatocellular carcinoma. HepG2 cells cultured in the serum of rats fed with Biejiajian Pills for 48 h were examined for β-catenin expression using immunofluorescence, β-catenin/TCF4 complex activity with luciferase, and expressions of the downstream proteins cyclin D1 and MMP-2 using qRT-PCR. Biejiajian Pills-treated sera significantly reduced the expressions of cytoplasmic and nuclear β-catenin protein, cyclin D1 and MMP-2 proteins and lowered the activities of β-catenin/TCF4 complex. Biejiajian Pills may serve as a potential anti-tumor agent, whose effect might be mediated by inhibiting the Wnt/β-catenin pathway.

  11. 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.

  12. Detection of signals in noise

    CERN Document Server

    Whalen, Anthony D; Declaris, Nicholas

    1971-01-01

    Detection of Signals in Noise serves as an introduction to the principles and applications of the statistical theory of signal detection. The book discusses probability and random processes; narrowband signals, their complex representation, and their properties described with the aid of the Hilbert transform; and Gaussian-derived processes. The text also describes the application of hypothesis testing for the detection of signals and the fundamentals required for statistical detection of signals in noise. Problem exercises, references, and a supplementary bibliography are included after each c

  13. 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.

  14. IQM: an extensible and portable open source application for image and signal analysis in Java.

    Science.gov (United States)

    Kainz, Philipp; Mayrhofer-Reinhartshuber, Michael; Ahammer, Helmut

    2015-01-01

    Image and signal analysis applications are substantial in scientific research. Both open source and commercial packages provide a wide range of functions for image and signal analysis, which are sometimes supported very well by the communities in the corresponding fields. Commercial software packages have the major drawback of being expensive and having undisclosed source code, which hampers extending the functionality if there is no plugin interface or similar option available. However, both variants cannot cover all possible use cases and sometimes custom developments are unavoidable, requiring open source applications. In this paper we describe IQM, a completely free, portable and open source (GNU GPLv3) image and signal analysis application written in pure Java. IQM does not depend on any natively installed libraries and is therefore runnable out-of-the-box. Currently, a continuously growing repertoire of 50 image and 16 signal analysis algorithms is provided. The modular functional architecture based on the three-tier model is described along the most important functionality. Extensibility is achieved using operator plugins, and the development of more complex workflows is provided by a Groovy script interface to the JVM. We demonstrate IQM's image and signal processing capabilities in a proof-of-principle analysis and provide example implementations to illustrate the plugin framework and the scripting interface. IQM integrates with the popular ImageJ image processing software and is aiming at complementing functionality rather than competing with existing open source software. Machine learning can be integrated into more complex algorithms via the WEKA software package as well, enabling the development of transparent and robust methods for image and signal analysis.

  15. An Intrinsic Value System for Developing Multiple Invariant Representations with Incremental Slowness Learning

    Directory of Open Access Journals (Sweden)

    Matthew David Luciw

    2013-05-01

    Full Text Available Curiosity Driven Modular Incremental Slow Feature Analysis (CD-MISFA;~cite{cdmisfa} is a recently introduced model of intrinsically-motivated invariance learning, which shows how curiosity enables the orderly formation of multiple stable sensory representations, through which the agent can simplify its complex sensory input. Here, we first discuss the computational properties of the CD-MISFA model itself, followed by a discussion of neurophysiological analogs fulfilling similar functional roles. CD-MISFA combines 1. unsupervised representation learning through the slowness principle, 2. generation of an intrinsic reward signal through the learning progress of the developing features, and 3. balancing of exploration and exploitation in order to maximize learning progress and quickly learn multiple feature sets for perceptual simplification. Experimental results on synthetic observations and on the iCub robot show that the intrinsic value system is an essential component to representation learning, further, the model explores such that the representations are typically learned in order from least to most costly, as predicted by the theory of Artificial Curiosity.

  16. 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.

  17. Regulator of G-protein signaling - 5 (RGS5 is a novel repressor of hedgehog signaling.

    Directory of Open Access Journals (Sweden)

    William M Mahoney

    Full Text Available Hedgehog (Hh signaling plays fundamental roles in morphogenesis, tissue repair, and human disease. Initiation of Hh signaling is controlled by the interaction of two multipass membrane proteins, patched (Ptc and smoothened (Smo. Recent studies identify Smo as a G-protein coupled receptor (GPCR-like protein that signals through large G-protein complexes which contain the Gαi subunit. We hypothesize Regulator of G-Protein Signaling (RGS proteins, and specifically RGS5, are endogenous repressors of Hh signaling via their ability to act as GTPase activating proteins (GAPs for GTP-bound Gαi, downstream of Smo. In support of this hypothesis, we demonstrate that RGS5 over-expression inhibits sonic hedgehog (Shh-mediated signaling and osteogenesis in C3H10T1/2 cells. Conversely, signaling is potentiated by siRNA-mediated knock-down of RGS5 expression, but not RGS4 expression. Furthermore, using immuohistochemical analysis and co-immunoprecipitation (Co-IP, we demonstrate that RGS5 is present with Smo in primary cilia. This organelle is required for canonical Hh signaling in mammalian cells, and RGS5 is found in a physical complex with Smo in these cells. We therefore conclude that RGS5 is an endogenous regulator of Hh-mediated signaling and that RGS proteins are potential targets for novel therapeutics in Hh-mediated diseases.

  18. Nuclear pore complex-mediated modulation of TCR signaling is required for naïve CD4+ T cell homeostasis.

    Science.gov (United States)

    Borlido, Joana; Sakuma, Stephen; Raices, Marcela; Carrette, Florent; Tinoco, Roberto; Bradley, Linda M; D'Angelo, Maximiliano A

    2018-05-07

    Nuclear pore complexes (NPCs) are channels connecting the nucleus with the cytoplasm. We report that loss of the tissue-specific NPC component Nup210 causes a severe deficit of naïve CD4 + T cells. Nup210-deficient CD4 + T lymphocytes develop normally but fail to survive in the periphery. The decreased survival results from both an impaired ability to transmit tonic T cell receptor (TCR) signals and increased levels of Fas, which sensitize Nup210 -/- naïve CD4 + T cells to Fas-mediated cell death. Mechanistically, Nup210 regulates these processes by modulating the expression of Cav2 (encoding Caveolin-2) and Jun at the nuclear periphery. Whereas the TCR-dependent and CD4 + T cell-specific upregulation of Cav2 is critical for proximal TCR signaling, cJun expression is required for STAT3-dependent repression of Fas. Our results uncover an unexpected role for Nup210 as a cell-intrinsic regulator of TCR signaling and T cell homeostasis and expose NPCs as key players in the adaptive immune system.

  19. Complexity in practice: understanding primary care as a complex adaptive system

    Directory of Open Access Journals (Sweden)

    Beverley Ellis

    2010-06-01

    Conclusions The results are real-world exemplars of the emergent properties of complex adaptive systems. Improving clinical governance in primary care requires both complex social interactions and underpinning informatics. The socio-technical lessons learned from this research should inform future management approaches.

  20. Synergistic effect of signaling from receptors of soluble platelet agonists and outside-in signaling in formation of a stable fibrinogen-integrin αIIbβ3-actin cytoskeleton complex.

    Science.gov (United States)

    Budnik, Ivan; Shenkman, Boris; Savion, Naphtali

    2015-01-01

    Thrombus formation in the injured vessel wall is a highly complex process involving various blood-born components that go through specific temporal and spatial changes as observed by intravital videomicroscopy. Platelets bind transiently to the developing thrombus and may either become stably incorporated into or disengage from the thrombus. The aim of the present study was to reveal the processes involved in the formation of a stable thrombus. Platelet-rich plasma and washed platelets were studied by the aggregometer. The aggregate stability was challenged by eptifibatide. Platelet Triton-insoluble fraction was prepared and the actin and αIIb content in the cytoskeleton was analyzed by western blot. Maximal actin polymerization is achieved 1min after platelet activation while maximal αIIbβ3-actin cytoskeleton association requires 5 to 10min of activation and fibrinogen-mediated platelet-to-platelet bridging. Thus, actin polymerization is dependent on platelet activation and requires neither αIIbβ3 integrin occupation nor platelet aggregation. Formation of a stable aggregate requires platelet activation for more than 1min, complete increase in actin cytoskeleton fraction and partial association of αIIbβ3 with the actin cytoskeleton. However, direct αIIbβ3 activation is not sufficient for cytoskeleton complex formation. Thus, stable αIIbβ3-fibrinogen interaction, representing stable aggregate, is achieved after more than 1min agonist activation, involving inside-out and outside-in signaling but not after direct integrin activation, involving only outside-in signaling. Formation of a stable fibrinogen-αIIbβ3-actin cytoskeleton complex is the result of the combined effect of platelet stimulation by soluble agonists, activation of αIIbβ3, fibrinogen binding and platelet-to-platelet bridging. Copyright © 2014 Elsevier Ltd. All rights reserved.