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Sample records for neural traffic signal

  1. Traffic Signal Cycle Lengths

    Data.gov (United States)

    Town of Chapel Hill, North Carolina — Traffic signal location list for the town of Chapel Hill. This data set includes light cycle information as well as as intersection information.The Town of Chapel...

  2. Neural network system for traffic flow management

    Science.gov (United States)

    Gilmore, John F.; Elibiary, Khalid J.; Petersson, L. E. Rickard

    1992-09-01

    Atlanta will be the home of several special events during the next five years ranging from the 1996 Olympics to the 1994 Super Bowl. When combined with the existing special events (Braves, Falcons, and Hawks games, concerts, festivals, etc.), the need to effectively manage traffic flow from surface streets to interstate highways is apparent. This paper describes a system for traffic event response and management for intelligent navigation utilizing signals (TERMINUS) developed at Georgia Tech for adaptively managing special event traffic flows in the Atlanta, Georgia area. TERMINUS (the original name given Atlanta, Georgia based upon its role as a rail line terminating center) is an intelligent surface street signal control system designed to manage traffic flow in Metro Atlanta. The system consists of three components. The first is a traffic simulation of the downtown Atlanta area around Fulton County Stadium that models the flow of traffic when a stadium event lets out. Parameters for the surrounding area include modeling for events during various times of day (such as rush hour). The second component is a computer graphics interface with the simulation that shows the traffic flows achieved based upon intelligent control system execution. The final component is the intelligent control system that manages surface street light signals based upon feedback from control sensors that dynamically adapt the intelligent controller's decision making process. The intelligent controller is a neural network model that allows TERMINUS to control the configuration of surface street signals to optimize the flow of traffic away from special events.

  3. Traffic Signals in School Zones

    OpenAIRE

    Kevin S Lee; Bullock, Darcy M.

    2003-01-01

    Traffic signals are used to control the right of way at intersections. Strict engineering guidelines are published in the Manual on Uniform Traffic Control Devices (MUTCD) that engineers use to determine if a traffic signal is “warranted”. The warrants provide consistent national balance between mobility, safety, efficiency, and costs. However, signalized intersections are often viewed by the general public as safer then unsignalized intersections. This belief is often heightened when there a...

  4. Stochastic Model of Traffic Jam and Traffic Signal Control

    Science.gov (United States)

    Shin, Ji-Sun; Cui, Cheng-You; Lee, Tae-Hong; Lee, Hee-Hyol

    Traffic signal control is an effective method to solve the traffic jam. and forecasting traffic density has been known as an important part of the Intelligent Transportation System (ITS). The several methods of the traffic signal control are known such as random walk method, Neuron Network method, Bayesian Network method, and so on. In this paper, we propose a new method of a traffic signal control using a predicted distribution of traffic jam based on a Dynamic Bayesian Network model. First, a forecasting model to predict a probabilistic distribution of the traffic jam during each period of traffic lights is built. As the forecasting model, the Dynamic Bayesian Network is used to predict the probabilistic distribution of a density of the traffic jam. According to measurement of two crossing points for each cycle, the inflow and outflow of each direction and the number of standing vehicles at former cycle are obtained. The number of standing vehicle at k-th cycle will be calculated synchronously. Next, the probabilistic distribution of the density of standing vehicle in each cycle will be predicted using the Dynamic Bayesian Network constructed for the traffic jam. And then a control rule to adjust the split and the cycle to increase the probability between a lower limit and ceiling of the standing vehicles is deduced. As the results of the simulation using the actual traffic data of Kitakyushu city, the effectiveness of the method is shown.

  5. modified traffic s modified traffic signal phasing at traffic warden ...

    African Journals Online (AJOL)

    eobe

    centred' notion in traffic engineering is now being replaced by the new 'human centred' notion which takes all road users into consideration in its planning, design and operations and attaches more importance to vulnerable traffic participants such as the pedestrians [4]. Thus the pedestrian traffic safety management at TWC ...

  6. Light signals for road traffic control.

    NARCIS (Netherlands)

    Schreuder, D.A.

    1981-01-01

    Signals for road traffic control are a major constituent of the modern traffic scene, particularly in built-up areas. A vast amount of research has been executed in the last two decennia, resulting in a fairly generally accepted view on what the requirements for effective traffic lights are. For the

  7. modified traffic s modified traffic signal phasing at traffic warden ...

    African Journals Online (AJOL)

    eobe

    1111, 2, 3, 2, 3, 2, 3, 2, 3 DEPARTMENT OF CIVIL ENGINEERING, UNIVERSITY OF ILORIN, ILORIN, KWARA STATE. NIGERIA. E-mail addresses: 1111 ... centred' notion in traffic engineering is now being replaced by the new 'human centred' ..... Federal Highway Administration, 2009. [5] Alhajyaseen, W. K. M., Asano, ...

  8. 40 CFR 93.128 - Traffic signal synchronization projects.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 20 2010-07-01 2010-07-01 false Traffic signal synchronization... synchronization projects. Traffic signal synchronization projects may be approved, funded, and implemented without... include such regionally significant traffic signal synchronization projects. ...

  9. System and method for traffic signal timing estimation

    KAUST Repository

    Dumazert, Julien

    2015-12-30

    A method and system for estimating traffic signals. The method and system can include constructing trajectories of probe vehicles from GPS data emitted by the probe vehicles, estimating traffic signal cycles, combining the estimates, and computing the traffic signal timing by maximizing a scoring function based on the estimates. Estimating traffic signal cycles can be based on transition times of the probe vehicles starting after a traffic signal turns green.

  10. Neural Membrane Signaling Platforms

    Directory of Open Access Journals (Sweden)

    Ron Wallace

    2010-06-01

    Full Text Available Throughout much of the history of biology, the cell membrane was functionally defined as a semi-permeable barrier separating aqueous compartments, and an anchoring site for proteins. Little attention was devoted to its possible regulatory role in intracellular molecular processes and neuron electrical signaling. This article reviews the history of membrane studies and the current state of the art. Emphasis is placed on natural and artificial membrane studies of electric field effects on molecular organization, especially as these may relate to impulse propagation in neurons. Implications of these studies for new designs in artificial intelligence are briefly examined.

  11. Neural membrane signaling platforms.

    Science.gov (United States)

    Wallace, Ron

    2010-06-10

    Throughout much of the history of biology, the cell membrane was functionally defined as a semi-permeable barrier separating aqueous compartments, and an anchoring site for proteins. Little attention was devoted to its possible regulatory role in intracellular molecular processes and neuron electrical signaling. This article reviews the history of membrane studies and the current state of the art. Emphasis is placed on natural and artificial membrane studies of electric field effects on molecular organization, especially as these may relate to impulse propagation in neurons. Implications of these studies for new designs in artificial intelligence are briefly examined.

  12. Delays at signalized intersections with exhaustive traffic control

    NARCIS (Netherlands)

    Boon, M.A.A.; Adan, I.J.B.F.; Winands, E.M.M.; Down, D.G.

    2012-01-01

    In this paper, we study a traffic intersection with vehicle-actuated traffic signal control. Traffic lights stay green until all lanes within a group are emptied. Assuming general renewal arrival processes, we derive exact limiting distributions of the delays under heavy traffic (HT) conditions.

  13. SignalGuru: Leveraging mobile phones for collaborative traffic signal schedule advisory

    OpenAIRE

    Koukoumidis, Emmanouil; Peh, Li-Shiuan; Martonosi, Margaret

    2011-01-01

    While traffic signals are necessary to safely control competing flows of traffic, they inevitably enforce a stop-and-go movement pattern that increases fuel consumption, reduces traffic flow and causes traffic jams. These side effects can be alleviated by providing drivers and their onboard computational devices (e.g., vehicle computer, smartphone) with information about the schedule of the traffic signals ahead. Based on when the signal ahead will turn green, drivers can then adjust speed so...

  14. [Glutamate signaling and neural plasticity].

    Science.gov (United States)

    Watanabe, Masahiko

    2013-07-01

    Proper functioning of the nervous system relies on the precise formation of neural circuits during development. At birth, neurons have redundant synaptic connections not only to their proper targets but also to other neighboring cells. Then, functional neural circuits are formed during early postnatal development by the selective strengthening of necessary synapses and weakening of surplus connections. Synaptic connections are also modified so that projection fields of active afferents expand at the expense of lesser ones. We have studied the molecular mechanisms underlying these activity-dependent prunings and the plasticity of synaptic circuitry using gene-engineered mice defective in the glutamatergic signaling system. NMDA-type glutamate receptors are critically involved in the establishment of the somatosensory pathway ascending from the brainstem trigeminal nucleus to the somatosensory cortex. Without NMDA receptors, whisker-related patterning fails to develop, whereas lesion-induced plasticity occurs normally during the critical period. In contrast, mice lacking the glutamate transporters GLAST or GLT1 are selectively impaired in the lesion-induced critical plasticity of cortical barrels, although whisker-related patterning itself develops normally. In the developing cerebellum, multiple climbing fibers initially innervating given Purkinje cells are eliminated one by one until mono-innervation is achieved. In this pruning process, P/Q-type Ca2+ channels expressed on Purkinje cells are critically involved by the selective strengthening of single main climbing fibers against other lesser afferents. Therefore, the activation of glutamate receptors that leads to an activity-dependent increase in the intracellular Ca2+ concentration plays a key role in the pruning of immature synaptic circuits into functional circuits. On the other hand, glutamate transporters appear to control activity-dependent plasticity among afferent fields, presumably through adjusting

  15. Network traffic anomaly prediction using Artificial Neural Network

    Science.gov (United States)

    Ciptaningtyas, Hening Titi; Fatichah, Chastine; Sabila, Altea

    2017-03-01

    As the excessive increase of internet usage, the malicious software (malware) has also increase significantly. Malware is software developed by hacker for illegal purpose(s), such as stealing data and identity, causing computer damage, or denying service to other user[1]. Malware which attack computer or server often triggers network traffic anomaly phenomena. Based on Sophos's report[2], Indonesia is the riskiest country of malware attack and it also has high network traffic anomaly. This research uses Artificial Neural Network (ANN) to predict network traffic anomaly based on malware attack in Indonesia which is recorded by Id-SIRTII/CC (Indonesia Security Incident Response Team on Internet Infrastructure/Coordination Center). The case study is the highest malware attack (SQL injection) which has happened in three consecutive years: 2012, 2013, and 2014[4]. The data series is preprocessed first, then the network traffic anomaly is predicted using Artificial Neural Network and using two weight update algorithms: Gradient Descent and Momentum. Error of prediction is calculated using Mean Squared Error (MSE) [7]. The experimental result shows that MSE for SQL Injection is 0.03856. So, this approach can be used to predict network traffic anomaly.

  16. Cognitive Control Signals for Neural Prosthetics

    National Research Council Canada - National Science Library

    S. Musallam; B. D. Corneil; B. Greger; H. Scherberger; R. A. Andersen

    2004-01-01

    Recent development of neural prosthetics for assisting paralyzed patients has focused on decoding intended hand trajectories from motor cortical neurons and using this signal to control external devices...

  17. A model of traffic signs recognition with convolutional neural network

    Science.gov (United States)

    Hu, Haihe; Li, Yujian; Zhang, Ting; Huo, Yi; Kuang, Wenqing

    2016-10-01

    In real traffic scenes, the quality of captured images are generally low due to some factors such as lighting conditions, and occlusion on. All of these factors are challengeable for automated recognition algorithms of traffic signs. Deep learning has provided a new way to solve this kind of problems recently. The deep network can automatically learn features from a large number of data samples and obtain an excellent recognition performance. We therefore approach this task of recognition of traffic signs as a general vision problem, with few assumptions related to road signs. We propose a model of Convolutional Neural Network (CNN) and apply the model to the task of traffic signs recognition. The proposed model adopts deep CNN as the supervised learning model, directly takes the collected traffic signs image as the input, alternates the convolutional layer and subsampling layer, and automatically extracts the features for the recognition of the traffic signs images. The proposed model includes an input layer, three convolutional layers, three subsampling layers, a fully-connected layer, and an output layer. To validate the proposed model, the experiments are implemented using the public dataset of China competition of fuzzy image processing. Experimental results show that the proposed model produces a recognition accuracy of 99.01 % on the training dataset, and yield a record of 92% on the preliminary contest within the fourth best.

  18. Multi-Modal Intelligent Traffic Signal Systems GPS

    Data.gov (United States)

    Department of Transportation — Data were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to...

  19. Neural network signal understanding for instrumentation

    DEFF Research Database (Denmark)

    Pau, L. F.; Johansen, F. S.

    1990-01-01

    A report is presented on the use of neural signal interpretation theory and techniques for the purpose of classifying the shapes of a set of instrumentation signals, in order to calibrate devices, diagnose anomalies, generate tuning/settings, and interpret the measurement results. Neural signal...... understanding research is surveyed, and the selected implementation and its performance in terms of correct classification rates and robustness to noise are described. Formal results on neural net training time and sensitivity to weights are given. A theory for neural control using functional link nets is given......, and an explanation facility designed to help neural signal understanding is described. The results are compared to those obtained with a knowledge-based signal interpretation system using the same instrument and data...

  20. Development of an electronic vehicular traffic signal controller ...

    African Journals Online (AJOL)

    This paper presents the design, construction, and test of an electronic signal controller for urban vehicular traffic control. The design is based on a series of fixed-time signal plans for different time zones of the day and can accommodate sixty-four signal plans. Finite state machine concept was used in the design of the signal ...

  1. Computer-Assisted Traffic Engineering Using Assignment, Optimal Signal Setting, and Modal Split

    Science.gov (United States)

    1978-05-01

    Methods of traffic assignment, traffic signal setting, and modal split analysis are combined in a set of computer-assisted traffic engineering programs. The system optimization and user optimization traffic assignments are described. Travel time func...

  2. Light Emitting Diode (LED) circular traffic signal lifetime management system.

    Science.gov (United States)

    2011-02-01

    The objective of this research is to build lifetime curves for red, yellow, and green LED circular traffic signals through 20,000-hr. accelerated stress testing of samples operating under Louisianas environmental conditions.

  3. Improving traffic signal management and operations : a basic service model.

    Science.gov (United States)

    2009-12-01

    This report provides a guide for achieving a basic service model for traffic signal management and : operations. The basic service model is based on simply stated and defensible operational objectives : that consider the staffing level, expertise and...

  4. Toward an optimal convolutional neural network for traffic sign recognition

    Science.gov (United States)

    Habibi Aghdam, Hamed; Jahani Heravi, Elnaz; Puig, Domenec

    2015-12-01

    Convolutional Neural Networks (CNN) beat the human performance on German Traffic Sign Benchmark competition. Both the winner and the runner-up teams trained CNNs to recognize 43 traffic signs. However, both networks are not computationally efficient since they have many free parameters and they use highly computational activation functions. In this paper, we propose a new architecture that reduces the number of the parameters 27% and 22% compared with the two networks. Furthermore, our network uses Leaky Rectified Linear Units (ReLU) as the activation function that only needs a few operations to produce the result. Specifically, compared with the hyperbolic tangent and rectified sigmoid activation functions utilized in the two networks, Leaky ReLU needs only one multiplication operation which makes it computationally much more efficient than the two other functions. Our experiments on the Gertman Traffic Sign Benchmark dataset shows 0:6% improvement on the best reported classification accuracy while it reduces the overall number of parameters 85% compared with the winner network in the competition.

  5. The design of traffic signal coordinated control

    Science.gov (United States)

    Guo, Xueting; Sun, Hongsheng; Wang, Xifu

    2017-05-01

    Traffic as the tertiary industry is an important pillar industry to support the normal development of the economy. But now China's road traffic development and economic development has shown a great imbalance and fault phenomenon, which greatly inhibited the normal development of China's economy. Now in many large and medium-sized cities in China are implementing green belt construction. The so-called green band is when the road conditions to meet the conditions for the establishment of the green band, the sections of the intersection of several planning to a traffic coordination control system, so that when the driver at a specific speed can be achieved without stopping the continuous Through the intersection. Green belt can effectively reduce the delay and queuing length of vehicle driving, the normal function of urban roads and reduce the economic losses caused by traffic congestion is a great help. In this paper, the theoretical basis of the design of the coordinated control system is described. Secondly, the green time offset is calculated by the analytic method and the green band is established. And then the VISSIM software is used to simulate the traffic system before and after the improvement. Finally, the results of the two simulations are compared.

  6. TRAFFIC TIME SERIES FORECASTING BY FEEDFORWARD NEURAL NETWORK: A CASE STUDY BASED ON TRAFFIC DATA OF MONROE

    Directory of Open Access Journals (Sweden)

    M. Raeesi

    2014-10-01

    Full Text Available Short time prediction is one of the most important factors in intelligence transportation system (ITS. In this research, the use of feed forward neural network for traffic time-series prediction is presented. In this paper, the traffic in one direction of the road segment is predicted. The input of the neural network is the time delay data exported from the road traffic data of Monroe city. The time delay data is used for training the network. For generating the time delay data, the traffic data related to the first 300 days of 2008 is used. The performance of the feed forward neural network model is validated using the real observation data of the 301st day.

  7. Multi-Modal Intelligent Traffic Signal Systems Signal Plans for Roadside Equipment

    Data.gov (United States)

    Department of Transportation — Data were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to...

  8. Traffic sign recognition based on deep convolutional neural network

    Science.gov (United States)

    Yin, Shi-hao; Deng, Ji-cai; Zhang, Da-wei; Du, Jing-yuan

    2017-11-01

    Traffic sign recognition (TSR) is an important component of automated driving systems. It is a rather challenging task to design a high-performance classifier for the TSR system. In this paper, we propose a new method for TSR system based on deep convolutional neural network. In order to enhance the expression of the network, a novel structure (dubbed block-layer below) which combines network-in-network and residual connection is designed. Our network has 10 layers with parameters (block-layer seen as a single layer): the first seven are alternate convolutional layers and block-layers, and the remaining three are fully-connected layers. We train our TSR network on the German traffic sign recognition benchmark (GTSRB) dataset. To reduce overfitting, we perform data augmentation on the training images and employ a regularization method named "dropout". The activation function we employ in our network adopts scaled exponential linear units (SELUs), which can induce self-normalizing properties. To speed up the training, we use an efficient GPU to accelerate the convolutional operation. On the test dataset of GTSRB, we achieve the accuracy rate of 99.67%, exceeding the state-of-the-art results.

  9. Imaging Posture Veils Neural Signals

    Directory of Open Access Journals (Sweden)

    Robert T Thibault

    2016-10-01

    Full Text Available Whereas modern brain imaging often demands holding body positions incongruent with everyday life, posture governs both neural activity and cognitive performance. Humans commonly perform while upright; yet, many neuroimaging methodologies require participants to remain motionless and adhere to non-ecological comportments within a confined space. This inconsistency between ecological postures and imaging constraints undermines the transferability and generalizability of many a neuroimaging assay.Here we highlight the influence of posture on brain function and behavior. Specifically, we challenge the tacit assumption that brain processes and cognitive performance are comparable across a spectrum of positions. We provide an integrative synthesis regarding the increasingly prominent influence of imaging postures on autonomic function, mental capacity, sensory thresholds, and neural activity. Arguing that neuroimagers and cognitive scientists could benefit from considering the influence posture wields on both general functioning and brain activity, we examine existing imaging technologies and the potential of portable and versatile imaging devices (e.g., functional near infrared spectroscopy. Finally, we discuss ways that accounting for posture may help unveil the complex brain processes of everyday cognition.

  10. [The history of optical signals for traffic regulation].

    Science.gov (United States)

    Draeger, J; Harsch, V

    2008-04-01

    For signal transmission in traffic today, different optical, acoustic, or other physical or technical means are used for information. The different kinds of traffic (water navigation, road and rail, and, later air transport) made traffic regulation necessary early on. This regulation, from its very beginning in ancient times, began by means of optical signals; nowadays, this remains the most important method. From the very start, minimum requirements for the navigator's vision, color discrimination, dark adaptation, and even visual field were needed. For historical reasons, it was in seafaring medicine that these first developed. Besides the development of the different signals, methods for checking the requirements were soon developed. National and international requirements have been very different. Only within the last 50 years has international cooperation led to the acceptance of general standards for the different traffic modes. This article discusses the technical development of optical signals for the different kinds of traffic, from ancient times to the present, and explains the development of minimum requirements for the different visual functions.

  11. Emergency vehicle traffic signal preemption system

    Science.gov (United States)

    Bachelder, Aaron D. (Inventor); Foster, Conrad F. (Inventor)

    2011-01-01

    An emergency vehicle traffic light preemption system for preemption of traffic lights at an intersection to allow safe passage of emergency vehicles. The system includes a real-time status monitor of an intersection which is relayed to a control module for transmission to emergency vehicles as well as to a central dispatch office. The system also provides for audio warnings at an intersection to protect pedestrians who may not be in a position to see visual warnings or for various reasons cannot hear the approach of emergency vehicles. A transponder mounted on an emergency vehicle provides autonomous control so the vehicle operator can attend to getting to an emergency and not be concerned with the operation of the system. Activation of a priority-code (i.e. Code-3) situation provides communications with each intersection being approached by an emergency vehicle and indicates whether the intersection is preempted or if there is any conflict with other approaching emergency vehicles. On-board diagnostics handle various information including heading, speed, and acceleration sent to a control module which is transmitted to an intersection and which also simultaneously receives information regarding the status of an intersection. Real-time communications and operations software allow central and remote monitoring, logging, and command of intersections and vehicles.

  12. Neural synchronization via potassium signaling

    DEFF Research Database (Denmark)

    Postnov, Dmitry E; Ryazanova, Ludmila S; Mosekilde, Erik

    2006-01-01

    Using a relatively simple model we examine how variations of the extracellular potassium concentration can give rise to synchronization of two nearby pacemaker cells. With the volume of the extracellular space and the rate of potassium diffusion as control parameters, the dual nature...... junctional coupling, potassium signaling gives rise to considerable changes of the cellular response to external stimuli....

  13. Reconstruction of periodic signals using neural networks

    Directory of Open Access Journals (Sweden)

    José Danilo Rairán Antolines

    2014-01-01

    Full Text Available In this paper, we reconstruct a periodic signal by using two neural networks. The first network is trained to approximate the period of a signal, and the second network estimates the corresponding coefficients of the signal's Fourier expansion. The reconstruction strategy consists in minimizing the mean-square error via backpro-pagation algorithms over a single neuron with a sine transfer function. Additionally, this paper presents mathematical proof about the quality of the approximation as well as a first modification of the algorithm, which requires less data to reach the same estimation; thus making the algorithm suitable for real-time implementations.

  14. Small-time Scale Network Traffic Prediction Based on Complex-valued Neural Network

    Science.gov (United States)

    Yang, Bin

    2017-07-01

    Accurate models play an important role in capturing the significant characteristics of the network traffic, analyzing the network dynamic, and improving the forecasting accuracy for system dynamics. In this study, complex-valued neural network (CVNN) model is proposed to further improve the accuracy of small-time scale network traffic forecasting. Artificial bee colony (ABC) algorithm is proposed to optimize the complex-valued and real-valued parameters of CVNN model. Small-scale traffic measurements data namely the TCP traffic data is used to test the performance of CVNN model. Experimental results reveal that CVNN model forecasts the small-time scale network traffic measurement data very accurately

  15. Forecasting of Congestion in Traffic Neural Network Modelling Using Duffing Holmes Oscillator

    Science.gov (United States)

    Mrgole, Anamarija L.; Čelan, Marko; Mesarec, Beno

    2017-10-01

    Forecasting of congestion in traffic with Neural Network is an innovative and new process of identification and detection of chaotic features in time series analysis. With the use of Duffing Holmes Oscillator, we estimate the emergence of traffic flow congestion when the traffic load on a specific section of the road and in a specific time period is close to exceeding the capacity of the road infrastructure. The orientated model is validated in six locations with a specific requirement. The paper points out the issue of importance of traffic flow forecasting and simulations for preventing or rerouting possible short term traffic flow congestions.

  16. Time-delay neural network for audio monitoring of road traffic and vehicle classification

    Science.gov (United States)

    Nooralahiyan, Amir Y.; Lopez, Louis; Mckewon, Denis; Ahmadi, Masoud

    1997-02-01

    The aim of this research is to investigate the feasibility of developing a cost effective traffic monitoring detector for the purpose of reliable on-line vehicle classification to aid traffic management systems. The detector used was a directional microphone connected to a DAT recorder. The digital signal was preprocessed by LPC (Linear Predictive Coding) parameter conversion based on autocorrelation analysis. A Time Delay Neural Network (TDNN) was chosen to classify individual travelling vehicles based on their speed-independent acoustic signature. The network was trained and tested with real data for four types of vehicles. The paper provides a description of the TDNN architecture and training algorithm and an overview of the LPC pre-processing and feature extraction technique as applied to audio monitoring of road traffic. The performance of TDNN vehicle classification, convergence and accuracy for the training patterns are fully illustrated. In generalizing to a limited number of test patterns available, 100% accuracy in classification was achieved. The net was also robust to changes in the starting position of the acoustic waveforms with 86% accuracy for the same test data set.

  17. Neural basis of multisensory looming signals.

    Science.gov (United States)

    Tyll, Sascha; Bonath, Björn; Schoenfeld, Mircea Ariel; Heinze, Hans-Jochen; Ohl, Frank W; Noesselt, Tömme

    2013-01-15

    Approaching or looming signals are often related to extremely relevant environmental events (e.g. threats or collisions) making these signals critical for survival. However, the neural network underlying multisensory looming processing is not yet fully understood. Using functional magnetic resonance imaging (fMRI) we identified the neural correlates of audiovisual looming processing in humans: audiovisual looming (vs. receding) signals enhance fMRI-responses in low-level visual and auditory areas plus multisensory cortex (superior temporal sulcus; plus parietal and frontal structures). When characterizing the fMRI-response profiles for multisensory looming stimuli, we found significant enhancements relative to the mean and maximum of unisensory responses in looming-sensitive visual and auditory cortex plus STS. Superadditive enhancements were observed in visual cortex. Subject-specific region-of-interest analyses further revealed superadditive response profiles within all sensory-specific looming-sensitive structures plus bilateral STS for audiovisual looming vs. summed unisensory looming conditions. Finally, we observed enhanced connectivity of bilateral STS with low-level visual areas in the context of looming processing. This enhanced coupling of STS with unisensory regions might potentially serve to enhance the salience of unisensory stimulus features and is accompanied by superadditive fMRI-responses. We suggest that this preference in neural signaling for looming stimuli effectively informs animals to avoid potential threats or collisions. Copyright © 2012 Elsevier Inc. All rights reserved.

  18. Model for Detection and Classification of DDoS Traffic Based on Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    D. Peraković

    2017-06-01

    Full Text Available Detection of DDoS (Distributed Denial of Service traffic is of great importance for the availability protection of services and other information and communication resources. The research presented in this paper shows the application of artificial neural networks in the development of detection and classification model for three types of DDoS attacks and legitimate network traffic. Simulation results of developed model showed accuracy of 95.6% in classification of pre-defined classes of traffic.

  19. A computerized traffic control algorithm to determine optimal traffic signal settings. Ph.D. Thesis - Toledo Univ.

    Science.gov (United States)

    Seldner, K.

    1977-01-01

    An algorithm was developed to optimally control the traffic signals at each intersection using a discrete time traffic model applicable to heavy or peak traffic. Off line optimization procedures were applied to compute the cycle splits required to minimize the lengths of the vehicle queues and delay at each intersection. The method was applied to an extensive traffic network in Toledo, Ohio. Results obtained with the derived optimal settings are compared with the control settings presently in use.

  20. Dividing traffic cluster into parts by signal control

    Science.gov (United States)

    Nagatani, Takashi

    2018-02-01

    When a cluster of vehicles with various speeds moves through the series of signals, the cluster breaks down by stopping at signals and results in smaller groups of vehicles. We present the nonlinear-map model of the motion of vehicles controlled by the signals. We study the breakup of a cluster of vehicles through the series of signals. The cluster of vehicles is divided into various groups by controlling the cycle time of signals. The vehicles within each group move with the same mean velocity. The breakup of the traffic cluster depends highly on the signal control. The dependence of dividing on both cycle time and vehicular speed is clarified. Also, we investigate the effect of the irregular interval between signals on dividing.

  1. The Denver region traffic signal system improvement program : planning for management and operations

    Science.gov (United States)

    2009-04-01

    The Denver Regional Council of Governments (DRCOG) works with over 30 local jurisdictions on the Traffic Signal System Improvement Program (TSSIP), a combination of management and operations strategies designed to time and coordinate traffic signals ...

  2. TRAFFIC SIGNALS MODELLING WITH LONG DISTANCE COMMUNICATIONS USING PIC MICROCONTROLLER

    Directory of Open Access Journals (Sweden)

    Ahmet ÖZEK

    2004-04-01

    Full Text Available Today, microcontrollers are widely used in control and automation systems. For the automation of traffic signalization, the crossroads need to be controlled by microcontrollers and for the automation of more than one crossroad the communication of more than one microcontroller is required. In some special cases, the intercommunication of microcontrollers is required to change crossroad status and obtain continuous flow (green weave synchronization. In this study, a method is proposed to control the traffic flow on a hospital road with two crossroads located several km. apart from each other. For the purpose of changing the crossroads status to have a continuous flow of the traffic, the series of PIC16F877 microcontroller is used.

  3. Driving behavior and control in traffic system with two kinds of signals

    Science.gov (United States)

    Nagatani, Takashi; Hino, Yuki

    2014-06-01

    We study the vehicular traffic controlled by two kinds of signals which are positioned with a periodic configuration. We propose a microscopic model to explore the driving behavior in the traffic system with two kinds of signals. The control method of traffic flow by the combination of two kinds of signals is proposed. The dynamic model is described by the nonlinear map model and the CA model. The driving behavior is clarified for the traffic system controlled by two kinds of signals. The fundamental diagrams are derived for various combinations of two kinds of signals. The traffic flow through two kinds of signals is compared with that of a single kind of signals. The traffic flow displays the complex behavior different from the conventional traffic with a single kind of signals.

  4. Classification and Prediction of Traffic Flow Based on Real Data Using Neural Networks

    Science.gov (United States)

    Pamuła, Teresa

    2012-12-01

    This paper presents a method of classification of time series of traffic flow, on the section of the main road leading into the city of Gliwice. Video detectors recorded traffic volume data was used, covering the period of one year in 5-minute intervals - from June 2011 to May 2012. In order to classify the data a statistical analysis was performed, which resulted in the proposition of splitting the daily time series into four classes. The series were smoothed to obtain hourly flow rates. The classification was performed using neural networks with different structures and using a variable number of input data. The purpose of classification is the prediction of traffic flow rates in the afternoon basing on the morning traffic and the assessment of daily traffic volumes for a particular day of the week. The results can be utilized by intelligent urban traffic management systems.

  5. Traffic Congestion Evaluation and Signal Control Optimization Based on Wireless Sensor Networks: Model and Algorithms

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2012-01-01

    Full Text Available This paper presents the model and algorithms for traffic flow data monitoring and optimal traffic light control based on wireless sensor networks. Given the scenario that sensor nodes are sparsely deployed along the segments between signalized intersections, an analytical model is built using continuum traffic equation and develops the method to estimate traffic parameter with the scattered sensor data. Based on the traffic data and principle of traffic congestion formation, we introduce the congestion factor which can be used to evaluate the real-time traffic congestion status along the segment and to predict the subcritical state of traffic jams. The result is expected to support the timing phase optimization of traffic light control for the purpose of avoiding traffic congestion before its formation. We simulate the traffic monitoring based on the Mobile Century dataset and analyze the performance of traffic light control on VISSIM platform when congestion factor is introduced into the signal timing optimization model. The simulation result shows that this method can improve the spatial-temporal resolution of traffic data monitoring and evaluate traffic congestion status with high precision. It is helpful to remarkably alleviate urban traffic congestion and decrease the average traffic delays and maximum queue length.

  6. Efficient queue length detection at traffic signals using probe vehicle data and data fusion

    OpenAIRE

    Neumann, Thorsten

    2009-01-01

    In this paper, a new method for the detection of queue lengths at traffic signals is described. Based on conventional probe vehicle data and implementing an extremely flexible data fusion approach for the integration of nearly arbitrary additional traffic information, it provides an efficient way to get high-quality estimates for the traffic states at traffic signals. A systematic evaluation based on extensive simulations addresses several issues concerning quality and demonstrates both th...

  7. Traffic Command Gesture Recognition for Virtual Urban Scenes Based on a Spatiotemporal Convolution Neural Network

    Directory of Open Access Journals (Sweden)

    Chunyong Ma

    2018-01-01

    Full Text Available Intelligent recognition of traffic police command gestures increases authenticity and interactivity in virtual urban scenes. To actualize real-time traffic gesture recognition, a novel spatiotemporal convolution neural network (ST-CNN model is presented. We utilized Kinect 2.0 to construct a traffic police command gesture skeleton (TPCGS dataset collected from 10 volunteers. Subsequently, convolution operations on the locational change of each skeletal point were performed to extract temporal features, analyze the relative positions of skeletal points, and extract spatial features. After temporal and spatial features based on the three-dimensional positional information of traffic police skeleton points were extracted, the ST-CNN model classified positional information into eight types of Chinese traffic police gestures. The test accuracy of the ST-CNN model was 96.67%. In addition, a virtual urban traffic scene in which real-time command tests were carried out was set up, and a real-time test accuracy rate of 93.0% was achieved. The proposed ST-CNN model ensured a high level of accuracy and robustness. The ST-CNN model recognized traffic command gestures, and such recognition was found to control vehicles in virtual traffic environments, which enriches the interactive mode of the virtual city scene. Traffic command gesture recognition contributes to smart city construction.

  8. Active voltammetric microsensors with neural signal processing.

    Energy Technology Data Exchange (ETDEWEB)

    Vogt, M. C.

    1998-12-11

    Many industrial and environmental processes, including bioremediation, would benefit from the feedback and control information provided by a local multi-analyte chemical sensor. For most processes, such a sensor would need to be rugged enough to be placed in situ for long-term remote monitoring, and inexpensive enough to be fielded in useful numbers. The multi-analyte capability is difficult to obtain from common passive sensors, but can be provided by an active device that produces a spectrum-type response. Such new active gas microsensor technology has been developed at Argonne National Laboratory. The technology couples an electrocatalytic ceramic-metallic (cermet) microsensor with a voltammetric measurement technique and advanced neural signal processing. It has been demonstrated to be flexible, rugged, and very economical to produce and deploy. Both narrow interest detectors and wide spectrum instruments have been developed around this technology. Much of this technology's strength lies in the active measurement technique employed. The technique involves applying voltammetry to a miniature electrocatalytic cell to produce unique chemical ''signatures'' from the analytes. These signatures are processed with neural pattern recognition algorithms to identify and quantify the components in the analyte. The neural signal processing allows for innovative sampling and analysis strategies to be employed with the microsensor. In most situations, the whole response signature from the voltammogram can be used to identify, classify, and quantify an analyte, without dissecting it into component parts. This allows an instrument to be calibrated once for a specific gas or mixture of gases by simple exposure to a multi-component standard rather than by a series of individual gases. The sampled unknown analytes can vary in composition or in concentration, the calibration, sensing, and processing methods of these active voltammetric microsensors can

  9. Urban Traffic Signal System Control Structural Optimization Based on Network Analysis

    Directory of Open Access Journals (Sweden)

    Li Wang

    2013-01-01

    Full Text Available Advanced urban traffic signal control systems such as SCOOT and SCATS normally coordinate traffic network using multilevel hierarchical control mechanism. In this mechanism, several key intersections will be selected from traffic signal network and the network will be divided into different control subareas. Traditionally, key intersection selection and control subareas division are executed according to dynamic traffic counts and link length between intersections, which largely rely on traffic engineers’ experience. However, it omits important inherent characteristics of traffic network topology. In this paper, we will apply network analysis approach into these two aspects for traffic system control structure optimization. Firstly, the modified C-means clustering algorithm will be proposed to assess the importance of intersections in traffic network and furthermore determine the key intersections based on three indexes instead of merely on traffic counts in traditional methods. Secondly, the improved network community discovery method will be used to give more reasonable evidence in traffic control subarea division. Finally, to test the effectiveness of network analysis approach, a hardware-in-loop simulation environment composed of regional traffic control system, microsimulation software and signal controller hardware, will be built. Both traditional method and proposed approach will be implemented on simulation test bed to evaluate traffic operation performance indexes, for example, travel time, stop times, delay and average vehicle speed. Simulation results show that the proposed network analysis approach can improve the traffic control system operation performance effectively.

  10. Pattern Recognition and Classification of Fatal Traffic Accidents in Israel A Neural Network Approach

    DEFF Research Database (Denmark)

    Prato, Carlo Giacomo; Gitelman, Victoria; Bekhor, Shlomo

    2011-01-01

    This article provides a broad picture of fatal traffic accidents in Israel to answer an increasing need of addressing compelling problems, designing preventive measures, and targeting specific population groups with the objective of reducing the number of traffic fatalities. The analysis focuses...... on 1,793 fatal traffic accidents occurred during the period between 2003 and 2006 and applies Kohonen and feed-forward back-propagation neural networks with the objective of extracting from the data typical patterns and relevant factors. Kohonen neural networks reveal five compelling accident patterns......: (1) single-vehicle accidents of young drivers, (2) multiple-vehicle accidents between young drivers, (3) accidents involving motorcyclists or cyclists, (4) accidents where elderly pedestrians crossed in urban areas, and (5) accidents where children and teenagers cross major roads in small urban areas...

  11. Sensitivity Analysis of Wavelet Neural Network Model for Short-Term Traffic Volume Prediction

    Directory of Open Access Journals (Sweden)

    Jinxing Shen

    2013-01-01

    Full Text Available In order to achieve a more accurate and robust traffic volume prediction model, the sensitivity of wavelet neural network model (WNNM is analyzed in this study. Based on real loop detector data which is provided by traffic police detachment of Maanshan, WNNM is discussed with different numbers of input neurons, different number of hidden neurons, and traffic volume for different time intervals. The test results show that the performance of WNNM depends heavily on network parameters and time interval of traffic volume. In addition, the WNNM with 4 input neurons and 6 hidden neurons is the optimal predictor with more accuracy, stability, and adaptability. At the same time, a much better prediction record will be achieved with the time interval of traffic volume are 15 minutes. In addition, the optimized WNNM is compared with the widely used back-propagation neural network (BPNN. The comparison results indicated that WNNM produce much lower values of MAE, MAPE, and VAPE than BPNN, which proves that WNNM performs better on short-term traffic volume prediction.

  12. Vehicular traffic flow through a series of signals with cycle time generated by a logistic map

    Science.gov (United States)

    Nagatani, Takashi; Sugiyama, Naoki

    2013-02-01

    We study the dynamical behavior of vehicular traffic through a series of traffic signals. The vehicular traffic is controlled with the use of the cycle time generated by a logistic map. Each signal changes periodically with a cycle time, and the cycle time varies from signal to signal. The nonlinear dynamic model of the vehicular motion is presented by a nonlinear map including the logistic map. The vehicular traffic exhibits very complex behavior on varying both the cycle time and the logistic-map parameter a. For a>3, the arrival time shows a linear dependence on the cycle time. Also, the dependence of vehicular motion on parameter a is clarified.

  13. Fuzzy cellular model of signal controlled traffic stream

    OpenAIRE

    Płaczek, Bartłomiej

    2011-01-01

    Microscopic traffic models have recently gained considerable importance as a mean of optimising traffic control strategies. Computationally efficient and sufficiently accurate microscopic traffic models have been developed based on the cellular automata theory. However, the real-time application of the available cellular automata models in traffic control systems is a difficult task due to their discrete and stochastic nature. This paper introduces a novel method of traffic streams modelling,...

  14. Multi-Modal Intelligent Traffic Signal Systems (MMITSS) Basic Safety Message

    Data.gov (United States)

    Department of Transportation — Data were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to...

  15. Multi-Modal Intelligent Traffic Signal Systems Vehicle Trajectories for Roadside Equipment

    Data.gov (United States)

    Department of Transportation — Data were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to...

  16. A Dynamic Traffic Signal Timing Model and its Algorithm for Junction of Urban Road

    DEFF Research Database (Denmark)

    Cai, Yanguang; Cai, Hao

    2012-01-01

    As an important part of Intelligent Transportation System, the scientific traffic signal timing of junction can improve the efficiency of urban transport. This paper presents a novel dynamic traffic signal timing model. According to the characteristics of the model, hybrid chaotic quantum evoluti...

  17. Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration Algorithm

    OpenAIRE

    Wang, Li; Lin, Shimin; Yang, Jingfeng; Zhang, Nanfeng; Yang, Ji; Li, Yong; Zhou, Handong; Yang, Feng; Li, Zhifu

    2017-01-01

    Traffic congestion is a common problem in many countries, especially in big cities. At present, China’s urban road traffic accidents occur frequently, the occurrence frequency is high, the accident causes traffic congestion, and accidents cause traffic congestion and vice versa. The occurrence of traffic accidents usually leads to the reduction of road traffic capacity and the formation of traffic bottlenecks, causing the traffic congestion. In this paper, the formation and propagation of tra...

  18. Neural signal registration and analysis of axons grown in microchannels

    Science.gov (United States)

    Pigareva, Y.; Malishev, E.; Gladkov, A.; Kolpakov, V.; Bukatin, A.; Mukhina, I.; Kazantsev, V.; Pimashkin, A.

    2016-08-01

    Registration of neuronal bioelectrical signals remains one of the main physical tools to study fundamental mechanisms of signal processing in the brain. Neurons generate spiking patterns which propagate through complex map of neural network connectivity. Extracellular recording of isolated axons grown in microchannels provides amplification of the signal for detailed study of spike propagation. In this study we used neuronal hippocampal cultures grown in microfluidic devices combined with microelectrode arrays to investigate a changes of electrical activity during neural network development. We found that after 5 days in vitro after culture plating the spiking activity appears first in microchannels and on the next 2-3 days appears on the electrodes of overall neural network. We conclude that such approach provides a convenient method to study neural signal processing and functional structure development on a single cell and network level of the neuronal culture.

  19. Using Artificial Neural Networks for ECG Signals Denoising

    Directory of Open Access Journals (Sweden)

    Zoltán Germán-Salló

    2010-12-01

    Full Text Available The authors have investigated some potential applications of artificial neural networks in electrocardiografic (ECG signal prediction. For this, the authors used an adaptive multilayer perceptron structure to predict the signal. The proposed procedure uses an artificial neural network based learning structure to estimate the (n+1th sample from n previous samples To train and adjust the network weights, the backpropagation (BP algorithm was used. In this paper, prediction of ECG signals (as time series using multi-layer feedforward neural networks will be described. The results are evaluated through approximation error which is defined as the difference between the predicted and the original signal.The prediction procedure is carried out (simulated in MATLAB environment, using signals from MIT-BIH arrhythmia database. Preliminary results are encouraging enough to extend the proposed method for other types of data signals.

  20. Hybrid digital signal processing and neural networks applications in PWRs

    Energy Technology Data Exchange (ETDEWEB)

    Eryurek, E.; Upadhyaya, B.R.; Kavaklioglu, K.

    1991-12-31

    Signal validation and plant subsystem tracking in power and process industries require the prediction of one or more state variables. Both heteroassociative and auotassociative neural networks were applied for characterizing relationships among sets of signals. A multi-layer neural network paradigm was applied for sensor and process monitoring in a Pressurized Water Reactor (PWR). This nonlinear interpolation technique was found to be very effective for these applications.

  1. Towards a magnetoresistive platform for neural signal recording

    Science.gov (United States)

    Sharma, P. P.; Gervasoni, G.; Albisetti, E.; D'Ercoli, F.; Monticelli, M.; Moretti, D.; Forte, N.; Rocchi, A.; Ferrari, G.; Baldelli, P.; Sampietro, M.; Benfenati, F.; Bertacco, R.; Petti, D.

    2017-05-01

    A promising strategy to get deeper insight on brain functionalities relies on the investigation of neural activities at the cellular and sub-cellular level. In this framework, methods for recording neuron electrical activity have gained interest over the years. Main technological challenges are associated to finding highly sensitive detection schemes, providing considerable spatial and temporal resolution. Moreover, the possibility to perform non-invasive assays would constitute a noteworthy benefit. In this work, we present a magnetoresistive platform for the detection of the action potential propagation in neural cells. Such platform allows, in perspective, the in vitro recording of neural signals arising from single neurons, neural networks and brain slices.

  2. Prediction of road traffic death rate using neural networks optimised by genetic algorithm.

    Science.gov (United States)

    Jafari, Seyed Ali; Jahandideh, Sepideh; Jahandideh, Mina; Asadabadi, Ebrahim Barzegari

    2015-01-01

    Road traffic injuries (RTIs) are realised as a main cause of public health problems at global, regional and national levels. Therefore, prediction of road traffic death rate will be helpful in its management. Based on this fact, we used an artificial neural network model optimised through Genetic algorithm to predict mortality. In this study, a five-fold cross-validation procedure on a data set containing total of 178 countries was used to verify the performance of models. The best-fit model was selected according to the root mean square errors (RMSE). Genetic algorithm, as a powerful model which has not been introduced in prediction of mortality to this extent in previous studies, showed high performance. The lowest RMSE obtained was 0.0808. Such satisfactory results could be attributed to the use of Genetic algorithm as a powerful optimiser which selects the best input feature set to be fed into the neural networks. Seven factors have been known as the most effective factors on the road traffic mortality rate by high accuracy. The gained results displayed that our model is very promising and may play a useful role in developing a better method for assessing the influence of road traffic mortality risk factors.

  3. Neural crest specification: tissues, signals, and transcription factors.

    Science.gov (United States)

    Rogers, C D; Jayasena, C S; Nie, S; Bronner, M E

    2012-01-01

    The neural crest is a transient population of multipotent and migratory cells unique to vertebrate embryos. Initially derived from the borders of the neural plate, these cells undergo an epithelial to mesenchymal transition to leave the central nervous system, migrate extensively in the periphery, and differentiate into numerous diverse derivatives. These include but are not limited to craniofacial cartilage, pigment cells, and peripheral neurons and glia. Attractive for their similarities to stem cells and metastatic cancer cells, neural crest cells are a popular model system for studying cell/tissue interactions and signaling factors that influence cell fate decisions and lineage transitions. In this review, we discuss the mechanisms required for neural crest formation in various vertebrate species, focusing on the importance of signaling factors from adjacent tissues and conserved gene regulatory interactions, which are required for induction and specification of the ectodermal tissue that will become neural crest. Copyright © 2011 Wiley Periodicals, Inc.

  4. A Network Traffic Prediction Model Based on Quantum-Behaved Particle Swarm Optimization Algorithm and Fuzzy Wavelet Neural Network

    OpenAIRE

    Kun Zhang; Zhao Hu; Xiao-Ting Gan; Jian-Bo Fang

    2016-01-01

    Due to the fact that the fluctuation of network traffic is affected by various factors, accurate prediction of network traffic is regarded as a challenging task of the time series prediction process. For this purpose, a novel prediction method of network traffic based on QPSO algorithm and fuzzy wavelet neural network is proposed in this paper. Firstly, quantum-behaved particle swarm optimization (QPSO) was introduced. Then, the structure and operation algorithms of WFNN are presented. The pa...

  5. An Intelligent Vehicular Traffic Signal Control System with State Flow Chart Design and FPGA Prototyping

    Directory of Open Access Journals (Sweden)

    UMAIR SAEEDSOLANGI

    2017-04-01

    Full Text Available The problem of vehicular traffic congestion is a persistent constraint in the socio-economic development of Pakistan. This paper presents design and implementation of an intelligent traffic controller based on FPGA (Field Programmable Gate Array to provide an efficient traffic management by optimizing functioning of traffic lights which will result in minimizing traffic congestion at intersections. The existent Traffic Signal system in Pakistan is fixed-time based and offers only Open Loop method for Traffic Control. The Intelligent Traffic Controller presented here uses feedback sensors to read the Traffic density present at a four way intersection to provide an efficient alternative for better supervisory Control of Traffic flow. The traffic density based control logic has been developed in a State Flow Chart for improved visualization of State Machine based operation, and implemented as a Subsystem in Simulink and transferred into VHDL (Hardware Description Language code using HDL Coder for reducing development time and time to market, which are essential to capitalize Embedded Systems Market. The VHDL code is synthesized with Altera QUARTUS, simulated timing waveform is obtained to verify correctness of the algorithm for different Traffic Scenarios. For implementation purpose estimations were obtained for Cyclone-III and Stratix-III.

  6. Effects of speed bottleneck on traffic flow with feedback control signal

    Science.gov (United States)

    Zhu, Kangli; Bi, Jiantao; Wu, Jianjun; Li, Shubin

    2016-09-01

    Various car-following models (CMs) have been developed to capture the complex characteristics of microscopic traffic flow, among which the coupled map CM can better reveal and reflect various phenomena of practical traffic flow. Capacity change at bottleneck contributes to high-density traffic flow upstream the bottleneck and contains very complex dynamic behavior. In this paper, we analyze the effect of speed bottleneck on the spatial-temporal evolution characteristics of traffic flow, and propose a method to reduce traffic congestion with the feedback control signal based on CM. Simulation results highlight the potential of using the feedback signal to control the stop-and-go wave and furthermore to alleviate the traffic congestion effectively.

  7. Advancing interconnect density for spiking neural network hardware implementations using traffic-aware adaptive network-on-chip routers.

    Science.gov (United States)

    Carrillo, Snaider; Harkin, Jim; McDaid, Liam; Pande, Sandeep; Cawley, Seamus; McGinley, Brian; Morgan, Fearghal

    2012-09-01

    The brain is highly efficient in how it processes information and tolerates faults. Arguably, the basic processing units are neurons and synapses that are interconnected in a complex pattern. Computer scientists and engineers aim to harness this efficiency and build artificial neural systems that can emulate the key information processing principles of the brain. However, existing approaches cannot provide the dense interconnect for the billions of neurons and synapses that are required. Recently a reconfigurable and biologically inspired paradigm based on network-on-chip (NoC) and spiking neural networks (SNNs) has been proposed as a new method of realising an efficient, robust computing platform. However, the use of the NoC as an interconnection fabric for large-scale SNNs demands a good trade-off between scalability, throughput, neuron/synapse ratio and power consumption. This paper presents a novel traffic-aware, adaptive NoC router, which forms part of a proposed embedded mixed-signal SNN architecture called EMBRACE (EMulating Biologically-inspiRed ArChitectures in hardwarE). The proposed adaptive NoC router provides the inter-neuron connectivity for EMBRACE, maintaining router communication and avoiding dropped router packets by adapting to router traffic congestion. Results are presented on throughput, power and area performance analysis of the adaptive router using a 90 nm CMOS technology which outperforms existing NoCs in this domain. The adaptive behaviour of the router is also verified on a Stratix II FPGA implementation of a 4 × 2 router array with real-time traffic congestion. The presented results demonstrate the feasibility of using the proposed adaptive NoC router within the EMBRACE architecture to realise large-scale SNNs on embedded hardware. Copyright © 2012 Elsevier Ltd. All rights reserved.

  8. Traffic data for local emissions monitoring at a signalized intersection

    NARCIS (Netherlands)

    Bigazzi, A.; Lint, J.W.C. van; Klunder, G.; Stelwagen, U.; Ligterink, N.E.

    2010-01-01

    In order to assist planning efforts for air pollution-responsive dynamic traffic management (DTM) systems, this research assesses the accuracy of local emissions monitoring based on traffic data and models. The study quantifies the benefits of increased data resolution for short-term emissions

  9. Small-signal neural models and their applications.

    Science.gov (United States)

    Basu, Arindam

    2012-02-01

    This paper introduces the use of the concept of small-signal analysis, commonly used in circuit design, for understanding neural models. We show that neural models, varying in complexity from Hodgkin-Huxley to integrate and fire have similar small-signal models when their corresponding differential equations are close to the same bifurcation with respect to input current. Three applications of small-signal neural models are shown. First, some of the properties of cortical neurons described by Izhikevich are explained intuitively through small-signal analysis. Second, we use small-signal models for deriving parameters for a simple neural model (such as resonate and fire) from a more complicated but biophysically relevant one like Morris-Lecar. We show similarity in the subthreshold behavior of the simple and complicated model when they are close to a Hopf bifurcation and a saddle-node bifurcation. Hence, this is useful to correctly tune simple neural models for large-scale cortical simulations. Finaly, the biasing regime of a silicon ion channel is derived by comparing its small-signal model with a Hodgkin-Huxley-type model.

  10. Neural processing of auditory signals and modular neural control for sound tropism of walking machines

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Pasemann, Frank; Fischer, Joern

    2005-01-01

    . The parameters of these networks are optimized by an evolutionary algorithm. In addition, a simple modular neural controller then generates the desired different walking patterns such that the machine walks straight, then turns towards a switched-on sound source, and then stops near to it....... and a neural preprocessing system together with a modular neural controller are used to generate a sound tropism of a four-legged walking machine. The neural preprocessing network is acting as a low-pass filter and it is followed by a network which discerns between signals coming from the left or the right...

  11. Analysis of traffic signal work backlog in Louisiana : technical assistance report.

    Science.gov (United States)

    1995-07-01

    A review of Traffic Services' traffic signal work records reveals the source of the backlog. During the 1980's, the department experienced personnel cutbacks and hiring freezes that caused the number of field personnel to drop from 40 to 24. Simultan...

  12. Traffic Signal Green-Wave Control Strategy Based on Divers’ Behaviors

    National Research Council Canada - National Science Library

    Ovidiu Tomescu; Vicente Ramón Tomás López; Florin Codruț Nemțanu; Iulian Bățroș

    2011-01-01

    .... The paper presents a new model for a bidirectional green wave traffic signal control which is improved by the results of analyzing the relation between vehicles/drivers behavior and the movement...

  13. Life expectancy evaluation and development of a replacement schedule for LED traffic signals.

    Science.gov (United States)

    2011-03-01

    This research details a field study of LED traffic signals in Missouri and develops a replacement schedule : based on key findings. Rates of degradation were statistically analyzed using Analysis of Variance : (ANOVA). Results of this research will p...

  14. Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System.

    Science.gov (United States)

    Kim, Sungkon; Lee, Jungwhee; Park, Min-Seok; Jo, Byung-Wan

    2009-01-01

    This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM) systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN) was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.

  15. Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System

    Directory of Open Access Journals (Sweden)

    Min-Seok Park

    2009-10-01

    Full Text Available This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.

  16. Applying Genetic Programming with Substructure Discovery to a Traffic Signal Control Problem

    Science.gov (United States)

    Kumagai, Juncichi; Ojima, Yasuo; Takashige, Souichi; Kameya, Yoshitaka; Sato, Taisuke

    Nowadays the increase of traffic causes numerous serious traffic jams, and traffic signals are desired to work adaptively for dynamic traffic flows. In this paper, we view such a problem of traffic signal control as a multi-agent problem where each signal has a controlling agent, and aim to make the agents work cooperatively depending on the traffic status. To build such an agent program automatically, we introduce genetic programming (GP), an evolutionary method for program construction. In GP, it is known as important to encapsulate the substructures of a program which leads to higher fitness to the environment, and we propose a new encapsulation method using an efficient technique for discovering frequent substructures, which has been recently proposed in the data mining field. We also conducted a simulation with a real traffic data, and confirmed that GP with our encapsulation method outperforms the normal GP. It is also observed that the best individual has a communication part that chooses an appropriate communication area and adapts to the traffic status.

  17. An Improved Algebraic Method for Transit Signal Priority Scheme and Its Impact on Traffic Emission

    OpenAIRE

    Yanjie Ji; Bo Hu; Jing Han; Dounan Tang

    2014-01-01

    Transit signal priority has a positive effect on improving traffic congestion and reducing transit delay and also has an influence on traffic emission. In this paper, an optimal transit signal priority scheme based on an improved algebraic method was developed and its impact on vehicle emission was evaluated as well. The improved algebraic method was proposed on the basis of classical algebraic method and has improvements in three aspects. First, the calculation rules of split loss are more r...

  18. Prediction of Ship Traffic Flow Based on BP Neural Network and Markov Model

    Directory of Open Access Journals (Sweden)

    Lv Pengfei

    2016-01-01

    Full Text Available This paper discusses the distribution regularity of ship arrival and departure and the method of prediction of ship traffic flow. Depict the frequency histograms of ships arriving to port every day and fit the curve of the frequency histograms with a variety of distribution density function by using the mathematical statistic methods based on the samples of ship-to-port statistics of Fangcheng port nearly a year. By the chi-square testing: the fitting with Negative Binomial distribution and t-Location Scale distribution are superior to normal distribution and Logistic distribution in the branch channel; the fitting with Logistic distribution is superior to normal distribution, Negative Binomial distribution and t-Location Scale distribution in main channel. Build the BP neural network and Markov model based on BP neural network model to forecast ship traffic flow of Fangcheng port. The new prediction model is superior to BP neural network model by comparing the relative residuals of predictive value, which means the new model can improve the prediction accuracy.

  19. Automatic Speech Recognition from Neural Signals: A Focused Review.

    Science.gov (United States)

    Herff, Christian; Schultz, Tanja

    2016-01-01

    Speech interfaces have become widely accepted and are nowadays integrated in various real-life applications and devices. They have become a part of our daily life. However, speech interfaces presume the ability to produce intelligible speech, which might be impossible due to either loud environments, bothering bystanders or incapabilities to produce speech (i.e., patients suffering from locked-in syndrome). For these reasons it would be highly desirable to not speak but to simply envision oneself to say words or sentences. Interfaces based on imagined speech would enable fast and natural communication without the need for audible speech and would give a voice to otherwise mute people. This focused review analyzes the potential of different brain imaging techniques to recognize speech from neural signals by applying Automatic Speech Recognition technology. We argue that modalities based on metabolic processes, such as functional Near Infrared Spectroscopy and functional Magnetic Resonance Imaging, are less suited for Automatic Speech Recognition from neural signals due to low temporal resolution but are very useful for the investigation of the underlying neural mechanisms involved in speech processes. In contrast, electrophysiologic activity is fast enough to capture speech processes and is therefor better suited for ASR. Our experimental results indicate the potential of these signals for speech recognition from neural data with a focus on invasively measured brain activity (electrocorticography). As a first example of Automatic Speech Recognition techniques used from neural signals, we discuss the Brain-to-text system.

  20. Automatic Speech Recognition from Neural Signals: A Focused Review

    Directory of Open Access Journals (Sweden)

    Christian Herff

    2016-09-01

    Full Text Available Speech interfaces have become widely accepted and are nowadays integrated in various real-life applications and devices. They have become a part of our daily life. However, speech interfaces presume the ability to produce intelligible speech, which might be impossible due to either loud environments, bothering bystanders or incapabilities to produce speech (i.e.~patients suffering from locked-in syndrome. For these reasons it would be highly desirable to not speak but to simply envision oneself to say words or sentences. Interfaces based on imagined speech would enable fast and natural communication without the need for audible speech and would give a voice to otherwise mute people.This focused review analyzes the potential of different brain imaging techniques to recognize speech from neural signals by applying Automatic Speech Recognition technology. We argue that modalities based on metabolic processes, such as functional Near Infrared Spectroscopy and functional Magnetic Resonance Imaging, are less suited for Automatic Speech Recognition from neural signals due to low temporal resolution but are very useful for the investigation of the underlying neural mechanisms involved in speech processes. In contrast, electrophysiologic activity is fast enough to capture speech processes and is therefor better suited for ASR. Our experimental results indicate the potential of these signals for speech recognition from neural data with a focus on invasively measured brain activity (electrocorticography. As a first example of Automatic Speech Recognition techniques used from neural signals, we discuss the emph{Brain-to-text} system.

  1. MIMO Lyapunov Theory-Based RBF Neural Classifier for Traffic Sign Recognition

    Directory of Open Access Journals (Sweden)

    King Hann Lim

    2012-01-01

    Full Text Available Lyapunov theory-based radial basis function neural network (RBFNN is developed for traffic sign recognition in this paper to perform multiple inputs multiple outputs (MIMO classification. Multidimensional input is inserted into RBF nodes and these nodes are linked with multiple weights. An iterative weight adaptation scheme is hence designed with regards to the Lyapunov stability theory to obtain a set of optimum weights. In the design, the Lyapunov function has to be well selected to construct an energy space with a single global minimum. Weight gain is formed later to obey the Lyapunov stability theory. Detail analysis and discussion on the proposed classifier’s properties are included in the paper. The performance comparisons between the proposed classifier and some existing conventional techniques are evaluated using traffic sign patterns. Simulation results reveal that our proposed system achieved better performance with lower number of training iterations.

  2. DECISION WITH ARTIFICIAL NEURAL NETWORKS IN DISCRETE EVENT SIMULATION MODELS ON A TRAFFIC SYSTEM

    Directory of Open Access Journals (Sweden)

    Marília Gonçalves Dutra da Silva

    2016-04-01

    Full Text Available ABSTRACT This work aims to demonstrate the use of a mechanism to be applied in the development of the discrete-event simulation models that perform decision operations through the implementation of an artificial neural network. Actions that involve complex operations performed by a human agent in a process, for example, are often modeled in simplified form with the usual mechanisms of simulation software. Therefore, it was chosen a traffic system controlled by a traffic officer with a flow of vehicles and pedestrians to demonstrate the proposed solution. From a module built in simulation software itself, it was possible to connect the algorithm for intelligent decision to the simulation model. The results showed that the model elaborated responded as expected when it was submitted to actions, which required different decisions to maintain the operation of the system with changes in the flow of people and vehicles.

  3. Temporal Classification Error Compensation of Convolutional Neural Network for Traffic Sign Recognition

    Science.gov (United States)

    Yoon, Seungjong; Kim, Eungtae

    2017-02-01

    In this paper, we propose the method that classifies the traffic signs by using Convolutional Neural Network(CNN) and compensates the error rate of CNN using the temporal correlation between adjacent successive frames. Instead of applying a conventional CNN architecture with more layers, Temporal Classification Error Compensation(TCEC) is proposed to improve the error rate in the architecture which has less nodes and layers than a conventional CNN. Experimental results show that the complexity of the proposed method could be reduced by 50% compared with that of the conventional CNN with same layers, and the error rate could be improved by about 3%.

  4. Neural Processing of Auditory Signals and Modular Neural Control for Sound Tropism of Walking Machines

    Directory of Open Access Journals (Sweden)

    Hubert Roth

    2008-11-01

    Full Text Available The specialized hairs and slit sensillae of spiders (Cupiennius salei can sense the airflow and auditory signals in a low-frequency range. They provide the sensor information for reactive behavior, like e.g. capturing a prey. In analogy, in this paper a setup is described where two microphones and a neural preprocessing system together with a modular neural controller are used to generate a sound tropism of a four-legged walking machine. The neural preprocessing network is acting as a low-pass filter and it is followed by a network which discerns between signals coming from the left or the right. The parameters of these networks are optimized by an evolutionary algorithm. In addition, a simple modular neural controller then generates the desired different walking patterns such that the machine walks straight, then turns towards a switched-on sound source, and then stops near to it.

  5. Modeling and Simulation of Road Traffic Noise Using Artificial Neural Network and Regression.

    Science.gov (United States)

    Honarmand, M; Mousavi, S M

    2014-04-01

    Modeling and simulation of noise pollution has been done in a large city, where the population is over 2 millions. Two models of artificial neural network and regression were developed to predict in-city road traffic noise pollution with using the data of noise measurements and vehicle counts at three points of the city for a period of 12 hours. The MATLAB and DATAFIT softwares were used for simulation. The predicted results of noise level were compared with the measured noise levels in three stations. The values of normalized bias, sum of squared errors, mean of squared errors, root mean of squared errors, and squared correlation coefficient calculated for each model show the results of two models are suitable, and the predictions of artificial neural network are closer to the experimental data.

  6. A Network Traffic Prediction Model Based on Quantum-Behaved Particle Swarm Optimization Algorithm and Fuzzy Wavelet Neural Network

    Directory of Open Access Journals (Sweden)

    Kun Zhang

    2016-01-01

    Full Text Available Due to the fact that the fluctuation of network traffic is affected by various factors, accurate prediction of network traffic is regarded as a challenging task of the time series prediction process. For this purpose, a novel prediction method of network traffic based on QPSO algorithm and fuzzy wavelet neural network is proposed in this paper. Firstly, quantum-behaved particle swarm optimization (QPSO was introduced. Then, the structure and operation algorithms of WFNN are presented. The parameters of fuzzy wavelet neural network were optimized by QPSO algorithm. Finally, the QPSO-FWNN could be used in prediction of network traffic simulation successfully and evaluate the performance of different prediction models such as BP neural network, RBF neural network, fuzzy neural network, and FWNN-GA neural network. Simulation results show that QPSO-FWNN has a better precision and stability in calculation. At the same time, the QPSO-FWNN also has better generalization ability, and it has a broad prospect on application.

  7. Signaling in large-scale neural networks

    DEFF Research Database (Denmark)

    Berg, Rune W; Hounsgaard, Jørn

    2009-01-01

    We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity. The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing. The possible advantages of this m......We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity. The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing. The possible advantages...... of this metabolically costly organization are analyzed by comparing with synaptically less intense networks driven by the intrinsic response properties of the network neurons....

  8. Effect of Mixed Traffic Flow on Control Delay at Signalized ...

    African Journals Online (AJOL)

    profoundly affect the well being of the transportation of goods and passengers in cities. There are several methods available for operational analysis including mathematical models and traffic simulation models; however the Highway Capacity Manual (HCM) has been widely accepted and applied as a standard method.

  9. Application of the minimum fuel neural network to music signals

    DEFF Research Database (Denmark)

    Harbo, Anders La-Cour

    2004-01-01

    Finding an optimal representation of a signal in an over-complete dictionary is often quite difficult. Since general results in this field are not very application friendly it truly helps to specify the framework as much as possible. We investigate the method Minimum Fuel Neural Network (MFNN...

  10. Models of Acetylcholine and Dopamine Signals Differentially Improve Neural Representations

    Science.gov (United States)

    Holca-Lamarre, Raphaël; Lücke, Jörg; Obermayer, Klaus

    2017-01-01

    Biological and artificial neural networks (ANNs) represent input signals as patterns of neural activity. In biology, neuromodulators can trigger important reorganizations of these neural representations. For instance, pairing a stimulus with the release of either acetylcholine (ACh) or dopamine (DA) evokes long lasting increases in the responses of neurons to the paired stimulus. The functional roles of ACh and DA in rearranging representations remain largely unknown. Here, we address this question using a Hebbian-learning neural network model. Our aim is both to gain a functional understanding of ACh and DA transmission in shaping biological representations and to explore neuromodulator-inspired learning rules for ANNs. We model the effects of ACh and DA on synaptic plasticity and confirm that stimuli coinciding with greater neuromodulator activation are over represented in the network. We then simulate the physiological release schedules of ACh and DA. We measure the impact of neuromodulator release on the network's representation and on its performance on a classification task. We find that ACh and DA trigger distinct changes in neural representations that both improve performance. The putative ACh signal redistributes neural preferences so that more neurons encode stimulus classes that are challenging for the network. The putative DA signal adapts synaptic weights so that they better match the classes of the task at hand. Our model thus offers a functional explanation for the effects of ACh and DA on cortical representations. Additionally, our learning algorithm yields performances comparable to those of state-of-the-art optimisation methods in multi-layer perceptrons while requiring weaker supervision signals and interacting with synaptically-local weight updates. PMID:28690509

  11. Traffic Signal Optimization with Transit Priority: A Person-based Approach

    OpenAIRE

    Christofa, Eleni

    2012-01-01

    Traffic responsive signal control with Transit Signal Priority (TSP) is a strategy that is increasingly used to improve transit operations in urban networks. However, none of the existing real-time signal control systems have explicitly incorporated the passenger occupancy of transit vehicles in granting priority or have effectively addressed issues such as the provision of priority to transit vehicles traveling in conflicting directions at signalized intersections. The contribution of this d...

  12. Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.

    Science.gov (United States)

    Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng

    2017-04-10

    This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

  13. Estimating Neural Signal Dynamics in the Human Brain

    Directory of Open Access Journals (Sweden)

    Christopher W Tyler

    2011-06-01

    Full Text Available Although brain imaging methods are highly effective for localizing the effects of neural activation throughout the human brain in terms of the blood oxygenation level dependent (BOLD response, there is currently no way to estimate the underlying neural signal dynamics in generating the BOLD response in each local activation region (except for processes slower than the BOLD time course. Knowledge of the neural signal is critical information if spatial mapping is to progress to the analysis of dynamic information flow through the cortical networks as the brain performs its tasks. We introduce an analytic approach that provides a new level of conceptualization and specificity in the study of brain processing by noninvasive methods. This technique allows us to use brain imaging methods to determine the dynamics of local neural population responses to their native temporal resolution throughout the human brain, with relatively narrow confidence intervals on many response properties. The ability to characterize local neural dynamics in the human brain represents a significant enhancement of brain imaging capabilities, with potential application from general cognitive studies to assessment of neuropathologies.

  14. Use of artificial neural networks in biosensor signal classification

    Directory of Open Access Journals (Sweden)

    Vlastimil Dohnal

    2008-01-01

    Full Text Available Biosensors are analytical devices that transforms chemical information, ranging from the concentration of a specific sample component to total composition analysis, into an analytical signal and that utilizes a biochemical mechanism for the chemical recognition. The complexity of biosensor construction and generation of measured signal requires the development of new method for signal eva­luation and its possible defects recognition. A new method based on artificial neural networks (ANN was developed for recognition of characteristic behavior of signals joined with malfunction of sensor. New algorithm uses unsupervised Kohonen self-organizing neural networks. The work with ANN has two phases – adaptation and prediction. During the adaptation step the classification model is build. Measured data form groups after projection into two-dimensional space based on theirs similarity. After identification of these groups and establishing the connection with signal disorders ANN can be used for evaluation of newly measured signals. This algorithm was successfully applied for 540 signal classification obtained from immobilized acetylcholinesterase biosensor measurement of organophosphate and carbamate pesticides in vegetables, fruits, spices, potatoes and soil samples. From six different signal defects were successfully classified four – low response after substrate addition, equilibration at high values, slow equilibration after substrate addition respectively low sensitivity on syntostigmine.

  15. Fuzzy Logic in Traffic Engineering: A Review on Signal Control

    Directory of Open Access Journals (Sweden)

    Milan Koukol

    2015-01-01

    Full Text Available Since 1965 when the fuzzy logic and fuzzy algebra were introduced by Lotfi Zadeh, the fuzzy theory successfully found its applications in the wide range of subject fields. This is mainly due to its ability to process various data, including vague or uncertain data, and provide results that are suitable for the decision making. This paper aims to provide comprehensive overview of literature on fuzzy control systems used for the management of the road traffic flow at road junctions. Several theoretical approaches from basic fuzzy models from the late 1970s to most recent combinations of real-time data with fuzzy inference system and genetic algorithms are mentioned and discussed throughout the paper. In most cases, fuzzy logic controllers provide considerable improvements in the efficiency of traffic junctions’ management.

  16. The Use of Adaptive Traffic Signal Systems Based on Floating Car Data

    Directory of Open Access Journals (Sweden)

    Vittorio Astarita

    2017-01-01

    Full Text Available This paper presents a simple concept which has not been, up to now, thoroughly explored in scientific research: the use of information coming from the network of Internet connected mobile devices (on vehicles to regulate traffic light systems. Three large-scale changes are going to shape the future of transportation and could lead to the regulation of traffic signal system based on floating car data (FCD: (i the implementation of Internet connected cars with global navigation satellite (GNSS system receivers and the autonomous car revolution; (ii the spreading of mobile cooperative Web 2.0 and the extension to connected vehicles; (iii an increasing need for sustainability of transportation in terms of energy efficiency, traffic safety, and environmental issues. Up to now, the concept of floating car data (FCD has only been extensively used to obtain traffic information and estimate traffic parameters. Traffic lights regulation based on FCD technology has not been fully researched since the implementation requires new ideas and algorithms. This paper intends to provide a seminal insight into the important issue of adaptive traffic light based on FCD by presenting ideas that can be useful to researchers and engineers in the long-term task of developing new algorithms and systems that may revolutionize the way traffic lights are regulated.

  17. Music Signal Processing Using Vector Product Neural Networks

    Science.gov (United States)

    Fan, Z. C.; Chan, T. S.; Yang, Y. H.; Jang, J. S. R.

    2017-05-01

    We propose a novel neural network model for music signal processing using vector product neurons and dimensionality transformations. Here, the inputs are first mapped from real values into three-dimensional vectors then fed into a three-dimensional vector product neural network where the inputs, outputs, and weights are all three-dimensional values. Next, the final outputs are mapped back to the reals. Two methods for dimensionality transformation are proposed, one via context windows and the other via spectral coloring. Experimental results on the iKala dataset for blind singing voice separation confirm the efficacy of our model.

  18. Neural systems for choice and valuation with counterfactual learning signals.

    Science.gov (United States)

    Tobia, M J; Guo, R; Schwarze, U; Boehmer, W; Gläscher, J; Finckh, B; Marschner, A; Büchel, C; Obermayer, K; Sommer, T

    2014-04-01

    The purpose of this experiment was to test a computational model of reinforcement learning with and without fictive prediction error (FPE) signals to investigate how counterfactual consequences contribute to acquired representations of action-specific expected value, and to determine the functional neuroanatomy and neuromodulator systems that are involved. 80 male participants underwent dietary depletion of either tryptophan or tyrosine/phenylalanine to manipulate serotonin (5HT) and dopamine (DA), respectively. They completed 80 rounds (240 trials) of a strategic sequential investment task that required accepting interim losses in order to access a lucrative state and maximize long-term gains, while being scanned. We extended the standard Q-learning model by incorporating both counterfactual gains and losses into separate error signals. The FPE model explained the participants' data significantly better than a model that did not include counterfactual learning signals. Expected value from the FPE model was significantly correlated with BOLD signal change in the ventromedial prefrontal cortex (vmPFC) and posterior orbitofrontal cortex (OFC), whereas expected value from the standard model did not predict changes in neural activity. The depletion procedure revealed significantly different neural responses to expected value in the vmPFC, caudate, and dopaminergic midbrain in the vicinity of the substantia nigra (SN). Differences in neural activity were not evident in the standard Q-learning computational model. These findings demonstrate that FPE signals are an important component of valuation for decision making, and that the neural representation of expected value incorporates cortical and subcortical structures via interactions among serotonergic and dopaminergic modulator systems. Copyright © 2013 Elsevier Inc. All rights reserved.

  19. Research and design of intelligent distributed traffic signal light control system based on CAN bus

    Science.gov (United States)

    Chen, Yu

    2007-12-01

    Intelligent distributed traffic signal light control system was designed based on technologies of infrared, CAN bus, single chip microprocessor (SCM), etc. The traffic flow signal is processed with the core of SCM AT89C51. At the same time, the SCM controls the CAN bus controller SJA1000/transceiver PCA82C250 to build a CAN bus communication system to transmit data. Moreover, up PC realizes to connect and communicate with SCM through USBCAN chip PDIUSBD12. The distributed traffic signal light control system with three control styles of Vehicle flux, remote and PC is designed. This paper introduces the system composition method and parts of hardware/software design in detail.

  20. Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods.

    Science.gov (United States)

    Arcos-García, Álvaro; Álvarez-García, Juan A; Soria-Morillo, Luis M

    2018-01-31

    This paper presents a Deep Learning approach for traffic sign recognition systems. Several classification experiments are conducted over publicly available traffic sign datasets from Germany and Belgium using a Deep Neural Network which comprises Convolutional layers and Spatial Transformer Networks. Such trials are built to measure the impact of diverse factors with the end goal of designing a Convolutional Neural Network that can improve the state-of-the-art of traffic sign classification task. First, different adaptive and non-adaptive stochastic gradient descent optimisation algorithms such as SGD, SGD-Nesterov, RMSprop and Adam are evaluated. Subsequently, multiple combinations of Spatial Transformer Networks placed at distinct positions within the main neural network are analysed. The recognition rate of the proposed Convolutional Neural Network reports an accuracy of 99.71% in the German Traffic Sign Recognition Benchmark, outperforming previous state-of-the-art methods and also being more efficient in terms of memory requirements. Copyright © 2018 Elsevier Ltd. All rights reserved.

  1. Vehicular motion in counter traffic flow through a series of signals controlled by a phase shift

    Science.gov (United States)

    Nagatani, Takashi; Tobita, Kazuhiro

    2012-10-01

    We study the dynamical behavior of counter traffic flow through a sequence of signals (traffic lights) controlled by a phase shift. There are two lanes for the counter traffic flow: the first lane is for east-bound vehicles and the second lane is for west-bound vehicles. The green-wave strategy is studied in the counter traffic flow where the phase shift of signals in the second lane has opposite sign to that in the first lane. A nonlinear dynamic model of the vehicular motion is presented by nonlinear maps at a low density. There is a distinct difference between the traffic flow in the first lane and that in the second lane. The counter traffic flow exhibits very complex behavior on varying the cycle time, the phase difference, and the split. Also, the fundamental diagram is derived by the use of the cellular automaton (CA) model. The dependence of east-bound and west-bound vehicles on cycle time, phase difference, and density is clarified.

  2. Exposure to lateral collision in signalized intersections with protected left turn under different traffic control strategies.

    Science.gov (United States)

    Midenet, Sophie; Saunier, Nicolas; Boillot, Florence

    2011-11-01

    This paper proposes an original definition of the exposure to lateral collision in signalized intersections and discusses the results of a real world experiment. This exposure is defined as the duration of situations where the stream that is given the right-of-way goes through the conflict zone while road users are waiting in the cross-traffic approach. This measure, obtained from video sensors, makes it possible to compare different operating conditions such as different traffic signal strategies. The data from a real world experiment is used, where the adaptive real-time strategy CRONOS (ContRol Of Networks by Optimization of Switchovers) and a time-plan strategy with vehicle-actuated ranges alternately controlled an isolated intersection near Paris. Hourly samples with similar traffic volumes are compared and the exposure to lateral collision is different in various areas of the intersection and various traffic conditions for the two strategies. The total exposure under peak hour traffic conditions drops by roughly 5 min/h with the CRONOS strategy compared to the time-plan strategy, which occurs mostly on entry streams. The results are analyzed through the decomposition of cycles in phase sequences and recommendations are made for traffic control strategies. Copyright © 2011 Elsevier Ltd. All rights reserved.

  3. Driver Compliance with Traffic Signal Indications in Two Ghanaian ...

    African Journals Online (AJOL)

    Driver response to signal indications was monitored at a selected number of signalised intersections and signal-controlled pedestrian crossings within the Kumasi and Accra metropolitan areas. The objective of the study was to establish the scale of red-light running among drivers in the two cities. Out of 189,628 vehicle ...

  4. [A telemetery system for neural signal acquiring and processing].

    Science.gov (United States)

    Wang, Min; Song, Yongji; Suen, Jiantao; Zhao, Yiliang; Jia, Aibin; Zhu, Jianping

    2011-02-01

    Recording and extracting characteristic brain signals in freely moving animals is the basic and significant requirement in the study of brain-computer interface (BCI). To record animal's behaving and extract characteristic brain signals simultaneously could help understand the complex behavior of neural ensembles. Here, a system was established to record and analyse extracellular discharge in freely moving rats for the study of BCI. It comprised microelectrode and micro-driver assembly, analog front end (AFE), programmer system on chip (PSoC), wireless communication and the LabVIEW used as the platform for the graphic user interface.

  5. Stochastic resonance with colored noise for neural signal detection.

    Science.gov (United States)

    Duan, Fabing; Chapeau-Blondeau, François; Abbott, Derek

    2014-01-01

    We analyze signal detection with nonlinear test statistics in the presence of colored noise. In the limits of small signal and weak noise correlation, the optimal test statistic and its performance are derived under general conditions, especially concerning the type of noise. We also analyze, for a threshold nonlinearity-a key component of a neural model, the conditions for noise-enhanced performance, establishing that colored noise is superior to white noise for detection. For a parallel array of nonlinear elements, approximating neurons, we demonstrate even broader conditions allowing noise-enhanced detection, via a form of suprathreshold stochastic resonance.

  6. Fate Specification of Neural Plate Border by Canonical Wnt Signaling and Grhl3 is Crucial for Neural Tube Closure.

    Science.gov (United States)

    Kimura-Yoshida, Chiharu; Mochida, Kyoko; Ellwanger, Kristina; Niehrs, Christof; Matsuo, Isao

    2015-06-01

    During primary neurulation, the separation of a single-layered ectodermal sheet into the surface ectoderm (SE) and neural tube specifies SE and neural ectoderm (NE) cell fates. The mechanisms underlying fate specification in conjunction with neural tube closure are poorly understood. Here, by comparing expression profiles between SE and NE lineages, we observed that uncommitted progenitor cells, expressing stem cell markers, are present in the neural plate border/neural fold prior to neural tube closure. Our results also demonstrated that canonical Wnt and its antagonists, DKK1/KREMEN1, progressively specify these progenitors into SE or NE fates in accord with the progress of neural tube closure. Additionally, SE specification of the neural plate border via canonical Wnt signaling is directed by the grainyhead-like 3 (Grhl3) transcription factor. Thus, we propose that the fate specification of uncommitted progenitors in the neural plate border by canonical Wnt signaling and its downstream effector Grhl3 is crucial for neural tube closure. This study implicates that failure in critical genetic factors controlling fate specification of progenitor cells in the neural plate border/neural fold coordinated with neural tube closure may be potential causes of human neural tube defects.

  7. A NEURAL NETWORK BASED TRAFFIC-AWARE FORWARDING STRATEGY IN NAMED DATA NETWORKING

    Directory of Open Access Journals (Sweden)

    Parisa Bazmi

    2016-11-01

    Full Text Available Named Data Networking (NDN is a new Internet architecture which has been proposed to eliminate TCP/IP Internet architecture restrictions. This architecture is abstracting away the notion of host and working based on naming datagrams. However, one of the major challenges of NDN is supporting QoS-aware forwarding strategy so as to forward Interest packets intelligently over multiple paths based on the current network condition. In this paper, Neural Network (NN Based Traffic-aware Forwarding strategy (NNTF is introduced in order to determine an optimal path for Interest forwarding. NN is embedded in NDN routers to select next hop dynamically based on the path overload probability achieved from the NN. This solution is characterized by load balancing and QoS-awareness via monitoring the available path and forwarding data on the traffic-aware shortest path. The performance of NNTF is evaluated using ndnSIM which shows the efficiency of this scheme in terms of network QoS improvementof17.5% and 72% reduction in network delay and packet drop respectively.

  8. Multi-Modal Intelligent Traffic Signal Systems (MMITSS) impacts assessment.

    Science.gov (United States)

    2015-08-01

    The study evaluates the potential network-wide impacts of the Multi-Modal Intelligent Transportation Signal System : (MMITSS) based on a field data analysis utilizing data collected from a MMITSS prototype and a simulation analysis. : The Intelligent...

  9. Neural crest specification by noncanonical Wnt signaling and PAR-1

    Science.gov (United States)

    Ossipova, Olga; Sokol, Sergei Y.

    2011-01-01

    Neural crest (NC) cells are multipotent progenitors that form at the neural plate border, undergo epithelial-mesenchymal transition and migrate to diverse locations in vertebrate embryos to give rise to many cell types. Multiple signaling factors, including Wnt proteins, operate during early embryonic development to induce the NC cell fate. Whereas the requirement for the Wnt/β-catenin pathway in NC specification has been well established, a similar role for Wnt proteins that do not stabilize β-catenin has remained unclear. Our gain- and loss-of-function experiments implicate Wnt11-like proteins in NC specification in Xenopus embryos. In support of this conclusion, modulation of β-catenin-independent signaling through Dishevelled and Ror2 causes predictable changes in premigratory NC. Morpholino-mediated depletion experiments suggest that Wnt11R, a Wnt protein that is expressed in neuroectoderm adjacent to the NC territory, is required for NC formation. Wnt11-like signals might specify NC by altering the localization and activity of the serine/threonine polarity kinase PAR-1 (also known as microtubule-associated regulatory kinase or MARK), which itself plays an essential role in NC formation. Consistent with this model, PAR-1 RNA rescues NC markers in embryos in which noncanonical Wnt signaling has been blocked. These experiments identify novel roles for Wnt11R and PAR-1 in NC specification and reveal an unexpected connection between morphogenesis and cell fate. PMID:22110058

  10. On the electric signal direction indicator for teh control of road traffic ...

    African Journals Online (AJOL)

    An electronic signal direction indicator (ESDI) for the control of road traffic has been designed, constructed and studied. The construction was done using 555 timer IC, a transistor-transistor logic compatible device that can operate in several modes as the major active element. The ESDI system circuit is reliable, satisfactorily ...

  11. Lossless Compression Schemes for ECG Signals Using Neural Network Predictors

    Directory of Open Access Journals (Sweden)

    C. Eswaran

    2007-01-01

    Full Text Available This paper presents lossless compression schemes for ECG signals based on neural network predictors and entropy encoders. Decorrelation is achieved by nonlinear prediction in the first stage and encoding of the residues is done by using lossless entropy encoders in the second stage. Different types of lossless encoders, such as Huffman, arithmetic, and runlength encoders, are used. The performances of the proposed neural network predictor-based compression schemes are evaluated using standard distortion and compression efficiency measures. Selected records from MIT-BIH arrhythmia database are used for performance evaluation. The proposed compression schemes are compared with linear predictor-based compression schemes and it is shown that about 11% improvement in compression efficiency can be achieved for neural network predictor-based schemes with the same quality and similar setup. They are also compared with other known ECG compression methods and the experimental results show that superior performances in terms of the distortion parameters of the reconstructed signals can be achieved with the proposed schemes.

  12. Best response game of traffic on road network of non-signalized intersections

    Science.gov (United States)

    Yao, Wang; Jia, Ning; Zhong, Shiquan; Li, Liying

    2018-01-01

    This paper studies the traffic flow in a grid road network with non-signalized intersections. The nature of the drivers in the network is simulated such that they play an iterative snowdrift game with other drivers. A cellular automata model is applied to study the characteristics of the traffic flow and the evolution of the behaviour of the drivers during the game. The drivers use best-response as their strategy to update rules. Three major findings are revealed. First, the cooperation rate in simulation experiences staircase-shaped drop as cost to benefit ratio r increases, and cooperation rate can be derived analytically as a function of cost to benefit ratio r. Second, we find that higher cooperation rate corresponds to higher average speed, lower density and higher flow. This reveals that defectors deteriorate the efficiency of traffic on non-signalized intersections. Third, the system experiences more randomness when the density is low because the drivers will not have much opportunity to update strategy when the density is low. These findings help to show how the strategy of drivers in a traffic network evolves and how their interactions influence the overall performance of the traffic system.

  13. The Combined Effect of Signal Strength and Background Traffic Load on Speech Quality in IEEE 802.11 WLAN

    Directory of Open Access Journals (Sweden)

    P. Pocta

    2011-04-01

    Full Text Available This paper deals with measurements of the combined effect of signal strength and background traffic load on speech quality in IEEE 802.11 WLAN. The ITU-T G.729AB encoding scheme is deployed in this study and the Distributed Internet Traffic Generator (D-ITG is used for the purpose of background traffic generation. The speech quality and background traffic load are assessed by means of the accomplished PESQ algorithm and Wireshark network analyzer, respectively. The results show that background traffic load has a bit higher impact on speech quality than signal strength when both effects are available together. Moreover, background traffic load also partially masks the impact of signal strength. The reasons for those findings are particularly discussed. The results also suggest some implications for designers of wireless networks providing VoIP service.

  14. Signal Processing in Periodically Forced Gradient Frequency Neural Networks.

    Science.gov (United States)

    Kim, Ji Chul; Large, Edward W

    2015-01-01

    Oscillatory instability at the Hopf bifurcation is a dynamical phenomenon that has been suggested to characterize active non-linear processes observed in the auditory system. Networks of oscillators poised near Hopf bifurcation points and tuned to tonotopically distributed frequencies have been used as models of auditory processing at various levels, but systematic investigation of the dynamical properties of such oscillatory networks is still lacking. Here we provide a dynamical systems analysis of a canonical model for gradient frequency neural networks driven by a periodic signal. We use linear stability analysis to identify various driven behaviors of canonical oscillators for all possible ranges of model and forcing parameters. The analysis shows that canonical oscillators exhibit qualitatively different sets of driven states and transitions for different regimes of model parameters. We classify the parameter regimes into four main categories based on their distinct signal processing capabilities. This analysis will lead to deeper understanding of the diverse behaviors of neural systems under periodic forcing and can inform the design of oscillatory network models of auditory signal processing.

  15. Generalized sample entropy analysis for traffic signals based on similarity measure

    Science.gov (United States)

    Shang, Du; Xu, Mengjia; Shang, Pengjian

    2017-05-01

    Sample entropy is a prevailing method used to quantify the complexity of a time series. In this paper a modified method of generalized sample entropy and surrogate data analysis is proposed as a new measure to assess the complexity of a complex dynamical system such as traffic signals. The method based on similarity distance presents a different way of signals patterns match showing distinct behaviors of complexity. Simulations are conducted over synthetic data and traffic signals for providing the comparative study, which is provided to show the power of the new method. Compared with previous sample entropy and surrogate data analysis, the new method has two main advantages. The first one is that it overcomes the limitation about the relationship between the dimension parameter and the length of series. The second one is that the modified sample entropy functions can be used to quantitatively distinguish time series from different complex systems by the similar measure.

  16. Dual roles for spike signaling in cortical neural populations

    Directory of Open Access Journals (Sweden)

    Dana eBallard

    2011-06-01

    Full Text Available A prominent feature of signaling in cortical neurons is that of randomness in the action potential. The output of a typical pyramidal cell can be well fit with a Poisson model, and variations in the Poisson rate repeatedly have been shown to be correlated with stimuli. However while the rate provides a very useful characterization of neural spike data, it may not be the most fundamental description of the signaling code. Recent data showing γ frequency range multi-cell action potential correlations, together with spike timing dependent plasticity, are spurring a re-examination of the classical model, since precise timing codes imply that the generation of spikes is essentially deterministic. Could the observed Poisson randomness and timing determinism reflect two separate modes of communication, or do they somehow derive from a single process? We investigate in a timing-based model whether the apparent incompatibility between these probabilistic and deterministic observations may be resolved by examining how spikes could be used in the underlying neural circuits. The crucial component of this model draws on dual roles for spike signaling. In learning receptive fields from ensembles of inputs, spikes need to behave probabilistically, whereas for fast signaling of individual stimuli, the spikes need to behave deterministically. Our simulations show that this combination is possible if deterministic signals using γ latency coding are probabilistically routed through different members of a cortical cell population at different times. This model exhibits standard features characteristic of Poisson models such as orientation tuning post-stimulus histograms and exponential interval histograms. In addition it makes testable predictions that follow from the γ latency coding.

  17. Artificial neural network based approach to EEG signal simulation.

    Science.gov (United States)

    Tomasevic, Nikola M; Neskovic, Aleksandar M; Neskovic, Natasa J

    2012-06-01

    In this paper a new approach to the electroencephalogram (EEG) signal simulation based on the artificial neural networks (ANN) is proposed. The aim was to simulate the spontaneous human EEG background activity based solely on the experimentally acquired EEG data. Therefore, an EEG measurement campaign was conducted on a healthy awake adult in order to obtain an adequate ANN training data set. As demonstration of the performance of the ANN based approach, comparisons were made against autoregressive moving average (ARMA) filtering based method. Comprehensive quantitative and qualitative statistical analysis showed clearly that the EEG process obtained by the proposed method was in satisfactory agreement with the one obtained by measurements.

  18. Fault Tolerant Neural Network for ECG Signal Classification Systems

    Directory of Open Access Journals (Sweden)

    MERAH, M.

    2011-08-01

    Full Text Available The aim of this paper is to apply a new robust hardware Artificial Neural Network (ANN for ECG classification systems. This ANN includes a penalization criterion which makes the performances in terms of robustness. Specifically, in this method, the ANN weights are normalized using the auto-prune method. Simulations performed on the MIT ? BIH ECG signals, have shown that significant robustness improvements are obtained regarding potential hardware artificial neuron failures. Moreover, we show that the proposed design achieves better generalization performances, compared to the standard back-propagation algorithm.

  19. The Preventive Signaling Maintenance Crew Scheduling Problem for European Railway Traffic Management system (ERTMS)

    DEFF Research Database (Denmark)

    Mohammad Pour, Shahrzad; Stidsen, Thomas Jacob Riis; Rasmussen, Kourosh Marjani

    A railway system is a large and complex infrastructure, which requires continuous maintenance in order to function correctly. Proper maintenance is critical but can also be costly. In this paper we consider the practical case of planning the preventive maintenance of railway signals in Jutland......, the western part of Denmark. This case is particularly interesting, since the entire railway signalling system is currently being upgraded to the new European Railway Traffic Management System (ERTMS) standard. The new signals need continuous maintenance and in this article we plan the distribution of crew...

  20. Traffic analysis and signal processing in optical packet switched networks

    DEFF Research Database (Denmark)

    Fjelde, Tina

    2002-01-01

    Gbit/s demultiplexing and 2x10 to 20 Gbit/s multiplexing. Lastly, the IWC’s capabilities as an optical logic gate for enabling more complex signal processing are demonstrated and four applications hereof are discussed. Logic OR and AND are verified in full at 10 Gbit/s using PRBS sequences coupled...... into an MI. Moreover, logic XOR is demonstrated in an MZI at 10 and 20 Gbit/s with good results. Using an MI, the excellent performance of a novel scheme for MPLS label swapping exploiting logic XOR is demonstrated at 10 Gbit/s with a negligible 0.4 dB penalty. Finally, three novel schemes are described...

  1. 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.

  2. Multiobjective Reinforcement Learning for Traffic Signal Control Using Vehicular Ad Hoc Network

    Science.gov (United States)

    Houli, Duan; Zhiheng, Li; Yi, Zhang

    2010-12-01

    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.

  3. Automated embolic signal detection using Deep Convolutional Neural Network.

    Science.gov (United States)

    Sombune, Praotasna; Phienphanich, Phongphan; Phuechpanpaisal, Sutanya; Muengtaweepongsa, Sombat; Ruamthanthong, Anuchit; Tantibundhit, Charturong

    2017-07-01

    This work investigated the potential of Deep Neural Network in detection of cerebral embolic signal (ES) from transcranial Doppler ultrasound (TCD). The resulting system is aimed to couple with TCD devices in diagnosing a risk of stroke in real-time with high accuracy. The Adaptive Gain Control (AGC) approach developed in our previous study is employed to capture suspected ESs in real-time. By using spectrograms of the same TCD signal dataset as that of our previous work as inputs and the same experimental setup, Deep Convolutional Neural Network (CNN), which can learn features while training, was investigated for its ability to bypass the traditional handcrafted feature extraction and selection process. Extracted feature vectors from the suspected ESs are later determined whether they are of an ES, artifact (AF) or normal (NR) interval. The effectiveness of the developed system was evaluated over 19 subjects going under procedures generating emboli. The CNN-based system could achieve in average of 83.0% sensitivity, 80.1% specificity, and 81.4% accuracy, with considerably much less time consumption in development. The certainly growing set of training samples and computational resources will contribute to high performance. Besides having potential use in various clinical ES monitoring settings, continuation of this promising study will benefit developments of wearable applications by leveraging learnable features to serve demographic differentials.

  4. 49 CFR 236.401 - Automatic block signal system and interlocking standards applicable to traffic control systems.

    Science.gov (United States)

    2010-10-01

    ... TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Traffic Control Systems Standards § 236.401 Automatic... 49 Transportation 4 2010-10-01 2010-10-01 false Automatic block signal system and interlocking standards applicable to traffic control systems. 236.401 Section 236.401 Transportation Other Regulations...

  5. The influence of traffic signal solutions on self-reported road-crossing behavior.

    Science.gov (United States)

    Di Stasi, Leandro L; Megías, Alberto; Cándido, Antonio; Maldonado, Antonio; Catena, Andrés

    2015-01-07

    Injury to pedestrians is a major safety hazard in many countries. Since the beginning of the last century, modern cities have been designed around the use of motor vehicles despite the unfavourable interactions between the vehicles and pedestrians. This push towards urbanization resulted in a substantial number of crashes and fatalities involving pedestrians every day, all over the world. Thus, improving the design of urban cities and townships is a pressing issue for modern society. The study presented here provides a characterization of pedestrian safety problems, with the emphasis on signalized crosswalks (i.e. traffic signal) design solutions. We tested the impact of seven different traffic light configurations (steady [green, yellow, and red], flashing [green, yellow, and red], and light off) on pedestrian self-reported road-crossing behavior, using a 11-point scale -ranging from 0 ("I never cross in this situation") to 10 ("I always cross in this situation"). Results showed that mandatory solutions (steady green vs. steady red) are the best solutions to avoid unsafe pedestrian behaviors while crossing controlled intersections (frequency of crossing: Mgreen = 9.4 ± 1 vs. Mred = 2.6 ± 2). These findings offer important guidelines for the design of future traffic signals for encouraging a pedestrian/transit-friendly environment.

  6. An FGF3-BMP Signaling Axis Regulates Caudal Neural Tube Closure, Neural Crest Specification and Anterior-Posterior Axis Extension.

    Science.gov (United States)

    Anderson, Matthew J; Schimmang, Thomas; Lewandoski, Mark

    2016-05-01

    During vertebrate axis extension, adjacent tissue layers undergo profound morphological changes: within the neuroepithelium, neural tube closure and neural crest formation are occurring, while within the paraxial mesoderm somites are segmenting from the presomitic mesoderm (PSM). Little is known about the signals between these tissues that regulate their coordinated morphogenesis. Here, we analyze the posterior axis truncation of mouse Fgf3 null homozygotes and demonstrate that the earliest role of PSM-derived FGF3 is to regulate BMP signals in the adjacent neuroepithelium. FGF3 loss causes elevated BMP signals leading to increased neuroepithelium proliferation, delay in neural tube closure and premature neural crest specification. We demonstrate that elevated BMP4 depletes PSM progenitors in vitro, phenocopying the Fgf3 mutant, suggesting that excessive BMP signals cause the Fgf3 axis defect. To test this in vivo we increased BMP signaling in Fgf3 mutants by removing one copy of Noggin, which encodes a BMP antagonist. In such mutants, all parameters of the Fgf3 phenotype were exacerbated: neural tube closure delay, premature neural crest specification, and premature axis termination. Conversely, genetically decreasing BMP signaling in Fgf3 mutants, via loss of BMP receptor activity, alleviates morphological defects. Aberrant apoptosis is observed in the Fgf3 mutant tailbud. However, we demonstrate that cell death does not cause the Fgf3 phenotype: blocking apoptosis via deletion of pro-apoptotic genes surprisingly increases all Fgf3 defects including causing spina bifida. We demonstrate that this counterintuitive consequence of blocking apoptosis is caused by the increased survival of BMP-producing cells in the neuroepithelium. Thus, we show that FGF3 in the caudal vertebrate embryo regulates BMP signaling in the neuroepithelium, which in turn regulates neural tube closure, neural crest specification and axis termination. Uncovering this FGF3-BMP signaling axis is

  7. Theta signal as the neural signature of social exclusion.

    Science.gov (United States)

    Cristofori, Irene; Moretti, Laura; Harquel, Sylvain; Posada, Andres; Deiana, Gianluca; Isnard, Jean; Mauguière, François; Sirigu, Angela

    2013-10-01

    The feeling of being excluded from a social interaction triggers social pain, a sensation as intense as actual physical pain. Little is known about the neurophysiological underpinnings of social pain. We addressed this issue using intracranial electroencephalography in 15 patients performing a ball game where inclusion and exclusion blocks were alternated. Time-frequency analyses showed an increase in power of theta-band oscillations during exclusion in the anterior insula (AI) and posterior insula, the subgenual anterior cingulate cortex (sACC), and the fusiform "face area" (FFA). Interestingly, the AI showed an initial fast response to exclusion but the signal rapidly faded out. Activity in the sACC gradually increased and remained significant thereafter. This suggests that the AI may signal social pain by detecting emotional distress caused by the exclusion, whereas the sACC may be linked to the learning aspects of social pain. Theta activity in the FFA was time-locked to the observation of a player poised to exclude the participant, suggesting that the FFA encodes the social value of faces. Taken together, our findings suggest that theta activity represents the neural signature of social pain. The time course of this signal varies across regions important for processing emotional features linked to social information.

  8. Two multichannel integrated circuits for neural recording and signal processing.

    Science.gov (United States)

    Obeid, Iyad; Morizio, James C; Moxon, Karen A; Nicolelis, Miguel A L; Wolf, Patrick D

    2003-02-01

    We have developed, manufactured, and tested two analog CMOS integrated circuit "neurochips" for recording from arrays of densely packed neural electrodes. Device A is a 16-channel buffer consisting of parallel noninverting amplifiers with a gain of 2 V/V. Device B is a 16-channel two-stage analog signal processor with differential amplification and high-pass filtering. It features selectable gains of 250 and 500 V/V as well as reference channel selection. The resulting amplifiers on Device A had a mean gain of 1.99 V/V with an equivalent input noise of 10 microV(rms). Those on Device B had mean gains of 53.4 and 47.4 dB with a high-pass filter pole at 211 Hz and an equivalent input noise of 4.4 microV(rms). Both devices were tested in vivo with electrode arrays implanted in the somatosensory cortex.

  9. Modulation of hippocampal neural plasticity by glucose-related signaling.

    Science.gov (United States)

    Mainardi, Marco; Fusco, Salvatore; Grassi, Claudio

    2015-01-01

    Hormones and peptides involved in glucose homeostasis are emerging as important modulators of neural plasticity. In this regard, increasing evidence shows that molecules such as insulin, insulin-like growth factor-I, glucagon-like peptide-1, and ghrelin impact on the function of the hippocampus, which is a key area for learning and memory. Indeed, all these factors affect fundamental hippocampal properties including synaptic plasticity (i.e., synapse potentiation and depression), structural plasticity (i.e., dynamics of dendritic spines), and adult neurogenesis, thus leading to modifications in cognitive performance. Here, we review the main mechanisms underlying the effects of glucose metabolism on hippocampal physiology. In particular, we discuss the role of these signals in the modulation of cognitive functions and their potential implications in dysmetabolism-related cognitive decline.

  10. Correlated EEG Signals Simulation Based on Artificial Neural Networks.

    Science.gov (United States)

    Tomasevic, Nikola M; Neskovic, Aleksandar M; Neskovic, Natasa J

    2017-08-01

    In recent years, simulation of the human electroencephalogram (EEG) data found its important role in medical domain and neuropsychology. In this paper, a novel approach to simulation of two cross-correlated EEG signals is proposed. The proposed method is based on the principles of artificial neural networks (ANN). Contrary to the existing EEG data simulators, the ANN-based approach was leveraged solely on the experimentally acquired EEG data. More precisely, measured EEG data were utilized to optimize the simulator which consisted of two ANN models (each model responsible for generation of one EEG sequence). In order to acquire the EEG recordings, the measurement campaign was carried out on a healthy awake adult having no cognitive, physical or mental load. For the evaluation of the proposed approach, comprehensive quantitative and qualitative statistical analysis was performed considering probability distribution, correlation properties and spectral characteristics of generated EEG processes. The obtained results clearly indicated the satisfactory agreement with the measurement data.

  11. Modulation of Hippocampal Neural Plasticity by Glucose-Related Signaling

    Directory of Open Access Journals (Sweden)

    Marco Mainardi

    2015-01-01

    Full Text Available Hormones and peptides involved in glucose homeostasis are emerging as important modulators of neural plasticity. In this regard, increasing evidence shows that molecules such as insulin, insulin-like growth factor-I, glucagon-like peptide-1, and ghrelin impact on the function of the hippocampus, which is a key area for learning and memory. Indeed, all these factors affect fundamental hippocampal properties including synaptic plasticity (i.e., synapse potentiation and depression, structural plasticity (i.e., dynamics of dendritic spines, and adult neurogenesis, thus leading to modifications in cognitive performance. Here, we review the main mechanisms underlying the effects of glucose metabolism on hippocampal physiology. In particular, we discuss the role of these signals in the modulation of cognitive functions and their potential implications in dysmetabolism-related cognitive decline.

  12. Lymphotropic Virions Affect Chemokine Receptor-Mediated Neural Signaling and Apoptosis: Implications for Human Immunodeficiency Virus Type 1-Associated Dementia

    Science.gov (United States)

    Zheng, Jialin; Ghorpade, Anuja; Niemann, Douglas; Cotter, Robin L.; Thylin, Michael R.; Epstein, Leon; Swartz, Jennifer M.; Shepard, Robin B.; Liu, Xiaojuan; Nukuna, Adeline; Gendelman, Howard E.

    1999-01-01

    Chemokine receptors pivotal for human immunodeficiency virus type 1 (HIV-1) infection in lymphocytes and macrophages (CCR3, CCR5, and CXCR4) are expressed on neural cells (microglia, astrocytes, and/or neurons). It is these cells which are damaged during progressive HIV-1 infection of the central nervous system. We theorize that viral coreceptors could effect neural cell damage during HIV-1-associated dementia (HAD) without simultaneously affecting viral replication. To these ends, we studied the ability of diverse viral strains to affect intracellular signaling and apoptosis of neurons, astrocytes, and monocyte-derived macrophages. Inhibition of cyclic AMP, activation of inositol 1,4,5-trisphosphate, and apoptosis were induced by diverse HIV-1 strains, principally in neurons. Virions from T-cell-tropic (T-tropic) strains (MN, IIIB, and Lai) produced the most significant alterations in signaling of neurons and astrocytes. The HIV-1 envelope glycoprotein, gp120, induced markedly less neural damage than purified virions. Macrophage-tropic (M-tropic) strains (ADA, JR-FL, Bal, MS-CSF, and DJV) produced the least neural damage, while 89.6, a dual-tropic HIV-1 strain, elicited intermediate neural cell damage. All T-tropic strain-mediated neuronal impairments were blocked by the CXCR4 antibody, 12G5. In contrast, the M-tropic strains were only partially blocked by 12G5. CXCR4-mediated neuronal apoptosis was confirmed in pure populations of rat cerebellar granule neurons and was blocked by HA1004, an inhibitor of calcium/calmodulin-dependent protein kinase II, protein kinase A, and protein kinase C. Taken together, these results suggest that progeny HIV-1 virions can influence neuronal signal transduction and apoptosis. This process occurs, in part, through CXCR4 and is independent of CD4 binding. T-tropic viruses that traffic in and out of the brain during progressive HIV-1 disease may play an important role in HAD neuropathogenesis. PMID:10482576

  13. Acquiring neural signals for developing a perception and cognition model

    Science.gov (United States)

    Li, Wei; Li, Yunyi; Chen, Genshe; Shen, Dan; Blasch, Erik; Pham, Khanh; Lynch, Robert

    2012-06-01

    The understanding of how humans process information, determine salience, and combine seemingly unrelated information is essential to automated processing of large amounts of information that is partially relevant, or of unknown relevance. Recent neurological science research in human perception, and in information science regarding contextbased modeling, provides us with a theoretical basis for using a bottom-up approach for automating the management of large amounts of information in ways directly useful for human operators. However, integration of human intelligence into a game theoretic framework for dynamic and adaptive decision support needs a perception and cognition model. For the purpose of cognitive modeling, we present a brain-computer-interface (BCI) based humanoid robot system to acquire brainwaves during human mental activities of imagining a humanoid robot-walking behavior. We use the neural signals to investigate relationships between complex humanoid robot behaviors and human mental activities for developing the perception and cognition model. The BCI system consists of a data acquisition unit with an electroencephalograph (EEG), a humanoid robot, and a charge couple CCD camera. An EEG electrode cup acquires brainwaves from the skin surface on scalp. The humanoid robot has 20 degrees of freedom (DOFs); 12 DOFs located on hips, knees, and ankles for humanoid robot walking, 6 DOFs on shoulders and arms for arms motion, and 2 DOFs for head yaw and pitch motion. The CCD camera takes video clips of the human subject's hand postures to identify mental activities that are correlated to the robot-walking behaviors. We use the neural signals to investigate relationships between complex humanoid robot behaviors and human mental activities for developing the perception and cognition model.

  14. Influence of the complex-shape light signal on the neural network

    Science.gov (United States)

    Melnikov, Leonid A.; Novosselova, Anna V.; Blinova, Nadejda V.

    1999-03-01

    The effect of external signals of different shapes (constant, serrated and others) on the ring neural network modeling the visual perception is investigated numerically. New specific features in the dynamics of the neural network, such as the excitation, the swapping and the depression, were observed. The cooperative amplication of the external signal and the memory effect have been observed.

  15. Proceedings of the IEEE 2003 Neural Networks for Signal Processing Workshop

    DEFF Research Database (Denmark)

    Larsen, Jan

    methodology and real-world application domains and is widely entering into everyday solutions adopted by research and industry, going far beyond “traditional” neural networks and academic examples. As reflected in this collection, contemporary neural networks for signal processing combine many ideas from......This proceeding contains refereed papers presented at the thirteenth IEEE Workshop on Neural Networks for Signal Processing (NNSP’2003), held at the Atria-Mercure Conference Center, Toulouse, France, September 17-19, 2003. The Neural Networks for Signal Processing Technical Committee of the IEEE...... Signal Processing Society organized the workshop with sponsorship of the Signal Processing Society and the co-operation of the IEEE Neural Networks Society. The IEEE Press published the previous twelve volumes of the NNSP Workshop proceedings in a hardbound volume. This year, the bound volume...

  16. Disrupted dorsal neural tube BMP signaling in the cilia mutant Arl13bhnn stems from abnormal Shh signaling

    Science.gov (United States)

    Horner, Vanessa L.; Caspary, Tamara

    2011-01-01

    In the embryonic neural tube, multiple signaling pathways work in concert to create functional neuronal circuits in the adult spinal cord. In the ventral neural tube, Sonic hedgehog (Shh) acts as a graded morphogen to specify neurons necessary for movement. In the dorsal neural tube, bone morphogenetic protein (BMP) and Wnt signals cooperate to specify neurons involved in sensation. Several signaling pathways, including Shh, rely on primary cilia in vertebrates. In this study, we used a mouse mutant with abnormal cilia, Arl13bhnn, to study the relationship between cilia, cell signaling, and neural tube patterning. Alr13bhnn mutants have abnormal ventral neural tube patterning due to disrupted Shh signaling; in addition, dorsal patterning defects occur, but the cause of these is unknown. Here we show that the Arl13bhnn dorsal patterning defects result from abnormal BMP signaling. In addition, we find that Wnt ligands are abnormally expressed in Arl13bhnn mutants; surprisingly, however, downstream Wnt signaling is normal. We demonstrate that Arl13b is required non-autonomously for BMP signaling and Wnt ligand expression, indicating that the abnormal Shh signaling environment in Arl13bhnn embryos indirectly causes dorsal defects. PMID:21539826

  17. Neural signalling of food healthiness associated with emotion processing

    Directory of Open Access Journals (Sweden)

    Uwe eHerwig

    2016-02-01

    Full Text Available The ability to differentiate healthy from unhealthy foods is important in order to promote good health. Food, however, may have an emotional connotation, which could be inversely related to healthiness. The neurobiological background of differentiating healthy and unhealthy food and its relations to emotion processing are not yet well understood. We addressed the neural activations, particularly considering the single subject level, when one evaluates a food item to be of a higher, compared to a lower grade of healthiness with a particular view on emotion processing brain regionsThirty-seven healthy subjects underwent functional magnetic resonance imaging while evaluating the healthiness of food presented as photographs with a subsequent rating on a visual analogue scale. We compared individual evaluations of high and low healthiness of food items and also considered gender differences.We found increased activation when food was evaluated to be healthy in the left dorsolateral prefrontal cortex and precuneus in whole brain analyses. In ROI analyses, perceived and rated higher healthiness was associated with lower amygdala activity and higher ventral striatal and orbitofrontal cortex activity. Females exerted a higher activation in midbrain areas when rating food items as being healthy.Our results underline the close relationship between food and emotion processing, which makes sense considering evolutionary aspects. Actively evaluating and deciding whether food is healthy is accompanied by neural signalling associated with reward and self-relevance, which could promote salutary nutrition behaviour. The involved brain regions may be amenable to mechanisms of emotion regulation in the context of psychotherapeutic regulation of food intake.

  18. Slit/Robo1 signaling regulates neural tube development by balancing neuroepithelial cell proliferation and differentiation

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Guang; Li, Yan; Wang, Xiao-yu [Key Laboratory for Regenerative Medicine of The Ministry of Education, Department of Histology and Embryology, School of Medicine, Jinan University, Guangzhou 510632 (China); Han, Zhe [Institute of Vascular Biological Sciences, Guangdong Pharmaceutical University, Guangzhou 510224 (China); Chuai, Manli [College of Life Sciences Biocentre, University of Dundee, Dundee DD1 5EH (United Kingdom); Wang, Li-jing [Institute of Vascular Biological Sciences, Guangdong Pharmaceutical University, Guangzhou 510224 (China); Ho Lee, Kenneth Ka [Stem Cell and Regeneration Thematic Research Programme, School of Biomedical Sciences, Chinese University of Hong Kong, Shatin (Hong Kong); Geng, Jian-guo, E-mail: jgeng@umich.edu [Institute of Vascular Biological Sciences, Guangdong Pharmaceutical University, Guangzhou 510224 (China); Department of Biologic and Materials Sciences, University of Michigan School of Dentistry, Ann Arbor, MI 48109 (United States); Yang, Xuesong, E-mail: yang_xuesong@126.com [Key Laboratory for Regenerative Medicine of The Ministry of Education, Department of Histology and Embryology, School of Medicine, Jinan University, Guangzhou 510632 (China)

    2013-05-01

    Formation of the neural tube is the morphological hallmark for development of the embryonic central nervous system (CNS). Therefore, neural tube development is a crucial step in the neurulation process. Slit/Robo signaling was initially identified as a chemo-repellent that regulated axon growth cone elongation, but its role in controlling neural tube development is currently unknown. To address this issue, we investigated Slit/Robo1 signaling in the development of chick neCollege of Life Sciences Biocentre, University of Dundee, Dundee DD1 5EH, UKural tube and transgenic mice over-expressing Slit2. We disrupted Slit/Robo1 signaling by injecting R5 monoclonal antibodies into HH10 neural tubes to block the Robo1 receptor. This inhibited the normal development of the ventral body curvature and caused the spinal cord to curl up into a S-shape. Next, Slit/Robo1 signaling on one half-side of the chick embryo neural tube was disturbed by electroporation in ovo. We found that the morphology of the neural tube was dramatically abnormal after we interfered with Slit/Robo1 signaling. Furthermore, we established that silencing Robo1 inhibited cell proliferation while over-expressing Robo1 enhanced cell proliferation. We also investigated the effects of altering Slit/Robo1 expression on Sonic Hedgehog (Shh) and Pax7 expression in the developing neural tube. We demonstrated that over-expressing Robo1 down-regulated Shh expression in the ventral neural tube and resulted in the production of fewer HNK-1{sup +} migrating neural crest cells (NCCs). In addition, Robo1 over-expression enhanced Pax7 expression in the dorsal neural tube and increased the number of Slug{sup +} pre-migratory NCCs. Conversely, silencing Robo1 expression resulted in an enhanced Shh expression and more HNK-1{sup +} migrating NCCs but reduced Pax7 expression and fewer Slug{sup +} pre-migratory NCCs were observed. In conclusion, we propose that Slit/Robo1 signaling is involved in regulating neural tube

  19. The relationship between Sonic hedgehog signalling, cilia and neural tube defects

    Science.gov (United States)

    Murdoch, Jennifer N.; Copp, Andrew J.

    2013-01-01

    The Hedgehog signalling pathway is essential for many aspects of normal embryonic development, including formation and patterning of the neural tube. Absence of Shh ligand is associated with the midline defect holoprosencephaly, while increased Shh signalling is associated with exencephaly and spina bifida. To complicate this apparently simple relationship, mutation of proteins required for function of cilia often leads to impaired Shh signalling and to disruption of neural tube closure. In this manuscript, we review the literature on Shh pathway mutants and discuss the relationship between Shh signalling, cilia and neural tube defects. PMID:20544799

  20. Signaling and transcriptional regulation in neural crest specification and migration: lessons from xenopus embryos.

    Science.gov (United States)

    Pegoraro, Caterina; Monsoro-Burq, Anne H

    2013-01-01

    The neural crest is a population of highly migratory and multipotent cells, which arises from the border of the neural plate in vertebrate embryos. In the last few years, the molecular actors of neural crest early development have been intensively studied, notably by using the frog embryo, as a prime model for the analysis of the earliest embryonic inductions. In addition, tremendous progress has been made in understanding the molecular and cellular basis of Xenopus cranial neural crest migration, by combining in vitro and in vivo analysis. In this review, we examine how the action of previously known neural crest-inducing signals [bone morphogenetic protein (BMP), wingless-int (Wnt), fibroblast growth factor (FGF)] is controlled by newly discovered modulators during early neural plate border patterning and neural crest specification. This regulation controls the induction of key transcription factors that cooperate to pattern the premigratory neural crest progenitors. These data are discussed in the perspective of the gene regulatory network that controls neural and neural crest patterning. We then address recent findings on noncanonical Wnt signaling regulation, cell polarization, and collective cell migration which highlight how cranial neural crest cells populate their target tissue, the branchial arches, in vivo. More than ever, the neural crest stands as a powerful and attractive model to decipher complex vertebrate regulatory circuits in vivo. Copyright © 2012 Wiley Periodicals, Inc.

  1. Effects of traffic noise on tree frog stress levels, immunity, and color signaling.

    Science.gov (United States)

    Troïanowski, Mathieu; Mondy, Nathalie; Dumet, Adeline; Arcanjo, Caroline; Lengagne, Thierry

    2017-10-01

    During the last decade, many studies have focused on the detrimental effects of noise pollution on acoustic communication. Surprisingly, although it is known that noise exposure strongly influences health in humans, studies on wildlife remain scarce. In order to gain insight into the consequences of traffic noise exposure, we experimentally manipulated traffic noise exposure as well as the endocrine status of animals to investigate physiological and phenotypic consequences of noise pollution in an anuran species. We showed that noise exposure increased stress hormone level and induced an immunosuppressive effect. In addition, both traffic noise exposure and stress hormone application negatively impacted H. arborea vocal sac coloration. Moreover, our results suggest profound changes in sexual selection processes because the best quality males with initial attractive vocal sac coloration were the most impacted by noise. Hence, our study suggests that the recent increases in anthropogenic noise worldwide might affect a broader range of animal species than previously thought, because of alteration of visual signals and immunity. Generalizing these results to other taxa is crucial for the conservation of biodiversity in an increasingly noisy world. © 2017 Society for Conservation Biology.

  2. Using pulse width modulation for wireless transmission of neural signals in multichannel neural recording systems.

    Science.gov (United States)

    Yin, Ming; Ghovanloo, Maysam

    2009-08-01

    We have used a well-known technique in wireless communication, pulse width modulation (PWM) of time division multiplexed (TDM) signals, within the architecture of a novel wireless integrated neural recording (WINeR) system. We have evaluated the performance of the PWM-based architecture and indicated its accuracy and potential sources of error through detailed theoretical analysis, simulations, and measurements on a setup consisting of a 15-channel WINeR prototype as the transmitter and two types of receivers; an Agilent 89600 vector signal analyzer and a custom wideband receiver, with 36 and 75 MHz of maximum bandwidth, respectively. Furthermore, we present simulation results from a realistic MATLAB-Simulink model of the entire WINeR system to observe the system behavior in response to changes in various parameters. We have concluded that the 15-ch WINeR prototype, which is fabricated in a 0.5- mum standard CMOS process and consumes 4.5 mW from +/-1.5 V supplies, can acquire and wirelessly transmit up to 320 k-samples/s to a 75-MHz receiver with 8.4 bits of resolution, which is equivalent to a wireless data rate of approximately 2.56 Mb/s.

  3. Signal transduction meets vesicle traffic: the software and hardware of GLUT4 translocation.

    Science.gov (United States)

    Klip, Amira; Sun, Yi; Chiu, Tim Ting; Foley, Kevin P

    2014-05-15

    Skeletal muscle is the major tissue disposing of dietary glucose, a function regulated by insulin-elicited signals that impart mobilization of GLUT4 glucose transporters to the plasma membrane. This phenomenon, also central to adipocyte biology, has been the subject of intense and productive research for decades. We focus on muscle cell studies scrutinizing insulin signals and vesicle traffic in a spatiotemporal manner. Using the analogy of an integrated circuit to approach the intersection between signal transduction and vesicle mobilization, we identify signaling relays ("software") that engage structural/mechanical elements ("hardware") to enact the rapid mobilization and incorporation of GLUT4 into the cell surface. We emphasize how insulin signal transduction switches from tyrosine through lipid and serine phosphorylation down to activation of small G proteins of the Rab and Rho families, describe key negative regulation step of Rab GTPases through the GTPase-activating protein activity of the Akt substrate of 160 kDa (AS160), and focus on the mechanical effectors engaged by Rabs 8A and 10 (the molecular motor myosin Va), and the Rho GTPase Rac1 (actin filament branching and severing through Arp2/3 and cofilin). Finally, we illustrate how actin filaments interact with myosin 1c and α-Actinin4 to promote vesicle tethering as preamble to fusion with the membrane. Copyright © 2014 the American Physiological Society.

  4. Traffic accident reconstruction and an approach for prediction of fault rates using artificial neural networks: A case study in Turkey.

    Science.gov (United States)

    Can Yilmaz, Ali; Aci, Cigdem; Aydin, Kadir

    2016-08-17

    Currently, in Turkey, fault rates in traffic accidents are determined according to the initiative of accident experts (no speed analyses of vehicles just considering accident type) and there are no specific quantitative instructions on fault rates related to procession of accidents which just represents the type of collision (side impact, head to head, rear end, etc.) in No. 2918 Turkish Highway Traffic Act (THTA 1983). The aim of this study is to introduce a scientific and systematic approach for determination of fault rates in most frequent property damage-only (PDO) traffic accidents in Turkey. In this study, data (police reports, skid marks, deformation, crush depth, etc.) collected from the most frequent and controversial accident types (4 sample vehicle-vehicle scenarios) that consist of PDO were inserted into a reconstruction software called vCrash. Sample real-world scenarios were simulated on the software to generate different vehicle deformations that also correspond to energy-equivalent speed data just before the crash. These values were used to train a multilayer feedforward artificial neural network (MFANN), function fitting neural network (FITNET, a specialized version of MFANN), and generalized regression neural network (GRNN) models within 10-fold cross-validation to predict fault rates without using software. The performance of the artificial neural network (ANN) prediction models was evaluated using mean square error (MSE) and multiple correlation coefficient (R). It was shown that the MFANN model performed better for predicting fault rates (i.e., lower MSE and higher R) than FITNET and GRNN models for accident scenarios 1, 2, and 3, whereas FITNET performed the best for scenario 4. The FITNET model showed the second best results for prediction for the first 3 scenarios. Because there is no training phase in GRNN, the GRNN model produced results much faster than MFANN and FITNET models. However, the GRNN model had the worst prediction results. The

  5. The neural subjective frame: from bodily signals to perceptual consciousness

    Science.gov (United States)

    Park, Hyeong-Dong; Tallon-Baudry, Catherine

    2014-01-01

    The report ‘I saw the stimulus’ operationally defines visual consciousness, but where does the ‘I’ come from? To account for the subjective dimension of perceptual experience, we introduce the concept of the neural subjective frame. The neural subjective frame would be based on the constantly updated neural maps of the internal state of the body and constitute a neural referential from which first person experience can be created. We propose to root the neural subjective frame in the neural representation of visceral information which is transmitted through multiple anatomical pathways to a number of target sites, including posterior insula, ventral anterior cingulate cortex, amygdala and somatosensory cortex. We review existing experimental evidence showing that the processing of external stimuli can interact with visceral function. The neural subjective frame is a low-level building block of subjective experience which is not explicitly experienced by itself which is necessary but not sufficient for perceptual experience. It could also underlie other types of subjective experiences such as self-consciousness and emotional feelings. Because the neural subjective frame is tightly linked to homeostatic regulations involved in vigilance, it could also make a link between state and content consciousness. PMID:24639580

  6. Discrimination of Cylinders with Different Wall Thicknesses using Neural Networks and Simulated Dolphin Sonar Signals

    DEFF Research Database (Denmark)

    Andersen, Lars Nonboe; Au, Whitlow; Larsen, Jan

    1999-01-01

    This paper describes a method integrating neural networks into a system for recognizing underwater objects. The system is based on a combination of simulated dolphin sonar signals, simulated auditory filters and artificial neural networks. The system is tested on a cylinder wall thickness...

  7. p53 as the main traffic controller of the cell signaling network.

    Science.gov (United States)

    Sebastian, Sinto; Azzariti, Amalia; Silvestris, Nicola; Porcelli, Letizia; Russo, Antonio; Paradiso, Angelo

    2010-06-01

    Among different pathological conditions that affect human beings, cancer has received a great deal of attention primarily because it leads to significant morbidity and mortality. This is essentially due to increasing world-wide incidence of this disease and the inability to discover the cause and molecular mechanisms by which normal human cells acquire the characteristics that define cancer cells. Since the discovery of p53 over a quarter of a century ago, it is now recognized that virtually all cell fate pathways of live cells and the decision to die are under the control of p53. Such extensive involvement indicates that p53 protein is acting as a major traffic controller in the cell signaling network. In cancer cells, many cell signaling pathways of normal human cells are rerouted towards immortalization and this is accomplished by the corruption of the main controllers of cell signaling pathways such as p53. This review highlights how p53 signaling activity is altered in cancer cells so that cells acquire the hallmarks of cancer including deregulated infinite self replicative potential.

  8. An RFID-Based Intelligent Vehicle Speed Controller Using Active Traffic Signals

    Directory of Open Access Journals (Sweden)

    Joshué Pérez

    2010-06-01

    Full Text Available These days, mass-produced vehicles benefit from research on Intelligent Transportation System (ITS. One prime example of ITS is vehicle Cruise Control (CC, which allows it to maintain a pre-defined reference speed, to economize on fuel or energy consumption, to avoid speeding fines, or to focus all of the driver’s attention on the steering of the vehicle. However, achieving efficient Cruise Control is not easy in roads or urban streets where sudden changes of the speed limit can happen, due to the presence of unexpected obstacles or maintenance work, causing, in inattentive drivers, traffic accidents. In this communication we present a new Infrastructure to Vehicles (I2V communication and control system for intelligent speed control, which is based upon Radio Frequency Identification (RFID technology for identification of traffic signals on the road, and high accuracy vehicle speed measurement with a Hall effect-based sensor. A fuzzy logic controller, based on sensor fusion of the information provided by the I2V infrastructure, allows the efficient adaptation of the speed of the vehicle to the circumstances of the road. The performance of the system is checked empirically, with promising results.

  9. An RFID-Based Intelligent Vehicle Speed Controller Using Active Traffic Signals

    Science.gov (United States)

    Pérez, Joshué; Seco, Fernando; Milanés, Vicente; Jiménez, Antonio; Díaz, Julio C.; de Pedro, Teresa

    2010-01-01

    These days, mass-produced vehicles benefit from research on Intelligent Transportation System (ITS). One prime example of ITS is vehicle Cruise Control (CC), which allows it to maintain a pre-defined reference speed, to economize on fuel or energy consumption, to avoid speeding fines, or to focus all of the driver’s attention on the steering of the vehicle. However, achieving efficient Cruise Control is not easy in roads or urban streets where sudden changes of the speed limit can happen, due to the presence of unexpected obstacles or maintenance work, causing, in inattentive drivers, traffic accidents. In this communication we present a new Infrastructure to Vehicles (I2V) communication and control system for intelligent speed control, which is based upon Radio Frequency Identification (RFID) technology for identification of traffic signals on the road, and high accuracy vehicle speed measurement with a Hall effect-based sensor. A fuzzy logic controller, based on sensor fusion of the information provided by the I2V infrastructure, allows the efficient adaptation of the speed of the vehicle to the circumstances of the road. The performance of the system is checked empirically, with promising results. PMID:22219692

  10. A real-time traffic control method for the intersection with pre-signals under the phase swap sorting strategy.

    Science.gov (United States)

    Bie, Yiming; Liu, Zhiyuan; Wang, Yinhai

    2017-01-01

    To deal with the conflicts between left-turn and through traffic streams and increase the discharge capacity, this paper addresses the pre-signal which is implemented at a signalized intersection. Such an intersection with pre-signal is termed as a tandem intersection. For the tandem intersection, phase swap sorting strategy is deemed as the most effective phasing scheme in view of some exclusive merits, such as easier compliance of drivers, and shorter sorting area. However, a major limitation of the phase swap sorting strategy is not considered in previous studies: if one or more vehicle is left at the sorting area after the signal light turns to red, the capacity of the approach would be dramatically dropped. Besides, previous signal control studies deal with a fixed timing plan that is not adaptive with the fluctuation of traffic flows. Therefore, to cope with these two gaps, this paper firstly takes an in-depth analysis of the traffic flow operations at the tandem intersection. Secondly, three groups of loop detectors are placed to obtain the real-time vehicle information for adaptive signalization. The lane selection behavior in the sorting area is considered to set the green time for intersection signals. With the objective of minimizing the vehicle delay, the signal control parameters are then optimized based on a dynamic programming method. Finally, numerical experiments show that average vehicle delay and maximum queue length can be reduced under all scenarios.

  11. Intelligent Noise Removal from EMG Signal Using Focused Time-Lagged Recurrent Neural Network

    OpenAIRE

    Kale, S. N.; Dudul, S. V.

    2009-01-01

    Electromyography (EMG) signals can be used for clinical/biomedical application and modern human computer interaction. EMG signals acquire noise while traveling through tissue, inherent noise in electronics equipment, ambient noise, and so forth. ANN approach is studied for reduction of noise in EMG signal. In this paper, it is shown that Focused Time-Lagged Recurrent Neural Network (FTLRNN) can elegantly solve to reduce the noise from EMG signal. After rigorous computer simulations, authors d...

  12. Robust Clustering of Acoustic Emission Signals Using Neural Networks and Signal Subspace Projections

    Directory of Open Access Journals (Sweden)

    Vahid Emamian

    2003-03-01

    Full Text Available Acoustic emission-based techniques are being used for the nondestructive inspection of mechanical systems. For reliable automatic fault monitoring related to the generation and propagation of cracks, it is important to identify the transient crack-related signals in the presence of strong time-varying noise and other interference. A prominent difficulty is the inability to differentiate events due to crack growth from noise of various origins. This work presents a novel algorithm for automatic clustering and separation of acoustic emission (AE events based on multiple features extracted from the experimental data. The algorithm consists of two steps. In the first step, the noise is separated from the events of interest and subsequently removed using a combination of covariance analysis, principal component analysis (PCA, and differential time delay estimates. The second step processes the remaining data using a self-organizing map (SOM neural network, which outputs the noise and AE signals into separate neurons. To improve the efficiency of classification, the short-time Fourier transform (STFT is applied to retain the time-frequency features of the remaining events, reducing the dimension of the data. The algorithm is verified with two sets of data, and a correct classification ratio over 95% is achieved.

  13. Ant colony optimization algorithm for signal coordination of oversaturated traffic networks.

    Science.gov (United States)

    2010-05-01

    Traffic congestion is a daily and growing problem of the modern era in mostly all major cities in the world. : Increasing traffic demand strains the existing transportation system, leading to oversaturated network : conditions, especially at peak hou...

  14. Neural retina identity is specified by lens-derived BMP signals.

    Science.gov (United States)

    Pandit, Tanushree; Jidigam, Vijay K; Patthey, Cedric; Gunhaga, Lena

    2015-05-15

    The eye has served as a classical model to study cell specification and tissue induction for over a century. Nevertheless, the molecular mechanisms that regulate the induction and maintenance of eye-field cells, and the specification of neural retina cells are poorly understood. Moreover, within the developing anterior forebrain, how prospective eye and telencephalic cells are differentially specified is not well defined. In the present study, we have analyzed these issues by manipulating signaling pathways in intact chick embryo and explant assays. Our results provide evidence that at blastula stages, BMP signals inhibit the acquisition of eye-field character, but from neural tube/optic vesicle stages, BMP signals from the lens are crucial for the maintenance of eye-field character, inhibition of dorsal telencephalic cell identity and specification of neural retina cells. Subsequently, our results provide evidence that a Rax2-positive eye-field state is not sufficient for the progress to a neural retina identity, but requires BMP signals. In addition, our results argue against any essential role of Wnt or FGF signals during the specification of neural retina cells, but provide evidence that Wnt signals together with BMP activity are sufficient to induce cells of retinal pigment epithelial character. We conclude that BMP activity emanating from the lens ectoderm maintains eye-field identity, inhibits telencephalic character and induces neural retina cells. Our findings link the requirement of the lens ectoderm for neural retina specification with the molecular mechanism by which cells in the forebrain become specified as neural retina by BMP activity. © 2015. Published by The Company of Biologists Ltd.

  15. Effects of social sustainability signals on neural valuation signals and taste-experience of food products

    Directory of Open Access Journals (Sweden)

    Laura eEnax

    2015-09-01

    Full Text Available Value-based decision making occurs when individuals choose between different alternatives and place a value on each alternative and its attributes. Marketing actions frequently manipulate product attributes, by adding e.g., health claims on the packaging. A previous imaging study found that an emblem for organic products increased willingness to pay (WTP and activity in the ventral striatum (VS. The current study investigated neural and behavioral processes underlying the influence of Fair Trade (FT labeling on food valuation and choice. Sustainability is an important product attribute for many consumers, with FT signals being one way to highlight ethically sustainable production. Forty participants valuated products in combination with an FT emblem or no emblem and stated their WTP in a bidding task while in an MRI scanner. After that, participants tasted – objectively identical – chocolates, presented either as FT or as conventionally produced. In the fMRI task, WTP was significantly higher for FT products. FT labeling increased activity in regions important for reward-processing and salience, that is, in the VS, anterior and posterior cingulate, as well as superior frontal gyrus. Subjective value, that is, WTP was correlated with activity in the ventromedial prefrontal cortex (vmPFC. We find that the anterior cingulate, VS and superior frontal gyrus exhibit task-related increases in functional connectivity to the vmPFC when an FT product was evaluated, suggesting a network which alters valuation processes. We also found a significant taste-placebo effect, with higher experienced taste pleasantness and intensity for FT labeled chocolates. Our results reveal a possible neural mechanism underlying valuation processes of certified food products. The results are important in light of understanding current marketing trends as well as designing future interventions that aim at positively influencing food choice.

  16. Effects of social sustainability signaling on neural valuation signals and taste-experience of food products.

    Science.gov (United States)

    Enax, Laura; Krapp, Vanessa; Piehl, Alexandra; Weber, Bernd

    2015-01-01

    Value-based decision making occurs when individuals choose between different alternatives and place a value on each alternative and its attributes. Marketing actions frequently manipulate product attributes, by adding, e.g., health claims on the packaging. A previous imaging study found that an emblem for organic products increased willingness to pay (WTP) and activity in the ventral striatum (VS). The current study investigated neural and behavioral processes underlying the influence of Fair Trade (FT) labeling on food valuation and choice. Sustainability is an important product attribute for many consumers, with FT signals being one way to highlight ethically sustainable production. Forty participants valuated products in combination with an FT emblem or no emblem and stated their WTP in a bidding task while in an MRI scanner. After that, participants tasted-objectively identical-chocolates, presented either as "FT" or as "conventionally produced". In the fMRI task, WTP was significantly higher for FT products. FT labeling increased activity in regions important for reward-processing and salience, that is, in the VS, anterior and posterior cingulate, as well as superior frontal gyrus. Subjective value, that is, WTP was correlated with activity in the ventromedial prefrontal cortex (vmPFC). We find that the anterior cingulate, VS and superior frontal gyrus exhibit task-related increases in functional connectivity to the vmPFC when an FT product was evaluated. Effective connectivity analyses revealed a highly probable directed modulation of the vmPFC by those three regions, suggesting a network which alters valuation processes. We also found a significant taste-placebo effect, with higher experienced taste pleasantness and intensity for FT labeled chocolates. Our results reveal a possible neural mechanism underlying valuation processes of certified food products. The results are important in light of understanding current marketing trends as well as designing

  17. A Decline in Response Variability Improves Neural Signal Detection during Auditory Task Performance.

    Science.gov (United States)

    von Trapp, Gardiner; Buran, Bradley N; Sen, Kamal; Semple, Malcolm N; Sanes, Dan H

    2016-10-26

    The detection of a sensory stimulus arises from a significant change in neural activity, but a sensory neuron's response is rarely identical to successive presentations of the same stimulus. Large trial-to-trial variability would limit the central nervous system's ability to reliably detect a stimulus, presumably affecting perceptual performance. However, if response variability were to decrease while firing rate remained constant, then neural sensitivity could improve. Here, we asked whether engagement in an auditory detection task can modulate response variability, thereby increasing neural sensitivity. We recorded telemetrically from the core auditory cortex of gerbils, both while they engaged in an amplitude-modulation detection task and while they sat quietly listening to the identical stimuli. Using a signal detection theory framework, we found that neural sensitivity was improved during task performance, and this improvement was closely associated with a decrease in response variability. Moreover, units with the greatest change in response variability had absolute neural thresholds most closely aligned with simultaneously measured perceptual thresholds. Our findings suggest that the limitations imposed by response variability diminish during task performance, thereby improving the sensitivity of neural encoding and potentially leading to better perceptual sensitivity. The detection of a sensory stimulus arises from a significant change in neural activity. However, trial-to-trial variability of the neural response may limit perceptual performance. If the neural response to a stimulus is quite variable, then the response on a given trial could be confused with the pattern of neural activity generated when the stimulus is absent. Therefore, a neural mechanism that served to reduce response variability would allow for better stimulus detection. By recording from the cortex of freely moving animals engaged in an auditory detection task, we found that variability

  18. G-protein-coupled receptor signaling and neural tube closure defects.

    Science.gov (United States)

    Shimada, Issei S; Mukhopadhyay, Saikat

    2017-01-30

    Disruption of the normal mechanisms that mediate neural tube closure can result in neural tube defects (NTDs) with devastating consequences in affected patients. With the advent of next-generation sequencing, we are increasingly detecting mutations in multiple genes in NTD cases. However, our ability to determine which of these genes contribute to the malformation is limited by our understanding of the pathways controlling neural tube closure. G-protein-coupled receptors (GPCRs) comprise the largest family of transmembrane receptors in humans and have been historically favored as drug targets. Recent studies implicate several GPCRs and downstream signaling pathways in neural tube development and closure. In this review, we will discuss our current understanding of GPCR signaling pathways in pathogenesis of NTDs. Notable examples include the orphan primary cilia-localized GPCR, Gpr161 that regulates the basal suppression machinery of sonic hedgehog pathway by means of activation of cAMP-protein kinase A signaling in the neural tube, and protease-activated receptors that are activated by a local network of membrane-tethered proteases during neural tube closure involving the surface ectoderm. Understanding the role of these GPCR-regulated pathways in neural tube development and closure is essential toward identification of underlying genetic causes to prevent NTDs. Birth Defects Research 109:129-139, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  19. Prediction of Ship Traffic Flow Based on BP Neural Network and Markov Model

    OpenAIRE

    Lv Pengfei; Zhuang Yuan; Yang Kun

    2016-01-01

    This paper discusses the distribution regularity of ship arrival and departure and the method of prediction of ship traffic flow. Depict the frequency histograms of ships arriving to port every day and fit the curve of the frequency histograms with a variety of distribution density function by using the mathematical statistic methods based on the samples of ship-to-port statistics of Fangcheng port nearly a year. By the chi-square testing: the fitting with Negative Binomial distribution and t...

  20. Neural signal sampling via the low power wireless pico system.

    Science.gov (United States)

    Cieslewski, Grzegorz; Cheney, David; Gugel, Karl; Sanchez, Justin C; Principe, Jose C

    2006-01-01

    This paper presents a powerful new low power wireless system for sampling multiple channels of neural activity based on Texas Instruments MSP430 microprocessors and Nordic Semiconductor's ultra low power high bandwidth RF transmitters and receivers. The system's development process, component selection, features and test methodology are presented.

  1. Comparative evaluation of different wavelet thresholding methods for neural signal processing.

    Science.gov (United States)

    Barabino, Gianluca; Baldazzi, Giulia; Sulas, Eleonora; Carboni, Caterina; Raffo, Luigi; Pani, Danilo

    2017-07-01

    Neural signal decoding is the basis for the development of neuroprosthetic devices and systems. Depending on the part of the nervous system these signals are picked up from, different signal-to-noise ratios (SNR) can be experienced. Wavelet denoising is often adopted due to its capability of reducing, to some extent, the noise falling within the signal spectrum. Several variables influence the denoising quality, but usually the focus in on the selection of the best performing mother wavelet. However, the threshold definition and the way it is applied to the signal have a significant impact on the denoising quality, determining the amount of noise removed and the distortion introduced on the signal. This work presents a comparative analysis of different threshold definition and thresholding mechanisms on neural signals, either largely adopted for neural signal processing or not. In order to evaluate the quality of the denoising in terms of the introduced distortion, which is important when decoding is implemented through spike-sorting algorithms, a synthetic dataset built on real action potentials was used, creating signals with different SNR and characterized by an additive white Gaussian noise (AWGN). The obtained results reveal the superiority of an approach, originally conceived for noisy non-linear time series, over the more typical ones. When compared to the original signal, a correlation above 0.9 was obtained, while in terms of root mean square error (RMSE) an improvement of 13% and 33% was reported with respect to the Minimax and Universal thresholds respectively.

  2. Neural responses to multimodal ostensive signals in 5-month-old infants.

    Directory of Open Access Journals (Sweden)

    Eugenio Parise

    Full Text Available Infants' sensitivity to ostensive signals, such as direct eye contact and infant-directed speech, is well documented in the literature. We investigated how infants interpret such signals by assessing common processing mechanisms devoted to them and by measuring neural responses to their compounds. In Experiment 1, we found that ostensive signals from different modalities display overlapping electrophysiological activity in 5-month-old infants, suggesting that these signals share neural processing mechanisms independently of their modality. In Experiment 2, we found that the activation to ostensive signals from different modalities is not additive to each other, but rather reflects the presence of ostension in either stimulus stream. These data support the thesis that ostensive signals obligatorily indicate to young infants that communication is directed to them.

  3. Computationally efficient locally-recurrent neural networks for online signal processing

    CERN Document Server

    Hussain, A; Shim, I

    1999-01-01

    A general class of computationally efficient locally recurrent networks (CERN) is described for real-time adaptive signal processing. The structure of the CERN is based on linear-in-the- parameters single-hidden-layered feedforward neural networks such as the radial basis function (RBF) network, the Volterra neural network (VNN) and the functionally expanded neural network (FENN), adapted to employ local output feedback. The corresponding learning algorithms are derived and key structural and computational complexity comparisons are made between the CERN and conventional recurrent neural networks. Two case studies are performed involving the real- time adaptive nonlinear prediction of real-world chaotic, highly non- stationary laser time series and an actual speech signal, which show that a recurrent FENN based adaptive CERN predictor can significantly outperform the corresponding feedforward FENN and conventionally employed linear adaptive filtering models. (13 refs).

  4. Modulation of Hippocampal Neural Plasticity by Glucose-Related Signaling

    OpenAIRE

    Marco Mainardi; Salvatore Fusco; Claudio Grassi

    2015-01-01

    Hormones and peptides involved in glucose homeostasis are emerging as important modulators of neural plasticity. In this regard, increasing evidence shows that molecules such as insulin, insulin-like growth factor-I, glucagon-like peptide-1, and ghrelin impact on the function of the hippocampus, which is a key area for learning and memory. Indeed, all these factors affect fundamental hippocampal properties including synaptic plasticity (i.e., synapse potentiation and depression), structural p...

  5. An Artificial Neural Network Based Robot Controller that Uses Rat’s Brain Signals

    Directory of Open Access Journals (Sweden)

    Marsel Mano

    2013-04-01

    Full Text Available Brain machine interface (BMI has been proposed as a novel technique to control prosthetic devices aimed at restoring motor functions in paralyzed patients. In this paper, we propose a neural network based controller that maps rat’s brain signals and transforms them into robot movement. First, the rat is trained to move the robot by pressing the right and left lever in order to get food. Next, we collect brain signals with four implanted electrodes, two in the motor cortex and two in the somatosensory cortex area. The collected data are used to train and evaluate different artificial neural controllers. Trained neural controllers are employed online to map brain signals and transform them into robot motion. Offline and online classification results of rat’s brain signals show that the Radial Basis Function Neural Networks (RBFNN outperforms other neural networks. In addition, online robot control results show that even with a limited number of electrodes, the robot motion generated by RBFNN matched the motion generated by the left and right lever position.

  6. A VLSI field-programmable mixed-signal array to perform neural signal processing and neural modeling in a prosthetic system.

    Science.gov (United States)

    Bamford, Simeon A; Hogri, Roni; Giovannucci, Andrea; Taub, Aryeh H; Herreros, Ivan; Verschure, Paul F M J; Mintz, Matti; Del Giudice, Paolo

    2012-07-01

    A very-large-scale integration field-programmable mixed-signal array specialized for neural signal processing and neural modeling has been designed. This has been fabricated as a core on a chip prototype intended for use in an implantable closed-loop prosthetic system aimed at rehabilitation of the learning of a discrete motor response. The chosen experimental context is cerebellar classical conditioning of the eye-blink response. The programmable system is based on the intimate mixing of switched capacitor analog techniques with low speed digital computation; power saving innovations within this framework are presented. The utility of the system is demonstrated by the implementation of a motor classical conditioning model applied to eye-blink conditioning in real time with associated neural signal processing. Paired conditioned and unconditioned stimuli were repeatedly presented to an anesthetized rat and recordings were taken simultaneously from two precerebellar nuclei. These paired stimuli were detected in real time from this multichannel data. This resulted in the acquisition of a trigger for a well-timed conditioned eye-blink response, and repetition of unpaired trials constructed from the same data led to the extinction of the conditioned response trigger, compatible with natural cerebellar learning in awake animals.

  7. Neural basis of impaired safety signaling in Obsessive Compulsive Disorder.

    Science.gov (United States)

    Apergis-Schoute, Annemieke M; Gillan, Claire M; Fineberg, Naomi A; Fernandez-Egea, Emilio; Sahakian, Barbara J; Robbins, Trevor W

    2017-03-21

    The ability to assign safety to stimuli in the environment is integral to everyday functioning. A key brain region for this evaluation is the ventromedial prefrontal cortex (vmPFC). To investigate the importance of vmPFC safety signaling, we used neuroimaging of Pavlovian fear reversal, a paradigm that involves flexible updating when the contingencies for a threatening (CS+) and safe (CS-) stimulus reverse, in a prototypical disorder of inflexible behavior influenced by anxiety, Obsessive Compulsive Disorder (OCD). Skin conductance responses in OCD patients (n = 43) failed to differentiate during reversal compared with healthy controls (n = 35), although significant differentiation did occur during early conditioning and amygdala BOLD signaling was unaffected in these patients. Increased vmPFC activation (for CS+ > CS-) during early conditioning predicted the degree of generalization in OCD patients during reversal, whereas vmPFC safety signals were absent throughout learning in these patients. Regions of the salience network (dorsal anterior cingulate, insula, and thalamus) showed early learning task-related hyperconnectivity with the vmPFC in OCD, consistent with biased processing of the CS+. Our findings reveal an absence of vmPFC safety signaling in OCD, undermining flexible threat updating and explicit contingency knowledge. Although differential threat learning can occur to some extent in the absence of vmPFC safety signals, effective CS- signaling becomes crucial during conflicting threat and safety cues. These results promote further investigation of vmPFC safety signaling in other anxiety disorders, with potential implications for the development of exposure-based therapies, in which safety signaling is likely to play a key role.

  8. Perlecan is required for FGF-2 signaling in the neural stem cell niche

    Directory of Open Access Journals (Sweden)

    Aurelien Kerever

    2014-03-01

    Full Text Available In the adult subventricular zone (neurogenic niche, neural stem cells double-positive for two markers of subsets of neural stem cells in the adult central nervous system, glial fibrillary acidic protein and CD133, lie in proximity to fractones and to blood vessel basement membranes, which contain the heparan sulfate proteoglycan perlecan. Here, we demonstrate that perlecan deficiency reduces the number of both GFAP/CD133-positive neural stem cells in the subventricular zone and new neurons integrating into the olfactory bulb. We also show that FGF-2 treatment induces the expression of cyclin D2 through the activation of the Akt and Erk1/2 pathways and promotes neurosphere formation in vitro. However, in the absence of perlecan, FGF-2 fails to promote neurosphere formation. These results suggest that perlecan is a component of the neurogenic niche that regulates FGF-2 signaling and acts by promoting neural stem cell self-renewal and neurogenesis.

  9. ERNN: a biologically inspired feedforward neural network to discriminate emotion from EEG signal.

    Science.gov (United States)

    Khosrowabadi, Reza; Quek, Chai; Ang, Kai Keng; Wahab, Abdul

    2014-03-01

    Emotions play an important role in human cognition, perception, decision making, and interaction. This paper presents a six-layer biologically inspired feedforward neural network to discriminate human emotions from EEG. The neural network comprises a shift register memory after spectral filtering for the input layer, and the estimation of coherence between each pair of input signals for the hidden layer. EEG data are collected from 57 healthy participants from eight locations while subjected to audio-visual stimuli. Discrimination of emotions from EEG is investigated based on valence and arousal levels. The accuracy of the proposed neural network is compared with various feature extraction methods and feedforward learning algorithms. The results showed that the highest accuracy is achieved when using the proposed neural network with a type of radial basis function.

  10. The performance evaluation of a new neural network based traffic management scheme for a satellite communication network

    Science.gov (United States)

    Ansari, Nirwan; Liu, Dequan

    1991-01-01

    A neural-network-based traffic management scheme for a satellite communication network is described. The scheme consists of two levels of management. The front end of the scheme is a derivation of Kohonen's self-organization model to configure maps for the satellite communication network dynamically. The model consists of three stages. The first stage is the pattern recognition task, in which an exemplar map that best meets the current network requirements is selected. The second stage is the analysis of the discrepancy between the chosen exemplar map and the state of the network, and the adaptive modification of the chosen exemplar map to conform closely to the network requirement (input data pattern) by means of Kohonen's self-organization. On the basis of certain performance criteria, whether a new map is generated to replace the original chosen map is decided in the third stage. A state-dependent routing algorithm, which arranges the incoming call to some proper path, is used to make the network more efficient and to lower the call block rate. Simulation results demonstrate that the scheme, which combines self-organization and the state-dependent routing mechanism, provides better performance in terms of call block rate than schemes that only have either the self-organization mechanism or the routing mechanism.

  11. Processing of signals from an ion-elective electrode array by a neural network

    NARCIS (Netherlands)

    Bos, M.; Bos, A.; van der Linden, W.E.

    1990-01-01

    Neural network software is described for processing the signals of arrays of ion-selective electrodes. The performance of the software was tested in the simultaneous determination of calcium and copper(II) ions in binary mixtures of copper(II) nitrate and calcium chloride and the simultaneous

  12. Reward Motivation Accelerates the Onset of Neural Novelty Signals in Humans to 85 Milliseconds

    National Research Council Canada - National Science Library

    Bunzeck, Nico; Doeller, Christian F; Fuentemilla, Lluis; Dolan, Raymond J; Duzel, Emrah

    2009-01-01

    ... are rewarded [8] . In human recognition memory studies, on the other hand, reward is not used to motivate the detection of novel or familiar items. Remarkably, the possibility that the timing of neural novelty signals might be affected if the discrimination of novel and familiar items is rewarded has not yet been tested. Indeed, novelty proces...

  13. Neural cell adhesion molecule induces intracellular signaling via multiple mechanisms of Ca2+ homeostasis

    DEFF Research Database (Denmark)

    Kiryushko, Darya; Korshunova, Irina; Berezin, Vladimir

    2006-01-01

    The neural cell adhesion molecule (NCAM) plays a pivotal role in the development of the nervous system, promoting neuronal differentiation via homophilic (NCAM-NCAM) as well as heterophilic (NCAM-fibroblast growth factor receptor [FGFR]) interactions. NCAM-induced intracellular signaling has been...

  14. Semantic Congruence Accelerates the Onset of the Neural Signals of Successful Memory Encoding.

    Science.gov (United States)

    Packard, Pau A; Rodríguez-Fornells, Antoni; Bunzeck, Nico; Nicolás, Berta; de Diego-Balaguer, Ruth; Fuentemilla, Lluís

    2017-01-11

    As the stream of experience unfolds, our memory system rapidly transforms current inputs into long-lasting meaningful memories. A putative neural mechanism that strongly influences how input elements are transformed into meaningful memory codes relies on the ability to integrate them with existing structures of knowledge or schemas. However, it is not yet clear whether schema-related integration neural mechanisms occur during online encoding. In the current investigation, we examined the encoding-dependent nature of this phenomenon in humans. We showed that actively integrating words with congruent semantic information provided by a category cue enhances memory for words and increases false recall. The memory effect of such active integration with congruent information was robust, even with an interference task occurring right after each encoding word list. In addition, via electroencephalography, we show in 2 separate studies that the onset of the neural signals of successful encoding appeared early (∼400 ms) during the encoding of congruent words. That the neural signals of successful encoding of congruent and incongruent information followed similarly ∼200 ms later suggests that this earlier neural response contributed to memory formation. We propose that the encoding of events that are congruent with readily available contextual semantics can trigger an accelerated onset of the neural mechanisms, supporting the integration of semantic information with the event input. This faster onset would result in a long-lasting and meaningful memory trace for the event but, at the same time, make it difficult to distinguish it from plausible but never encoded events (i.e., related false memories). Conceptual or schema congruence has a strong influence on long-term memory. However, the question of whether schema-related integration neural mechanisms occur during online encoding has yet to be clarified. We investigated the neural mechanisms reflecting how the active

  15. A four-channel microelectronic system for neural signal regeneration

    Energy Technology Data Exchange (ETDEWEB)

    Xie Shushan; Wang Zhigong; Li Wenyuan [Institute of RF- and OE-ICs, Southeast University, Nanjing 210096 (China); Lue Xiaoying; Pan Haixian, E-mail: zgwang@seu.edu.c [State Key Laboratory of Bio-Electronics, Southeast University, Nanjing 210096 (China)

    2009-12-15

    This paper presents a microelectronic system which is capable of making a signal record and functional electric stimulation of an injured spinal cord. As a requirement of implantable engineering for the regeneration microelectronic system, the system is of low noise, low power, small size and high performance. A front-end circuit and two high performance OPAs (operational amplifiers) have been designed for the system with different functions, and the two OPAs are a low-noise low-power two-stage OPA and a constant-g{sub m} RTR input and output OPA. The system has been realized in CSMC 0.5-{mu}m CMOS technology. The test results show that the system satisfies the demands of neuron signal regeneration. (semiconductor integrated circuits)

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

    Science.gov (United States)

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

    2017-05-01

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

  17. Cooperative intersection collision avoidance system limited to stop sign and traffic signal violations (CICAS-V).

    Science.gov (United States)

    2008-09-30

    The objective of the Cooperative Intersection Collision Avoidance System for Violations (CICAS-V) Project is to develop and field-test a comprehensive system to reduce the number of crashes at intersections due to violations of traffic control device...

  18. The impact of the conversion of incandescent bulbs to the LED light source in traffic signals in Houston : a step toward sustainable control devices.

    Science.gov (United States)

    2015-06-01

    With the slowing of the American economy since 2008, it has become imperative that municipalities : identify areas in which costs can be reduced while still providing needed services to its constituents. The : use of traffic signals equipped with lig...

  19. FGF signalling regulates chromatin organisation during neural differentiation via mechanisms that can be uncoupled from transcription.

    Directory of Open Access Journals (Sweden)

    Nishal S Patel

    Full Text Available Changes in higher order chromatin organisation have been linked to transcriptional regulation; however, little is known about how such organisation alters during embryonic development or how it is regulated by extrinsic signals. Here we analyse changes in chromatin organisation as neural differentiation progresses, exploiting the clear spatial separation of the temporal events of differentiation along the elongating body axis of the mouse embryo. Combining fluorescence in situ hybridisation with super-resolution structured illumination microscopy, we show that chromatin around key differentiation gene loci Pax6 and Irx3 undergoes both decompaction and displacement towards the nuclear centre coincident with transcriptional onset. Conversely, down-regulation of Fgf8 as neural differentiation commences correlates with a more peripheral nuclear position of this locus. During normal neural differentiation, fibroblast growth factor (FGF signalling is repressed by retinoic acid, and this vitamin A derivative is further required for transcription of neural genes. We show here that exposure to retinoic acid or inhibition of FGF signalling promotes precocious decompaction and central nuclear positioning of differentiation gene loci. Using the Raldh2 mutant as a model for retinoid deficiency, we further find that such changes in higher order chromatin organisation are dependent on retinoid signalling. In this retinoid deficient condition, FGF signalling persists ectopically in the elongating body, and importantly, we find that inhibiting FGF receptor (FGFR signalling in Raldh2-/- embryos does not rescue differentiation gene transcription, but does elicit both chromatin decompaction and nuclear position change. These findings demonstrate that regulation of higher order chromatin organisation during differentiation in the embryo can be uncoupled from the machinery that promotes transcription and, for the first time, identify FGF as an extrinsic signal that

  20. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.

    Science.gov (United States)

    Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adeli, Hojjat

    2017-09-27

    An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. Astrocytic Calcium Waves Signal Brain Injury to Neural Stem and Progenitor Cells

    Directory of Open Access Journals (Sweden)

    Anna Kraft

    2017-03-01

    Full Text Available Brain injuries, such as stroke or trauma, induce neural stem cells in the subventricular zone (SVZ to a neurogenic response. Very little is known about the molecular cues that signal tissue damage, even over large distances, to the SVZ. Based on our analysis of gene expression patterns in the SVZ, 48 hr after an ischemic lesion caused by middle cerebral artery occlusion, we hypothesized that the presence of an injury might be transmitted by an astrocytic traveling calcium wave rather than by diffusible factors or hypoxia. Using a newly established in vitro system we show that calcium waves induced in an astrocytic monolayer spread to neural stem and progenitor cells and increase their self-renewal as well as migratory behavior. These changes are due to an upregulation of the Notch signaling pathway. This introduces the concept of propagating astrocytic calcium waves transmitting brain injury signals over long distances.

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

    Science.gov (United States)

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

    2009-07-01

    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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Vega, J.J. [Departamento del Acelerador, Gerencia de Ciencias Ambientales, Instituto Nacional de Investigaciones Nucleares, Apartado Postal 18-1027, Mexico D.F. 11801 (Mexico)], E-mail: jjvc@nuclear.inin.mx; Reynoso, R. [Departamento del Acelerador, Gerencia de Ciencias Ambientales, Instituto Nacional de Investigaciones Nucleares, Apartado Postal 18-1027, Mexico D.F. 11801 (Mexico); Calvet, H. Carrillo [Laboratorio de Dinamica no Lineal, Facultad de Ciencias, Universidad Nacional Autonoma de Mexico, Mexico D.F. 04510 (Mexico)

    2009-07-21

    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.

  4. Neural Network Based Recognition of Signal Patterns in Application to Automatic Testing of Rails

    Directory of Open Access Journals (Sweden)

    Tomasz Ciszewski

    2006-01-01

    Full Text Available The paper describes the application of neural network for recognition of signal patterns in measuring data gathered by the railroad ultrasound testing car. Digital conversion of the measuring signal allows to store and process large quantities of data. The elaboration of smart, effective and automatic procedures recognizing the obtained patterns on the basisof measured signal amplitude has been presented. The test shows only two classes of pattern recognition. In authors’ opinion if we deliver big enough quantity of training data, presented method is applicable to a system that recognizes many classes.

  5. Radial glial neural progenitors regulate nascent brain vascular network stabilization via inhibition of Wnt signaling.

    Directory of Open Access Journals (Sweden)

    Shang Ma

    Full Text Available The cerebral cortex performs complex cognitive functions at the expense of tremendous energy consumption. Blood vessels in the brain are known to form stereotypic patterns that facilitate efficient oxygen and nutrient delivery. Yet little is known about how vessel development in the brain is normally regulated. Radial glial neural progenitors are well known for their central role in orchestrating brain neurogenesis. Here we show that, in the late embryonic cortex, radial glial neural progenitors also play a key role in brain angiogenesis, by interacting with nascent blood vessels and regulating vessel stabilization via modulation of canonical Wnt signaling. We find that ablation of radial glia results in vessel regression, concomitant with ectopic activation of Wnt signaling in endothelial cells. Direct activation of Wnt signaling also results in similar vessel regression, while attenuation of Wnt signaling substantially suppresses regression. Radial glial ablation and ectopic Wnt pathway activation leads to elevated endothelial expression of matrix metalloproteinases, while inhibition of metalloproteinase activity significantly suppresses vessel regression. These results thus reveal a previously unrecognized role of radial glial progenitors in stabilizing nascent brain vascular network and provide novel insights into the molecular cascades through which target neural tissues regulate vessel stabilization and patterning during development and throughout life.

  6. A low power multichannel analog front end for portable neural signal recordings.

    Science.gov (United States)

    Obeid, Iyad; Nicolelis, Miguel A L; Wolf, Patrick D

    2004-02-15

    We present the design and testing of a 16-channel analog amplifier for processing neural signals. Each channel has the following features: (1) variable gain (70-94 dB), (2) four high pass Bessel filter poles (f(-3 dB)=445 Hz), (3) five low pass Bessel filter poles (f(-3 dB)=6.6 kHz), and (4) differential amplification with a user selectable reference channel to reject common mode background biological noise. Processed signals are time division multiplexed and sampled by an on-board 12-bit analog to digital converter at up to 62.5k samples/s per channel. The board is powered by two low dropout voltage regulators which may be supplied by a single battery. The board measures 8.1 cm x 9.9 cm, weighs 50 g, and consumes up to 130 mW. Its low input-referred noise (1.0 microV(RMS)) makes it possible to process low amplitude neural signals; the board was successfully tested in vivo to process cortically derived extracellular action potentials in primates. Signals processed by this board were compared to those generated by a commercially available system and were found to be nearly identical. Background noise generated by mastication was substantially attenuated by the selectable reference circuit. The described circuit is light weight and low power and is used as a component of a wearable multichannel neural telemetry system.

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

    Science.gov (United States)

    Lee, Daewoo

    2015-01-01

    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.

  8. Global Synchronization Measurement of Multivariate Neural Signals with Massively Parallel Nonlinear Interdependence Analysis.

    Science.gov (United States)

    Chen, Dan; Li, Xiaoli; Cui, Dong; Wang, Lizhe; Lu, Dongchuan

    2014-01-01

    The estimation of synchronization amongst multiple brain regions is a critical issue in understanding brain functions. There is a lack of an appropriate approach which is capable of 1) measuring the direction and strength of synchronization of activities of multiple brain regions, and 2) adapting to the quickly increasing sizes and scales of neural signals. Nonlinear Interdependence (NLI) analysis is an effective method for measuring synchronization direction and strength of bivariate neural signal. However, the method currently does not directly apply in handling multivariate signal. Its application in practice has also long been largely hampered by the ultra-high complexity of NLI algorithms. Aiming at these problems, this study 1) extends the conventional NLI to quantify the global synchronization of multivariate neural signals, and 2) develops a parallelized NLI method with general-purpose computing on the graphics processing unit (GPGPU), namely, G-NLI. The approach performs synchronization measurement in a massively parallel manner. The G-NLI has improved the runtime performance by more than 1000 times comparing to the original sequential NLI. Meanwhile, the G-NLI was employed to analyze 10-channel local field potential (LFP) recordings from a patient suffering from temporal lobe epilepsy. The results demonstrate that the proposed G-NLI method can support real-time global synchronization measurement and it could be successful in localization of epileptic focus.

  9. Intrinsic lens potential of neural retina inhibited by Notch signaling as the cause of lens transdifferentiation.

    Science.gov (United States)

    Iida, Hideaki; Ishii, Yasuo; Kondoh, Hisato

    2017-01-15

    Embryonic neural retinas of avians produce lenses under spreading culture conditions. This phenomenon has been regarded as a paradigm of transdifferentiation due to the overt change in cell type. Here we elucidated the underlying mechanisms. Retina-to-lens transdifferentiation occurs in spreading cultures, suggesting that it is triggered by altered cell-cell interactions. Thus, we tested the involvement of Notch signaling based on its role in retinal neurogenesis. Starting from E8 retina, a small number of crystallin-expressing lens cells began to develop after 20 days in control spreading cultures. By contrast, addition of Notch signal inhibitors to cultures after day 2 strongly promoted lens development beginning at day 11, and a 10-fold increase in δ-crystallin expression level. After Notch signal inhibition, transcription factor genes that regulate the early stage of eye development, Prox1 and Pitx3, were sequentially activated. These observations indicate that the lens differentiation potential is intrinsic to the neural retina, and this potential is repressed by Notch signaling during normal embryogenesis. Therefore, Notch suppression leads to lens transdifferentiation by disinhibiting the neural retina-intrinsic program of lens development. Copyright © 2016 Elsevier Inc. All rights reserved.

  10. Human Age Recognition by Electrocardiogram Signal Based on Artificial Neural Network

    Science.gov (United States)

    Dasgupta, Hirak

    2016-12-01

    The objective of this work is to make a neural network function approximation model to detect human age from the electrocardiogram (ECG) signal. The input vectors of the neural network are the Katz fractal dimension of the ECG signal, frequencies in the QRS complex, male or female (represented by numeric constant) and the average of successive R-R peak distance of a particular ECG signal. The QRS complex has been detected by short time Fourier transform algorithm. The successive R peak has been detected by, first cutting the signal into periods by auto-correlation method and then finding the absolute of the highest point in each period. The neural network used in this problem consists of two layers, with Sigmoid neuron in the input and linear neuron in the output layer. The result shows the mean of errors as -0.49, 1.03, 0.79 years and the standard deviation of errors as 1.81, 1.77, 2.70 years during training, cross validation and testing with unknown data sets, respectively.

  11. Ultra-low-power and robust digital-signal-processing hardware for implantable neural interface microsystems.

    Science.gov (United States)

    Narasimhan, S; Chiel, H J; Bhunia, S

    2011-04-01

    Implantable microsystems for monitoring or manipulating brain activity typically require on-chip real-time processing of multichannel neural data using ultra low-power, miniaturized electronics. In this paper, we propose an integrated-circuit/architecture-level hardware design framework for neural signal processing that exploits the nature of the signal-processing algorithm. First, we consider different power reduction techniques and compare the energy efficiency between the ultra-low frequency subthreshold and conventional superthreshold design. We show that the superthreshold design operating at a much higher frequency can achieve comparable energy dissipation by taking advantage of extensive power gating. It also provides significantly higher robustness of operation and yield under large process variations. Next, we propose an architecture level preferential design approach for further energy reduction by isolating the critical computation blocks (with respect to the quality of the output signal) and assigning them higher delay margins compared to the noncritical ones. Possible delay failures under parameter variations are confined to the noncritical components, allowing graceful degradation in quality under voltage scaling. Simulation results using prerecorded neural data from the sea-slug (Aplysia californica) show that the application of the proposed design approach can lead to significant improvement in total energy, without compromising the output signal quality under process variations, compared to conventional design approaches.

  12. Wireless transmission of neural signals using entropy and mutual information compression.

    Science.gov (United States)

    Craciun, Stefan; Cheney, David; Gugel, Karl; Sanchez, Justin C; Principe, Jose C

    2011-02-01

    Two of the most critical tasks when designing a portable wireless neural recording system are to limit power consumption and to efficiently use the limited bandwidth. It is known that for most wireless devices the majority of power is consumed by the wireless transmitter and it often represents the bottleneck of the overall design. This paper compares two compression techniques that take advantage of the sparseness of the neural spikes in neural recordings using an information theoretic formalism to enhance the well-established vector quantization (VQ) algorithm. The two discriminative VQ algorithms are applied to neuronal recordings proving their ability to accurately reconstruct action potential (AP) regions of the neuronal signal while compressing background activity without using thresholds. The two operational modes presented offer distinct characteristics to lossy compression. The first approach requires no preprocessing or prior knowledge of the signal while the second requires a training set of spikes to obtain AP templates. The compression algorithms are implemented on an on-board digital signal processor (DSP) and results show that power consumption is decreased while the bandwidth is more efficiently utilized. The compression algorithms have been tested in real time on a hardware platform (PICO DSP ) enhanced with the DSP which runs the algorithm before sending the compressed data to a wireless transmitter. The compression ratios obtained range from 70:1 and 40:1 depending on the signal to noise ratio (SNR) of the input signal. The spike sorting accuracy in the reconstructed data is 95% compatible to the original neural data.

  13. Quality-on-Demand Compression of EEG Signals for Telemedicine Applications Using Neural Network Predictors

    Directory of Open Access Journals (Sweden)

    N. Sriraam

    2011-01-01

    Full Text Available A telemedicine system using communication and information technology to deliver medical signals such as ECG, EEG for long distance medical services has become reality. In either the urgent treatment or ordinary healthcare, it is necessary to compress these signals for the efficient use of bandwidth. This paper discusses a quality on demand compression of EEG signals using neural network predictors for telemedicine applications. The objective is to obtain a greater compression gains at a low bit rate while preserving the clinical information content. A two-stage compression scheme with a predictor and an entropy encoder is used. The residue signals obtained after prediction is first thresholded using various levels of thresholds and are further quantized and then encoded using an arithmetic encoder. Three neural network models, single-layer and multi-layer perceptrons and Elman network are used and the results are compared with linear predictors such as FIR filters and AR modeling. The fidelity of the reconstructed EEG signal is assessed quantitatively using parameters such as PRD, SNR, cross correlation and power spectral density. It is found from the results that the quality of the reconstructed signal is preserved at a low PRD thereby yielding better compression results compared to results obtained using lossless scheme.

  14. Quality-on-Demand Compression of EEG Signals for Telemedicine Applications Using Neural Network Predictors.

    Science.gov (United States)

    Sriraam, N

    2011-01-01

    A telemedicine system using communication and information technology to deliver medical signals such as ECG, EEG for long distance medical services has become reality. In either the urgent treatment or ordinary healthcare, it is necessary to compress these signals for the efficient use of bandwidth. This paper discusses a quality on demand compression of EEG signals using neural network predictors for telemedicine applications. The objective is to obtain a greater compression gains at a low bit rate while preserving the clinical information content. A two-stage compression scheme with a predictor and an entropy encoder is used. The residue signals obtained after prediction is first thresholded using various levels of thresholds and are further quantized and then encoded using an arithmetic encoder. Three neural network models, single-layer and multi-layer perceptrons and Elman network are used and the results are compared with linear predictors such as FIR filters and AR modeling. The fidelity of the reconstructed EEG signal is assessed quantitatively using parameters such as PRD, SNR, cross correlation and power spectral density. It is found from the results that the quality of the reconstructed signal is preserved at a low PRD thereby yielding better compression results compared to results obtained using lossless scheme.

  15. Traffic design and signal timing of staggered intersections based on a sorting strategy

    National Research Council Canada - National Science Library

    Cai, Zhengyi; Xiong, Manchu; Ma, Dongfang; Wang, Dianhai

    2016-01-01

    ...–right type of staggered intersection by channelization and signal phasing, based on a sorting strategy and pre-signal, which reduce the amount of lost time during the signal cycle using the split...

  16. Comparison of MLP neural network and neuro-fuzzy system in transcranial Doppler signals recorded from the cerebral vessels.

    Science.gov (United States)

    Hardalaç, Firat

    2008-04-01

    Transcranial Doppler signals recorded from cerebral vessels of 110 patients were transferred to a personal computer by using a 16 bit sound card. Spectral analyses of Transcranial Doppler signals were performed for determining the Multi Layer Perceptron (MLP) neural network and neuro Ankara-fuzzy system inputs. In order to do a good interpretation and rapid diagnosis, FFT parameters of Transcranial Doppler signals classified using MLP neural network and neuro-fuzzy system. Our findings demonstrated that 92% correct classification rate was obtained from MLP neural network, and 86% correct classification rate was obtained from neuro-fuzzy system.

  17. CONTROLLING TRAFFIC FLOW IN MULTILANE-ISOLATED INTERSECTION USING ANFIS APPROACH TECHNIQUES

    Directory of Open Access Journals (Sweden)

    G. R. LAI

    2015-08-01

    Full Text Available Many controllers have applied the Adaptive Neural-Fuzzy Inference System (ANFIS concept for optimizing the controller performance. However, there are less traffic signal controllers developed using the ANFIS concept. ANFIS traffic signal controller with its fuzzy rule base and its ability to learn from a set of sample data could improve the performance of Existing traffic signal controlling system to reduce traffic congestions at most of the busy traffic intersections in city such as Kuala Lumpur, Malaysia. The aim of this research is to develop an ANFIS traffic signals controller for multilane-isolated four approaches intersections in order to ease traffic congestions at traffic intersections. The new concept to generate sample data for ANFIS training is introduced in this research. The sample data is generated based on fuzzy rules and can be analysed using tree diagram. This controller is simulated on multilane-isolated traffic intersection model developed using M/M/1 queuing theory and its performance in terms of average waiting time, queue length and delay time are compared with traditional controllers and fuzzy controller. Simulation result shows that the average waiting time, queue length, and delay time of ANFIS traffic signal controller are the lowest as compared to the other three controllers. In conclusion, the efficiency and performance of ANFIS controller are much better than that of fuzzy and traditional controllers in different traffic volumes.

  18. Global exponential stability and dissipativity of generalized neural networks with time-varying delay signals.

    Science.gov (United States)

    Manivannan, R; Samidurai, R; Cao, Jinde; Alsaedi, Ahmed; Alsaadi, Fuad E

    2017-03-01

    This paper investigates the problems of exponential stability and dissipativity of generalized neural networks (GNNs) with time-varying delay signals. By constructing a novel Lyapunov-Krasovskii functionals (LKFs) with triple integral terms that contain more advantages of the state vectors of the neural networks, and the upper bound on the time-varying delay signals are formulated. We employ a new integral inequality technique (IIT), free-matrix-based (FMB) integral inequality approach, and Wirtinger double integral inequality (WDII) technique together with the reciprocally convex combination (RCC) approach to bound the time derivative of the LKFs. An improved exponential stability and strictly (Q,S,R)-γ-dissipative conditions of the addressed systems are represented by the linear matrix inequalities (LMIs). Finally, four interesting numerical examples are developed to verify the usefulness of the proposed method with a practical application to a biological network. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. Intelligent traffic signals : extending the range of self-organization in the BML model.

    Science.gov (United States)

    2013-04-01

    The two-dimensional traffic model of Biham, Middleton and Levine (Phys. Rev. A, 1992) is : a simple cellular automaton that exhibits a wide range of complex behavior. It consists of both : northbound and eastbound cars traveling on a rectangular arra...

  20. Comparison of five bicycle facility designs in signalized intersections using traffic conflict studies

    DEFF Research Database (Denmark)

    Madsen, Tanja Kidholm Osmann; Lahrmann, Harry Spaabæk

    2017-01-01

    Highlights •Traffic conflict study comparing cyclists’ relative risk for five bicycle layouts. •Watchdog video analysis software applied to reduce video data. •Video analysis software necessary to conduct larger conflict studies. •Recessed bicycle track seems to provide the highest safety level...

  1. Defective ALK5 signaling in the neural crest leads to increased postmigratory neural crest cell apoptosis and severe outflow tract defects

    Directory of Open Access Journals (Sweden)

    Sucov Henry M

    2006-11-01

    Full Text Available Abstract Background Congenital cardiovascular diseases are the most common form of birth defects in humans. A substantial portion of these defects has been associated with inappropriate induction, migration, differentiation and patterning of pluripotent cardiac neural crest stem cells. While TGF-β-superfamily signaling has been strongly implicated in neural crest cell development, the detailed molecular signaling mechanisms in vivo are still poorly understood. Results We deleted the TGF-β type I receptor Alk5 specifically in the mouse neural crest cell lineage. Failure in signaling via ALK5 leads to severe cardiovascular and pharyngeal defects, including inappropriate remodeling of pharyngeal arch arteries, abnormal aortic sac development, failure in pharyngeal organ migration and persistent truncus arteriosus. While ALK5 is not required for neural crest cell migration, our results demonstrate that it plays an important role in the survival of post-migratory cardiac neural crest cells. Conclusion Our results demonstrate that ALK5-mediated signaling in neural crest cells plays an essential cell-autonomous role in the pharyngeal and cardiac outflow tract development.

  2. Signalling through the Type 1 Insulin-Like Growth Factor Receptor (IGF1R Interacts with Canonical Wnt Signalling to Promote Neural Proliferation in Developing Brain

    Directory of Open Access Journals (Sweden)

    Qichen Hu

    2012-05-01

    Full Text Available Signalling through the IGF1R [type 1 IGF (insulin-like growth factor receptor] and canonical Wnt signalling are two signalling pathways that play critical roles in regulating neural cell generation and growth. To determine whether the signalling through the IGF1R can interact with the canonical Wnt signalling pathway in neural cells in vivo, we studied mutant mice with altered IGF signalling. We found that in mice with blunted IGF1R expression specifically in nestin-expressing neural cells (IGF1RNestin–KO mice the abundance of neural β-catenin was significantly reduced. Blunting IGF1R expression also markedly decreased: (i the activity of a LacZ (β-galactosidase reporter transgene that responds to Wnt nuclear signalling (LacZTCF reporter transgene and (ii the number of proliferating neural precursors. In contrast, overexpressing IGF-I (insulin-like growth factor I in brain markedly increased the activity of the LacZTCF reporter transgene. Consistently, IGF-I treatment also markedly increased the activity of the LacZTCF reporter transgene in embryonic neuron cultures that are derived from LacZTCF Tg (transgenic mice. Importantly, increasing the abundance of β-catenin in IGF1RNestin–KO embryonic brains by suppressing the activity of GSK3β (glycogen synthase kinase-3β significantly alleviated the phenotypic changes induced by IGF1R deficiency. These phenotypic changes includes: (i retarded brain growth, (ii reduced precursor proliferation and (iii decreased neuronal number. Our current data, consistent with our previous study of cultured oligodendrocytes, strongly support the concept that IGF signalling interacts with canonical Wnt signalling in the developing brain to promote neural proliferation. The interaction of IGF and canonical Wnt signalling plays an important role in normal brain development by promoting neural precursor proliferation.

  3. Reconfigurable embedded system architecture for next-generation Neural Signal Processing.

    Science.gov (United States)

    Balasubramanian, Karthikeyan; Obeid, Iyad

    2010-01-01

    This work presents a new architectural framework for next generation Neural Signal Processing (NSP). The essential features of the NSP hardware platform include scalability, reconfigurability, real-time processing ability and data storage. This proposed framework has been implemented in a proof-of-concept NSP prototype using an embedded system architecture synthesized in a Xilinx(®)Virtex(®)5 development board. The prototype includes a threshold-based spike detector and a fuzzy logic-based spike sorter.

  4. Detecting and Predicting Muscle Fatigue during Typing By SEMG Signal Processing and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Elham Ghoochani

    2011-03-01

    Full Text Available Introduction: Repetitive strain injuries are one of the most prevalent problems in occupational diseases. Repetition, vibration and bad postures of the extremities are physical risk factors related to work that can cause chronic musculoskeletal disorders. Repetitive work on a computer with low level contraction requires the posture to be maintained for a long time, which can cause muscle fatigue. Muscle fatigue in shoulders and neck is one of the most prevalent problems reported with computer users especially during typing. Surface electromyography (SEMG signals are used for detecting muscle fatigue as a non-invasive method. Material and Methods: Nine healthy females volunteered for signal recoding during typing. EMG signals were recorded from the trapezius muscle, which is subjected to muscle fatigue during typing.  After signal analysis and feature extraction, detecting and predicting muscle fatigue was performed by using the MLP artificial neural network. Results: Recorded signals were analyzed in time and frequency domains for feature extraction. Results of classification showed that the MLP neural network can detect and predict muscle fatigue during typing with 80.79 % ± 1.04% accuracy. Conclusion: Intelligent classification and prediction of muscle fatigue can have many applications in human factors engineering (ergonomics, rehabilitation engineering and biofeedback equipment for mitigating the injuries of repetitive works.

  5. EEG signal classification using PSO trained RBF neural network for epilepsy identification

    Directory of Open Access Journals (Sweden)

    Sandeep Kumar Satapathy

    Full Text Available The electroencephalogram (EEG is a low amplitude signal generated in the brain, as a result of information flow during the communication of several neurons. Hence, careful analysis of these signals could be useful in understanding many human brain disorder diseases. One such disease topic is epileptic seizure identification, which can be identified via a classification process of the EEG signal after preprocessing with the discrete wavelet transform (DWT. To classify the EEG signal, we used a radial basis function neural network (RBFNN. As shown herein, the network can be trained to optimize the mean square error (MSE by using a modified particle swarm optimization (PSO algorithm. The key idea behind the modification of PSO is to introduce a method to overcome the problem of slow searching in and around the global optimum solution. The effectiveness of this procedure was verified by an experimental analysis on a benchmark dataset which is publicly available. The result of our experimental analysis revealed that the improvement in the algorithm is significant with respect to RBF trained by gradient descent and canonical PSO. Here, two classes of EEG signals were considered: the first being an epileptic and the other being non-epileptic. The proposed method produced a maximum accuracy of 99% as compared to the other techniques. Keywords: Electroencephalography, Radial basis function neural network, Particle swarm optimization, Discrete wavelet transform, Machine learning

  6. Genetic algorithm for the optimization of features and neural networks in ECG signals classification.

    Science.gov (United States)

    Li, Hongqiang; Yuan, Danyang; Ma, Xiangdong; Cui, Dianyin; Cao, Lu

    2017-01-31

    Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

  7. Genetic algorithm for the optimization of features and neural networks in ECG signals classification

    Science.gov (United States)

    Li, Hongqiang; Yuan, Danyang; Ma, Xiangdong; Cui, Dianyin; Cao, Lu

    2017-01-01

    Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

  8. on the electronic signal direction indicator for the control of road traffic

    African Journals Online (AJOL)

    2006-02-14

    Feb 14, 2006 ... An electronic signal direction indicator (ESDI) for the control of road traffic has been designed, constructed and ... voltage (signal) level or a low voltage level. That is, it will either be set or reset, and the state of the output can be changed with proper input signals. ..... Belts, J. (1970): Signal Processing and.

  9. A probablistic neural network classification system for signal and image processing

    Energy Technology Data Exchange (ETDEWEB)

    Bowman, B. [Lawrence Livermore National Lab., CA (United States)

    1994-11-15

    The Acoustical Heart Valve Analysis Package is a system for signal and image processing and classification. It is being developed in both Matlab and C, to provide an interactive, interpreted environment, and has been optimized for large scale matrix operations. It has been used successfully to classify acoustic signals from implanted prosthetic heart valves in human patients, and will be integrated into a commercial Heart Valve Screening Center. The system uses several standard signal processing algorithms, as well as supervised learning techniques using the probabilistic neural network (PNN). Although currently used for the acoustic heart valve application, the algorithms and modular design allow it to be used for other applications, as well. We will describe the signal classification system, and show results from a set of test valves.

  10. RBF neural network prediction on weak electrical signals in Aloe vera var. chinensis

    Science.gov (United States)

    Wang, Lanzhou; Zhao, Jiayin; Wang, Miao

    2008-10-01

    A Gaussian radial base function (RBF) neural network forecast on signals in the Aloe vera var. chinensis by the wavelet soft-threshold denoised as the time series and using the delayed input window chosen at 50, is set up to forecast backward. There was the maximum amplitude at 310.45μV, minimum -75.15μV, average value -2.69μV and Aloe vera var. chinensis respectively. The electrical signal in Aloe vera var. chinensis is a sort of weak, unstable and low frequency signals. A result showed that it is feasible to forecast plant electrical signals for the timing by the RBF. The forecast data can be used as the preferences for the intelligent autocontrol system based on the adaptive characteristic of plants to achieve the energy saving on the agricultural production in the plastic lookum or greenhouse.

  11. Cancellation of artifacts in ECG signals using a normalized adaptive neural filter.

    Science.gov (United States)

    Wu, Yunfeng; Rangayyan, Rangaraj M; Ng, Sin-Chun

    2007-01-01

    Denoising electrocardiographic (ECG) signals is an essential procedure prior to their analysis. In this paper, we present a normalized adaptive neural filter (NANF) for cancellation of artifacts in ECG signals. The normalized filter coefficients are updated by the steepest-descent algorithm; the adaptation process is designed to minimize the difference between second-order estimated output values and the desired artifact-free ECG signals. Empirical results with benchmark data show that the adaptive artifact canceller that includes the NANF can effectively remove muscle-contraction artifacts and high-frequency noise in ambulatory ECG recordings, leading to a high signal-to-noise ratio. Moreover, the performance of the NANF in terms of the root-mean-squared error, normalized correlation coefficient, and filtered artifact entropy is significantly better than that of the popular least-mean-square (LMS) filter.

  12. SOME EMPIRICAL RELATIONS BETWEEN TRAVEL SPEED, TRAFFIC VOLUME AND TRAFFIC COMPOSITION IN URBAN ARTERIALS

    Directory of Open Access Journals (Sweden)

    Eleni I. VLAHOGIANNI, Ph.D.

    2007-01-01

    Full Text Available The effects of traffic mix (the percentage of cars, trucks, buses and so on are of particular interest in the speed-volume relationship in urban signalized arterials under various geometric and control characteristics. The paper presents some empirical observations on the relation between travel speed, traffic volume and traffic composition in urban signalized arterials. A methodology based on emerging self-organizing structures of neural networks to identify regions in the speed-volume relationship with respect to traffic composition and Bayesian networks to evaluate the effect of different types of motorized vehicles on prevailing traffic conditions is proposed. Results based on data from a large urban network indicate that the variability in traffic conditions can be described by eight regions in speed-volume relationship with respect to traffic composition. Further evaluation of the effect of motorized vehicles in each region separately indicates that the effect of traffic composition decreases with the onset of congestion. Moreover, taxis and motorcycles are the primary affecting parameter of the form of the speed-volume relationship in urban arterials.

  13. Embryonic requirements for ErbB signaling in neural crest development and adult pigment pattern formation

    Science.gov (United States)

    Budi, Erine H.; Patterson, Larissa B.; Parichy, David M.

    2009-01-01

    SUMMARY Vertebrate pigment cells are derived from neural crest cells and are a useful system for studying neural crest-derived traits during post-embryonic development. In zebrafish, neural crest-derived melanophores differentiate during embryogenesis to produce stripes in the early larva. Dramatic changes to the pigment pattern occur subsequently during the larva-to-adult transformation, or metamorphosis. At this time, embryonic melanophores are replaced by newly differentiating metamorphic melanophores that form the adult stripes. Mutants with normal embryonic/early larval pigment patterns but defective adult patterns identify factors required uniquely to establish, maintain, or recruit the latent precursors to metamorphic melanophores. We show that one such mutant, picasso, lacks most metamorphic melanophores and results from mutations in the ErbB gene erbb3b, encoding an EGFR-like receptor tyrosine kinase. To identify critical periods for ErbB activities, we treated fish with pharmacological ErbB inhibitors and also knocked-down erbb3b by morpholino injection. These analyses reveal an embryonic critical period for ErbB signaling in promoting later pigment pattern metamorphosis, despite the normal patterning of embryonic/early larval melanophores. We further demonstrate a peak requirement during neural crest migration that correlates with early defects in neural crest pathfinding and peripheral ganglion formation. Finally, we show that erbb3b activities are both autonomous and non-autonomous to the metamorphic melanophore lineage. These data identify a very early, embryonic, requirement for erbb3b in the development of much later metamorphic melanophores, and suggest complex modes by which ErbB signals promote adult pigment pattern development. PMID:18508863

  14. Neural plasticity in the gastrointestinal tract: chronic inflammation, neurotrophic signals, and hypersensitivity.

    Science.gov (United States)

    Demir, Ihsan Ekin; Schäfer, Karl-Herbert; Tieftrunk, Elke; Friess, Helmut; Ceyhan, Güralp O

    2013-04-01

    Neural plasticity is not only the adaptive response of the central nervous system to learning, structural damage or sensory deprivation, but also an increasingly recognized common feature of the gastrointestinal (GI) nervous system during pathological states. Indeed, nearly all chronic GI disorders exhibit a disease-stage-dependent, structural and functional neuroplasticity. At structural level, GI neuroplasticity usually comprises local tissue hyperinnervation (neural sprouting, neural, and ganglionic hypertrophy) next to hypoinnervated areas, a switch in the neurochemical (neurotransmitter/neuropeptide) code toward preferential expression of neuropeptides which are frequently present in nociceptive neurons (e.g., substance P/SP, calcitonin-gene-related-peptide/CGRP) and of ion channels (TRPV1, TRPA1, PAR2), and concomitant activation of peripheral neural glia. The functional counterpart of these structural alterations is altered neuronal electric activity, leading to organ dysfunction (e.g., impaired motility and secretion), together with reduced sensory thresholds, resulting in hypersensitivity and pain. The present review underlines that neural plasticity in all GI organs, starting from esophagus, stomach, small and large intestine to liver, gallbladder, and pancreas, actually exhibits common phenotypes and mechanisms. Careful appraisal of these GI neuroplastic alterations reveals that--no matter which etiology, i.e., inflammatory, infectious, neoplastic/malignant, or degenerative--neural plasticity in the GI tract primarily occurs in the presence of chronic tissue- and neuro-inflammation. It seems that studying the abundant trophic and activating signals which are generated during this neuro-immune-crosstalk represents the key to understand the remarkable neuroplasticity of the GI tract.

  15. Intra-day signal instabilities affect decoding performance in an intracortical neural interface system

    Science.gov (United States)

    Perge, János A.; Homer, Mark L.; Malik, Wasim Q.; Cash, Sydney; Eskandar, Emad; Friehs, Gerhard; Donoghue, John P.; Hochberg, Leigh R.

    2013-06-01

    Objective. Motor neural interface systems (NIS) aim to convert neural signals into motor prosthetic or assistive device control, allowing people with paralysis to regain movement or control over their immediate environment. Effector or prosthetic control can degrade if the relationship between recorded neural signals and intended motor behavior changes. Therefore, characterizing both biological and technological sources of signal variability is important for a reliable NIS. Approach. To address the frequency and causes of neural signal variability in a spike-based NIS, we analyzed within-day fluctuations in spiking activity and action potential amplitude recorded with silicon microelectrode arrays implanted in the motor cortex of three people with tetraplegia (BrainGate pilot clinical trial, IDE). Main results. 84% of the recorded units showed a statistically significant change in apparent firing rate (3.8 ± 8.71 Hz or 49% of the mean rate) across several-minute epochs of tasks performed on a single session, and 74% of the units showed a significant change in spike amplitude (3.7 ± 6.5 µV or 5.5% of mean spike amplitude). 40% of the recording sessions showed a significant correlation in the occurrence of amplitude changes across electrodes, suggesting array micro-movement. Despite the relatively frequent amplitude changes, only 15% of the observed within-day rate changes originated from recording artifacts such as spike amplitude change or electrical noise, while 85% of the rate changes most likely emerged from physiological mechanisms. Computer simulations confirmed that systematic rate changes of individual neurons could produce a directional ‘bias’ in the decoded neural cursor movements. Instability in apparent neuronal spike rates indeed yielded a directional bias in 56% of all performance assessments in participant cursor control (n = 2 participants, 108 and 20 assessments over two years), resulting in suboptimal performance in these sessions

  16. Respiratory signal prediction based on adaptive boosting and multi-layer perceptron neural network

    Science.gov (United States)

    Sun, W. Z.; Jiang, M. Y.; Ren, L.; Dang, J.; You, T.; Yin, F.-F.

    2017-09-01

    To improve the prediction accuracy of respiratory signals using adaptive boosting and multi-layer perceptron neural network (ADMLP-NN) for gated treatment of moving target in radiation therapy. The respiratory signals acquired using a real-time position management (RPM) device from 138 previous 4DCT scans were retrospectively used in this study. The ADMLP-NN was composed of several artificial neural networks (ANNs) which were used as weaker predictors to compose a stronger predictor. The respiratory signal was initially smoothed using a Savitzky-Golay finite impulse response smoothing filter (S-G filter). Then, several similar multi-layer perceptron neural networks (MLP-NNs) were configured to estimate future respiratory signal position from its previous positions. Finally, an adaptive boosting (Adaboost) decision algorithm was used to set weights for each MLP-NN based on the sample prediction error of each MLP-NN. Two prediction methods, MLP-NN and ADMLP-NN (MLP-NN plus adaptive boosting), were evaluated by calculating correlation coefficient and root-mean-square-error between true and predicted signals. For predicting 500 ms ahead of prediction, average correlation coefficients were improved from 0.83 (MLP-NN method) to 0.89 (ADMLP-NN method). The average of root-mean-square-error (relative unit) for 500 ms ahead of prediction using ADMLP-NN were reduced by 27.9%, compared to those using MLP-NN. The preliminary results demonstrate that the ADMLP-NN respiratory prediction method is more accurate than the MLP-NN method and can improve the respiration prediction accuracy.

  17. Urban Traffic Signal Control for Fuel Economy (Economie d’Essence Grace a la Commande des Feux de Circulation en Zone Urbaine),

    Science.gov (United States)

    1980-01-01

    Laboratory/Laboratoire des moteurs ; Director/Dreer - SUMMARY The Metropolitan Toronto Roads and Traffic Department and the Engine Laboratory of the...rising energy costs. In consequence, the provision of an efficient traffic signal control system has become extremely important in urban areas. Through...visibility, pavement structure, markings and turning radii. Therefore, the test section is essentially -2- free -flow during both the offpeak and rush hour

  18. Social discounting involves modulation of neural value signals by temporoparietal junction

    Science.gov (United States)

    Strombach, Tina; Weber, Bernd; Hangebrauk, Zsofia; Kenning, Peter; Karipidis, Iliana I.; Tobler, Philippe N.; Kalenscher, Tobias

    2015-01-01

    Most people are generous, but not toward everyone alike: generosity usually declines with social distance between individuals, a phenomenon called social discounting. Despite the pervasiveness of social discounting, social distance between actors has been surprisingly neglected in economic theory and neuroscientific research. We used functional magnetic resonance imaging (fMRI) to study the neural basis of this process to understand the neural underpinnings of social decision making. Participants chose between selfish and generous alternatives, yielding either a large reward for the participant alone, or smaller rewards for the participant and another individual at a particular social distance. We found that generous choices engaged the temporoparietal junction (TPJ). In particular, the TPJ activity was scaled to the social-distance–dependent conflict between selfish and generous motives during prosocial choice, consistent with ideas that the TPJ promotes generosity by facilitating overcoming egoism bias. Based on functional coupling data, we propose and provide evidence for a biologically plausible neural model according to which the TPJ supports social discounting by modulating basic neural value signals in the ventromedial prefrontal cortex to incorporate social-distance–dependent other-regarding preferences into an otherwise exclusively own-reward value representation. PMID:25605887

  19. Enhancement of signal sensitivity in a heterogeneous neural network refined from synaptic plasticity

    Energy Technology Data Exchange (ETDEWEB)

    Li Xiumin; Small, Michael, E-mail: ensmall@polyu.edu.h, E-mail: 07901216r@eie.polyu.edu.h [Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon (Hong Kong)

    2010-08-15

    Long-term synaptic plasticity induced by neural activity is of great importance in informing the formation of neural connectivity and the development of the nervous system. It is reasonable to consider self-organized neural networks instead of prior imposition of a specific topology. In this paper, we propose a novel network evolved from two stages of the learning process, which are respectively guided by two experimentally observed synaptic plasticity rules, i.e. the spike-timing-dependent plasticity (STDP) mechanism and the burst-timing-dependent plasticity (BTDP) mechanism. Due to the existence of heterogeneity in neurons that exhibit different degrees of excitability, a two-level hierarchical structure is obtained after the synaptic refinement. This self-organized network shows higher sensitivity to afferent current injection compared with alternative archetypal networks with different neural connectivity. Statistical analysis also demonstrates that it has the small-world properties of small shortest path length and high clustering coefficients. Thus the selectively refined connectivity enhances the ability of neuronal communications and improves the efficiency of signal transmission in the network.

  20. Artificial Neural Network-Based Early-Age Concrete Strength Monitoring Using Dynamic Response Signals.

    Science.gov (United States)

    Kim, Junkyeong; Lee, Chaggil; Park, Seunghee

    2017-06-07

    Concrete is one of the most common materials used to construct a variety of civil infrastructures. However, since concrete might be susceptible to brittle fracture, it is essential to confirm the strength of concrete at the early-age stage of the curing process to prevent unexpected collapse. To address this issue, this study proposes a novel method to estimate the early-age strength of concrete, by integrating an artificial neural network algorithm with a dynamic response measurement of the concrete material. The dynamic response signals of the concrete, including both electromechanical impedances and guided ultrasonic waves, are obtained from an embedded piezoelectric sensor module. The cross-correlation coefficient of the electromechanical impedance signals and the amplitude of the guided ultrasonic wave signals are selected to quantify the variation in dynamic responses according to the strength of the concrete. Furthermore, an artificial neural network algorithm is used to verify a relationship between the variation in dynamic response signals and concrete strength. The results of an experimental study confirm that the proposed approach can be effectively applied to estimate the strength of concrete material from the early-age stage of the curing process.

  1. Altered neural reward and loss processing and prediction error signalling in depression

    Science.gov (United States)

    Ubl, Bettina; Kuehner, Christine; Kirsch, Peter; Ruttorf, Michaela

    2015-01-01

    Dysfunctional processing of reward and punishment may play an important role in depression. However, functional magnetic resonance imaging (fMRI) studies have shown heterogeneous results for reward processing in fronto-striatal regions. We examined neural responsivity associated with the processing of reward and loss during anticipation and receipt of incentives and related prediction error (PE) signalling in depressed individuals. Thirty medication-free depressed persons and 28 healthy controls performed an fMRI reward paradigm. Regions of interest analyses focused on neural responses during anticipation and receipt of gains and losses and related PE-signals. Additionally, we assessed the relationship between neural responsivity during gain/loss processing and hedonic capacity. When compared with healthy controls, depressed individuals showed reduced fronto-striatal activity during anticipation of gains and losses. The groups did not significantly differ in response to reward and loss outcomes. In depressed individuals, activity increases in the orbitofrontal cortex and nucleus accumbens during reward anticipation were associated with hedonic capacity. Depressed individuals showed an absence of reward-related PEs but encoded loss-related PEs in the ventral striatum. Depression seems to be linked to blunted responsivity in fronto-striatal regions associated with limited motivational responses for rewards and losses. Alterations in PE encoding might mirror blunted reward- and enhanced loss-related associative learning in depression. PMID:25567763

  2. Integration of Signals along Orthogonal Axes of the Vertebrate Neural Tube Controls Progenitor Competence and Increases Cell Diversity

    Science.gov (United States)

    Sasai, Noriaki; Kutejova, Eva; Briscoe, James

    2014-01-01

    A relatively small number of signals are responsible for the variety and pattern of cell types generated in developing embryos. In part this is achieved by exploiting differences in the concentration or duration of signaling to increase cellular diversity. In addition, however, changes in cellular competence—temporal shifts in the response of cells to a signal—contribute to the array of cell types generated. Here we investigate how these two mechanisms are combined in the vertebrate neural tube to increase the range of cell types and deliver spatial control over their location. We provide evidence that FGF signaling emanating from the posterior of the embryo controls a change in competence of neural progenitors to Shh and BMP, the two morphogens that are responsible for patterning the ventral and dorsal regions of the neural tube, respectively. Newly generated neural progenitors are exposed to FGF signaling, and this maintains the expression of the Nk1-class transcription factor Nkx1.2. Ventrally, this acts in combination with the Shh-induced transcription factor FoxA2 to specify floor plate cells and dorsally in combination with BMP signaling to induce neural crest cells. As development progresses, the intersection of FGF with BMP and Shh signals is interrupted by axis elongation, resulting in the loss of Nkx1.2 expression and allowing the induction of ventral and dorsal interneuron progenitors by Shh and BMP signaling to supervene. Hence a similar mechanism increases cell type diversity at both dorsal and ventral poles of the neural tube. Together these data reveal that tissue morphogenesis produces changes in the coincidence of signals acting along orthogonal axes of the neural tube and this is used to define spatial and temporal transitions in the competence of cells to interpret morphogen signaling. PMID:25026549

  3. Feature reconstruction of LFP signals based on PLSR in the neural information decoding study.

    Science.gov (United States)

    Yonghui Dong; Zhigang Shang; Mengmeng Li; Xinyu Liu; Hong Wan

    2017-07-01

    To solve the problems of Signal-to-Noise Ratio (SNR) and multicollinearity when the Local Field Potential (LFP) signals is used for the decoding of animal motion intention, a feature reconstruction of LFP signals based on partial least squares regression (PLSR) in the neural information decoding study is proposed in this paper. Firstly, the feature information of LFP coding band is extracted based on wavelet transform. Then the PLSR model is constructed by the extracted LFP coding features. According to the multicollinearity characteristics among the coding features, several latent variables which contribute greatly to the steering behavior are obtained, and the new LFP coding features are reconstructed. Finally, the K-Nearest Neighbor (KNN) method is used to classify the reconstructed coding features to verify the decoding performance. The results show that the proposed method can achieve the highest accuracy compared to the other three methods and the decoding effect of the proposed method is robust.

  4. Employment and comparison of different Artificial Neural Networks for epilepsy diagnosis from EEG signals.

    Science.gov (United States)

    Sezer, Esma; Işik, Hakan; Saracoğlu, Esra

    2012-02-01

    In this study, it has been intended to analyze Electroencephalography (EEG) signals by Wavelet Transform (WT) for diagnosis of epilepsy, to employ various Artificial Neural Networks (ANNs) for the signals' automatic classification. Furthermore, carrying out a performance comparison has been aimed. Three EEG signals have been decomposed into frequency sub bands by WT and the feature vectors have been extracted from these sub bands. In order to reduce the sizes of the extracted feature vectors, Principal Component Analysis (PCA) method has been applied when necessary and these feature vectors have been classified by five different ANNs as either epileptic or healthy. The performance evaluation has been carried out by conducting ROC analysis for the used ANN models that and their comparisons have also been included.

  5. Accelerometer signal-based human activity recognition using augmented autoregressive model coefficients and artificial neural nets.

    Science.gov (United States)

    Khan, A M; Lee, Y K; Kim, T S

    2008-01-01

    Automatic recognition of human activities is one of the important and challenging research areas in proactive and ubiquitous computing. In this work, we present some preliminary results of recognizing human activities using augmented features extracted from the activity signals measured using a single triaxial accelerometer sensor and artificial neural nets. The features include autoregressive (AR) modeling coefficients of activity signals, signal magnitude areas (SMA), and title angles (TA). We have recognized four human activities using AR coefficients (ARC) only, ARC with SMA, and ARC with SMA and TA. With the last augmented features, we have achieved the recognition rate above 99% for all four activities including lying, standing, walking, and running. With our proposed technique, real time recognition of some human activities is possible.

  6. Neural crest-derived mesenchymal cells require Wnt signaling for their development and drive invagination of the telencephalic midline.

    Directory of Open Access Journals (Sweden)

    Youngshik Choe

    Full Text Available Embryonic neural crest cells contribute to the development of the craniofacial mesenchyme, forebrain meninges and perivascular cells. In this study, we investigated the function of ß-catenin signaling in neural crest cells abutting the dorsal forebrain during development. In the absence of ß-catenin signaling, neural crest cells failed to expand in the interhemispheric region and produced ectopic smooth muscle cells instead of generating dermal and calvarial mesenchyme. In contrast, constitutive expression of stabilized ß-catenin in neural crest cells increased the number of mesenchymal lineage precursors suggesting that ß-catenin signaling is necessary for the expansion of neural crest-derived mesenchymal cells. Interestingly, the loss of neural crest-derived mesenchymal stem cells (MSCs leads to failure of telencephalic midline invagination and causes ventricular system defects. This study shows that ß-catenin signaling is required for the switch of neural crest cells to MSCs and mediates the expansion of MSCs to drive the formation of mesenchymal structures of the head. Furthermore, loss of these structures causes striking defects in forebrain morphogenesis.

  7. Hybrid fuzzy logic committee neural networks for recognition of swallow acceleration signals.

    Science.gov (United States)

    Das, A; Reddy, N P; Narayanan, J

    2001-02-01

    Biological signals are complex and often require intelligent systems for recognition of characteristic signals. In order to improve the reliability of the recognition or automated diagnostic systems, hybrid fuzzy logic committee neural networks were developed and the system was used for recognition of swallow acceleration signals from artifacts. Two sets of fuzzy logic-committee networks (FCN) each consisting of seven member networks were developed, trained and evaluated. The FCN-I was used to recognize dysphagic swallow from artifacts, and the second committee FCN-II was used to recognize normal swallow from artifacts. Several networks were trained and the best seven were recruited into each committee. Acceleration signals from the throat were bandpass filtered, and several parameters were extracted and fed to the fuzzy logic block of either FCN-I or FCN-II. The fuzzified membership values were fed to the committee of neural networks which provided the signal classification. A majority opinion of the member networks was used to arrive at the final decision. Evaluation results revealed that FCN correctly identified 16 out of 16 artifacts and 31 out of 33 dysphagic swallows. In two cases, the decision was ambiguous due to the lack of a majority opinion. FCN-II correctly identified 24 out of 24 normal swallows, and 28 out of 29 artifacts. In one case, the decision was ambiguous due to the lack of a majority opinion. The present hybrid intelligent system consisting of fuzzy logic and committee networks provides a reliable tool for recognition and classification of acceleration signals due to swallowing.

  8. Modeling fMRI signals can provide insights into neural processing in the cerebral cortex

    Science.gov (United States)

    Sharifian, Fariba; Heikkinen, Hanna; Vigário, Ricardo

    2015-01-01

    Every stimulus or task activates multiple areas in the mammalian cortex. These distributed activations can be measured with functional magnetic resonance imaging (fMRI), which has the best spatial resolution among the noninvasive brain imaging methods. Unfortunately, the relationship between the fMRI activations and distributed cortical processing has remained unclear, both because the coupling between neural and fMRI activations has remained poorly understood and because fMRI voxels are too large to directly sense the local neural events. To get an idea of the local processing given the macroscopic data, we need models to simulate the neural activity and to provide output that can be compared with fMRI data. Such models can describe neural mechanisms as mathematical functions between input and output in a specific system, with little correspondence to physiological mechanisms. Alternatively, models can be biomimetic, including biological details with straightforward correspondence to experimental data. After careful balancing between complexity, computational efficiency, and realism, a biomimetic simulation should be able to provide insight into how biological structures or functions contribute to actual data processing as well as to promote theory-driven neuroscience experiments. This review analyzes the requirements for validating system-level computational models with fMRI. In particular, we study mesoscopic biomimetic models, which include a limited set of details from real-life networks and enable system-level simulations of neural mass action. In addition, we discuss how recent developments in neurophysiology and biophysics may significantly advance the modelling of fMRI signals. PMID:25972586

  9. Larger Neural Responses Produce BOLD Signals That Begin Earlier in Time

    Directory of Open Access Journals (Sweden)

    Serena eThompson

    2014-06-01

    Full Text Available Functional MRI analyses commonly rely on the assumption that the temporal dynamics of hemodynamic response functions (HRFs are independent of the amplitude of the neural signals that give rise to them. The validity of this assumption is particularly important for techniques that use fMRI to resolve sub-second timing distinctions between responses, in order to make inferences about the ordering of neural processes. Whether or not the detailed shape of the HRF is independent of neural response amplitude remains an open question, however. We performed experiments in which we measured responses in primary visual cortex (V1 to large, contrast-reversing checkerboards at a range of contrast levels, which should produce varying amounts of neural activity. Ten subjects (ages 22-52 were studied in each of two experiments using 3 Tesla scanners. We used rapid, 250 msec, temporal sampling (repetition time, or TR and both short and long inter-stimulus interval (ISI stimulus presentations. We tested for a systematic relationship between the onset of the HRF and its amplitude across conditions, and found a strong negative correlation between the two measures when stimuli were separated in time (long- and medium-ISI experiments, but not the short-ISI experiment. Thus, stimuli that produce larger neural responses, as indexed by HRF amplitude, also produced HRFs with shorter onsets. The relationship between amplitude and latency was strongest in voxels with lowest mean-normalized variance (i.e., parenchymal voxels. The onset differences observed in the longer-ISI experiments are likely attributable to mechanisms of neurovascular coupling, since they are substantially larger than reported differences in the onset of action potentials in V1 as a function of response amplitude.

  10. Noncoding RNA mediated traffic of foreign mRNA into chloroplasts reveals a novel signaling mechanism in plants.

    Directory of Open Access Journals (Sweden)

    Gustavo Gómez

    Full Text Available Communication between chloroplasts and the nucleus is one of the milestones of the evolution of plants on earth. Proteins encoded by ancestral chloroplast-endogenous genes were transferred to the nucleus during the endosymbiotic evolution and originated this communication, which is mainly dependent on specific transit-peptides. However, the identification of nuclear-encoded proteins targeted to the chloroplast lacking these canonical signals suggests the existence of an alternative cellular pathway tuning this metabolic crosstalk. Non-coding RNAS (NcRNAs are increasingly recognized as regulators of gene expression as they play roles previously believed to correspond to proteins. Avsunviroidae family viroids are the only noncoding functional RNAs that have been reported to traffic inside the chloroplasts. Elucidating mechanisms used by these pathogens to enter this organelle will unearth novel transport pathways in plant cells. Here we show that a viroid-derived NcRNA acting as a 5'UTR-end mediates the functional import of Green Fluorescent Protein (GFP mRNA into chloroplast. This claim is supported by the observation at confocal microscopy of a selective accumulation of GFP in the chloroplast of the leaves expressing the chimeric vd-5'UTR/GFP and by the detection of the GFP mRNA in chloroplasts isolated from cells expressing this construct. These results support the existence of an alternative signaling mechanism in plants between the host cell and chloroplasts, where an ncRNA functions as a key regulatory molecule to control the accumulation of nuclear-encoded proteins in this organelle. In addition, our findings provide a conceptual framework to develop new biotechnological tools in systems using plant chloroplast as bioreactors. Finally, viroids of the family Avsunviroidae have probably evolved to subvert this signaling mechanism to regulate their differential traffic into the chloroplast of infected cells.

  11. Neural Mechanisms for Acoustic Signal Detection under Strong Masking in an Insect.

    Science.gov (United States)

    Kostarakos, Konstantinos; Römer, Heiner

    2015-07-22

    produces an extremely noisy sound, yet the second species still detects its own song. Using intracellular recording techniques we identified two neural mechanisms underlying the surprising behavioral signal detection at the level of single identified interneurons. These neural mechanisms for signal detection are likely to be important for other sensory modalities as well, where noise in the communication channel creates similar problems. Also, they may be used for the development of algorithms for the filtering of specific signals in technical microphones or hearing aids. Copyright © 2015 Kostarakos and Römer.

  12. AUTOMATIC RECOGNITION OF BOTH INTER AND INTRA CLASSES OF DIGITAL MODULATED SIGNALS USING ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    JIDE JULIUS POPOOLA

    2014-04-01

    Full Text Available In radio communication systems, signal modulation format recognition is a significant characteristic used in radio signal monitoring and identification. Over the past few decades, modulation formats have become increasingly complex, which has led to the problem of how to accurately and promptly recognize a modulation format. In addressing these challenges, the development of automatic modulation recognition systems that can classify a radio signal’s modulation format has received worldwide attention. Decision-theoretic methods and pattern recognition solutions are the two typical automatic modulation recognition approaches. While decision-theoretic approaches use probabilistic or likelihood functions, pattern recognition uses feature-based methods. This study applies the pattern recognition approach based on statistical parameters, using an artificial neural network to classify five different digital modulation formats. The paper deals with automatic recognition of both inter-and intra-classes of digitally modulated signals in contrast to most of the existing algorithms in literature that deal with either inter-class or intra-class modulation format recognition. The results of this study show that accurate and prompt modulation recognition is possible beyond the lower bound of 5 dB commonly acclaimed in literature. The other significant contribution of this paper is the usage of the Python programming language which reduces computational complexity that characterizes other automatic modulation recognition classifiers developed using the conventional MATLAB neural network toolbox.

  13. Astrocytic Calcium Waves Signal Brain Injury to Neural Stem and Progenitor Cells.

    Science.gov (United States)

    Kraft, Anna; Jubal, Eduardo Rosales; von Laer, Ruth; Döring, Claudia; Rocha, Adriana; Grebbin, Moyo; Zenke, Martin; Kettenmann, Helmut; Stroh, Albrecht; Momma, Stefan

    2017-03-14

    Brain injuries, such as stroke or trauma, induce neural stem cells in the subventricular zone (SVZ) to a neurogenic response. Very little is known about the molecular cues that signal tissue damage, even over large distances, to the SVZ. Based on our analysis of gene expression patterns in the SVZ, 48 hr after an ischemic lesion caused by middle cerebral artery occlusion, we hypothesized that the presence of an injury might be transmitted by an astrocytic traveling calcium wave rather than by diffusible factors or hypoxia. Using a newly established in vitro system we show that calcium waves induced in an astrocytic monolayer spread to neural stem and progenitor cells and increase their self-renewal as well as migratory behavior. These changes are due to an upregulation of the Notch signaling pathway. This introduces the concept of propagating astrocytic calcium waves transmitting brain injury signals over long distances. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

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

    DEFF Research Database (Denmark)

    Pedersen, Anders Gorm; Engelbrecht, Jacob

    1995-01-01

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

  15. Cardiac modulation of startle is altered in depersonalization-/derealization disorder: Evidence for impaired brainstem representation of baro-afferent neural traffic.

    Science.gov (United States)

    Schulz, André; Matthey, Jan Hendrik; Vögele, Claus; Schaan, Violetta; Schächinger, Hartmut; Adler, Julia; Beutel, Manfred E; Michal, Matthias

    2016-06-30

    Patients with depersonalization-/derealization disorder (DPD) show altered heartbeat-evoked brain potentials, which are considered psychophysiological indicators of cortical representation of visceral-afferent neural signals. The aim of the current investigation was to clarify whether the impaired CNS representation of visceral-afferent neural signals in DPD is restricted to the cortical level or is also present in sub-cortical structures. We used cardiac modulation of startle (CMS) to assess baro-afferent signal transmission at brainstem level in 22 DPD and 23 healthy control individuals. The CMS paradigm involved acoustic startle stimuli (105dB(A), 50ms) elicited 0, 100, 200, 300, 400 and 500ms after a cardiac R-wave. In healthy control individuals, we observed lower startle responses at 100 and 300ms than at 0 and 400ms after an R-wave. In DPD patients, no effect of the cardiac cycle on startle response magnitude was found. We conclude that the representation of visceral-afferent neural signals at brainstem level may be deficient in DPD. This effect may be due to increased peripheral sympathetic tone or to dysregulated signal processing at brainstem level. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  16. Real-time neural signals of perceptual priming with unfamiliar geometric shapes.

    Science.gov (United States)

    Voss, Joel L; Paller, Ken A

    2010-07-07

    Perceptual priming is a type of item-specific implicit memory that is distinct from explicit memory. Neural signals of the processing responsible for perceptual priming can be difficult to isolate due to concurrent conceptual processing and explicit recognition. We successfully identified neural correlates of perceptual priming by using minimally meaningful, difficult-to-recognize, kaleidoscope images. Human participants were required to quickly indicate the number of colors present in each stimulus, and priming was shown by faster and more accurate visual discriminations for repeated compared with initial presentations. Electroencephalographic responses linked with this differential perceptual fluency were identified as negative potentials 100-300 ms poststimulus onset. Furthermore, different potentials recorded during initial presentations were indicative of perceptual learning, in that their amplitude predicted the magnitude of later priming. These electrophysiological findings show that the degree of perceptual learning engaged upon first encountering a novel visual stimulus predicts the degree of perceptual fluency experienced when the stimulus is processed a second time. It is thus possible to isolate multiple neural processing stages relevant to perceptual priming by using real-time measures of relevant neurophysiological activity in conjunction with experimental circumstances that limit the contaminating influences of other neurocognitive events.

  17. Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

    Science.gov (United States)

    Smith, Aaron; Evans, Michael; Downey, Joseph

    2017-01-01

    National Aeronautics and Space Administration (NASA)'s future communication architecture is evaluating cognitive technologies and increased system intelligence. These technologies are expected to reduce the operational complexity of the network, increase science data return, and reduce interference to self and others. In order to increase situational awareness, signal classification algorithms could be applied to identify users and distinguish sources of interference. A significant amount of previous work has been done in the area of automatic signal classification for military and commercial applications. As a preliminary step, we seek to develop a system with the ability to discern signals typically encountered in satellite communication. Proposed is an automatic modulation classifier which utilizes higher order statistics (cumulants) and an estimate of the signal-to-noise ratio. These features are extracted from baseband symbols and then processed by a neural network for classification. The modulation types considered are phase-shift keying (PSK), amplitude and phase-shift keying (APSK),and quadrature amplitude modulation (QAM). Physical layer properties specific to the Digital Video Broadcasting - Satellite- Second Generation (DVB-S2) standard, such as pilots and variable ring ratios, are also considered. This paper will provide simulation results of a candidate modulation classifier, and performance will be evaluated over a range of signal-to-noise ratios, frequency offsets, and nonlinear amplifier distortions.

  18. Encoding physiological signals as images for affective state recognition using convolutional neural networks.

    Science.gov (United States)

    Guangliang Yu; Xiang Li; Dawei Song; Xiaozhao Zhao; Peng Zhang; Yuexian Hou; Bin Hu

    2016-08-01

    Affective state recognition based on multiple modalities of physiological signals has been a hot research topic. Traditional methods require designing hand-crafted features based on domain knowledge, which is time-consuming and has not achieved a satisfactory performance. On the other hand, conducting classification on raw signals directly can also cause some problems, such as the interference of noise and the curse of dimensionality. To address these problems, we propose a novel approach that encodes different modalities of data as images and use convolutional neural networks (CNN) to perform the affective state recognition task. We validate our aproach on the DECAF dataset in comparison with two state-of-the-art methods, i.e., the Support Vector Machines (SVM) and Random Forest (RF). Experimental results show that our aproach outperforms the baselines by 5% to 9%.

  19. Classification of seismic signals at Villarrica volcano (Chile) using neural networks and genetic algorithms

    Science.gov (United States)

    Curilem, Gloria; Vergara, Jorge; Fuentealba, Gustavo; Acuña, Gonzalo; Chacón, Max

    2009-02-01

    Each volcano has its own unique seismic activity. The aim of this work is to construct a system able to classify seismic signals for the Villarrica volcano, one of the most active volcanoes in South America. Since seismic signals are the result of particular processes inside the volcano's structure, they can be used to forecast volcanic activity. This paper describes the different kinds of seismic signals recorded at the Villarrica volcano and their significance. Three kind of signals were considered as most representative of this volcano's activity: the long-period, the tremor, and the energetic tremor signals. A classifier is implemented to read the seismic registers at 30-second intervals, extract the most relevant features of each interval, and classify them into one of the three kinds of signals considered as most representative of this particular volcano. To do so, 1033 different kinds of 30-s signals were extracted and classified by a human expert. A feature extraction process was applied to obtain the main characteristics of each of them. This process was developed using criteria which have been shown by others to effectively classify seismic signals, based on the experience of a human expert. The classifier was implemented with a Multi-Layer Perceptron (MLP) artificial neural network whose architecture and training process were optimized by means of a genetic algorithm. This technique searched for the most adequate MLP configuration to improve the classification performance, optimizing the number of hidden neurons, the transfer functions of the neurons, and the training algorithm. The optimization process also performed a feature selection to reduce the number of signal features, optimizing the number of network inputs. The results show that the optimized classifier reaches more than 93% exactitude. identifying the signals of each kind. The amplitude of the signals is the most important feature for its classification, followed by its frequency content. The

  20. Rabconnectin-3a regulates vesicle endocytosis and canonical Wnt signaling in zebrafish neural crest migration.

    Directory of Open Access Journals (Sweden)

    Adam M Tuttle

    2014-05-01

    Full Text Available Cell migration requires dynamic regulation of cell-cell signaling and cell adhesion. Both of these processes involve endocytosis, lysosomal degradation, and recycling of ligand-receptor complexes and cell adhesion molecules from the plasma membrane. Neural crest (NC cells in vertebrates are highly migratory cells, which undergo an epithelial-mesenchymal transition (EMT to leave the neural epithelium and migrate throughout the body to give rise to many different derivatives. Here we show that the v-ATPase interacting protein, Rabconnectin-3a (Rbc3a, controls intracellular trafficking events and Wnt signaling during NC migration. In zebrafish embryos deficient in Rbc3a, or its associated v-ATPase subunit Atp6v0a1, many NC cells fail to migrate and misregulate expression of cadherins. Surprisingly, endosomes in Rbc3a- and Atp6v0a1-deficient NC cells remain immature but still acidify. Rbc3a loss-of-function initially downregulates several canonical Wnt targets involved in EMT, but later Frizzled-7 accumulates at NC cell membranes, and nuclear B-catenin levels increase. Presumably due to this later Wnt signaling increase, Rbc3a-deficient NC cells that fail to migrate become pigment progenitors. We propose that Rbc3a and Atp6v0a1 promote endosomal maturation to coordinate Wnt signaling and intracellular trafficking of Wnt receptors and cadherins required for NC migration and cell fate determination. Our results suggest that different v-ATPases and associated proteins may play cell-type-specific functions in intracellular trafficking in many contexts.

  1. Extruded Bread Classification on the Basis of Acoustic Emission Signal With Application of Artificial Neural Networks

    Science.gov (United States)

    Świetlicka, Izabela; Muszyński, Siemowit; Marzec, Agata

    2015-04-01

    The presented work covers the problem of developing a method of extruded bread classification with the application of artificial neural networks. Extruded flat graham, corn, and rye breads differening in water activity were used. The breads were subjected to the compression test with simultaneous registration of acoustic signal. The amplitude-time records were analyzed both in time and frequency domains. Acoustic emission signal parameters: single energy, counts, amplitude, and duration acoustic emission were determined for the breads in four water activities: initial (0.362 for rye, 0.377 for corn, and 0.371 for graham bread), 0.432, 0.529, and 0.648. For classification and the clustering process, radial basis function, and self-organizing maps (Kohonen network) were used. Artificial neural networks were examined with respect to their ability to classify or to cluster samples according to the bread type, water activity value, and both of them. The best examination results were achieved by the radial basis function network in classification according to water activity (88%), while the self-organizing maps network yielded 81% during bread type clustering.

  2. 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.

  3. Transfer functions for protein signal transduction: application to a model of striatal neural plasticity.

    Directory of Open Access Journals (Sweden)

    Gabriele Scheler

    Full Text Available We present a novel formulation for biochemical reaction networks in the context of protein signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of "source" species, which are interpreted as input signals. Signals are transmitted to all other species in the system (the "target" species with a specific delay and with a specific transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and even recalled to build dynamical models on the basis of state changes. By separating the temporal and the magnitudinal domain we can greatly simplify the computational model, circumventing typical problems of complex dynamical systems. The transfer function transformation of biochemical reaction systems can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant novel insights while remaining a fully testable and executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modularizations that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. Remarkably, we found that overall interconnectedness depends on the magnitude of inputs, with higher connectivity at low input concentrations and significant modularization at moderate to high input concentrations. This general result, which directly follows from the properties of

  4. Compound developmental eye disorders following inactivation of TGFβ signaling in neural-crest stem cells

    Directory of Open Access Journals (Sweden)

    Suter Ueli

    2005-12-01

    Full Text Available Abstract Background Development of the eye depends partly on the periocular mesenchyme derived from the neural crest (NC, but the fate of NC cells in mammalian eye development and the signals coordinating the formation of ocular structures are poorly understood. Results Here we reveal distinct NC contributions to both anterior and posterior mesenchymal eye structures and show that TGFβ signaling in these cells is crucial for normal eye development. In the anterior eye, TGFβ2 released from the lens is required for the expression of transcription factors Pitx2 and Foxc1 in the NC-derived cornea and in the chamber-angle structures of the eye that control intraocular pressure. TGFβ enhances Foxc1 and induces Pitx2 expression in cell cultures. As in patients carrying mutations in PITX2 and FOXC1, TGFβ signal inactivation in NC cells leads to ocular defects characteristic of the human disorder Axenfeld-Rieger's anomaly. In the posterior eye, NC cell-specific inactivation of TGFβ signaling results in a condition reminiscent of the human disorder persistent hyperplastic primary vitreous. As a secondary effect, retinal patterning is also disturbed in mutant mice. Conclusion In the developing eye the lens acts as a TGFβ signaling center that controls the development of eye structures derived from the NC. Defective TGFβ signal transduction interferes with NC-cell differentiation and survival anterior to the lens and with normal tissue morphogenesis and patterning posterior to the lens. The similarity to developmental eye disorders in humans suggests that defective TGFβ signal modulation in ocular NC derivatives contributes to the pathophysiology of these diseases.

  5. A neural network method for identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites

    DEFF Research Database (Denmark)

    Nielsen, Henrik; Engelbrecht, Jacob; Brunak, Søren

    1997-01-01

    We have developed a new method for the identication of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequences. The method performs signicantly better than previous prediction schemes, and can easily be applied to genome......-wide data sets. Discrimination between cleaved signal peptides and uncleaved N-terminal signal-anchor sequences is also possible, though with lower precision....

  6. Standardization of light signals for road traffic control. Contribution in: Speed enforcement, visibility, and effects of traffic control measures on drivers, Transportation Research Record No. 811, p. 14-15, Transportation Research Board, National Academies of Sciences, Washington, D.C., 1981.

    NARCIS (Netherlands)

    Schreuder, D.A.

    1981-01-01

    A recent technical report on road-traffic-control signals prepared by the International Commission on Illumination is briefly discussed. The report represents a first step toward international standardisation of traffic signal lights in order to benefit trade and transportation. The principal

  7. USP9X deubiquitylating enzyme maintains RAPTOR protein levels, mTORC1 signalling and proliferation in neural progenitors.

    Science.gov (United States)

    Bridges, Caitlin R; Tan, Men-Chee; Premarathne, Susitha; Nanayakkara, Devathri; Bellette, Bernadette; Zencak, Dusan; Domingo, Deepti; Gecz, Jozef; Murtaza, Mariyam; Jolly, Lachlan A; Wood, Stephen A

    2017-03-24

    USP9X, is highly expressed in neural progenitors and, essential for neural development in mice. In humans, mutations in USP9X are associated with neurodevelopmental disorders. To understand USP9X's role in neural progenitors, we studied the effects of altering its expression in both the human neural progenitor cell line, ReNcell VM, as well as neural stem and progenitor cells derived from Nestin-cre conditionally deleted Usp9x mice. Decreasing USP9X resulted in ReNcell VM cells arresting in G0 cell cycle phase, with a concomitant decrease in mTORC1 signalling, a major regulator of G0/G1 cell cycle progression. Decreased mTORC1 signalling was also observed in Usp9x-null neurospheres and embryonic mouse brains. Further analyses revealed, (i) the canonical mTORC1 protein, RAPTOR, physically associates with Usp9x in embryonic brains, (ii) RAPTOR protein level is directly proportional to USP9X, in both loss- and gain-of-function experiments in cultured cells and, (iii) USP9X deubiquitlyating activity opposes the proteasomal degradation of RAPTOR. EdU incorporation assays confirmed Usp9x maintains the proliferation of neural progenitors similar to Raptor-null and rapamycin-treated neurospheres. Interestingly, loss of Usp9x increased the number of sphere-forming cells consistent with enhanced neural stem cell self-renewal. To our knowledge, USP9X is the first deubiquitylating enzyme shown to stabilize RAPTOR.

  8. Implementing eigenvector methods/probabilistic neural networks for analysis of EEG signals.

    Science.gov (United States)

    Ubeyli, Elif Derya

    2008-11-01

    A new approach based on the implementation of probabilistic neural network (PNN) is presented for classification of electroencephalogram (EEG) signals. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Because of the importance of making the right decision, the present work is carried out for searching better classification procedures for the EEG signals. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The aim of the study is classification of the EEG signals by the combination of eigenvector methods and the PNN. The purpose is to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. The present research demonstrated that the power levels of the power spectral density (PSD) estimates obtained by the eigenvector methods are the features which well represent the EEG signals and the PNN trained on these features achieved high classification accuracies.

  9. SMAD4-mediated WNT signaling controls the fate of cranial neural crest cells during tooth morphogenesis

    Science.gov (United States)

    Li, Jingyuan; Huang, Xiaofeng; Xu, Xun; Mayo, Julie; Bringas, Pablo; Jiang, Rulang; Wang, Songling; Chai, Yang

    2011-01-01

    TGFβ/BMP signaling regulates the fate of multipotential cranial neural crest (CNC) cells during tooth and jawbone formation as these cells differentiate into odontoblasts and osteoblasts, respectively. The functional significance of SMAD4, the common mediator of TGFβ/BMP signaling, in regulating the fate of CNC cells remains unclear. In this study, we investigated the mechanism of SMAD4 in regulating the fate of CNC-derived dental mesenchymal cells through tissue-specific inactivation of Smad4. Ablation of Smad4 results in defects in odontoblast differentiation and dentin formation. Moreover, ectopic bone-like structures replaced normal dentin in the teeth of Osr2-IresCre;Smad4fl/fl mice. Despite the lack of dentin, enamel formation appeared unaffected in Osr2-IresCre;Smad4fl/fl mice, challenging the paradigm that the initiation of enamel development depends on normal dentin formation. At the molecular level, loss of Smad4 results in downregulation of the WNT pathway inhibitors Dkk1 and Sfrp1 and in the upregulation of canonical WNT signaling, including increased β-catenin activity. More importantly, inhibition of the upregulated canonical WNT pathway in Osr2-IresCre;Smad4fl/fl dental mesenchyme in vitro partially rescued the CNC cell fate change. Taken together, our study demonstrates that SMAD4 plays a crucial role in regulating the interplay between TGFβ/BMP and WNT signaling to ensure the proper CNC cell fate decision during organogenesis. PMID:21490069

  10. Real-Time Neural Signals Decoding onto Off-the-Shelf DSP Processors for Neuroprosthetic Applications.

    Science.gov (United States)

    Pani, Danilo; Barabino, Gianluca; Citi, Luca; Meloni, Paolo; Raspopovic, Stanisa; Micera, Silvestro; Raffo, Luigi

    2016-09-01

    The control of upper limb neuroprostheses through the peripheral nervous system (PNS) can allow restoring motor functions in amputees. At present, the important aspect of the real-time implementation of neural decoding algorithms on embedded systems has been often overlooked, notwithstanding the impact that limited hardware resources have on the efficiency/effectiveness of any given algorithm. Present study is addressing the optimization of a template matching based algorithm for PNS signals decoding that is a milestone for its real-time, full implementation onto a floating-point digital signal processor (DSP). The proposed optimized real-time algorithm achieves up to 96% of correct classification on real PNS signals acquired through LIFE electrodes on animals, and can correctly sort spikes of a synthetic cortical dataset with sufficiently uncorrelated spike morphologies (93% average correct classification) comparably to the results obtained with top spike sorter (94% on average on the same dataset). The power consumption enables more than 24 h processing at the maximum load, and latency model has been derived to enable a fair performance assessment. The final embodiment demonstrates the real-time performance onto a low-power off-the-shelf DSP, opening to experiments exploiting the efferent signals to control a motor neuroprosthesis.

  11. The anti-motility signaling mechanism of TGFβ3 that controls cell traffic during skin wound healing

    Directory of Open Access Journals (Sweden)

    Arum Han

    2012-09-01

    When skin is wounded, migration of epidermal keratinocytes at the wound edge initiates within hours, whereas migration of dermal fibroblasts toward the wounded area remains undetectable until several days later. This “cell type traffic” regulation ensures proper healing of the wound, as disruptions of the regulation could either cause delay of wound healing or result in hypertrophic scars. TGFβ3 is the critical traffic controller that selectively halts migration of the dermal, but not epidermal, cells to ensure completion of wound re-epithelialization prior to wound remodeling. However, the mechanism of TGFβ3's anti-motility signaling has never been investigated. We report here that activated TβRII transmits the anti-motility signal of TGFβ3 in full to TβRI, since expression of the constitutively activated TβRI-TD mutant was sufficient to replace TGFβ3 to block PDGF-bb-induced dermal fibroblast migration. Second, the three components of R-Smad complex are all required. Individual downregulation of Smad2, Smad3 or Smad4 prevented TGFβ3 from inhibiting dermal fibroblast migration. Third, Protein Kinase Array allowed us to identify the protein kinase A (PKA as a specific downstream effector of R-Smads in dermal fibroblasts. Activation of PKA alone blocked PDGF-bb-induced dermal fibroblast migration, just like TGFβ3. Downregulation of PKA's catalytic subunit nullified the anti-motility signaling of TGFβ3. This is the first report on anti-motility signaling mechanism by TGFβ family cytokines. Significance of this finding is not only limited to wound healing but also to other human disorders, such as heart attack and cancer, where the diseased cells have often managed to avoid the anti-motility effect of TGFβ.

  12. Novel Mutation of LRP6 Identified in Chinese Han Population Links Canonical WNT Signaling to Neural Tube Defects.

    Science.gov (United States)

    Shi, Zhiwen; Yang, Xueyan; Li, Bin-Bin; Chen, Shuxia; Yang, Luming; Cheng, Liangping; Zhang, Ting; Wang, Hongyan; Zheng, Yufang

    2017-09-29

    Neural tube defects (NTDs), the second most frequent cause of human congenital abnormalities, are debilitating birth defects due to failure of neural tube closure. It has been shown that noncanonical WNT/planar cell polarity (PCP) signaling is required for convergent extension (CE), the initiation step of neural tube closure (NTC). But the effect of canonical WNT//β-catenin signaling during NTC is still elusive. LRP6 (low density lipoprotein receptor related proteins 6) was identified as a co-receptor for WNT/β-catenin signaling, but recent studies showed that it also can mediate WNT/PCP signaling. In this study, we screened mutations in the LRP6 gene in 343 NTDs and 215 ethnically matched normal controls of Chinese Han population. Three rare missense mutations (c.1514A>G, p.Y505C); c.2984A>G, p.D995G; and c.4280C>A, p.P1427Q) of the LRP6 gene were identified in Chinese NTD patients. The Y505C mutation is a loss-of-function mutation on both WNT/β-catenin and PCP signaling. The D995G mutation only partially lost inhibition on PCP signaling without affecting WNT/β-catenin signaling. The P1427Q mutation dramatically increased WNT/β-catenin signaling but only mildly loss of inhibition on PCP signaling. All three mutations failed to rescue CE defects caused by lrp6 morpholino oligos knockdown in zebrafish. Of interest, when overexpressed, D995G did not induce any defects, but Y505C and P1427Q caused more severe CE defects in zebrafish. Our results suggested that over-active canonical WNT signaling induced by gain-of-function mutation in LRP6 could also contribute to human NTDs, and a balanced WNT/β-catenin and PCP signaling is probably required for proper neural tube development. Birth Defects Research, 2017.© 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  13. Phosphorylation of Sox9 is required for neural crest delamination and is regulated downstream of BMP and canonical Wnt signaling.

    Science.gov (United States)

    Liu, Jessica A J; Wu, Ming-Hoi; Yan, Carol H; Chau, Bolton K H; So, Henry; Ng, Alvis; Chan, Alan; Cheah, Kathryn S E; Briscoe, James; Cheung, Martin

    2013-02-19

    Coordination of neural crest cell (NCC) induction and delamination is orchestrated by several transcription factors. Among these, Sry-related HMG box-9 (Sox9) and Snail2 have been implicated in both the induction of NCC identity and, together with phoshorylation, NCC delamination. How phosphorylation effects this function has not been clear. Here we show, in the developing chick neural tube, that phosphorylation of Sox9 on S64 and S181 facilitates its SUMOylation, and the phosphorylated forms of Sox9 are essential for trunk neural crest delamination. Both phosphorylation and to a lesser extent SUMOylation, of Sox9 are required to cooperate with Snail2 to promote delamination. Moreover, bone morphogenetic protein and canonical Wnt signaling induce phosphorylation of Sox9, thereby connecting extracellular signals with the delamination of NCCs. Together the data suggest a model in which extracellular signals initiate phosphorylation of Sox9 and its cooperation with Snail2 to induce NCC delamination.

  14. A study on a robot arm driven by three-dimensional trajectories predicted from non-invasive neural signals.

    Science.gov (United States)

    Kim, Yoon Jae; Park, Sung Woo; Yeom, Hong Gi; Bang, Moon Suk; Kim, June Sic; Chung, Chun Kee; Kim, Sungwan

    2015-08-20

    A brain-machine interface (BMI) should be able to help people with disabilities by replacing their lost motor functions. To replace lost functions, robot arms have been developed that are controlled by invasive neural signals. Although invasive neural signals have a high spatial resolution, non-invasive neural signals are valuable because they provide an interface without surgery. Thus, various researchers have developed robot arms driven by non-invasive neural signals. However, robot arm control based on the imagined trajectory of a human hand can be more intuitive for patients. In this study, therefore, an integrated robot arm-gripper system (IRAGS) that is driven by three-dimensional (3D) hand trajectories predicted from non-invasive neural signals was developed and verified. The IRAGS was developed by integrating a six-degree of freedom robot arm and adaptive robot gripper. The system was used to perform reaching and grasping motions for verification. The non-invasive neural signals, magnetoencephalography (MEG) and electroencephalography (EEG), were obtained to control the system. The 3D trajectories were predicted by multiple linear regressions. A target sphere was placed at the terminal point of the real trajectories, and the system was commanded to grasp the target at the terminal point of the predicted trajectories. The average correlation coefficient between the predicted and real trajectories in the MEG case was [Formula: see text] ([Formula: see text]). In the EEG case, it was [Formula: see text] ([Formula: see text]). The success rates in grasping the target plastic sphere were 18.75 and 7.50 % with MEG and EEG, respectively. The success rates of touching the target were 52.50 and 58.75 % respectively. A robot arm driven by 3D trajectories predicted from non-invasive neural signals was implemented, and reaching and grasping motions were performed. In most cases, the robot closely approached the target, but the success rate was not very high because

  15. A quantum theory for the irreplaceable role of docosahexaenoic acid in neural cell signalling throughout evolution.

    Science.gov (United States)

    Crawford, Michael A; Broadhurst, C Leigh; Guest, Martin; Nagar, Atulya; Wang, Yiqun; Ghebremeskel, Kebreab; Schmidt, Walter F

    2013-01-01

    Six hundred million years ago, the fossil record displays the sudden appearance of intracellular detail and the 32 phyla. The "Cambrian Explosion" marks the onset of dominant aerobic life. Fossil intracellular structures are so similar to extant organisms that they were likely made with similar membrane lipids and proteins, which together provided for organisation and specialisation. While amino acids could be synthesised over 4 billion years ago, only oxidative metabolism allows for the synthesis of highly unsaturated fatty acids, thus producing novel lipid molecular species for specialised cell membranes. Docosahexaenoic acid (DHA) provided the core for the development of the photoreceptor, and conversion of photons into electricity stimulated the evolution of the nervous system and brain. Since then, DHA has been conserved as the principle acyl component of photoreceptor synaptic and neuronal signalling membranes in the cephalopods, fish, amphibian, reptiles, birds, mammals and humans. This extreme conservation in electrical signalling membranes despite great genomic change suggests it was DHA dictating to DNA rather than the generally accepted other way around. We offer a theoretical explanation based on the quantum mechanical properties of DHA for such extreme conservation. The unique molecular structure of DHA allows for quantum transfer and communication of π-electrons, which explains the precise depolarisation of retinal membranes and the cohesive, organised neural signalling which characterises higher intelligence. Copyright © 2012 Elsevier Ltd. All rights reserved.

  16. EEG signal classification method based on fractal features and neural network.

    Science.gov (United States)

    Phothisonothai, Montri; Nakagawa, Masahiro

    2008-01-01

    In this paper, we propose a method to classify electroencephalogram (EEG) signal recorded from left- and right-hand movement imaginations. Three subjects (two males and one female) are volunteered to participate in the experiment. We use a technique of complexity measure based on fractal analysis to reveal feature patterns in the EEG signal. Effective algorithm, namely, detrended fluctuation analysis (DFA) has been selected to estimate embedded fractal dimension (FD) values between relaxing and imaging states of the recorded EEG signal. To show the waveform of FDs, we use a windowing-based method or called time-dependent fractal dimension (TDFD) and the Kullback-Leibler (K-L) divergence. Two feature parameters; K-L divergence and different expected values are proposed to be input variables of the classifier. Finally, featured data are classified by a three-layer feed-forward neural network based on a simple backpropagation algorithm. Experimental results can be considerably applied in a brain-computer interface (BCI) application and show that the proposed method is more effective than the conventional method by improving average classification rates of 87.5% and 88.3% for left- and right-hand movement imagery tasks, respectively.

  17. Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

    Directory of Open Access Journals (Sweden)

    M. Sreeshakthy

    2016-01-01

    Full Text Available Department of Computer Science and Engineering,Anna University Regional Centre, Coimbatore, Indiam.sribtechit@gmail.comJ. PreethiDepartment of Computer Science and EngineeringAnna University Regional Centre, Coimbatore, Indiapreethi17j@yahoo.comEmotions are very important in human decision handling, interaction and cognitive process. In this paper describes that recognize the human emotions from DEAP EEG dataset with different kind of methods. Audio – video based stimuli is used to extract the emotions. EEG signal is divided into different bands using discrete wavelet transformation with db8 wavelet function for further process. Statistical and energy based features are extracted from the bands, based on the features emotions are classified with feed forward neural network with weight optimized algorithm like PSO. Before that the particular band has to be selected based on the training performance of neural networks and then the emotions are classified. In this experimental result describes that the gamma and alpha bands are provides the accurate classification result with average classification rate of 90.3% of using NNRBF, 90.325% of using PNN, 96.3% of using PSO trained NN, 98.1 of using Cuckoo trained NN. At last the emotions are classified into two different groups like valence and arousal. Based on that identifies the person normal and abnormal behavioral using classified emotion.

  18. Spectral properties of multiple myoelectric signals: New insights into the neural origin of muscle synergies.

    Science.gov (United States)

    Frère, Julien

    2017-07-04

    It is still unclear if muscle synergies reflect neural strategies or mirror the underlying mechanical constraints. Therefore, this study aimed to verify the consistency of muscle groupings between the synergies based on the linear envelope (LE) of muscle activities and those incorporating the time-frequency (TF) features of the electromyographic (EMG) signals. Twelve healthy participants performed six 20-m walking trials at a comfort and fast self-selected speed, while the activity of eleven lower limb muscles was recorded by means of surface EMG. Wavelet-transformed EMG was used to obtain the TF pattern and muscle synergies were extracted by non-negative matrix factorization. When five muscle synergies were extracted, both methods defined similar muscle groupings whatever the walking speed. When accounting the reconstruction level of the initial dataset, a new TF synergy emerged. This new synergy dissociated the activity of the rectus femoris from those of the vastii muscles (synergy #1) and from the one of the tensor fascia latae (synergy #5). Overall, extracting TF muscle synergies supports the neural origin of muscle synergies and provides an opportunity to distinguish between prescriptive and descriptive muscle synergies. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

  19. AUTOMATIC SEGMENTATION OF BROADCAST AUDIO SIGNALS USING AUTO ASSOCIATIVE NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    P. Dhanalakshmi

    2010-12-01

    Full Text Available In this paper, we describe automatic segmentation methods for audio broadcast data. Today, digital audio applications are part of our everyday lives. Since there are more and more digital audio databases in place these days, the importance of effective management for audio databases have become prominent. Broadcast audio data is recorded from the Television which comprises of various categories of audio signals. Efficient algorithms for segmenting the audio broadcast data into predefined categories are proposed. Audio features namely Linear prediction coefficients (LPC, Linear prediction cepstral coefficients, and Mel frequency cepstral coefficients (MFCC are extracted to characterize the audio data. Auto Associative Neural Networks are used to segment the audio data into predefined categories using the extracted features. Experimental results indicate that the proposed algorithms can produce satisfactory results.

  20. A low-noise low-power amplifier for implantable device for neural signal acquisition.

    Science.gov (United States)

    Li, Ming-Ze; Tang, Kea-Tiong

    2009-01-01

    This paper presents a low-noise low-power amplifier for implantable device for neural signal acquisition. By operating MOS transistors in the subthreshold region, smaller low-frequency noise and lower power consumption can be achieved. A low power, low-noise common-drain buffer and a low-noise, high-linearity, low pass filter are used for high frequency noise filtering. Post-layout simulation shows the input referred noise of the system is 2.19microVrms from 10Hz to 10 KHz, power consumption is 55.8microW, and the NEF is 2.53. The amplifier was fabricated using a TSMC 0.18microm 1P6M CMOS process. Simulation results show that this low-noise, low-power amplifier is suitable for implantable device applications.

  1. G-protein-coupled receptors and localized signaling in the primary cilium during ventral neural tube patterning.

    Science.gov (United States)

    Hwang, Sun-Hee; Mukhopadhyay, Saikat

    2015-01-01

    The primary cilium is critical in sonic hedgehog (Shh)-dependent ventral patterning of the vertebrate neural tube. Most mutants that cause disruption of the cilium result in decreased Shh signaling in the neural tube. In contrast, mutations in the intraflagellar complex A (IFT-A) and the tubby family protein, Tulp3, result in increased Shh signaling in the neural tube. Proteomic analysis of Tulp3-binding proteins first pointed to the role of the IFT-A complex in trafficking Tulp3 into the cilia. Tulp3 directs trafficking of rhodopsin family G-protein-coupled receptors (GPCRs) to the cilia, suggesting the role of a GPCR in mediating the paradoxical effects of the Tulp3/IFT-A complex in causing increased Shh signaling. Gpr161 has recently been identified as a Tulp3/IFT-A-regulated GPCR that localizes to the primary cilium. A null knock-out mouse model of Gpr161 phenocopies Tulp3 and IFT-A mutants, and causes increased Shh signaling throughout the neural tube. In the absence of Shh, the bifunctional Gli transcription factors are proteolytically processed into repressor forms in a protein kinase A (PKA) -dependent and cilium-dependent manner. Gpr161 activity results in increased cAMP levels in a Gαs -coupled manner, and determines processing of Gli3. Shh signaling also results in removal of Gpr161 from the cilia, suggesting that Gpr161 functions in a positive feedback loop in the Shh pathway. As PKA-null and Gαs mutant embryos also exhibit increased Shh signaling in the neural tube, Gpr161 is a strong candidate for a GPCR that regulates ciliary cAMP levels, and activates PKA in close proximity to the cilia. © 2014 Wiley Periodicals, Inc.

  2. Decoding neural events from fMRI BOLD signal: A comparison of existing approaches and development of a new algorithm

    Science.gov (United States)

    Bush, Keith; Cisler, Josh

    2013-01-01

    Neuroimaging methodology predominantly relies on the blood oxygenation level dependent (BOLD) signal. While the BOLD signal is a valid measure of neuronal activity, variance in fluctuations of the BOLD signal are not only due to fluctuations in neural activity. Thus, a remaining problem in neuroimaging analyses is developing methods that ensure specific inferences about neural activity that are not confounded by unrelated sources of noise in the BOLD signal. Here, we develop and test a new algorithm for performing semi-blind (i.e., no knowledge of stimulus timings) deconvolution of the BOLD signal that treats the neural event as an observable, but intermediate, probabilistic representation of the system’s state. We test and compare this new algorithm against three other recent deconvolution algorithms under varied levels of autocorrelated and Gaussian noise, hemodynamic response function (HRF) misspecification, and observation sampling rate (i.e., TR). Further, we compare the algorithms’ performance using two models to simulate BOLD data: a convolution of neural events with a known (or misspecified) HRF versus a biophysically accurate balloon model of hemodynamics. We also examine the algorithms’ performance on real task data. The results demonstrated good performance of all algorithms, though the new algorithm generally outperformed the others (3.0% improvement) under simulated resting state experimental conditions exhibiting multiple, realistic confounding factors (as well as 10.3% improvement on a real Stroop task). The simulations also demonstrate that the greatest negative influence on deconvolution accuracy is observation sampling rate. Practical and theoretical implications of these results for improving inferences about neural activity from fMRI BOLD signal are discussed. PMID:23602664

  3. Optimization of neural network architecture for classification of radar jamming FM signals

    Science.gov (United States)

    Soto, Alberto; Mendoza, Ariadna; Flores, Benjamin C.

    2017-05-01

    The purpose of this study is to investigate several artificial Neural Network (NN) architectures in order to design a cognitive radar system capable of optimally distinguishing linear Frequency-Modulated (FM) signals from bandlimited Additive White Gaussian Noise (AWGN). The goal is to create a theoretical framework to determine an optimal NN architecture to achieve a Probability of Detection (PD) of 95% or higher and a Probability of False Alarm (PFA) of 1.5% or lower at 5 dB Signal to Noise Ratio (SNR). Literature research reveals that the frequency-domain power spectral densities characterize a signal more efficiently than its time-domain counterparts. Therefore, the input data is preprocessed by calculating the magnitude square of the Discrete Fourier Transform of the digitally sampled bandlimited AWGN and linear FM signals to populate a matrix containing N number of samples and M number of spectra. This matrix is used as input for the NN, and the spectra are divided as follows: 70% for training, 15% for validation, and 15% for testing. The study begins by experimentally deducing the optimal number of hidden neurons (1-40 neurons), then the optimal number of hidden layers (1-5 layers), and lastly, the most efficient learning algorithm. The training algorithms examined are: Resilient Backpropagation, Scaled Conjugate Gradient, Conjugate Gradient with Powell/Beale Restarts, Polak-Ribiére Conjugate Gradient, and Variable Learning Rate Backpropagation. We determine that an architecture with ten hidden neurons (or higher), one hidden layer, and a Scaled Conjugate Gradient for training algorithm encapsulates an optimal architecture for our application.

  4. Fatty acid–induced gut-brain signaling attenuates neural and behavioral effects of sad emotion in humans

    OpenAIRE

    Van Oudenhove, Lukas; Mckie, Shane; Lassman, Daniel; Uddin, Bilal; Paine, Peter; Coen, Steven; Gregory, Lloyd; Tack, Jan; Aziz, Qasim

    2011-01-01

    Although a relationship between emotional state and feeding behavior is known to exist, the interactions between signaling initiated by stimuli in the gut and exteroceptively generated emotions remain incompletely understood. Here, we investigated the interaction between nutrient-induced gut-brain signaling and sad emotion induced by musical and visual cues at the behavioral and neural level in healthy nonobese subjects undergoing functional magnetic resonance imaging. Subjects received an in...

  5. Lingo-1 shRNA and Notch signaling inhibitor DAPT promote differentiation of neural stem/progenitor cells into neurons.

    Science.gov (United States)

    Wang, Jue; Ye, Zhizhong; Zheng, Shuhui; Chen, Luming; Wan, Yong; Deng, Yubin; Yang, Ruirui

    2016-03-01

    Determination of the exogenous factors that regulate differentiation of neural stem/progenitor cells into neurons, oligodendrocytes and astrocytes is an important step in the clinical therapy of spinal cord injury (SCI). The Notch pathway inhibits the differentiation of neural stem/progenitor cells and Lingo-1 is a strong negative regulator for myelination and axon growth. While Lingo-1 shRNA and N-[N-(3, 5-difluorophenacetyl)-1-alanyl]-S-Phenylglycinet-butylester (DAPT), a Notch pathway inhibitor, have been used separately to help repair SCI, the results have been unsatisfactory. Here we investigated and elucidated the preliminary mechanism for the effect of Lingo-1 shRNA and DAPT on neural stem/progenitor cells differentiation. We found that neural stem/progenitor cells from E14 rat embryos expressed Nestin, Sox-2 and Lingo-1, and we optimized the transduction of neural stem/progenitor cells using lentiviral vectors encoding Lingo-1 shRNA. The addition of DAPT decreased the expression of Notch intracellular domain (NICD) as well as the downstream genes Hes1 and Hes5. Expression of NeuN, CNPase and GFAP in DAPT treated cells and expression of NeuN in Lingo-1 shRNA treated cells confirmed differentiation of neural stem/progenitor cells into neurons, oligodendrocytes and astrocytes. These results revealed that while Lingo-1 shRNA and Notch signaling inhibitor DAPT both promoted differentiation of neural stem cells into neurons, only DAPT was capable of driving neural stem/progenitor cells differentiation into oligodendrocytes and astrocytes. Since we were able to show that both Lingo-1 shRNA and DAPT could drive neural stem/progenitor cells differentiation, our data might aid the development of more effective SCI therapies using Lingo-1 shRNA and DAPT. Copyright © 2015 Elsevier B.V. All rights reserved.

  6. Analysis of acoustic emission signals at austempering of steels using neural networks

    Science.gov (United States)

    Łazarska, Malgorzata; Wozniak, Tadeusz Z.; Ranachowski, Zbigniew; Trafarski, Andrzej; Domek, Grzegorz

    2017-05-01

    Bearing steel 100CrMnSi6-4 and tool steel C105U were used to carry out this research with the steels being austempered to obtain a martensitic-bainitic structure. During the process quite a large number of acoustic emissions (AE) were observed. These signals were then analysed using neural networks resulting in the identification of three groups of events of: high, medium and low energy and in addition their spectral characteristics were plotted. The results were presented in the form of diagrams of AE incidence as a function of time. It was demonstrated that complex transformations of austenite into martensite and bainite occurred when austempering bearing steel at 160 °C and tool steel at 130 °C respectively. The selected temperatures of isothermal quenching of the tested steels were within the area near to MS temperature, which affected the complex course of phase transition. The high activity of AE is a typical occurrence for martensitic transformation and this is the transformation mechanism that induces the generation of AE signals of higher energy in the first stage of transition. In the second stage of transformation, the initially nucleated martensite accelerates the occurrence of the next bainitic transformation.

  7. Classification of a Driver's cognitive workload levels using artificial neural network on ECG signals.

    Science.gov (United States)

    Tjolleng, Amir; Jung, Kihyo; Hong, Wongi; Lee, Wonsup; Lee, Baekhee; You, Heecheon; Son, Joonwoo; Park, Seikwon

    2017-03-01

    An artificial neural network (ANN) model was developed in the present study to classify the level of a driver's cognitive workload based on electrocardiography (ECG). ECG signals were measured on 15 male participants while they performed a simulated driving task as a primary task with/without an N-back task as a secondary task. Three time-domain ECG measures (mean inter-beat interval (IBI), standard deviation of IBIs, and root mean squared difference of adjacent IBIs) and three frequencydomain ECG measures (power in low frequency, power in high frequency, and ratio of power in low and high frequencies) were calculated. To compensate for individual differences in heart response during the driving tasks, a three-step data processing procedure was performed to ECG signals of each participant: (1) selection of two most sensitive ECG measures, (2) definition of three (low, medium, and high) cognitive workload levels, and (3) normalization of the selected ECG measures. An ANN model was constructed using a feed-forward network and scaled conjugate gradient as a back-propagation learning rule. The accuracy of the ANN classification model was found satisfactory for learning data (95%) and testing data (82%). Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Nogo Receptor Signaling Restricts Adult Neural Plasticity by Limiting Synaptic AMPA Receptor Delivery.

    Science.gov (United States)

    Jitsuki, Susumu; Nakajima, Waki; Takemoto, Kiwamu; Sano, Akane; Tada, Hirobumi; Takahashi-Jitsuki, Aoi; Takahashi, Takuya

    2016-01-01

    Experience-dependent plasticity is limited in the adult brain, and its molecular and cellular mechanisms are poorly understood. Removal of the myelin-inhibiting signaling protein, Nogo receptor (NgR1), restores adult neural plasticity. Here we found that, in NgR1-deficient mice, whisker experience-driven synaptic α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR) insertion in the barrel cortex, which is normally complete by 2 weeks after birth, lasts into adulthood. In vivo live imaging by two-photon microscopy revealed more AMPAR on the surface of spines in the adult barrel cortex of NgR1-deficient than on those of wild-type (WT) mice. Furthermore, we observed that whisker stimulation produced new spines in the adult barrel cortex of mutant but not WT mice, and that the newly synthesized spines contained surface AMPAR. These results suggest that Nogo signaling limits plasticity by restricting synaptic AMPAR delivery in coordination with anatomical plasticity. © The Author 2015. Published by Oxford University Press.

  9. Depression and treatment response: dynamic interplay of signaling pathways and altered neural processes.

    Science.gov (United States)

    Duric, Vanja; Duman, Ronald S

    2013-01-01

    Since the 1960s, when the first tricyclic and monoamine oxidase inhibitor antidepressant drugs were introduced, most of the ensuing agents were designed to target similar brain pathways that elevate serotonin and/or norepinephrine signaling. Fifty years later, the main goal of the current depression research is to develop faster-acting, more effective therapeutic agents with fewer side effects, as currently available antidepressants are plagued by delayed therapeutic onset and low response rates. Clinical and basic science research studies have made significant progress towards deciphering the pathophysiological events within the brain involved in development, maintenance, and treatment of major depressive disorder. Imaging and postmortem brain studies in depressed human subjects, in combination with animal behavioral models of depression, have identified a number of different cellular events, intracellular signaling pathways, proteins, and target genes that are modulated by stress and are potentially vital mediators of antidepressant action. In this review, we focus on several neural mechanisms, primarily within the hippocampus and prefrontal cortex, which have recently been implicated in depression and treatment response.

  10. Neural classifier of the communication damage size being a result of collision of vehicles in road traffic

    Directory of Open Access Journals (Sweden)

    Krystian WILK

    2010-01-01

    Full Text Available In the article the results of the attempts of MLP neural network application to define the size of a communication damage being the result of a road collision were presented. The size of the damage was used as a research parameter defined by the coefficient dependent on the cost of repair of the damaged vehicle and its market value. The elements of the damage mechanism determining the way of damage qualification were the inner factors of the system, that is; the technical features of the vehicles, the character features of the drivers, the influence of the weather conditions and the location of the event in time and space. The research was conducted on one thousand cases reported for liquidation in Silesian branch of one of the insurance companies. In the conducted research the working of the neural networks with the limited input data was checked.

  11. Analytical investigation on the minimum traffic delay at a three-phase signalized T-type intersection

    Science.gov (United States)

    Zhang, Hong-Ze; Jiang, Rui; Hu, Mao-Bin; Jia, Bin

    2017-02-01

    The traffic delay at intersections is crucial for the performance of urban traffic system. This paper analytically investigated the minimum traffic delay at a three-phase T-type intersection. We firstly demonstrate that the minimum traffic delay must be achieved on the surface of the 3D space constituted by the three constraints. Next, we prove that the minimum traffic delay must be achieved on the three borderlines of the surface. Finally, we show that the minimum delay is achieved either on one specific borderline or at the vertex of the surface. In the former case, extra green time is needed for the stream with largest demand, while no extra green time should be assigned to any stream in the latter case.

  12. Estrogen Stimulates Proliferation and Differentiation of Neural Stem/Progenitor Cells through Different Signal Transduction Pathways

    Directory of Open Access Journals (Sweden)

    Makiko Okada

    2010-10-01

    Full Text Available Our previous study indicated that both 17β-estradiol (E2, known to be an endogenous estrogen, and bisphenol A (BPA, known to be a xenoestrogen, could positively influence the proliferation or differentiation of neural stem/progenitor cells (NS/PCs. The aim of the present study was to identify the signal transduction pathways for estrogenic activities promoting proliferation and differentiation of NS/PCs via well known nuclear estrogen receptors (ERs or putative membrane-associated ERs. NS/PCs were cultured from the telencephalon of 15-day-old rat embryos. In order to confirm the involvement of nuclear ERs for estrogenic activities, their specific antagonist, ICI-182,780, was used. The presence of putative membrane-associated ER was functionally examined as to whether E2 can activate rapid intracellular signaling mechanism. In order to confirm the involvement of membrane-associated ERs for estrogenic activities, a cell-impermeable E2, bovine serum albumin-conjugated E2 (E2-BSA was used. We showed that E2 could rapidly activate extracellular signal-regulated kinases 1/2 (ERK 1/2, which was not inhibited by ICI-182,780. ICI-182,780 abrogated the stimulatory effect of these estrogens (E2 and BPA on the proliferation of NS/PCs, but not their effect on the differentiation of the NS/PCs into oligodendroglia. Furthermore, E2-BSA mimicked the activity of differentiation from NS/PCs into oligodendroglia, but not the activity of proliferation. Our study suggests that (1 the estrogen induced proliferation of NS/PCs is mediated via nuclear ERs; (2 the oligodendroglial generation from NS/PCs is likely to be stimulated via putative membrane‑associated ERs.

  13. Inhibitory control and trait aggression: neural and behavioral insights using the emotional stop signal task.

    Science.gov (United States)

    Pawliczek, Christina M; Derntl, Birgit; Kellermann, Thilo; Kohn, Nils; Gur, Ruben C; Habel, Ute

    2013-10-01

    Deficits in response inhibition and heightened impulsivity have been linked to psychiatric disorders and aggression. They have been investigated in clinical groups as well as individuals with trait characteristics, yielding insights into the underlying neural and behavioral mechanisms of response inhibition and impulsivity. The motor inhibition tasks employed in most studies, however, have lacked an emotional component, which is crucial given that both response inhibition and impulsivity attain salience within a socio-emotional context. For this fMRI study, we selected a group with high trait aggression (HA, n=17) and one with low trait aggression (LA, n=16) from 550 males who had completed an Aggression Questionnaire. Neural activation was compared to an emotional version (including angry and neutral faces) of the stop signal task. Behavioral results revealed impaired response inhibition in HA, associated with higher motor impulsivity. This was accompanied by attenuated activation in brain regions involved in response inhibition, including the pre-supplementary motor area (SMA) and motor cortex. Together, these findings offer evidence that a reduced inhibition capacity is present in HA. Notably, response inhibition improved during anger trials in both groups, suggesting a facilitation effect through heightened activation in the related brain regions. In both groups, inclusion of the anger stimuli enhanced the activation of the motor and somatosensory areas, which modulate executive control, and of limbic regions including the amygdala. In summary, the investigation of response inhibition in individuals with high and low trait characteristics affords useful insights into the underlying distinct processing mechanisms. It can contribute to the investigation of trait markers in a clinical context without having to deal with the complex mechanisms of a clinical disorder itself. In contrast, the mechanisms of emotional response inhibition did not differ between groups

  14. 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.

  15. Weak signal detection and propagation in diluted feed-forward neural network with recurrent excitation and inhibition

    Science.gov (United States)

    Wang, Jiang; Han, Ruixue; Wei, Xilei; Qin, Yingmei; Yu, Haitao; Deng, Bin

    2016-12-01

    Reliable signal propagation across distributed brain areas provides the basis for neural circuit function. Modeling studies on cortical circuits have shown that multilayered feed-forward networks (FFNs), if strongly and/or densely connected, can enable robust signal propagation. However, cortical networks are typically neither densely connected nor have strong synapses. This paper investigates under which conditions spiking activity can be propagated reliably across diluted FFNs. Extending previous works, we model each layer as a recurrent sub-network constituting both excitatory (E) and inhibitory (I) neurons and consider the effect of interactions between local excitation and inhibition on signal propagation. It is shown that elevation of cellular excitation-inhibition (EI) balance in the local sub-networks (layers) softens the requirement for dense/strong anatomical connections and thereby promotes weak signal propagation in weakly connected networks. By means of iterated maps, we show how elevated local excitability state compensates for the decreased gain of synchrony transfer function that is due to sparse long-range connectivity. Finally, we report that modulations of EI balance and background activity provide a mechanism for selectively gating and routing neural signal. Our results highlight the essential role of intrinsic network states in neural computation.

  16. Two major gate-keepers in the self-renewal of neural stem cells: Erk1/2 and PLCγ1 in FGFR signaling

    Directory of Open Access Journals (Sweden)

    Lee Jin-A

    2009-06-01

    Full Text Available Abstract Neural stem cells are undifferentiated precursor cells that proliferate, self-renew, and give rise to neuronal and glial lineages. Understanding the molecular mechanisms underlying their self-renewal is an important aspect in neural stem cell biology. The regulation mechanisms governing self-renewal of neural stem cells and the signaling pathways responsible for the proliferation and maintenance of adult stem cells remain largely unknown. In this issue of Molecular Brain [Ma DK et al. Molecular genetic analysis of FGFR1 signaling reveals distinct roles of MAPK and PLCγ1 activation for self-renewal of adult neural stem cells. Molecular Brain 2009, 2:16], characterized the different roles of MAPK and PLCγ1 in FGFR1 signaling in the self-renewal of neural stem cells. These novel findings provide insights into basic neural stem cell biology and clinical applications of potential stem-cell-based therapy.

  17. Diagnosis of epilepsy from electroencephalography signals using multilayer perceptron and Elman Artificial Neural Networks and Wavelet Transform.

    Science.gov (United States)

    Işik, Hakan; Sezer, Esma

    2012-02-01

    In this study, it has been intended to perform an automatic classification of Electroencephalography (EEG) signals via Artificial Neural Networks (ANN) and to investigate these signals using Wavelet Transform (WT) for diagnosing epilepsy syndrome. EEG signals have been decomposed into frequency sub-bands using WT and a set of feature vectors which were extracted from the sub-bands. Dimensions of these feature vectors have been reduced via Principal Component Analysis (PCA) method and then classified as epileptic or healthy using Multilayer Perceptron (MLP) and ELMAN ANN. Performance evaluation of the used ANN models have been carried out by performing Receiver Operation Characteristic (ROC) analysis.

  18. STAT3 signal that mediates the neural plasticity is involved in willed-movement training in focal ischemic rats.

    Science.gov (United States)

    Tang, Qing-Ping; Shen, Qin; Wu, Li-Xiang; Feng, Xiang-Ling; Liu, Hui; Wu, Bei; Huang, Xiao-Song; Wang, Gai-Qing; Li, Zhong-Hao; Liu, Zun-Jing

    2016-07-01

    Willed-movement training has been demonstrated to be a promising approach to increase motor performance and neural plasticity in ischemic rats. However, little is known regarding the molecular signals that are involved in neural plasticity following willed-movement training. To investigate the potential signals related to neural plasticity following willed-movement training, littermate rats were randomly assigned into three groups: middle cerebral artery occlusion, environmental modification, and willed-movement training. The infarct volume was measured 18 d after occlusion of the right middle cerebral artery. Reverse transcription-polymerase chain reaction (PCR) and immunofluorescence staining were used to detect the changes in the signal transducer and activator of transcription 3 (STAT3) mRNA and protein, respectively. A chromatin immunoprecipitation was used to investigate whether STAT3 bound to plasticity-related genes, such as brain-derived neurotrophic factor (BDNF), synaptophysin, and protein interacting with C kinase 1 (PICK1). In this study, we demonstrated that STAT3 mRNA and protein were markedly increased following 15-d willed-movement training in the ischemic hemispheres of the treated rats. STAT3 bound to BDNF, PICK1, and synaptophysin promoters in the neocortical cells of rats. These data suggest that the increased STAT3 levels after willed-movement training might play critical roles in the neural plasticity by directly regulating plasticity-related genes.

  19. Dynamics of BMP and Hes1/Hairy1 signaling in the dorsal neural tube underlies the transition from neural crest to definitive roof plate.

    Science.gov (United States)

    Nitzan, Erez; Avraham, Oshri; Kahane, Nitza; Ofek, Shai; Kumar, Deepak; Kalcheim, Chaya

    2016-03-24

    The dorsal midline region of the neural tube that results from closure of the neural folds is generally termed the roof plate (RP). However, this domain is highly dynamic and complex, and is first transiently inhabited by prospective neural crest (NC) cells that sequentially emigrate from the neuroepithelium. It only later becomes the definitive RP, the dorsal midline cells of the spinal cord. We previously showed that at the trunk level of the axis, prospective RP progenitors originate ventral to the premigratory NC and progressively reach the dorsal midline following NC emigration. However, the molecular mechanisms underlying the end of NC production and formation of the definitive RP remain virtually unknown. Based on distinctive cellular and molecular traits, we have defined an initial NC and a subsequent RP stage, allowing us to investigate the mechanisms responsible for the transition between the two phases. We demonstrate that in spite of the constant production of BMP4 in the dorsal tube at both stages, RP progenitors only transiently respond to the ligand and lose competence shortly before they arrive at their final location. In addition, exposure of dorsal tube cells at the NC stage to high levels of BMP signaling induces premature RP traits, such as Hes1/Hairy1, while concomitantly inhibiting NC production. Reciprocally, early inhibition of BMP signaling prevents Hairy1 mRNA expression at the RP stage altogether, suggesting that BMP is both necessary and sufficient for the development of this RP-specific trait. Furthermore, when Hes1/Hairy1 is misexpressed at the NC stage, it inhibits BMP signaling and downregulates BMPR1A/Alk3 mRNA expression, transcription of BMP targets such as Foxd3, cell-cycle progression, and NC emigration. Reciprocally, Foxd3 inhibits Hairy1, suggesting that repressive cross-interactions at the level of, and downstream from, BMP ensure the temporal separation between both lineages. Together, our data suggest that BMP signaling is

  20. Analysing the 21 cm signal from the epoch of reionization with artificial neural networks

    Science.gov (United States)

    Shimabukuro, Hayato; Semelin, Benoit

    2017-07-01

    The 21 cm signal from the epoch of reionization should be observed within the next decade. While a simple statistical detection is expected with Square Kilometre Array (SKA) pathfinders, the SKA will hopefully produce a full 3D mapping of the signal. To extract from the observed data constraints on the parameters describing the underlying astrophysical processes, inversion methods must be developed. For example, the Markov Chain Monte Carlo method has been successfully applied. Here, we test another possible inversion method: artificial neural networks (ANNs). We produce a training set that consists of 70 individual samples. Each sample is made of the 21 cm power spectrum at different redshifts produced with the 21cmFast code plus the value of three parameters used in the seminumerical simulations that describe astrophysical processes. Using this set, we train the network to minimize the error between the parameter values it produces as an output and the true values. We explore the impact of the architecture of the network on the quality of the training. Then we test the trained network on the new set of 54 test samples with different values of the parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameters at a given redshift, that including thermal noise and sample variance decreases the quality of the reconstruction and that using the power spectrum at several redshifts as an input to the ANN improves the quality of the reconstruction. We conclude that ANNs are a viable inversion method whose main strength is that they require a sparse exploration of the parameter space and thus should be usable with full numerical simulations.

  1. With a little help from my friends: androgens tap BDNF signaling pathways to alter neural circuits.

    Science.gov (United States)

    Ottem, E N; Bailey, D J; Jordan, C L; Breedlove, S M

    2013-06-03

    Gonadal androgens are critical for the development and maintenance of sexually dimorphic regions of the male nervous system, which is critical for male-specific behavior and physiological functioning. In rodents, the motoneurons of the spinal nucleus of the bulbocavernosus (SNB) provide a useful example of a neural system dependent on androgen. Unless rescued by perinatal androgens, the SNB motoneurons will undergo apoptotic cell death. In adulthood, SNB motoneurons remain dependent on androgen, as castration leads to somal atrophy and dendritic retraction. In a second vertebrate model, the zebra finch, androgens are critical for the development of several brain nuclei involved in song production in males. Androgen deprivation during a critical period during postnatal development disrupts song acquisition and dimorphic size-associated nuclei. Mechanisms by which androgens exert masculinizing effects in each model system remain elusive. Recent studies suggest that brain-derived neurotrophic factor (BDNF) may play a role in androgen-dependent masculinization and maintenance of both SNB motoneurons and song nuclei of birds. This review aims to summarize studies demonstrating that BDNF signaling via its tyrosine receptor kinase (TrkB) receptor may work cooperatively with androgens to maintain somal and dendritic morphology of SNB motoneurons. We further describe studies that suggest the cellular origin of BDNF is of particular importance in androgen-dependent regulation of SNB motoneurons. We review evidence that androgens and BDNF may synergistically influence song development and plasticity in bird species. Finally, we provide hypothetical models of mechanisms that may underlie androgen- and BDNF-dependent signaling pathways. Copyright © 2012 IBRO. Published by Elsevier Ltd. All rights reserved.

  2. Serotonin 2A Receptor Signaling Underlies LSD-induced Alteration of the Neural Response to Dynamic Changes in Music.

    Science.gov (United States)

    Barrett, Frederick S; Preller, Katrin H; Herdener, Marcus; Janata, Petr; Vollenweider, Franz X

    2017-09-28

    Classic psychedelic drugs (serotonin 2A, or 5HT2A, receptor agonists) have notable effects on music listening. In the current report, blood oxygen level-dependent (BOLD) signal was collected during music listening in 25 healthy adults after administration of placebo, lysergic acid diethylamide (LSD), and LSD pretreated with the 5HT2A antagonist ketanserin, to investigate the role of 5HT2A receptor signaling in the neural response to the time-varying tonal structure of music. Tonality-tracking analysis of BOLD data revealed that 5HT2A receptor signaling alters the neural response to music in brain regions supporting basic and higher-level musical and auditory processing, and areas involved in memory, emotion, and self-referential processing. This suggests a critical role of 5HT2A receptor signaling in supporting the neural tracking of dynamic tonal structure in music, as well as in supporting the associated increases in emotionality, connectedness, and meaningfulness in response to music that are commonly observed after the administration of LSD and other psychedelics. Together, these findings inform the neuropsychopharmacology of music perception and cognition, meaningful music listening experiences, and altered perception of music during psychedelic experiences. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  3. The effects of life stress and neural learning signals on fluid intelligence.

    Science.gov (United States)

    Friedel, Eva; Schlagenhauf, Florian; Beck, Anne; Dolan, Raymond J; Huys, Quentin J M; Rapp, Michael A; Heinz, Andreas

    2015-02-01

    Fluid intelligence (fluid IQ), defined as the capacity for rapid problem solving and behavioral adaptation, is known to be modulated by learning and experience. Both stressful life events (SLES) and neural correlates of learning [specifically, a key mediator of adaptive learning in the brain, namely the ventral striatal representation of prediction errors (PE)] have been shown to be associated with individual differences in fluid IQ. Here, we examine the interaction between adaptive learning signals (using a well-characterized probabilistic reversal learning task in combination with fMRI) and SLES on fluid IQ measures. We find that the correlation between ventral striatal BOLD PE and fluid IQ, which we have previously reported, is quantitatively modulated by the amount of reported SLES. Thus, after experiencing adversity, basic neuronal learning signatures appear to align more closely with a general measure of flexible learning (fluid IQ), a finding complementing studies on the effects of acute stress on learning. The results suggest that an understanding of the neurobiological correlates of trait variables like fluid IQ needs to take socioemotional influences such as chronic stress into account.

  4. SIGNAL CONTROLLED JUNCTIONS CALCULATIONS IN TRAFFIC-CAPACITY ASSESSMENT - AIMSUN, OMNITRANS, WEBSTER AND TP 10/2010 RESULTS COMPARISON

    Directory of Open Access Journals (Sweden)

    Ľubomír ČERNICKÝ

    2016-03-01

    Full Text Available Every increase in traffic volume on road network in towns can lead to overcrowding of road network. This results in undesirable external costs such as traffic congestions, which cause high loses in time during transportation, increased fuel consumption and thus higher production of greenhouse gases and noise. This all ultimately reduces the attractiveness of the area. The increase of traffic volume and therefrom derived traffic problems are needed to be solved during traffic-capacity assessment of every larger investment. The software can help to assess increased traffic in solved area and thus help authorities to make a right decision during approving of the investment plan. This article is focused on comparison of two software – Aimsun and OmniTrans, and calculations according to Webster and technical regulations for assessing junction capacity in the Slovak Republic. The packages outputs are also compared to the measured data at the assessed junction in this article. The analysis showed that outputs of various tools differ, generally all packages showed higher delays compared to measured data at the main road and lower delays compared to measured data at the side roads.

  5. Bearings Fault Diagnosis Based on Convolutional Neural Networks with 2-D Representation of Vibration Signals as Input

    Directory of Open Access Journals (Sweden)

    Zhang Wei

    2017-01-01

    Full Text Available Periodic vibration signals captured by the accelerometers carry rich information for bearing fault diagnosis. Existing methods mostly rely on hand-crafted time-consuming preprocessing of data to acquire suitable features. In this paper, we use an easy and effective method to transform the 1-D temporal vibration signal into a 2-D image. With the signal image, convolutional Neural Network (CNN is used to train the raw vibration data. As powerful feature extractor and classifier for image recognition, CNN can learn to acquire features most suitable for the classification task by being trained. With the image format of vibration signals, the neuron in fully-connected layer of CNN can see farther and capture the periodic feature of signals. According to the results of the experiments, when fed in enough training samples, the proposed method outperforms other common methods. The proposed method can also be applied to solve intelligent diagnosis problems of other machine systems.

  6. Traffic Monitor

    Science.gov (United States)

    1995-01-01

    Intelligent Vision Systems, Inc. (InVision) needed image acquisition technology that was reliable in bad weather for its TDS-200 Traffic Detection System. InVision researchers used information from NASA Tech Briefs and assistance from Johnson Space Center to finish the system. The NASA technology used was developed for Earth-observing imaging satellites: charge coupled devices, in which silicon chips convert light directly into electronic or digital images. The TDS-200 consists of sensors mounted above traffic on poles or span wires, enabling two sensors to view an intersection; a "swing and sway" feature to compensate for movement of the sensors; a combination of electronic shutter and gain control; and sensor output to an image digital signal processor, still frame video and optionally live video.

  7. Traffic signal summer camp

    Science.gov (United States)

    2001-11-01

    The Department of Transportation's new Intelligent Transportation System (ITS) program mandates that computing, communications, electronics, and other advanced technologies be applied to improving the capacity and safety of the nation's transportatio...

  8. T cell traffic signals.

    Science.gov (United States)

    Van Epps, Heather L

    2005-08-15

    In 1990, Charles Mackay and colleagues combined classical physiology with modern molecular biology to provide the first concrete evidence that naive and memory T cells follow distinct migratory routes out of the bloodstream--a discovery that helped invigorate the field of lymphocyte homing.

  9. Investigating the effect of traditional Persian music on ECG signals in young women using wavelet transform and neural networks.

    Science.gov (United States)

    Abedi, Behzad; Abbasi, Ataollah; Goshvarpour, Atefeh

    2017-05-01

    In the past few decades, several studies have reported the physiological effects of listening to music. The physiological effects of different music types on different people are different. In the present study, we aimed to examine the effects of listening to traditional Persian music on electrocardiogram (ECG) signals in young women. Twenty-two healthy females participated in this study. ECG signals were recorded under two conditions: rest and music. For each ECG signal, 20 morphological and wavelet-based features were selected. Artificial neural network (ANN) and probabilistic neural network (PNN) classifiers were used for the classification of ECG signals during and before listening to music. Collected data were separated into two data sets: train and test. Classification accuracies of 88% and 97% were achieved in train data sets using ANN and PNN, respectively. In addition, the test data set was employed for evaluating the classifiers, and classification rates of 84% and 93% were obtained using ANN and PNN, respectively. The present study investigated the effect of music on ECG signals based on wavelet transform and morphological features. The results obtained here can provide a good understanding on the effects of music on ECG signals to researchers.

  10. Forcast of TEXT plasma disruptions using soft X-rays as input signal in a neural network

    Energy Technology Data Exchange (ETDEWEB)

    Vannucci, A.; Oliveira, K.A.; Tajima, T.

    1998-03-03

    A feed-forward neural network with two hidden layers is used in this work to forecast major and minor disruptive instabilities in TEXT discharges. Using soft X-ray signals as input data, the neural net is trained with one disruptive plasma pulse, and a different disruptive discharge is used for validation. After being properly trained the networks, with the same set of weights. is then used to forecast disruptions in two others different plasma pulses. It is observed that the neural net is able to predict the incoming of a disruption more than 3 ms in advance. This time interval is almost three times longer than the one already obtained previously when magnetic signal from a Mirnov coil was used to feed the neural networks with. To our own eye we fail to see any indication of an upcoming disruption from the experimental data this far back from the time of disruption. Finally, from what we observe in the predictive behavior of our network, speculations are made whether the disruption triggering mechanism would be associated to an increase of the m = 2 magnetic island, that disturbs the central part of the plasma column afterwards or, in face of the results from this work, the initial perturbation would have occurred first in the central part of the plasma column, within the q = 1 magnetic surface, and then the m = 2 MHD mode would be destabilized afterwards.

  11. Multiphoton minimal inertia scanning for fast acquisition of neural activity signals.

    Science.gov (United States)

    Schuck, Renaud; Go, Mary Ann; Garasto, Stefania; Reynolds, Stephanie; Dragotti, Pier Luigi; Schultz, Simon

    2017-11-13

    Multi-photon laser scanning microscopy provides a powerful tool for monitoring the spatiotemporal dynamics of neural circuit activity. It is, however, intrinsically a point scanning technique. Standard raster scanning enables imaging at subcellular resolution; however, acquisition rates are limited by the size of the field of view to be scanned. Recently developed scanning strategies such as Travelling Salesman Scanning (TSS) have been developed to maximize cellular sampling rate by scanning only select regions in the field of view corresponding to locations of interest such as somata. However, such strategies are not optimized for the mechanical properties of galvanometric scanners. We thus aimed to develop a new scanning algorithm which produces minimal inertia trajectories, and compare its performance with existing scanning algorithms. Approach: We describe here the Adaptive Spiral Scanning (SSA) algorithm, which fits a set of near-circular trajectories to the cellular distribution to avoid inertial drifts of galvanometer position. We compare its performance to raster scanning and TSS in terms of cellular sampling frequency and signal-to-noise ratio (SNR). Main Results: Using surrogate neuron spatial position data, we show that SSA acquisition rates are an order of magnitude higher than those for raster scanning and generally exceed those achieved by TSS for neural densities comparable with those found in the cortex. We show that this result also holds true for in vitro hippocampal mouse brain slices bath loaded with the synthetic calcium dye Cal-520 AM. The ability of TSS to "park" the laser on each neuron along the scanning trajectory, however, enables higher SNR than SSA when all targets are precisely scanned. Raster scanning has the highest SNR but at a substantial cost in number of cells scanned. To understand the impact of sampling rate and SNR on functional calcium imaging, we used the Crame ́r-Rao Bound on evoked calcium traces recorded

  12. Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Prima Dewi Purnamasari

    2017-05-01

    Full Text Available The development of automatic emotion detection systems has recently gained significant attention due to the growing possibility of their implementation in several applications, including affective computing and various fields within biomedical engineering. Use of the electroencephalograph (EEG signal is preferred over facial expression, as people cannot control the EEG signal generated by their brain; the EEG ensures a stronger reliability in the psychological signal. However, because of its uniqueness between individuals and its vulnerability to noise, use of EEG signals can be rather complicated. In this paper, we propose a methodology to conduct EEG-based emotion recognition by using a filtered bispectrum as the feature extraction subsystem and an artificial neural network (ANN as the classifier. The bispectrum is theoretically superior to the power spectrum because it can identify phase coupling between the nonlinear process components of the EEG signal. In the feature extraction process, to extract the information contained in the bispectrum matrices, a 3D pyramid filter is used for sampling and quantifying the bispectrum value. Experiment results show that the mean percentage of the bispectrum value from 5 × 5 non-overlapped 3D pyramid filters produces the highest recognition rate. We found that reducing the number of EEG channels down to only eight in the frontal area of the brain does not significantly affect the recognition rate, and the number of data samples used in the training process is then increased to improve the recognition rate of the system. We have also utilized a probabilistic neural network (PNN as another classifier and compared its recognition rate with that of the back-propagation neural network (BPNN, and the results show that the PNN produces a comparable recognition rate and lower computational costs. Our research shows that the extracted bispectrum values of an EEG signal using 3D filtering as a feature extraction

  13. Radiation-induced glioblastoma signaling cascade regulates viability, apoptosis and differentiation of neural stem cells (NSC).

    Science.gov (United States)

    Ivanov, Vladimir N; Hei, Tom K

    2014-12-01

    Ionizing radiation alone or in combination with chemotherapy is the main treatment modality for brain tumors including glioblastoma. Adult neurons and astrocytes demonstrate substantial radioresistance; in contrast, human neural stem cells (NSC) are highly sensitive to radiation via induction of apoptosis. Irradiation of tumor cells has the potential risk of affecting the viability and function of NSC. In this study, we have evaluated the effects of irradiated glioblastoma cells on viability, proliferation and differentiation potential of non-irradiated (bystander) NSC through radiation-induced signaling cascades. Using media transfer experiments, we demonstrated significant effects of the U87MG glioblastoma secretome after gamma-irradiation on apoptosis in non-irradiated NSC. Addition of anti-TRAIL antibody to the transferred media partially suppressed apoptosis in NSC. Furthermore, we observed a dramatic increase in the production and secretion of IL8, TGFβ1 and IL6 by irradiated glioblastoma cells, which could promote glioblastoma cell survival and modify the effects of death factors in bystander NSC. While differentiation of NSC into neurons and astrocytes occurred efficiently with the corresponding differentiation media, pretreatment of NSC for 8 h with medium from irradiated glioblastoma cells selectively suppressed the differentiation of NSC into neurons, but not into astrocytes. Exogenous IL8 and TGFβ1 increased NSC/NPC survival, but also suppressed neuronal differentiation. On the other hand, IL6 was known to positively affect survival and differentiation of astrocyte progenitors. We established a U87MG neurosphere culture that was substantially enriched by SOX2(+) and CD133(+) glioma stem-like cells (GSC). Gamma-irradiation up-regulated apoptotic death in GSC via the FasL/Fas pathway. Media transfer experiments from irradiated GSC to non-targeted NSC again demonstrated induction of apoptosis and suppression of neuronal differentiation of NSC. In

  14. A Fully Integrated Wireless Compressed Sensing Neural Signal Acquisition System for Chronic Recording and Brain Machine Interface.

    Science.gov (United States)

    Liu, Xilin; Zhang, Milin; Xiong, Tao; Richardson, Andrew G; Lucas, Timothy H; Chin, Peter S; Etienne-Cummings, Ralph; Tran, Trac D; Van der Spiegel, Jan

    2016-07-18

    Reliable, multi-channel neural recording is critical to the neuroscience research and clinical treatment. However, most hardware development of fully integrated, multi-channel wireless neural recorders to-date, is still in the proof-of-concept stage. To be ready for practical use, the trade-offs between performance, power consumption, device size, robustness, and compatibility need to be carefully taken into account. This paper presents an optimized wireless compressed sensing neural signal recording system. The system takes advantages of both custom integrated circuits and universal compatible wireless solutions. The proposed system includes an implantable wireless system-on-chip (SoC) and an external wireless relay. The SoC integrates 16-channel low-noise neural amplifiers, programmable filters and gain stages, a SAR ADC, a real-time compressed sensing module, and a near field wireless power and data transmission link. The external relay integrates a 32 bit low-power microcontroller with Bluetooth 4.0 wireless module, a programming interface, and an inductive charging unit. The SoC achieves high signal recording quality with minimized power consumption, while reducing the risk of infection from through-skin connectors. The external relay maximizes the compatibility and programmability. The proposed compressed sensing module is highly configurable, featuring a SNDR of 9.78 dB with a compression ratio of 8×. The SoC has been fabricated in a 180 nm standard CMOS technology, occupying 2.1 mm × 0.6 mm silicon area. A pre-implantable system has been assembled to demonstrate the proposed paradigm. The developed system has been successfully used for long-term wireless neural recording in freely behaving rhesus monkey.

  15. A system of recurrent neural networks for modularising, parameterising and dynamic analysis of cell signalling networks.

    Science.gov (United States)

    Samarasinghe, S; Ling, H

    In this paper, we show how to extend our previously proposed novel continuous time Recurrent Neural Networks (RNN) approach that retains the advantage of continuous dynamics offered by Ordinary Differential Equations (ODE) while enabling parameter estimation through adaptation, to larger signalling networks using a modular approach. Specifically, the signalling network is decomposed into several sub-models based on important temporal events in the network. Each sub-model is represented by the proposed RNN and trained using data generated from the corresponding ODE model. Trained sub-models are assembled into a whole system RNN which is then subjected to systems dynamics and sensitivity analyses. The concept is illustrated by application to G1/S transition in cell cycle using Iwamoto et al. (2008) ODE model. We decomposed the G1/S network into 3 sub-models: (i) E2F transcription factor release; (ii) E2F and CycE positive feedback loop for elevating cyclin levels; and (iii) E2F and CycA negative feedback to degrade E2F. The trained sub-models accurately represented system dynamics and parameters were in good agreement with the ODE model. The whole system RNN however revealed couple of parameters contributing to compounding errors due to feedback and required refinement to sub-model 2. These related to the reversible reaction between CycE/CDK2 and p27, its inhibitor. The revised whole system RNN model very accurately matched dynamics of the ODE system. Local sensitivity analysis of the whole system model further revealed the most dominant influence of the above two parameters in perturbing G1/S transition, giving support to a recent hypothesis that the release of inhibitor p27 from Cyc/CDK complex triggers cell cycle stage transition. To make the model useful in a practical setting, we modified each RNN sub-model with a time relay switch to facilitate larger interval input data (≈20min) (original model used data for 30s or less) and retrained them that produced

  16. 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

  17. Agonist-Evoked Ca2+ Signaling in Enteric Glia Drives Neural Programs That Regulate Intestinal Motility in MiceSummary

    Directory of Open Access Journals (Sweden)

    Jonathon L. McClain

    2015-11-01

    Full Text Available Background & Aims: Gastrointestinal motility is regulated by enteric neural circuitry that includes enteric neurons and glia. Enteric glia monitor synaptic activity and exhibit responses to neurotransmitters that are encoded by intracellular calcium (Ca2+ signaling. What role evoked glial responses play in the neural regulation of gut motility is unknown. We tested how evoking Ca2+ signaling in enteric glia affects the neural control of intestinal motility. Methods: We used a novel chemogenetic mouse model that expresses the designer receptor hM3Dq under the transcriptional control of the glial fibrillary acidic protein (GFAP promoter (GFAP::hM3Dq mice to selectively trigger glial Ca2+ signaling. We used in situ Ca2+ imaging and immunohistochemistry to validate this model, and we assessed gut motility by measuring pellet output and composition, colonic bead expulsion time, small intestinal transit time, total gut transit time, colonic migrating motor complex (CMMC recordings, and muscle tension recordings. Results: Expression of the hM3Dq receptor is confined to GFAP-positive enteric glia in the intestines of GFAP::hM3Dq mice. In these mice, application of the hM3Dq agonist clozapine-N-oxide (CNO selectively triggers intracellular Ca2+ responses in enteric glia. Glial activation drove neurogenic contractions in the ileum and colon but had no effect on neurogenic relaxations. CNO enhanced the amplitude and frequency of CMMCs in ex vivo preparations of the colon, and CNO increased colonic motility in vivo. CNO had no effect on the composition of fecal matter, small intestinal transit, or whole gut transit. Conclusions: Glial excitability encoded by intracellular Ca2+ signaling functions to modulate excitatory enteric circuits. Selectively triggering glial Ca2+ signaling might be a novel strategy to improve gut function in motility disorders. Keywords: Autonomic, Chemogenetic, Enteric Nervous System, Intestine, Gut

  18. Neural mechanisms underlying the effects of face-based affective signals on memory for faces: a tentative model.

    Science.gov (United States)

    Tsukiura, Takashi

    2012-01-01

    In our daily lives, we form some impressions of other people. Although those impressions are affected by many factors, face-based affective signals such as facial expression, facial attractiveness, or trustworthiness are important. Previous psychological studies have demonstrated the impact of facial impressions on remembering other people, but little is known about the neural mechanisms underlying this psychological process. The purpose of this article is to review recent functional MRI (fMRI) studies to investigate the effects of face-based affective signals including facial expression, facial attractiveness, and trustworthiness on memory for faces, and to propose a tentative concept for understanding this affective-cognitive interaction. On the basis of the aforementioned research, three brain regions are potentially involved in the processing of face-based affective signals. The first candidate is the amygdala, where activity is generally modulated by both affectively positive and negative signals from faces. Activity in the orbitofrontal cortex (OFC), as the second candidate, increases as a function of perceived positive signals from faces; whereas activity in the insular cortex, as the third candidate, reflects a function of face-based negative signals. In addition, neuroscientific studies have reported that the three regions are functionally connected to the memory-related hippocampal regions. These findings suggest that the effects of face-based affective signals on memory for faces could be modulated by interactions between the regions associated with the processing of face-based affective signals and the hippocampus as a memory-related region.

  19. A negative modulatory role for rho and rho-associated kinase signaling in delamination of neural crest cells

    Science.gov (United States)

    Groysman, Maya; Shoval, Irit; Kalcheim, Chaya

    2008-01-01

    Background Neural crest progenitors arise as epithelial cells and then undergo a process of epithelial to mesenchymal transition that precedes the generation of cellular motility and subsequent migration. We aim at understanding the underlying molecular network. Along this line, possible roles of Rho GTPases that act as molecular switches to control a variety of signal transduction pathways remain virtually unexplored, as are putative interactions between Rho proteins and additional known components of this cascade. Results We investigated the role of Rho/Rock signaling in neural crest delamination. Active RhoA and RhoB are expressed in the membrane of epithelial progenitors and are downregulated upon delamination. In vivo loss-of-function of RhoA or RhoB or of overall Rho signaling by C3 transferase enhanced and/or triggered premature crest delamination yet had no effect on cell specification. Consistently, treatment of explanted neural primordia with membrane-permeable C3 or with the Rock inhibitor Y27632 both accelerated and enhanced crest emigration without affecting cell proliferation. These treatments altered neural crest morphology by reducing stress fibers, focal adhesions and downregulating membrane-bound N-cadherin. Reciprocally, activation of endogenous Rho by lysophosphatidic acid inhibited emigration while enhancing the above. Since delamination is triggered by BMP and requires G1/S transition, we examined their relationship with Rho. Blocking Rho/Rock function rescued crest emigration upon treatment with noggin or with the G1/S inhibitor mimosine. In the latter condition, cells emigrated while arrested at G1. Conversely, BMP4 was unable to rescue cell emigration when endogenous Rho activity was enhanced by lysophosphatidic acid. Conclusion Rho-GTPases, through Rock, act downstream of BMP and of G1/S transition to negatively regulate crest delamination by modifying cytoskeleton assembly and intercellular adhesion. PMID:18945340

  20. Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex

    Science.gov (United States)

    Chestek, Cynthia A.; Gilja, Vikash; Nuyujukian, Paul; Foster, Justin D.; Fan, Joline M.; Kaufman, Matthew T.; Churchland, Mark M.; Rivera-Alvidrez, Zuley; Cunningham, John P.; Ryu, Stephen I.; Shenoy, Krishna V.

    2011-08-01

    Cortically-controlled prosthetic systems aim to help disabled patients by translating neural signals from the brain into control signals for guiding prosthetic devices. Recent reports have demonstrated reasonably high levels of performance and control of computer cursors and prosthetic limbs, but to achieve true clinical viability, the long-term operation of these systems must be better understood. In particular, the quality and stability of the electrically-recorded neural signals require further characterization. Here, we quantify action potential changes and offline neural decoder performance over 382 days of recording from four intracortical arrays in three animals. Action potential amplitude decreased by 2.4% per month on average over the course of 9.4, 10.4, and 31.7 months in three animals. During most time periods, decoder performance was not well correlated with action potential amplitude (p > 0.05 for three of four arrays). In two arrays from one animal, action potential amplitude declined by an average of 37% over the first 2 months after implant. However, when using simple threshold-crossing events rather than well-isolated action potentials, no corresponding performance loss was observed during this time using an offline decoder. One of these arrays was effectively used for online prosthetic experiments over the following year. Substantial short-term variations in waveforms were quantified using a wireless system for contiguous recording in one animal, and compared within and between days for all three animals. Overall, this study suggests that action potential amplitude declines more slowly than previously supposed, and performance can be maintained over the course of multiple years when decoding from threshold-crossing events rather than isolated action potentials. This suggests that neural prosthetic systems may provide high performance over multiple years in human clinical trials.

  1. Planar polarization of Vangl2 in the vertebrate neural plate is controlled by Wnt and Myosin II signaling

    Directory of Open Access Journals (Sweden)

    Olga Ossipova

    2015-07-01

    Full Text Available The vertebrate neural tube forms as a result of complex morphogenetic movements, which require the functions of several core planar cell polarity (PCP proteins, including Vangl2 and Prickle. Despite the importance of these proteins for neurulation, their subcellular localization and the mode of action have remained largely unknown. Here we describe the anteroposterior planar cell polarity (AP-PCP of the cells in the Xenopus neural plate. At the neural midline, the Vangl2 protein is enriched at anterior cell edges and that this localization is directed by Prickle, a Vangl2-interacting protein. Our further analysis is consistent with the model, in which Vangl2 AP-PCP is established in the neural plate as a consequence of Wnt-dependent phosphorylation. Additionally, we uncover feedback regulation of Vangl2 polarity by Myosin II, reiterating a role for mechanical forces in PCP. These observations indicate that both Wnt signaling and Myosin II activity regulate cell polarity and cell behaviors during vertebrate neurulation.

  2. GSM Network Traffic Analysis | Ani | Nigerian Journal of Technology

    African Journals Online (AJOL)

    GSM networks are traffic intensive specifically the signaling traffic. Evolvement of effective and efficient performance management strategy requires accurate quantification of network signaling traffic volume along side with the user traffic volume. Inaccurate quantification may lead to serious network traffic congestion and ...

  3. Assessing the user experience of older adults using a neural network trained to recognize emotions from brain signals.

    Science.gov (United States)

    Meza-Kubo, Victoria; Morán, Alberto L; Carrillo, Ivan; Galindo, Gilberto; García-Canseco, Eloisa

    2016-08-01

    The use of Ambient Assisted Living (AAL) technologies as a means to cope with problems that arise due to an increasing and aging population is becoming usual. AAL technologies are used to prevent, cure and improve the wellness and health conditions of the elderly. However, their adoption and use by older adults is still a major challenge. User Experience (UX) evaluations aim at aiding on this task, by identifying the experience that a user has while interacting with an AAL technology under particular conditions. This may help designing better products and improve user engagement and adoption of AAL solutions. However, evaluating the UX of AAL technologies is a difficult task, due to the inherent limitations of their subjects and of the evaluation methods. In this study, we validated the feasibility of assessing the UX of older adults while they use a cognitive stimulation application using a neural network trained to recognize pleasant and unpleasant emotions from electroencephalography (EEG) signals by contrasting our results with those of additional self-report and qualitative analysis UX evaluations. Our study results provide evidence about the feasibility of assessing the UX of older adults using a neural network that take as input the EEG signals; the classification accuracy of our neural network ranges from 60.87% to 82.61%. As future work we will conduct additional UX evaluation studies using the three different methods, in order to appropriately validate these results. Copyright © 2016 Elsevier Inc. All rights reserved.

  4. A Hardware-Efficient Scalable Spike Sorting Neural Signal Processor Module for Implantable High-Channel-Count Brain Machine Interfaces.

    Science.gov (United States)

    Yang, Yuning; Boling, Sam; Mason, Andrew J

    2017-08-01

    Next-generation brain machine interfaces demand a high-channel-count neural recording system to wirelessly monitor activities of thousands of neurons. A hardware efficient neural signal processor (NSP) is greatly desirable to ease the data bandwidth bottleneck for a fully implantable wireless neural recording system. This paper demonstrates a complete multichannel spike sorting NSP module that incorporates all of the necessary spike detector, feature extractor, and spike classifier blocks. To meet high-channel-count and implantability demands, each block was designed to be highly hardware efficient and scalable while sharing resources efficiently among multiple channels. To process multiple channels in parallel, scalability analysis was performed, and the utilization of each block was optimized according to its input data statistics and the power, area and/or speed of each block. Based on this analysis, a prototype 32-channel spike sorting NSP scalable module was designed and tested on an FPGA using synthesized datasets over a wide range of signal to noise ratios. The design was mapped to 130 nm CMOS to achieve 0.75 μW power and 0.023 mm2 area consumptions per channel based on post synthesis simulation results, which permits scalability of digital processing to 690 channels on a 4×4 mm2 electrode array.

  5. Fatty acid-induced gut-brain signaling attenuates neural and behavioral effects of sad emotion in humans.

    Science.gov (United States)

    Van Oudenhove, Lukas; McKie, Shane; Lassman, Daniel; Uddin, Bilal; Paine, Peter; Coen, Steven; Gregory, Lloyd; Tack, Jan; Aziz, Qasim

    2011-08-01

    Although a relationship between emotional state and feeding behavior is known to exist, the interactions between signaling initiated by stimuli in the gut and exteroceptively generated emotions remain incompletely understood. Here, we investigated the interaction between nutrient-induced gut-brain signaling and sad emotion induced by musical and visual cues at the behavioral and neural level in healthy nonobese subjects undergoing functional magnetic resonance imaging. Subjects received an intragastric infusion of fatty acid solution or saline during neutral or sad emotion induction and rated sensations of hunger, fullness, and mood. We found an interaction between fatty acid infusion and emotion induction both in the behavioral readouts (hunger, mood) and at the level of neural activity in multiple pre-hypothesized regions of interest. Specifically, the behavioral and neural responses to sad emotion induction were attenuated by fatty acid infusion. These findings increase our understanding of the interplay among emotions, hunger, food intake, and meal-induced sensations in health, which may have important implications for a wide range of disorders, including obesity, eating disorders, and depression.

  6. Dual small-molecule targeting of SMAD signaling stimulates human induced pluripotent stem cells toward neural lineages.

    Directory of Open Access Journals (Sweden)

    Methichit Wattanapanitch

    Full Text Available Incurable neurological disorders such as Parkinson's disease (PD, Huntington's disease (HD, and Alzheimer's disease (AD are very common and can be life-threatening because of their progressive disease symptoms with limited treatment options. To provide an alternative renewable cell source for cell-based transplantation and as study models for neurological diseases, we generated induced pluripotent stem cells (iPSCs from human dermal fibroblasts (HDFs and then differentiated them into neural progenitor cells (NPCs and mature neurons by dual SMAD signaling inhibitors. Reprogramming efficiency was improved by supplementing the histone deacethylase inhibitor, valproic acid (VPA, and inhibitor of p160-Rho associated coiled-coil kinase (ROCK, Y-27632, after retroviral transduction. We obtained a number of iPS colonies that shared similar characteristics with human embryonic stem cells in terms of their morphology, cell surface antigens, pluripotency-associated gene and protein expressions as well as their in vitro and in vivo differentiation potentials. After treatment with Noggin and SB431542, inhibitors of the SMAD signaling pathway, HDF-iPSCs demonstrated rapid and efficient differentiation into neural lineages. Six days after neural induction, neuroepithelial cells (NEPCs were observed in the adherent monolayer culture, which had the ability to differentiate further into NPCs and neurons, as characterized by their morphology and the expression of neuron-specific transcripts and proteins. We propose that our study may be applied to generate neurological disease patient-specific iPSCs allowing better understanding of disease pathogenesis and drug sensitivity assays.

  7. Electroencephalogram Signals Processing for the Diagnosis of Petit mal and Grand mal Epilepsies Using an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    M. R. Arab

    2010-04-01

    Full Text Available In this study, a novel wavelet transform‐neural network method is presented. The presented method is used for theclassification of grand mal (clonic stage and petit mal (absence epilepsies into healthy, ictal and interictal (EEGs. Preprocessingis included to remove an artifact occurred by blinking and a wandering baseline (electrodes movement as well as an eyeballmovement artifact using the Discrete Wavelet Transformation (DWT. Denoising EEG signals from the AC power supplyfrequency with a suitable notch filter is another job of preprocessing. The preprocessing enhanced speed and accuracy of theprocessing stage (wavelet transform and neural network. The EEGs signals are categorized into normal and petit mal and clonicepilepsy by an expert neurologist. The categorization is confirmed by the Fast Fourier Transform (FFT analysis. The datasetincludes waves such as sharp, spike and spike‐slow wave. Through the Countinous Wavelet Transform (CWT of EEG records,transient features are accurately captured and separated and used as classifier input. We introduce a two‐stage classifier basedon the Learning Vector Quantization (LVQ neural network localized in both time and frequency contexts. The particularcoefficients of the Continuous Wavelet Transform (CWT are networks. The simulation results are very promising and theaccuracy of the proposed method obtained is of about 80%.

  8. Quantitative and kinetic profile of Wnt/β-catenin signaling components during human neural progenitor cell differentiation.

    Science.gov (United States)

    Mazemondet, Orianne; Hubner, Rayk; Frahm, Jana; Koczan, Dirk; Bader, Benjamin M; Weiss, Dieter G; Uhrmacher, Adelinde M; Frech, Moritz J; Rolfs, Arndt; Luo, Jiankai

    2011-12-01

    ReNcell VM is an immortalized human neural progenitor cell line with the ability to differentiate in vitro into astrocytes and neurons, in which the Wnt/β-catenin pathway is known to be involved. However, little is known about kinetic changes of this pathway in human neural progenitor cell differentiation. In the present study, we provide a quantitative profile of Wnt/β-catenin pathway dynamics showing its spatio-temporal regulation during ReNcell VM cell differentiation. We show first that T-cell factor dependent transcription can be activated by stabilized β-catenin. Furthermore, endogenous Wnt ligands, pathway receptors and signaling molecules are temporally controlled, demonstrating changes related to differentiation stages. During the first three hours of differentiation the signaling molecules LRP6, Dvl2 and β-catenin are spatio-temporally regulated between distinct cellular compartments. From 24 h onward, components of the Wnt/β-catenin pathway are strongly activated and regulated as shown by mRNA up-regulation of Wnt ligands (Wnt5a and Wnt7a), receptors including Frizzled-2, -3, -6, -7, and -9, and co-receptors, and target genes including Axin2. This detailed temporal profile of the Wnt/β-catenin pathway is a first step to understand, control and to orientate, in vitro, human neural progenitor cell differentiation.

  9. CONCEPTION OF USE VIBROACOUSTIC SIGNALS AND NEURAL NETWORKS FOR DIAGNOSING OF CHOSEN ELEMENTS OF INTERNAL COMBUSTION ENGINES IN CAR VEHICLES

    Directory of Open Access Journals (Sweden)

    Piotr CZECH

    2014-03-01

    Full Text Available Currently used diagnostics systems are not always efficient and do not give straightforward results which allow for the assessment of the technological condition of the engine or for the identification of the possible damages in their early stages of development. Growing requirements concerning durability, reliability, reduction of costs to minimum and decrease of negative influence on the natural environment are the reasons why there is a need to acquire information about the technological condition of each of the elements of a vehicle during its exploitation. One of the possibilities to achieve information about technological condition of a vehicle are vibroacoustic phenomena. Symptoms of defects, achieved as a result of advanced methods of vibroacoustic signals processing can serve as models which can be used during construction of intelligent diagnostic system based on artificial neural networks. The work presents conception of use artificial neural networks in the task of combustion engines diagnosis.

  10. [Retinoic acid signal pathway regulation of zebra fish tooth development through manipulation of the differentiation of neural crest].

    Science.gov (United States)

    Liu, Xin; Huang, Xing; Xu, Zhiyun; Yang, Deqin

    2016-04-01

    To investigate the mechanism of retinoic acid (RA) signal in dental evolution, RA is used to explore the influence of the mechanism on neural crest's migration during the early stage of zebra fish embryos. We divided embryos of wild type and transgenic line zebra fish into three groups. 1 x 10(-7) to 6 x 10(-7) mol x L(-1) RA and 1 x 10(-7) mo x L(-1) 4-diethylaminobenzaldehyde (DEAB) were added into egg water at 24 hpf for 9 h. Dimethyl sulfoxid (DMSO) with the concentration was used as control group. Then, antisense probes of dlx2a, dlx2b, and barxl were formulated to perform whole-mount in situ hybridization to check the expressions of the genes in 48 hpf to 72 hpf embryos. We observed fluorescence of transgenic line in 4 dpf embryos. We obtained three mRNA probes successfully. Compared with DMSO control group, a low concentration (1 x 10(-7) mol x L(-1)) of RA could up-regulate the expression of mRNA (barx1, dlx2a) in neural crest. Obvious migration trend was observed toward the pharyngeal arch in which teeth adhered. Transgenic fish had spreading fluorescence tendency in pharyngeal arch. However, a high concentration (4 x 10(-7) mol x L(-1)) of RA malformed the embryos and killed them after treatment. One third of the embryos of middle concentration (3 x 10(-7) mo x L(-1)) exhibited delayed development. DEAB resulted in neural crest dysplasia. The expression of barxl and dlx2a were suppressed, and the appearance of dlx2b in tooth was delayed. RA signal pathway can regulate the progenitors of tooth by controlling the growth of the neural crest and manipulating tooth development

  11. Economical Video Monitoring of Traffic

    Science.gov (United States)

    Houser, B. C.; Paine, G.; Rubenstein, L. D.; Parham, O. Bruce, Jr.; Graves, W.; Bradley, C.

    1986-01-01

    Data compression allows video signals to be transmitted economically on telephone circuits. Telephone lines transmit television signals to remote traffic-control center. Lines also carry command signals from center to TV camera and compressor at highway site. Video system with television cameras positioned at critical points on highways allows traffic controllers to determine visually, almost immediately, exact cause of traffic-flow disruption; e.g., accidents, breakdowns, or spills, almost immediately. Controllers can then dispatch appropriate emergency services and alert motorists to minimize traffic backups.

  12. Pax3 and Hippo Signaling Coordinate Melanocyte Gene Expression in Neural Crest

    Directory of Open Access Journals (Sweden)

    Lauren J. Manderfield

    2014-12-01

    Full Text Available Loss of Pax3, a developmentally regulated transcription factor expressed in premigratory neural crest, results in severe developmental defects and embryonic lethality. Although Pax3 mutations produce profound phenotypes, the intrinsic transcriptional activation exhibited by Pax3 is surprisingly modest. We postulated the existence of transcriptional coactivators that function with Pax3 to mediate developmental functions. A high-throughput screen identified the Hippo effector proteins Taz and Yap65 as Pax3 coactivators. Synergistic coactivation of target genes by Pax3-Taz/Yap65 requires DNA binding by Pax3, is Tead independent, and is regulated by Hippo kinases Mst1 and Lats2. In vivo, Pax3 and Yap65 colocalize in the nucleus of neural crest progenitors in the dorsal neural tube. Neural crest deletion of Taz and Yap65 results in embryo-lethal neural crest defects and decreased expression of the Pax3 target gene, Mitf. These results suggest that Pax3 activity is regulated by the Hippo pathway and that Pax factors are Hippo effectors.

  13. Adenosine signaling promotes neuronal, catecholaminergic differentiation of primary neural crest cells and CNS-derived CAD cells.

    Science.gov (United States)

    Bilodeau, Matthew L; Ji, Ming; Paris, Maryline; Andrisani, Ourania M

    2005-07-01

    In neural crest (NC) cultures cAMP signaling is an instructive signal in catecholaminergic, sympathoadrenal cell development. However, the extracellular signals activating the cAMP pathway during NC cell development have not been identified. We demonstrate that in avian NC cultures, evidenced by tyrosine hydroxylase expression and catecholamine biosynthesis, adenosine and not adrenergic signaling, together with BMP2, promotes sympathoadrenal cell development. In NC cultures, addition of the adenosine receptor agonist NECA in the presence of BMP2 promotes sympathoadrenal cell development, whereas the antagonist CGS 15943 or the adenosine degrading enzyme adenosine deaminase (ADA) suppresses TH expression. Importantly, NC cells express A2A and A2B receptors which couple with Gsalpha increasing intracellular cAMP. Employing the CNS-derived catecholaminergic CAD cell line, we also demonstrate that neuronal differentiation mediated by serum withdrawal is further enhanced by treatment with IBMX, a cAMP-elevating agent, or the adenosine receptor agonist NECA, acting via cAMP. By contrast, the adenosine receptor antagonist CGS 15943 or the adenosine degrading enzyme ADA inhibits CAD cell neuronal differentiation mediated by serum withdrawal. These results support that adenosine is a physiological signal in neuronal differentiation of the CNS-derived catecholaminergic CAD cell line and suggest that adenosine signaling is involved in NC cell development in vivo.

  14. A High-Performance Lossless Compression Scheme for EEG Signals Using Wavelet Transform and Neural Network Predictors

    Directory of Open Access Journals (Sweden)

    N. Sriraam

    2012-01-01

    Full Text Available Developments of new classes of efficient compression algorithms, software systems, and hardware for data intensive applications in today's digital health care systems provide timely and meaningful solutions in response to exponentially growing patient information data complexity and associated analysis requirements. Of the different 1D medical signals, electroencephalography (EEG data is of great importance to the neurologist for detecting brain-related disorders. The volume of digitized EEG data generated and preserved for future reference exceeds the capacity of recent developments in digital storage and communication media and hence there is a need for an efficient compression system. This paper presents a new and efficient high performance lossless EEG compression using wavelet transform and neural network predictors. The coefficients generated from the EEG signal by integer wavelet transform are used to train the neural network predictors. The error residues are further encoded using a combinational entropy encoder, Lempel-Ziv-arithmetic encoder. Also a new context-based error modeling is also investigated to improve the compression efficiency. A compression ratio of 2.99 (with compression efficiency of 67% is achieved with the proposed scheme with less encoding time thereby providing diagnostic reliability for lossless transmission as well as recovery of EEG signals for telemedicine applications.

  15. A signal pre-processing algorithm designed for the needs of hardware implementation of neural classifiers used in condition monitoring

    DEFF Research Database (Denmark)

    Dabrowski, Dariusz; Hashemiyan, Zahra; Adamczyk, Jan

    2015-01-01

    Gearboxes have a significant influence on the durability and reliability of a power transmission system. Currently, extensive research studies are being carried out to increase the reliability of gearboxes working in the energy industry, especially with a focus on planetary gears in wind turbines...... is to estimate the features of a vibration signal that are related to failures, e.g. misalignment and unbalance. These features can serve as the components of an input vector for a neural classifier. The approach proposed here has several important benefits: it is resistant to small speed fluctuations up to 7...

  16. A Flexible Terminal Approach to Sampled-Data Exponentially Synchronization of Markovian Neural Networks With Time-Varying Delayed Signals.

    Science.gov (United States)

    Cheng, Jun; Park, Ju H; Karimi, Hamid Reza; Shen, Hao

    2017-08-02

    This paper investigates the problem of sampled-data (SD) exponentially synchronization for a class of Markovian neural networks with time-varying delayed signals. Based on the tunable parameter and convex combination computational method, a new approach named flexible terminal approach is proposed to reduce the conservatism of delay-dependent synchronization criteria. The SD subject to stochastic sampling period is introduced to exhibit the general phenomena of reality. Novel exponential synchronization criterion are derived by utilizing uniform Lyapunov-Krasovskii functional and suitable integral inequality. Finally, numerical examples are provided to show the usefulness and advantages of the proposed design procedure.

  17. Characterization of Apoptosis Signaling Cascades During the Differentiation Process of Human Neural ReNcell VM Progenitor Cells In Vitro.

    Science.gov (United States)

    Jaeger, Alexandra; Fröhlich, Michael; Klum, Susanne; Lantow, Margareta; Viergutz, Torsten; Weiss, Dieter G; Kriehuber, Ralf

    2015-11-01

    Apoptosis is an essential physiological process accompanying the development of the central nervous system and human neurogenesis. However, the time scale and the underlying molecular mechanisms are yet poorly understood. Due to this fact, we investigated the functionality and general inducibility of apoptosis in the human neural ReNcell VM progenitor cell line during differentiation and also after exposure to staurosporine (STS) and ultraviolet B (UVB) irradiation. Transmission light microscopy, flow cytometry, and Western-/Immunoblot analysis were performed to compare proliferating and differentiating, in addition to STS- and UVB-treated cells. In particular, from 24 to 72 h post-initiation of differentiation, G0/G1 cell cycle arrest, increased loss of apoptotic cells, activation of pro-apoptotic BAX, Caspase-3, and cleavage of its substrate PARP were observed during cell differentiation and, to a higher extent, after treatment with STS and UVB. We conclude that redundant or defective cells are eliminated by apoptosis, while otherwise fully differentiated cells were less responsive to apoptosis induction by STS than proliferating cells, likely as a result of reduced APAF-1 expression, and increased levels of BCL-2. These data provide the evidence that apoptotic mechanisms in the neural ReNcell VM progenitor cell line are not only functional, but also inducible by external stimuli like growth factor withdrawal or treatment with STS and UVB, which marks this cell line as a suitable model to investigate apoptosis signaling pathways in respect to the differentiation processes of human neural progenitor cells in vitro.

  18. The Sustained Effect of Emotional Signals on Neural Processing in 12-Month-Olds

    Science.gov (United States)

    Leventon, Jacqueline S.; Bauer, Patricia J.

    2013-01-01

    Around the end of the first year of life, infants develop a social referencing ability -- using emotional information from others to guide their own behavior. Much research on social referencing has focused on changes in behavior in response to emotional information. The present study was an investigation of the changes in neural responses that…

  19. MEG and fMRI fusion for nonlinear estimation of neural and BOLD signal changes

    Directory of Open Access Journals (Sweden)

    Sergey M Plis

    2010-11-01

    Full Text Available The combined analysis of MEG/EEG and functional MRI measurements can lead to improvement in the description of the dynamical and spatial properties of brain activity. In this paper we empirically demonstrate this improvement using simulated and recorded task related MEG and fMRI activity. Neural activity estimates were derived using a dynamic Bayesian network with continuous real valued parameters by means of a sequential Monte Carlo technique. In synthetic data, we show that MEG and fMRI fusion improves estimation of the indirectly observed neural activity and smooths tracking of the BOLD response. In recordings of task related neural activity the combination of MEG and fMRI produces a result with greater SNR, that confirms the expectation arising from the nature of the experiment. The highly nonlinear model of the BOLD response poses a difficult inference problem for neural activity estimation; computational requirements are also high due to the time and space complexity. We show that joint analysis of the data improves the system's behavior by stabilizing the differential equations system and by requiring fewer computational resources.

  20. Sample Entropy Analysis of EEG Signals via Artificial Neural Networks to Model Patients’ Consciousness Level Based on Anesthesiologists Experience

    Directory of Open Access Journals (Sweden)

    George J. A. Jiang

    2015-01-01

    Full Text Available Electroencephalogram (EEG signals, as it can express the human brain’s activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA. Bispectral (BIS index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD method and analyzed using sample entropy (SampEn analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN model through using expert assessment of consciousness level (EACL which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.

  1. Augmented BMPRIA-mediated BMP signaling in cranial neural crest lineage leads to cleft palate formation and delayed tooth differentiation.

    Directory of Open Access Journals (Sweden)

    Lu Li

    Full Text Available The importance of BMP receptor Ia (BMPRIa mediated signaling in the development of craniofacial organs, including the tooth and palate, has been well illuminated in several mouse models of loss of function, and by its mutations associated with juvenile polyposis syndrome and facial defects in humans. In this study, we took a gain-of-function approach to further address the role of BMPR-IA-mediated signaling in the mesenchymal compartment during tooth and palate development. We generated transgenic mice expressing a constitutively active form of BmprIa (caBmprIa in cranial neural crest (CNC cells that contributes to the dental and palatal mesenchyme. Mice bearing enhanced BMPRIa-mediated signaling in CNC cells exhibit complete cleft palate and delayed odontogenic differentiation. We showed that the cleft palate defect in the transgenic animals is attributed to an altered cell proliferation rate in the anterior palatal mesenchyme and to the delayed palatal elevation in the posterior portion associated with ectopic cartilage formation. Despite enhanced activity of BMP signaling in the dental mesenchyme, tooth development and patterning in transgenic mice appeared normal except delayed odontogenic differentiation. These data support the hypothesis that a finely tuned level of BMPRIa-mediated signaling is essential for normal palate and tooth development.

  2. SU-F-E-09: Respiratory Signal Prediction Based On Multi-Layer Perceptron Neural Network Using Adjustable Training Samples

    Energy Technology Data Exchange (ETDEWEB)

    Sun, W; Jiang, M; Yin, F [Duke University Medical Center, Durham, NC (United States)

    2016-06-15

    Purpose: Dynamic tracking of moving organs, such as lung and liver tumors, under radiation therapy requires prediction of organ motions prior to delivery. The shift of moving organ may change a lot due to huge transform of respiration at different periods. This study aims to reduce the influence of that changes using adjustable training signals and multi-layer perceptron neural network (ASMLP). Methods: Respiratory signals obtained using a Real-time Position Management(RPM) device were used for this study. The ASMLP uses two multi-layer perceptron neural networks(MLPs) to infer respiration position alternately and the training sample will be updated with time. Firstly, a Savitzky-Golay finite impulse response smoothing filter was established to smooth the respiratory signal. Secondly, two same MLPs were developed to estimate respiratory position from its previous positions separately. Weights and thresholds were updated to minimize network errors according to Leverberg-Marquart optimization algorithm through backward propagation method. Finally, MLP 1 was used to predict 120∼150s respiration position using 0∼120s training signals. At the same time, MLP 2 was trained using 30∼150s training signals. Then MLP is used to predict 150∼180s training signals according to 30∼150s training signals. The respiration position is predicted as this way until it was finished. Results: In this experiment, the two methods were used to predict 2.5 minute respiratory signals. For predicting 1s ahead of response time, correlation coefficient was improved from 0.8250(MLP method) to 0.8856(ASMLP method). Besides, a 30% improvement of mean absolute error between MLP(0.1798 on average) and ASMLP(0.1267 on average) was achieved. For predicting 2s ahead of response time, correlation coefficient was improved from 0.61415 to 0.7098.Mean absolute error of MLP method(0.3111 on average) was reduced by 35% using ASMLP method(0.2020 on average). Conclusion: The preliminary results

  3. I can't wait! Neural reward signals in impulsive individuals exaggerate the difference between immediate and future rewards.

    Science.gov (United States)

    Schmidt, Barbara; Holroyd, Clay B; Debener, Stefan; Hewig, Johannes

    2017-03-01

    Waiting for rewards is difficult, and highly impulsive individuals with low self-control have an especially hard time with it. Here, we investigated whether neural responses to rewards in a delayed gratification task predict impulsivity and self-control. The EEG was recorded from participants engaged in a guessing game in which on each trial they could win either a large or small reward, paid either now or after 6 months. Ratings confirmed that participants preferred immediate, large rewards over small, delayed rewards. Electrophysiological reward signals reflecting the difference between immediate and future rewards predicted self-report measures of impulsivity and self-control. Further, these signals were highly reliable across two sessions over a 1-week interval, showing high temporal stability like stable personality traits. These results suggest that greater valuation of immediate rewards causes impulsive individuals to redirect control away from delayed rewards, indicating why it is so hard for them to wait. © 2016 Society for Psychophysiological Research.

  4. A CMOS power-efficient low-noise current-mode front-end amplifier for neural signal recording.

    Science.gov (United States)

    Wu, Chung-Yu; Chen, Wei-Ming; Kuo, Liang-Ting

    2013-04-01

    In this paper, a new current-mode front-end amplifier (CMFEA) for neural signal recording systems is proposed. In the proposed CMFEA, a current-mode preamplifier with an active feedback loop operated at very low frequency is designed as the first gain stage to bypass any dc offset current generated by the electrode-tissue interface and to achieve a low high-pass cutoff frequency below 0.5 Hz. No reset signal or ultra-large pseudo resistor is required. The current-mode preamplifier has low dc operation current to enhance low-noise performance and decrease power consumption. A programmable current gain stage is adopted to provide adjustable gain for adaptive signal scaling. A following current-mode filter is designed to adjust the low-pass cutoff frequency for different neural signals. The proposed CMFEA is designed and fabricated in 0.18-μm CMOS technology and the area of the core circuit is 0.076 mm(2). The measured high-pass cutoff frequency is as low as 0.3 Hz and the low-pass cutoff frequency is adjustable from 1 kHz to 10 kHz. The measured maximum current gain is 55.9 dB. The measured input-referred current noise density is 153 fA /√Hz , and the power consumption is 13 μW at 1-V power supply. The fabricated CMFEA has been successfully applied to the animal test for recording the seizure ECoG of Long-Evan rats.

  5. Simulation of signal flow in 3D reconstructions of an anatomically realistic neural network in rat vibrissal cortex.

    Science.gov (United States)

    Lang, Stefan; Dercksen, Vincent J; Sakmann, Bert; Oberlaender, Marcel

    2011-11-01

    The three-dimensional (3D) structure of neural circuits represents an essential constraint for information flow in the brain. Methods to directly monitor streams of excitation, at subcellular and millisecond resolution, are at present lacking. Here, we describe a pipeline of tools that allow investigating information flow by simulating electrical signals that propagate through anatomically realistic models of average neural networks. The pipeline comprises three blocks. First, we review tools that allow fast and automated acquisition of 3D anatomical data, such as neuron soma distributions or reconstructions of dendrites and axons from in vivo labeled cells. Second, we introduce NeuroNet, a tool for assembling the 3D structure and wiring of average neural networks. Finally, we introduce a simulation framework, NeuroDUNE, to investigate structure-function relationships within networks of full-compartmental neuron models at subcellular, cellular and network levels. We illustrate the pipeline by simulations of a reconstructed excitatory network formed between the thalamus and spiny stellate neurons in layer 4 (L4ss) of a cortical barrel column in rat vibrissal cortex. Exciting the ensemble of L4ss neurons with realistic input from an ensemble of thalamic neurons revealed that the location-specific thalamocortical connectivity may result in location-specific spiking of cortical cells. Specifically, a radial decay in spiking probability toward the column borders could be a general feature of signal flow in a barrel column. Our simulations provide insights of how anatomical parameters, such as the subcellular organization of synapses, may constrain spiking responses at the cellular and network levels. Copyright © 2011 Elsevier Ltd. All rights reserved.

  6. Multiple excitatory and inhibitory neural signals converge to fine-tune Caenorhabditis elegans feeding to food availability

    Science.gov (United States)

    Dallière, Nicolas; Bhatla, Nikhil; Luedtke, Zara; Ma, Dengke K.; Woolman, Jonathan; Walker, Robert J.; Holden-Dye, Lindy; O’Connor, Vincent

    2016-01-01

    How an animal matches feeding to food availability is a key question for energy homeostasis. We addressed this in the nematode Caenorhabditis elegans, which couples feeding to the presence of its food (bacteria) by regulating pharyngeal activity (pumping). We scored pumping in the presence of food and over an extended time course of food deprivation in wild-type and mutant worms to determine the neural substrates of adaptive behavior. Removal of food initially suppressed pumping but after 2 h this was accompanied by intermittent periods of high activity. We show pumping is fine-tuned by context-specific neural mechanisms and highlight a key role for inhibitory glutamatergic and excitatory cholinergic/peptidergic drives in the absence of food. Additionally, the synaptic protein UNC-31 [calcium-activated protein for secretion (CAPS)] acts through an inhibitory pathway not explained by previously identified contributions of UNC-31/CAPS to neuropeptide or glutamate transmission. Pumping was unaffected by laser ablation of connectivity between the pharyngeal and central nervous system indicating signals are either humoral or intrinsic to the enteric system. This framework in which control is mediated through finely tuned excitatory and inhibitory drives resonates with mammalian hypothalamic control of feeding and suggests that fundamental regulation of this basic animal behavior may be conserved through evolution from nematode to human.—Dallière, N., Bhatla, N., Luedtke, Z., Ma, D. K., Woolman, J., Walker, R. J., Holden-Dye, L., O’Connor, V. Multiple excitatory and inhibitory neural signals converge to fine-tune Caenorhabditis elegans feeding to food availability. PMID:26514165

  7. Neural network activation during a stop-signal task discriminates cocaine-dependent from non-drug-abusing men.

    Science.gov (United States)

    Elton, Amanda; Young, Jonathan; Smitherman, Sonet; Gross, Robin E; Mletzko, Tanja; Kilts, Clinton D

    2014-05-01

    Cocaine dependence is defined by a loss of inhibitory control over drug-use behaviors, mirrored by measurable impairments in laboratory tasks of inhibitory control. The current study tested the hypothesis that deficits in multiple subprocesses of behavioral control are associated with reliable neural-processing alterations that define cocaine addiction. While undergoing functional magnetic resonance imaging (fMRI), 38 cocaine-dependent men and 27 healthy control men performed a stop-signal task of motor inhibition. An independent component analysis on fMRI time courses identified task-related neural networks attributed to motor, visual, cognitive and affective processes. The statistical associations of these components with five different stop-signal task conditions were selected for use in a linear discriminant analysis to define a classifier for cocaine addiction from a subsample of 26 cocaine-dependent men and 18 controls. Leave-one-out cross-validation accurately classified 89.5% (39/44; chance accuracy = 26/44 = 59.1%) of subjects with 84.6% (22/26) sensitivity and 94.4% (17/18) specificity. The remaining 12 cocaine-dependent and 9 control men formed an independent test sample, for which accuracy of the classifier was 81.9% (17/21; chance accuracy = 12/21 = 57.1%) with 75% (9/12) sensitivity and 88.9% (8/9) specificity. The cocaine addiction classification score was significantly correlated with a measure of impulsiveness as well as the duration of cocaine use for cocaine-dependent men. The results of this study support the ability of a pattern of multiple neural network alterations associated with inhibitory motor control to define a binary classifier for cocaine addiction. © 2012 The Authors, Addiction Biology © 2012 Society for the Study of Addiction.

  8. Canonical Wnt/β-catenin signaling is required for maintenance but not activation of Pitx2 expression in neural crest during eye development.

    Science.gov (United States)

    Zacharias, Amanda L; Gage, Philip J

    2010-12-01

    Pitx2 is a paired-like homeodomain gene that acts as a key regulator of eye development. Despite its significance, upstream regulation of Pitx2 expression during eye development remains incompletely understood. We use neural crest-specific ablation of Ctnnb1 to demonstrate that canonical Wnt signaling is not required for initial activation of Pitx2 in neural crest. However, canonical Wnt signaling is subsequently required to maintain Pitx2 expression in the neural crest. Eye development in Ctnnb1-null mice appears grossly normal early but significant phenotypes emerge following loss of Pitx2 expression. LEF-1 and β-catenin bind Pitx2 promoter sequences in ocular neural crest, indicating a likely direct effect of canonical Wnt signaling on Pitx2 expression. Combining our data with previous reports, we propose a model wherein a sequential code of retinoic acid followed by canonical Wnt signaling are required for activation and maintenance of Pitx2 expression, respectively. Other key transcription factors in the neural crest, including Foxc1, do not require intact canonical Wnt signaling. Copyright © 2010 Wiley-Liss, Inc.

  9. Disrupting morphosyntactic and lexical semantic processing has opposite effects on the sample entropy of neural signals.

    Science.gov (United States)

    Fonseca, André; Boboeva, Vezha; Brederoo, Sanne; Baggio, Giosuè

    2015-04-16

    Converging evidence in neuroscience suggests that syntax and semantics are dissociable in brain space and time. However, it is possible that partly disjoint cortical networks, operating in successive time frames, still perform similar types of neural computations. To test the alternative hypothesis, we collected EEG data while participants read sentences containing lexical semantic or morphosyntactic anomalies, resulting in N400 and P600 effects, respectively. Next, we reconstructed phase space trajectories from EEG time series, and we measured the complexity of the resulting dynamical orbits using sample entropy - an index of the rate at which the system generates or loses information over time. Disrupting morphosyntactic or lexical semantic processing had opposite effects on sample entropy: it increased in the N400 window for semantic anomalies, and it decreased in the P600 window for morphosyntactic anomalies. These findings point to a fundamental divergence in the neural computations supporting meaning and grammar in language. Copyright © 2015 Elsevier B.V. All rights reserved.

  10. Classification of Human Emotions from EEG Signals using Statistical Features and Neural Network

    OpenAIRE

    Chai Tong Yuen; Woo San San; Tan Ching Seong; Mohamed Rizon

    2009-01-01

    A statistical based system for human emotions classification by using electroencephalogram (EEG) is proposed in this paper. The data used in this study is acquired using EEG and the emotions are elicited from six human subjects under the effect of emotion stimuli. This paper also proposed an emotion stimulation experiment using visual stimuli. From the EEG data, a total of six statistical features are computed and back-propagation neural network is applied for the classification of human emot...

  11. Calcium-mediated repression of β-catenin and its transcriptional signaling mediates neural crest cell death in an avian model of fetal alcohol syndrome.

    Science.gov (United States)

    Flentke, George R; Garic, Ana; Amberger, Ed; Hernandez, Marcos; Smith, Susan M

    2011-07-01

    Fetal alcohol syndrome (FAS) is a common birth defect in many societies. Affected individuals have neurodevelopmental disabilities and a distinctive craniofacial dysmorphology. These latter deficits originate during early development from the ethanol-mediated apoptotic depletion of cranial facial progenitors, a population known as the neural crest. We showed previously that this apoptosis is caused because acute ethanol exposure activates G-protein-dependent intracellular calcium within cranial neural crest progenitors, and this calcium transient initiates the cell death. The dysregulated signals that reside downstream of ethanol's calcium transient and effect neural crest death are unknown. Here we show that ethanol's repression of the transcriptional effector β-catenin causes the neural crest losses. Clinically relevant ethanol concentrations (22-78 mM) rapidly deplete nuclear β-catenin from neural crest progenitors, with accompanying losses of β-catenin transcriptional activity and downstream genes that govern neural crest induction, expansion, and survival. Using forced expression studies, we show that β-catenin loss of function (via dominant-negative T cell transcription factor [TCF]) recapitulates ethanol's effects on neural crest apoptosis, whereas β-catenin gain-of-function in ethanol's presence preserves neural crest survival. Blockade of ethanol's calcium transient using Bapta-AM normalizes β-catenin activity and prevents the neural crest losses, whereas ionomycin treatment is sufficient to destabilize β-catenin. We propose that ethanol's repression of β-catenin causes the neural crest losses in this model of FAS. β-Catenin is a novel target for ethanol's teratogenicity. β-Catenin/Wnt signals participate in many developmental events and its rapid and persistent dysregulation by ethanol may explain why the latter is such a potent teratogen. Copyright © 2011 Wiley-Liss, Inc.

  12. The Calcium-Mediated Repression of β-Catenin and Its Transcriptional Signaling Mediates Neural Crest Cell Death in an Avian Model of Fetal Alcohol Syndrome

    Science.gov (United States)

    Flentke, George R.; Garic, Ana; Amberger, Ed; Hernandez, Marcos; Smith, Susan M.

    2016-01-01

    Fetal Alcohol Syndrome (FAS) is a common birth defect in many societies. Affected individuals have neurodevelopmental disabilities and a distinctive craniofacial dysmorphology. These latter deficits originate during early development from the ethanol-mediated apoptotic depletion of cranial facial progenitors, a population known as the neural crest. We showed previously that this apoptosis is caused because acute ethanol exposure activates a G protein-dependent intracellular calcium within cranial neural crest progenitors, and this calcium transient initiates the cell death. The dysregulated signals that reside downstream of ethanol’s calcium transient and effect neural crest death are unknown. Here we show that ethanol’s repression of the transcriptional effector β-catenin causes the neural crest losses. Clinically-relevant ethanol concentrations (22–78 mM) rapidly deplete nuclear β-catenin from neural crest progenitors, with accompanying losses of β-catenin transcriptional activity and downstream genes that govern neural crest induction, expansion and survival. Using forced expression studies we show that β-catenin loss of function (via dominant-negative TCF) recapitulates ethanol’s effects on neural crest apoptosis, whereas β-catenin gain-of-function in ethanol’s presence preserves neural crest survival. Blockade of ethanol’s calcium transient using Bapta-AM normalizes β-catenin activity and prevents the neural crest losses, whereas ionomycin treatment is sufficient to destabilize β-catenin. We propose that ethanol’s repression of β-catenin causes the neural crest losses in this model of FAS. β-Catenin is a novel target for ethanol’s teratogenicity. β-Catenin/Wnt signals participate in many developmental events and its rapid and persistent dysregulation by ethanol may explain why the latter is such a potent teratogen. PMID:21630427

  13. Simultaneous in vivo recording of local brain temperature and electrophysiological signals with a novel neural probe

    Science.gov (United States)

    Fekete, Z.; Csernai, M.; Kocsis, K.; Horváth, Á. C.; Pongrácz, A.; Barthó, P.

    2017-06-01

    Objective. Temperature is an important factor for neural function both in normal and pathological states, nevertheless, simultaneous monitoring of local brain temperature and neuronal activity has not yet been undertaken. Approach. In our work, we propose an implantable, calibrated multimodal biosensor that facilitates the complex investigation of thermal changes in both cortical and deep brain regions, which records multiunit activity of neuronal populations in mice. The fabricated neural probe contains four electrical recording sites and a platinum temperature sensor filament integrated on the same probe shaft within a distance of 30 µm from the closest recording site. The feasibility of the simultaneous functionality is presented in in vivo studies. The probe was tested in the thalamus of anesthetized mice while manipulating the core temperature of the animals. Main results. We obtained multiunit and local field recordings along with measurement of local brain temperature with accuracy of 0.14 °C. Brain temperature generally followed core body temperature, but also showed superimposed fluctuations corresponding to epochs of increased local neural activity. With the application of higher currents, we increased the local temperature by several degrees without observable tissue damage between 34-39 °C. Significance. The proposed multifunctional tool is envisioned to broaden our knowledge on the role of the thermal modulation of neuronal activity in both cortical and deeper brain regions.

  14. KEAP1-modifying small molecule reveals muted NRF2 signaling responses in neural stem cells from Huntington's disease patients

    Science.gov (United States)

    Quinti, Luisa; Dayalan Naidu, Sharadha; Träger, Ulrike; Chen, Xiqun; Kegel-Gleason, Kimberly; Llères, David; Connolly, Colúm; Chopra, Vanita; Low, Cho; Moniot, Sébastien; Sapp, Ellen; Tousley, Adelaide R.; Vodicka, Petr; Van Kanegan, Michael J.; Kaltenbach, Linda S.; Crawford, Lisa A.; Fuszard, Matthew; Higgins, Maureen; Miller, James R. C.; Farmer, Ruth E.; Potluri, Vijay; Samajdar, Susanta; Meisel, Lisa; Zhang, Ningzhe; Snyder, Andrew; Stein, Ross; Hersch, Steven M.; Ellerby, Lisa M.; Schwarzschild, Michael A.; Steegborn, Clemens; Leavitt, Blair R.; Degterev, Alexei; Tabrizi, Sarah J.; Lo, Donald C.; DiFiglia, Marian; Thompson, Leslie M.; Dinkova-Kostova, Albena T.; Kazantsev, Aleksey G.

    2017-01-01

    The activity of the transcription factor nuclear factor-erythroid 2 p45-derived factor 2 (NRF2) is orchestrated and amplified through enhanced transcription of antioxidant and antiinflammatory target genes. The present study has characterized a triazole-containing inducer of NRF2 and elucidated the mechanism by which this molecule activates NRF2 signaling. In a highly selective manner, the compound covalently modifies a critical stress-sensor cysteine (C151) of the E3 ligase substrate adaptor protein Kelch-like ECH-associated protein 1 (KEAP1), the primary negative regulator of NRF2. We further used this inducer to probe the functional consequences of selective activation of NRF2 signaling in Huntington's disease (HD) mouse and human model systems. Surprisingly, we discovered a muted NRF2 activation response in human HD neural stem cells, which was restored by genetic correction of the disease-causing mutation. In contrast, selective activation of NRF2 signaling potently repressed the release of the proinflammatory cytokine IL-6 in primary mouse HD and WT microglia and astrocytes. Moreover, in primary monocytes from HD patients and healthy subjects, NRF2 induction repressed expression of the proinflammatory cytokines IL-1, IL-6, IL-8, and TNFα. Together, our results demonstrate a multifaceted protective potential of NRF2 signaling in key cell types relevant to HD pathology. PMID:28533375

  15. The obtaining of statistical characteristics of informative features of signals in the Autonomous information systems using neural networks

    Directory of Open Access Journals (Sweden)

    V. K. Hohlov

    2014-01-01

    Full Text Available The article studies a neural network approach to obtain the statistical characteristics of the input vector implementations of signal and noise at ill-conditioned matrices of correlation moments to solve the problems to select and reduce the vector dimensions of informative features at detection and recognition of signals and noise based on regression methods.A scientific novelty is determined by applying neural network algorithms for the efficient solution of problems to select the informative features and determine the parameters of regression algorithms in terms of the degeneracy or ill-conditioned data with unknown expectation and covariance matrices.The article proposes to use a single-layer neural network with no zero weights and activation functions to calculate the initial regression characteristics and the mean-square value error of multiple initial regression representations, which are necessary to justify the selection of informative features, reduce a dimension of sign vectors and implement the regression algorithms. It is shown that when excluding direct links between the inputs and their corresponding neurons, in the training network the weight coefficients of neuron inputs are the coefficients of initial multiple regression, the error meansquare value of multiple initial regression representations is calculated at the outputs of neurons. The article considers conditionality of the problem to calculate the matrix that is inverse one for matrix of correlation moments. It defines a condition number, which characterizes the relative error of stated task.The problem concerning the matrix condition of the correlation moment of informative signal features and noise arises when solving the problem to find the multiple coefficients of initial regression (MCIR and the residual mean-square values of the multiple regression representations. For obtaining the MCIR and finding the residual mean-square values the matrix of correlation moments of

  16. Development of an ex Vivo Method for Multi-unit Recording of Microbiota-Colonic-Neural Signaling in Real Time

    Directory of Open Access Journals (Sweden)

    Maria M. Buckley

    2018-02-01

    Full Text Available Background and Objectives: Bidirectional signaling between the gastrointestinal tract and the brain is vital for maintaining whole-body homeostasis. Moreover, emerging evidence implicates vagal afferent signaling in the modulation of host physiology by microbes, which are most abundant in the colon. This study aims to optimize and advance dissection and recording techniques to facilitate real-time recordings of afferent neural signals originating in the distal colon.New Protocol: This paper describes a dissection technique, which facilitates extracellular electrophysiological recordings from visceral pelvic, spinal and vagal afferent neurons in response to stimulation of the distal colon.Examples of Application: Focal application of 75 mM KCl to a section of distal colon with exposed submucosal or myenteric nerve cell bodies and sensory nerve endings evoked activity in the superior mesenteric plexus and the vagal nerve. Noradrenaline stimulated nerve activity in the superior mesenteric plexus, whereas application of carbachol stimulated vagal nerve activity. Exposure of an ex vivo section of distal colon with an intact colonic mucosa to peptidoglycan, but not lipopolysaccharide, evoked vagal nerve firing.Discussion: Previous studies have recorded vagal signaling evoked by bacteria in the small intestine. The technical advances of this dissection and recording technique facilitates recording of afferent nerve signals evoked in extrinsic sensory pathways by neuromodulatory reagents applied to the distal colon. Moreover, we have demonstrated vagal afferent activation evoked by bacterial products applied to the distal colonic mucosa. This protocol may contribute to our understanding of functional bowel disorders where gut-brain communication is dysfunctional, and facilitate real-time interrogation of microbiota-gut-brain signaling.

  17. Detecting causal interdependence in simulated neural signals based on pairwise and multivariate analysis.

    Science.gov (United States)

    Yang, C; Le Bouquin Jeannes, R; Faucon, G; Wendling, F

    2010-01-01

    Our objective is to analyze EEG signals recorded with depth electrodes during seizures in patients with drug-resistant epilepsy. Usually, different phases are observed during the seizure process, including a fast onset activity (FOA). We aim to determine how cerebral structures get involved during this FOA, in particular whether some structure can "drive" some other structures. This paper focuses on a linear Granger causality based measure to detect causal relation of interdependence in multivariate signals generated by a physiology-based model of coupled neuronal populations. When coupling between signals exists, statistical analysis supports the relevance of this index for characterizing the information flow and its direction among neuronal populations.

  18. Improved exponential convergence result for generalized neural networks including interval time-varying delayed signals.

    Science.gov (United States)

    Rajchakit, G; Saravanakumar, R; Ahn, Choon Ki; Karimi, Hamid Reza

    2017-02-01

    This article examines the exponential stability analysis problem of generalized neural networks (GNNs) including interval time-varying delayed states. A new improved exponential stability criterion is presented by establishing a proper Lyapunov-Krasovskii functional (LKF) and employing new analysis theory. The improved reciprocally convex combination (RCC) and weighted integral inequality (WII) techniques are utilized to obtain new sufficient conditions to ascertain the exponential stability result of such delayed GNNs. The superiority of the obtained results is clearly demonstrated by numerical examples. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. Application of Polynomial Neural Networks to Classification of Acoustic Warfare Signals

    Science.gov (United States)

    1993-04-01

    fcf’𔄁IC nf20 uqe o.’ C eouinq thil b4𔃺*e to yWash’nOntq .1u0tos Svverl’ t. O’at r ;0, rl’ -f’ Of 0ly ,"non s *( O no 4’ W~c’mt 1)$ ’T .c~qOuQ~O ’ llno...Report NOSC TD 1855, Naval Ocean Systems Center, San Diego, May, 1990. [34] Ghosh, J., L. Deuser, and S. Beck, "A neural network based hybrid system

  20. A low-power, low-noise neural-signal amplifier circuit in 90-nm CMOS.

    Science.gov (United States)

    Zarifi, M H; Frounchi, J; Farshchi, S; Judy, J W

    2008-01-01

    A fully-differential low-power low-noise preamplifier for biopotential and neural-recording applications is presented. This design, which has been simulated in a standard 90-nm CMOS process, consumes 30 microW from a 3-V power supply. The simulated integrated input-referred noise is 2.3 microV over 0.1 Hz to 20 kHz. The amplifier also provides an output swing of +/- 0.9 V with a THD of less than 0.1%

  1. Web traffic and firm performance

    DEFF Research Database (Denmark)

    Farooq, Omar; Aguenaou, Samir

    2013-01-01

    Does the traffic generated by websites of firms signal anything to stock market participants? Does higher web-traffic translate into availability of more information and therefore lower agency problems? And if answers to above questions are in affirmative, does higher web-traffic traffic translate...... into better firm performance? This paper aims to answer these questions by documenting a positive relationship between the extent of web-traffic and firm performance in the MENA region during the 2010. We argue that higher web-traffic lowers the agency problems in firms by disseminating more information...... to stock market participants. Consequently, lower agency problems translate into better performance. Furthermore, we also show that agency reducing role of web-traffic is more pronounced in regimes where information environment is already bad. For example, our results show stronger impact of web...

  2. Milling tool wear diagnosis by feed motor current signal using an artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Khajavi, Mehrdad Nouri; Nasernia, Ebrahim; Rostaghi, Mostafa [Dept. of Mechanical Engineering, Shahid Rajaee Teacher Training University, Tehran (Iran, Islamic Republic of)

    2016-11-15

    In this paper, a Multi-layer perceptron (MLP) neural network was used to predict tool wear in face milling. For this purpose, a series of experiments was conducted using a milling machine on a CK45 work piece. Tool wear was measured by an optical microscope. To improve the accuracy and reliability of the monitoring system, tool wear state was classified into five groups, namely, no wear, slight wear, normal wear, severe wear and broken tool. Experiments were conducted with the aforementioned tool wear states, and different machining conditions and data were extracted. An increase in current amplitude was observed as the tool wear increased. Furthermore, effects of parameters such as tool wear, feed, and cut depth on motor current consumption were analyzed. Considering the complexity of the wear state classification, a multi-layer neural network was used. The root mean square of motor current, feed, cut depth, and tool rpm were chosen as the input and amount of flank wear as the output of MLP. Results showed good performance of the designed tool wear monitoring system.

  3. Neural signal during immediate reward anticipation in schizophrenia: Relationship to real-world motivation and function.

    Science.gov (United States)

    Subramaniam, Karuna; Hooker, Christine I; Biagianti, Bruno; Fisher, Melissa; Nagarajan, Srikantan; Vinogradov, Sophia

    2015-01-01

    Amotivation in schizophrenia is a central predictor of poor functioning, and is thought to occur due to deficits in anticipating future rewards, suggesting that impairments in anticipating pleasure can contribute to functional disability in schizophrenia. In healthy comparison (HC) participants, reward anticipation is associated with activity in frontal-striatal networks. By contrast, schizophrenia (SZ) participants show hypoactivation within these frontal-striatal networks during this motivated anticipatory brain state. Here, we examined neural activation in SZ and HC participants during the anticipatory phase of stimuli that predicted immediate upcoming reward and punishment, and during the feedback/outcome phase, in relation to trait measures of hedonic pleasure and real-world functional capacity. SZ patients showed hypoactivation in ventral striatum during reward anticipation. Additionally, we found distinct differences between HC and SZ groups in their association between reward-related immediate anticipatory neural activity and their reported experience of pleasure. HC participants recruited reward-related regions in striatum that significantly correlated with subjective consummatory pleasure, while SZ patients revealed activation in attention-related regions, such as the IPL, which correlated with consummatory pleasure and functional capacity. These findings may suggest that SZ patients activate compensatory attention processes during anticipation of immediate upcoming rewards, which likely contribute to their functional capacity in daily life.

  4. Neural Correlates of Visual Spatial Attention in Electrocorticographic (ECoG Signals in Humans

    Directory of Open Access Journals (Sweden)

    Aysegul eGunduz

    2011-09-01

    Full Text Available Attention is a cognitive selection mechanism that allocates the limited processing resources of the brain to the sensory streams most relevant to our immediate goals, thereby enhancing responsiveness and behavioral performance. The underlying neural mechanisms of orienting attention are distributedacross a widespread cortical network. While aspects of this network have been extensively studied, details about the electrophysiological dynamics of this network are scarce. In this study, we investigated attentional networks using electrocorticographic (ECoG recordings from the surface ofthe brain, which combine broad spatial coverage with high temporal resolution, in five human subjects. ECoG was recorded when subjects covertly attended to a spatial location and responded to contrast changes in the presence of distractors in a modified Posner cueing task. ECoG amplitudes in the alpha, beta and gamma bands identified neural changes associated with covert attention and motor preparation/execution in the different stages of the task. The results show that attentional engagement was primarily associated with ECoG activity in the visual, prefrontal, premotor, and parietal cortices. Motor preparation/execution was associated with ECoG activity in premotor/sensorimotor cortices. In summary, our results illustrate rich and distributed cortical dynamics that are associated with orienting attention and the subsequent motor preparation and execution. These findings are largely consistent with and expand on primate studies using intracortical recordings and human functional neuroimaging studies.

  5. Neural signal during immediate reward anticipation in schizophrenia: Relationship to real-world motivation and function

    Directory of Open Access Journals (Sweden)

    Karuna Subramaniam

    2015-01-01

    Full Text Available Amotivation in schizophrenia is a central predictor of poor functioning, and is thought to occur due to deficits in anticipating future rewards, suggesting that impairments in anticipating pleasure can contribute to functional disability in schizophrenia. In healthy comparison (HC participants, reward anticipation is associated with activity in frontal–striatal networks. By contrast, schizophrenia (SZ participants show hypoactivation within these frontal–striatal networks during this motivated anticipatory brain state. Here, we examined neural activation in SZ and HC participants during the anticipatory phase of stimuli that predicted immediate upcoming reward and punishment, and during the feedback/outcome phase, in relation to trait measures of hedonic pleasure and real-world functional capacity. SZ patients showed hypoactivation in ventral striatum during reward anticipation. Additionally, we found distinct differences between HC and SZ groups in their association between reward-related immediate anticipatory neural activity and their reported experience of pleasure. HC participants recruited reward-related regions in striatum that significantly correlated with subjective consummatory pleasure, while SZ patients revealed activation in attention-related regions, such as the IPL, which correlated with consummatory pleasure and functional capacity. These findings may suggest that SZ patients activate compensatory attention processes during anticipation of immediate upcoming rewards, which likely contribute to their functional capacity in daily life.

  6. Neural signal during immediate reward anticipation in schizophrenia: Relationship to real-world motivation and function

    Science.gov (United States)

    Subramaniam, Karuna; Hooker, Christine I.; Biagianti, Bruno; Fisher, Melissa; Nagarajan, Srikantan; Vinogradov, Sophia

    2015-01-01

    Amotivation in schizophrenia is a central predictor of poor functioning, and is thought to occur due to deficits in anticipating future rewards, suggesting that impairments in anticipating pleasure can contribute to functional disability in schizophrenia. In healthy comparison (HC) participants, reward anticipation is associated with activity in frontal–striatal networks. By contrast, schizophrenia (SZ) participants show hypoactivation within these frontal–striatal networks during this motivated anticipatory brain state. Here, we examined neural activation in SZ and HC participants during the anticipatory phase of stimuli that predicted immediate upcoming reward and punishment, and during the feedback/outcome phase, in relation to trait measures of hedonic pleasure and real-world functional capacity. SZ patients showed hypoactivation in ventral striatum during reward anticipation. Additionally, we found distinct differences between HC and SZ groups in their association between reward-related immediate anticipatory neural activity and their reported experience of pleasure. HC participants recruited reward-related regions in striatum that significantly correlated with subjective consummatory pleasure, while SZ patients revealed activation in attention-related regions, such as the IPL, which correlated with consummatory pleasure and functional capacity. These findings may suggest that SZ patients activate compensatory attention processes during anticipation of immediate upcoming rewards, which likely contribute to their functional capacity in daily life. PMID:26413478

  7. Nitric oxide from inflammatory origin impairs neural stem cell proliferation by inhibiting epidermal growth factor receptor signaling

    Directory of Open Access Journals (Sweden)

    Bruno Pereira Carreira

    2014-10-01

    Full Text Available Neuroinflammation is characterized by activation of microglial cells, followed by production of nitric oxide (NO, which may have different outcomes on neurogenesis, favoring or inhibiting this process. In the present study, we investigated how the inflammatory mediator NO can affect proliferation of neural stem cells (NSC, and explored possible mechanisms underlying this effect. We investigated which mechanisms are involved in the regulation of NSC proliferation following treatment with an inflammatory stimulus (LPS plus IFN-γ, using a culture system of subventricular zone (SVZ-derived NSC mixed with microglia cells obtained from wild-type mice (iNOS+/+ or from iNOS knockout mice (iNOS-/-. We found an impairment of NSC cell proliferation in iNOS+/+ mixed cultures, which was not observed in iNOS-/- mixed cultures. Furthermore, the increased release of NO by activated iNOS+/+ microglial cells decreased the activation of the ERK/MAPK signaling pathway, which was concomitant with an enhanced nitration of the EGF receptor. Preventing nitrogen reactive species formation with MnTBAP, a scavenger of peroxynitrite, or using the peroxynitrite degradation catalyst FeTMPyP, cell proliferation and ERK signaling were restored to basal levels in iNOS+/+ mixed cultures. Moreover, exposure to the NO donor NOC-18 (100 µM, for 48 h, inhibited SVZ-derived NSC proliferation. Regarding the antiproliferative effect of NO, we found that NOC-18 caused the impairment of signaling through the ERK/MAPK pathway, which may be related to increased nitration of the EGF receptor in NSC. Using MnTBAP nitration was prevented, maintaining ERK signaling, rescuing NSC proliferation. We show that NO from inflammatory origin leads to a decreased function of the EGF receptor, which compromised proliferation of NSC. We also demonstrated that NO-mediated nitration of the EGF receptor caused a decrease in its phosphorylation, thus preventing regular proliferation signaling through the

  8. R-Peak Detection using Daubechies Wavelet and ECG Signal Classification using Radial Basis Function Neural Network

    Science.gov (United States)

    Rai, H. M.; Trivedi, A.; Chatterjee, K.; Shukla, S.

    2014-01-01

    This paper employed the Daubechies wavelet transform (WT) for R-peak detection and radial basis function neural network (RBFNN) to classify the electrocardiogram (ECG) signals. Five types of ECG beats: normal beat, paced beat, left bundle branch block (LBBB) beat, right bundle branch block (RBBB) beat and premature ventricular contraction (PVC) were classified. 500 QRS complexes were arbitrarily extracted from 26 records in Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, which are available on Physionet website. Each and every QRS complex was represented by 21 points from p1 to p21 and these QRS complexes of each record were categorized according to types of beats. The system performance was computed using four types of parameter evaluation metrics: sensitivity, positive predictivity, specificity and classification error rate. The experimental result shows that the average values of sensitivity, positive predictivity, specificity and classification error rate are 99.8%, 99.60%, 99.90% and 0.12%, respectively with RBFNN classifier. The overall accuracy achieved for back propagation neural network (BPNN), multilayered perceptron (MLP), support vector machine (SVM) and RBFNN classifiers are 97.2%, 98.8%, 99% and 99.6%, respectively. The accuracy levels and processing time of RBFNN is higher than or comparable with BPNN, MLP and SVM classifiers.

  9. Multiple excitatory and inhibitory neural signals converge to fine-tune Caenorhabditis elegans feeding to food availability.

    Science.gov (United States)

    Dallière, Nicolas; Bhatla, Nikhil; Luedtke, Zara; Ma, Dengke K; Woolman, Jonathan; Walker, Robert J; Holden-Dye, Lindy; O'Connor, Vincent

    2016-02-01

    How an animal matches feeding to food availability is a key question for energy homeostasis. We addressed this in the nematode Caenorhabditis elegans, which couples feeding to the presence of its food (bacteria) by regulating pharyngeal activity (pumping). We scored pumping in the presence of food and over an extended time course of food deprivation in wild-type and mutant worms to determine the neural substrates of adaptive behavior. Removal of food initially suppressed pumping but after 2 h this was accompanied by intermittent periods of high activity. We show pumping is fine-tuned by context-specific neural mechanisms and highlight a key role for inhibitory glutamatergic and excitatory cholinergic/peptidergic drives in the absence of food. Additionally, the synaptic protein UNC-31 [calcium-activated protein for secretion (CAPS)] acts through an inhibitory pathway not explained by previously identified contributions of UNC-31/CAPS to neuropeptide or glutamate transmission. Pumping was unaffected by laser ablation of connectivity between the pharyngeal and central nervous system indicating signals are either humoral or intrinsic to the enteric system. This framework in which control is mediated through finely tuned excitatory and inhibitory drives resonates with mammalian hypothalamic control of feeding and suggests that fundamental regulation of this basic animal behavior may be conserved through evolution from nematode to human. © FASEB.

  10. E3D hand movement velocity reconstruction using power spectral density of EEG signals and neural network.

    Science.gov (United States)

    Korik, A; Siddique, N; Sosnik, R; Coyle, D

    2015-08-01

    Three dimensional (3D) limb motion trajectory is predictable with a non-invasive brain-computer interface (BCI). To date, most non-invasive motion trajectory prediction BCIs use potential values of electroencephalographic (EEG) signals as the input to a multiple linear regression (mLR) based kinetic data estimator. We investigated the possible improvement in accuracy of 3D hand movement prediction (i.e., the correlation of registered and reconstructed hand velocities) by replacing raw EEG potentials with spectrum power values of specific EEG bands. We also investigated if a non-linear neural network based estimator outperformed the mLR approach. The spectrum power model provided significantly higher accuracy (R~0.60) compared to the similar EEG potentials based approach (R~0.45). Additionally, when replacing the mLR based kinetic data estimation module with a feed-forward neural network (NN) we found the NN based spectrum power model provided higher accuracy (R~0.70) compared to the similar mLR based approach (R~0.60).

  11. States versus rewards: dissociable neural prediction error signals underlying model-based and model-free reinforcement learning.

    Science.gov (United States)

    Gläscher, Jan; Daw, Nathaniel; Dayan, Peter; O'Doherty, John P

    2010-05-27

    Reinforcement learning (RL) uses sequential experience with situations ("states") and outcomes to assess actions. Whereas model-free RL uses this experience directly, in the form of a reward prediction error (RPE), model-based RL uses it indirectly, building a model of the state transition and outcome structure of the environment, and evaluating actions by searching this model. A state prediction error (SPE) plays a central role, reporting discrepancies between the current model and the observed state transitions. Using functional magnetic resonance imaging in humans solving a probabilistic Markov decision task, we found the neural signature of an SPE in the intraparietal sulcus and lateral prefrontal cortex, in addition to the previously well-characterized RPE in the ventral striatum. This finding supports the existence of two unique forms of learning signal in humans, which may form the basis of distinct computational strategies for guiding behavior. Copyright 2010 Elsevier Inc. All rights reserved.

  12. Fault Diagnosis System of Induction Motors Based on Neural Network and Genetic Algorithm Using Stator Current Signals

    Directory of Open Access Journals (Sweden)

    Tian Han

    2006-01-01

    Full Text Available This paper proposes an online fault diagnosis system for induction motors through the combination of discrete wavelet transform (DWT, feature extraction, genetic algorithm (GA, and neural network (ANN techniques. The wavelet transform improves the signal-to-noise ratio during a preprocessing. Features are extracted from motor stator current, while reducing data transfers and making online application available. GA is used to select the most significant features from the whole feature database and optimize the ANN structure parameter. Optimized ANN is trained and tested by the selected features of the measurement data of stator current. The combination of advanced techniques reduces the learning time and increases the diagnosis accuracy. The efficiency of the proposed system is demonstrated through motor faults of electrical and mechanical origins on the induction motors. The results of the test indicate that the proposed system is promising for the real-time application.

  13. Classification of EMG signals using artificial neural networks for virtual hand prosthesis control.

    Science.gov (United States)

    Mattioli, Fernando E R; Lamounier, Edgard A; Cardoso, Alexandre; Soares, Alcimar B; Andrade, Adriano O

    2011-01-01

    Computer-based training systems have been widely studied in the field of human rehabilitation. In health applications, Virtual Reality presents itself as an appropriate tool to simulate training environments without exposing the patients to risks. In particular, virtual prosthetic devices have been used to reduce the great mental effort needed by patients fitted with myoelectric prosthesis, during the training stage. In this paper, the application of Virtual Reality in a hand prosthesis training system is presented. To achieve this, the possibility of exploring Neural Networks in a real-time classification system is discussed. The classification technique used in this work resulted in a 95% success rate when discriminating 4 different hand movements.

  14. Estimating complicated baselines in analytical signals using the iterative training of Bayesian regularized artificial neural networks.

    Science.gov (United States)

    Mani-Varnosfaderani, Ahmad; Kanginejad, Atefeh; Gilany, Kambiz; Valadkhani, Abolfazl

    2016-10-12

    The present work deals with the development of a new baseline correction method based on the comparative learning capabilities of artificial neural networks. The developed method uses the Bayes probability theorem for prevention of the occurrence of the over-fitting and finding a generalized baseline. The developed method has been applied on simulated and real metabolomic gas-chromatography (GC) and Raman data sets. The results revealed that the proposed method can be used to handle different types of baselines with cave, convex, curvelinear, triangular and sinusoidal patterns. For further evaluation of the performances of this method, it has been compared with benchmarking baseline correction methods such as corner-cutting (CC), morphological weighted penalized least squares (MPLS), adaptive iteratively-reweighted penalized least squares (airPLS) and iterative polynomial fitting (iPF). In order to compare the methods, the projected difference resolution (PDR) criterion has been calculated for the data before and after the baseline correction procedure. The calculated values of PDR after the baseline correction using iBRANN, airPLS, MPLS, iPF and CC algorithms for the GC metabolomic data were 4.18, 3.64, 3.88, 1.88 and 3.08, respectively. The obtained results in this work demonstrated that the developed iterative Bayesian regularized neural network (iBRANN) method in this work thoroughly detects the baselines and is superior over the CC, MPLS, airPLS and iPF techniques. A graphical user interface has been developed for the suggested algorithm and can be used for easy implementation of the iBRANN algorithm for the correction of different chromatography, NMR and Raman data sets. Copyright © 2016 Elsevier B.V. All rights reserved.

  15. Striatal Activity and Reward Relativity: Neural Signals Encoding Dynamic Outcome Valuation.

    Science.gov (United States)

    Webber, Emily S; Mankin, David E; Cromwell, Howard C

    2016-01-01

    The striatum is a key brain region involved in reward processing. Striatal activity has been linked to encoding reward magnitude and integrating diverse reward outcome information. Recent work has supported the involvement of striatum in the valuation of outcomes. The present work extends this idea by examining striatal activity during dynamic shifts in value that include different levels and directions of magnitude disparity. A novel task was used to produce diverse relative reward effects on a chain of instrumental action. Rats (Rattus norvegicus) were trained to respond to cues associated with specific outcomes varying by food pellet magnitude. Animals were exposed to single-outcome sessions followed by mixed-outcome sessions, and neural activity was compared among identical outcome trials from the different behavioral contexts. Results recording striatal activity show that neural responses to different task elements reflect incentive contrast as well as other relative effects that involve generalization between outcomes or possible influences of outcome variety. The activity that was most prevalent was linked to food consumption and post-food consumption periods. Relative encoding was sensitive to magnitude disparity. A within-session analysis showed strong contrast effects that were dependent upon the outcome received in the immediately preceding trial. Significantly higher numbers of responses were found in ventral striatum linked to relative outcome effects. Our results support the idea that relative value can incorporate diverse relationships, including comparisons from specific individual outcomes to general behavioral contexts. The striatum contains these diverse relative processes, possibly enabling both a higher information yield concerning value shifts and a greater behavioral flexibility.

  16. Multi-scale quantitative precipitation forecasting using nonlinear and nonstationary teleconnection signals and artificial neural network models

    Science.gov (United States)

    Chang, Ni-Bin; Yang, Y. Jeffrey; Imen, Sanaz; Mullon, Lee

    2017-05-01

    Global sea surface temperature (SST) anomalies are observed to have a significant effect on terrestrial precipitation patterns throughout the United States. SST variations have been correlated with terrestrial precipitation via ocean-atmospheric interactions known as climate teleconnections. This study demonstrates how the scale effect could affect the forecasting accuracy with or without the inclusion of those newly discovered unknown teleconnection signals between Adirondack precipitation and SST anomaly in the Atlantic and Pacific oceans. Unique SST regions of both known and unknown telecommunication signals were extracted from the wavelet analysis and used as input variables in an artificial neural network (ANN) forecasting model. Monthly and seasonal scales were considered with respect to a host of long-term (30-year) nonlinear and nonstationary teleconnection signals detected locally at the study site of Adirondack. Similar intra-annual time-lag effects of SST on precipitation variability are salient at both time scales. Sensitivity analysis of four scenarios reveals that more improvements of the forecasting accuracy of the ANN model can be observed by including both known and unknown teleconnection patterns at both time scales, although such improvements are not salient. Research findings also highlight the importance of choosing the forecasting model at the seasonal scale to predict more accurate peak values and global trends of terrestrial precipitation in response to teleconnection signals. The scale shift from monthly to seasonal may improve results by 17% and 17 mm/day in terms of R squared and root of mean square error values, respectively, if both known and unknown SST regions are considered for forecasting.

  17. Neural cross-correlation and signal decorrelation: insights into coding of auditory space.

    Science.gov (United States)

    Saberi, Kourosh; Petrosyan, Agavni

    2005-07-07

    The auditory systems of humans and many other species use the difference in the time of arrival of acoustic signals at the two ears to compute the lateral position of sound sources. This computation is assumed to initially occur in an assembly of neurons organized along a frequency-by-delay surface. Mathematically, the computations are equivalent to a two-dimensional cross-correlation of the input signals at the two ears, with the position of the peak activity along this surface designating the position of the source in space. In this study, partially correlated signals to the two ears are used to probe the mechanisms for encoding spatial cues in stationary or dynamic (moving) signals. It is demonstrated that a cross-correlation model of the auditory periphery coupled with statistical decision theory can predict the patterns of performance by human subjects for both stationary and motion stimuli as a function of stimulus decorrelation. Implications of these findings for the existence of a unique cortical motion system are discussed.

  18. A hardware model of the auditory periphery to transduce acoustic signals into neural activity

    Directory of Open Access Journals (Sweden)

    Takashi eTateno

    2013-11-01

    Full Text Available To improve the performance of cochlear implants, we have integrated a microdevice into a model of the auditory periphery with the goal of creating a microprocessor. We constructed an artificial peripheral auditory system using a hybrid model in which polyvinylidene difluoride was used as a piezoelectric sensor to convert mechanical stimuli into electric signals. To produce frequency selectivity, the slit on a stainless steel base plate was designed such that the local resonance frequency of the membrane over the slit reflected the transfer function. In the acoustic sensor, electric signals were generated based on the piezoelectric effect from local stress in the membrane. The electrodes on the resonating plate produced relatively large electric output signals. The signals were fed into a computer model that mimicked some functions of inner hair cells, inner hair cell–auditory nerve synapses, and auditory nerve fibers. In general, the responses of the model to pure-tone burst and complex stimuli accurately represented the discharge rates of high-spontaneous-rate auditory nerve fibers across a range of frequencies greater than 1 kHz and middle to high sound pressure levels. Thus, the model provides a tool to understand information processing in the peripheral auditory system and a basic design for connecting artificial acoustic sensors to the peripheral auditory nervous system. Finally, we discuss the need for stimulus control with an appropriate model of the auditory periphery based on auditory brainstem responses that were electrically evoked by different temporal pulse patterns with the same pulse number.

  19. Stochastic resonance can enhance information transmission of supra-threshold neural signals.

    Science.gov (United States)

    Kawaguchi, Minato; Mino, Hiroyuki; Momose, Keiko; Durand, Dominique M

    2009-01-01

    Stochastic resonance (SR) has been shown to improve detection of sub-threshold signals with additive uncor-related background noise, not only in a single hippocampal CA1 neuron model, but in a population of hippocampal CA1 neuron models (Array-Enhanced Stochastic Resonance; AESR). However, most of the information in the CNS is transmitted through supra-threshold signals and the effect of stochastic resonance in neurons on these signals is unknown. Therefore, we investigate through computer simulations whether information transmission of supra-threshold input signal can be improved by uncorrelated noise in a population of hippocampal CA1 neuron models by supra-threshold stochastic resonance (SSR). The mutual information was estimated as an index of information transmission via total and noise entropies from the inter-spike interval (ISI) histograms of the spike trains generated by gathering each of spike trains in a population of hippocampal CA1 neuron models at N = 1, 2, 4, 10, 20 and 50. It was shown that the mutual information was maximized at a specific amplitude of uncorrelated noise, i.e., a typical curve of SR was observed when the number of neurons was greater than 10 with SSR. However, SSR did not affect the information transfer with a small number of neurons. In conclusion, SSR may play an important role in processing information such as memory formation in a population of hippocampal neurons.

  20. Neuropeptide Y Family Receptors Traffic via the Bardet-Biedl Syndrome Pathway to Signal in Neuronal Primary Cilia

    Directory of Open Access Journals (Sweden)

    Alexander V. Loktev

    2013-12-01

    Full Text Available Human monogenic obesity syndromes, including Bardet-Biedl syndrome (BBS, implicate neuronal primary cilia in regulation of energy homeostasis. Cilia in hypothalamic neurons have been hypothesized to sense and regulate systemic energy status, but the molecular mechanism of this signaling remains unknown. Here, we report a comprehensive localization screen of 42 G-protein-coupled receptors (GPCR revealing seven ciliary GPCRs, including the neuropeptide Y (NPY receptors NPY2R and NPY5R. We show that mice modeling BBS disease or obese tubby mice fail to localize NPY2R to cilia in the hypothalamus and that BBS mutant mice fail to activate c-fos or decrease food intake in response to the NPY2R ligand PYY3-36. We find that cells with ciliary NPY2R show augmented PYY3-36-dependent cAMP signaling. Our data demonstrate that ciliary targeting of NPY receptors is important for controlling energy balance in mammals, revealing a physiologically defined ligand-receptor pathway signaling within neuronal cilia.

  1. A Deep Generative Adversarial Architecture for Network-Wide Spatial-Temporal Traffic State Estimation

    OpenAIRE

    Liang, Yunyi; Cui, Zhiyong; Tian, Yu; Chen, Huimiao; Wang, Yinhai

    2018-01-01

    This study proposes a deep generative adversarial architecture (GAA) for network-wide spatial-temporal traffic state estimation. The GAA is able to combine traffic flow theory with neural networks and thus improve the accuracy of traffic state estimation. It consists of two Long Short-Term Memory Neural Networks (LSTM NNs) which capture correlation in time and space among traffic flow and traffic density. One of the LSTM NNs, called a discriminative network, aims to maximize the probability o...

  2. EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks.

    Science.gov (United States)

    Liu, Quan; Chen, Yi-Feng; Fan, Shou-Zen; Abbod, Maysam F; Shieh, Jiann-Shing

    2015-01-01

    In order to build a reliable index to monitor the depth of anesthesia (DOA), many algorithms have been proposed in recent years, one of which is sample entropy (SampEn), a commonly used and important tool to measure the regularity of data series. However, SampEn only estimates the complexity of signals on one time scale. In this study, a new approach is introduced using multiscale entropy (MSE) considering the structure information over different time scales. The entropy values over different time scales calculated through MSE are applied as the input data to train an artificial neural network (ANN) model using bispectral index (BIS) or expert assessment of conscious level (EACL) as the target. To test the performance of the new index's sensitivity to artifacts, we compared the results before and after filtration by multivariate empirical mode decomposition (MEMD). The new approach via ANN is utilized in real EEG signals collected from 26 patients before and after filtering by MEMD, respectively; the results show that is a higher correlation between index from the proposed approach and the gold standard compared with SampEn. Moreover, the proposed approach is more structurally robust to noise and artifacts which indicates that it can be used for monitoring the DOA more accurately.

  3. The classification of oximetry signals using Bayesian neural networks to assist in the detection of obstructive sleep apnoea syndrome.

    Science.gov (United States)

    Marcos, J V; Hornero, R; Alvarez, D; Nabney, I T; Del Campo, F; Zamarrón, C

    2010-03-01

    In the present study, multilayer perceptron (MLP) neural networks were applied to help in the diagnosis of obstructive sleep apnoea syndrome (OSAS). Oxygen saturation (SaO(2)) recordings from nocturnal pulse oximetry were used for this purpose. We performed time and spectral analysis of these signals to extract 14 features related to OSAS. The performance of two different MLP classifiers was compared: maximum likelihood (ML) and Bayesian (BY) MLP networks. A total of 187 subjects suspected of suffering from OSAS took part in the study. Their SaO(2) signals were divided into a training set with 74 recordings and a test set with 113 recordings. BY-MLP networks achieved the best performance on the test set with 85.58% accuracy (87.76% sensitivity and 82.39% specificity). These results were substantially better than those provided by ML-MLP networks, which were affected by overfitting and achieved an accuracy of 76.81% (86.42% sensitivity and 62.83% specificity). Our results suggest that the Bayesian framework is preferred to implement our MLP classifiers. The proposed BY-MLP networks could be used for early OSAS detection. They could contribute to overcome the difficulties of nocturnal polysomnography (PSG) and thus reduce the demand for these studies.

  4. Noncanonical transforming growth factor β (TGFβ) signaling in cranial neural crest cells causes tongue muscle developmental defects.

    Science.gov (United States)

    Iwata, Jun-ichi; Suzuki, Akiko; Pelikan, Richard C; Ho, Thach-Vu; Chai, Yang

    2013-10-11

    Microglossia is a congenital birth defect in humans and adversely impacts quality of life. In vertebrates, tongue muscle derives from the cranial mesoderm, whereas tendons and connective tissues in the craniofacial region originate from cranial neural crest (CNC) cells. Loss of transforming growth factor β (TGFβ) type II receptor in CNC cells in mice (Tgfbr2(fl/fl);Wnt1-Cre) causes microglossia due to a failure of cell-cell communication between cranial mesoderm and CNC cells during tongue development. However, it is still unclear how TGFβ signaling in CNC cells regulates the fate of mesoderm-derived myoblasts during tongue development. Here we show that activation of the cytoplasmic and nuclear tyrosine kinase 1 (ABL1) cascade in Tgfbr2(fl/fl);Wnt1-Cre mice results in a failure of CNC-derived cell differentiation followed by a disruption of TGFβ-mediated induction of growth factors and reduction of myogenic cell proliferation and differentiation activities. Among the affected growth factors, the addition of fibroblast growth factor 4 (FGF4) and neutralizing antibody for follistatin (FST; an antagonist of bone morphogenetic protein (BMP)) could most efficiently restore cell proliferation, differentiation, and organization of muscle cells in the tongue of Tgfbr2(fl/fl);Wnt1-Cre mice. Thus, our data indicate that CNC-derived fibroblasts regulate the fate of mesoderm-derived myoblasts through TGFβ-mediated regulation of FGF and BMP signaling during tongue development.

  5. Disruption of CXCR4 signaling in pharyngeal neural crest cells causes DiGeorge syndrome-like malformations.

    Science.gov (United States)

    Escot, Sophie; Blavet, Cédrine; Faure, Emilie; Zaffran, Stéphane; Duband, Jean-Loup; Fournier-Thibault, Claire

    2016-02-15

    DiGeorge syndrome (DGS) is a congenital disease causing cardiac outflow tract anomalies, craniofacial dysmorphogenesis, thymus hypoplasia, and mental disorders. It results from defective development of neural crest cells (NCs) that colonize the pharyngeal arches and contribute to lower jaw, neck and heart tissues. Although TBX1 has been identified as the main gene accounting for the defects observed in human patients and mouse models, the molecular mechanisms underlying DGS etiology are poorly identified. The recent demonstrations that the SDF1/CXCR4 axis is implicated in NC chemotactic guidance and impaired in cortical interneurons of mouse DGS models prompted us to search for genetic interactions between Tbx1, Sdf1 (Cxcl12) and Cxcr4 in pharyngeal NCs and to investigate the effect of altering CXCR4 signaling on the ontogeny of their derivatives, which are affected in DGS. Here, we provide evidence that Cxcr4 and Sdf1 are genetically downstream of Tbx1 during pharyngeal NC development and that reduction of CXCR4 signaling causes misrouting of pharyngeal NCs in chick and dramatic morphological alterations in the mandibular skeleton, thymus and cranial sensory ganglia. Our results therefore support the possibility of a pivotal role for the SDF1/CXCR4 axis in DGS etiology. © 2016. Published by The Company of Biologists Ltd.

  6. SEMICONDUCTOR INTEGRATED CIRCUITS: A four-channel microelectronic system for neural signal regeneration

    Science.gov (United States)

    Shushan, Xie; Zhigong, Wang; Xiaoying, Lü; Wenyuan, Li; Haixian, Pan

    2009-12-01

    This paper presents a microelectronic system which is capable of making a signal record and functional electric stimulation of an injured spinal cord. As a requirement of implantable engineering for the regeneration microelectronic system, the system is of low noise, low power, small size and high performance. A front-end circuit and two high performance OPAs (operational amplifiers) have been designed for the system with different functions, and the two OPAs are a low-noise low-power two-stage OPA and a constant-gm RTR input and output OPA. The system has been realized in CSMC 0.5-μm CMOS technology. The test results show that the system satisfies the demands of neuron signal regeneration.

  7. Lymphovascular and neural regulation of metastasis: Shared tumour signalling pathways and novel therapeutic approaches

    Science.gov (United States)

    Le, C.P.; Karnezis, T.; Achen, M. G.; Stacker, S.A.; Sloan, E.K.

    2014-01-01

    The progression of cancer is supported by a wide variety of non-neoplastic cell types which make up the tumour stroma, including immune cells, endothelial cells, cancer-associated fibroblasts and nerve fibres. These host cells contribute molecular signals that enhance primary tumour growth and provide physical avenues for metastatic dissemination. This article provides an overview of the role of blood vessels, lymphatic vessels and nerve fibres in the tumour microenvironment, and highlights the interconnected molecular signalling pathways that control their development and activation in cancer. Further the review highlights the known pharmacological agents which target these pathways and discusses the potential therapeutic uses of drugs that target angiogenesis, lymphangiogenesis and stress response pathways in the different stages of cancer care. PMID:24267548

  8. Simplest relationship between local field potential and intracellular signals in layered neural tissue.

    Science.gov (United States)

    Chizhov, Anton V; Sanchez-Aguilera, Alberto; Rodrigues, Serafim; de la Prida, Liset Menendez

    2015-12-01

    The relationship between the extracellularly measured electric field potential resulting from synaptic activity in an ensemble of neurons and intracellular signals in these neurons is an important but still open question. Based on a model neuron with a cylindrical dendrite and lumped soma, we derive a formula that substantiates a proportionality between the local field potential and the total somatic transmembrane current that emerges from the difference between the somatic and dendritic membrane potentials. The formula is tested by intra- and extracellular recordings of evoked synaptic responses in hippocampal slices. Additionally, the contribution of different membrane currents to the field potential is demonstrated in a two-population mean-field model. Our formalism, which allows for a simple estimation of unknown dendritic currents directly from somatic measurements, provides an interpretation of the local field potential in terms of intracellularly measurable synaptic signals. It is also applicable to the study of cortical activity using two-compartment neuronal population models.

  9. Feature Selection and Classification of Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach.

    Science.gov (United States)

    Erguzel, Turker Tekin; Ozekes, Serhat; Tan, Oguz; Gultekin, Selahattin

    2015-10-01

    Feature selection is an important step in many pattern recognition systems aiming to overcome the so-called curse of dimensionality. In this study, an optimized classification method was tested in 147 patients with major depressive disorder (MDD) treated with repetitive transcranial magnetic stimulation (rTMS). The performance of the combination of a genetic algorithm (GA) and a back-propagation (BP) neural network (BPNN) was evaluated using 6-channel pre-rTMS electroencephalographic (EEG) patterns of theta and delta frequency bands. The GA was first used to eliminate the redundant and less discriminant features to maximize classification performance. The BPNN was then applied to test the performance of the feature subset. Finally, classification performance using the subset was evaluated using 6-fold cross-validation. Although the slow bands of the frontal electrodes are widely used to collect EEG data for patients with MDD and provide quite satisfactory classification results, the outcomes of the proposed approach indicate noticeably increased overall accuracy of 89.12% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.904 using the reduced feature set. © EEG and Clinical Neuroscience Society (ECNS) 2014.

  10. Measurement of neural signals from inexpensive, wireless and dry EEG systems.

    Science.gov (United States)

    Grummett, T S; Leibbrandt, R E; Lewis, T W; DeLosAngeles, D; Powers, D M W; Willoughby, J O; Pope, K J; Fitzgibbon, S P

    2015-07-01

    Electroencephalography (EEG) is challenged by high cost, immobility of equipment and the use of inconvenient conductive gels. We compared EEG recordings obtained from three systems that are inexpensive, wireless, and/or dry (no gel), against recordings made with a traditional, research-grade EEG system, in order to investigate the ability of these 'non-traditional' systems to produce recordings of comparable quality to a research-grade system. The systems compared were: Emotiv EPOC (inexpensive and wireless), B-Alert (wireless), g.Sahara (dry) and g.HIamp (research-grade). We compared the ability of the systems to demonstrate five well-studied neural phenomena: (1) enhanced alpha activity with eyes closed versus open; (2) visual steady-state response (VSSR); (3) mismatch negativity; (4) P300; and (5) event-related desynchronization/synchronization. All systems measured significant alpha augmentation with eye closure, and were able to measure VSSRs (although these were smaller with g.Sahara). The B-Alert and g.Sahara were able to measure the three time-locked phenomena equivalently to the g.HIamp. The Emotiv EPOC did not have suitably located electrodes for two of the tasks and synchronization considerations meant that data from the time-locked tasks were not assessed. The results show that inexpensive, wireless, or dry systems may be suitable for experimental studies using EEG, depending on the research paradigm, and within the constraints imposed by their limited electrode placement and number.

  11. Experimental and Computational Studies of Cortical Neural Network Properties Through Signal Processing

    Science.gov (United States)

    Clawson, Wesley Patrick

    Previous studies, both theoretical and experimental, of network level dynamics in the cerebral cortex show evidence for a statistical phenomenon called criticality; a phenomenon originally studied in the context of phase transitions in physical systems and that is associated with favorable information processing in the context of the brain. The focus of this thesis is to expand upon past results with new experimentation and modeling to show a relationship between criticality and the ability to detect and discriminate sensory input. A line of theoretical work predicts maximal sensory discrimination as a functional benefit of criticality, which can then be characterized using mutual information between sensory input, visual stimulus, and neural response,. The primary finding of our experiments in the visual cortex in turtles and neuronal network modeling confirms this theoretical prediction. We show that sensory discrimination is maximized when visual cortex operates near criticality. In addition to presenting this primary finding in detail, this thesis will also address our preliminary results on change-point-detection in experimentally measured cortical dynamics.

  12. AKT signaling mediates IGF-I survival actions on otic neural progenitors.

    Directory of Open Access Journals (Sweden)

    Maria R Aburto

    Full Text Available BACKGROUND: Otic neurons and sensory cells derive from common progenitors whose transition into mature cells requires the coordination of cell survival, proliferation and differentiation programmes. Neurotrophic support and survival of post-mitotic otic neurons have been intensively studied, but the bases underlying the regulation of programmed cell death in immature proliferative otic neuroblasts remains poorly understood. The protein kinase AKT acts as a node, playing a critical role in controlling cell survival and cell cycle progression. AKT is activated by trophic factors, including insulin-like growth factor I (IGF-I, through the generation of the lipidic second messenger phosphatidylinositol 3-phosphate by phosphatidylinositol 3-kinase (PI3K. Here we have investigated the role of IGF-dependent activation of the PI3K-AKT pathway in maintenance of otic neuroblasts. METHODOLOGY/PRINCIPAL FINDINGS: By using a combination of organotypic cultures of chicken (Gallus gallus otic vesicles and acoustic-vestibular ganglia, Western blotting, immunohistochemistry and in situ hybridization, we show that IGF-I-activation of AKT protects neural progenitors from programmed cell death. IGF-I maintains otic neuroblasts in an undifferentiated and proliferative state, which is characterised by the upregulation of the forkhead box M1 (FoxM1 transcription factor. By contrast, our results indicate that post-mitotic p27(Kip-positive neurons become IGF-I independent as they extend their neuronal processes. Neurons gradually reduce their expression of the Igf1r, while they increase that of the neurotrophin receptor, TrkC. CONCLUSIONS/SIGNIFICANCE: Proliferative otic neuroblasts are dependent on the activation of the PI3K-AKT pathway by IGF-I for survival during the otic neuronal progenitor phase of early inner ear development.

  13. Continuous neural identifier for uncertain nonlinear systems with time delays in the input signal.

    Science.gov (United States)

    Alfaro-Ponce, M; Argüelles, A; Chairez, I

    2014-12-01

    Time-delay systems have been successfully used to represent the complexity of some dynamic systems. Time-delay is often used for modeling many real systems. Among others, biological and chemical plants have been described using time-delay terms with better results than those models that have not consider them. However, getting those models represented a challenge and sometimes the results were not so satisfactory. Non-parametric modeling offered an alternative to obtain suitable and usable models. Continuous neural networks (CNN) have been considered as a real alternative to provide models over uncertain non-parametric systems. This article introduces the design of a specific class of non-parametric model for uncertain time-delay system based on CNN considering the so-called delayed learning laws analysis. The convergence analysis as well as the learning laws were produced by means of a Lyapunov-Krasovskii functional. Three examples were developed to demonstrate the effectiveness of the modeling process forced by the identifier proposed in this study. The first example was a simple nonlinear model used as benchmark example. The second example regarded the human immunodeficiency virus dynamic behavior is used to show the performance of the suggested non-parametric identifier based on CNN for no fictitious neither academic models. Finally, a third example describing the evolution of hepatitis B virus served to test the identifier presented in this study and was also useful to provide evidence of its superior performance against a non-delayed identifier based on CNN. Copyright © 2014 Elsevier Ltd. All rights reserved.

  14. Lithium promotes neural precursor cell proliferation: evidence for the involvement of the non-canonical GSK-3β-NF-AT signaling

    Directory of Open Access Journals (Sweden)

    Qu Zhaoxia

    2011-05-01

    Full Text Available Abstract Lithium, a drug that has long been used to treat bipolar disorder and some other human pathogenesis, has recently been shown to stimulate neural precursor growth. However, the involved mechanism is not clear. Here, we show that lithium induces proliferation but not survival of neural precursor cells. Mechanistic studies suggest that the effect of lithium mainly involved activation of the transcription factor NF-AT and specific induction of a subset of proliferation-related genes. While NF-AT inactivation by specific inhibition of its upstream activator calcineurin antagonized the effect of lithium on the proliferation of neural precursor cells, specific inhibition of the NF-AT inhibitor GSK-3β, similar to lithium treatment, promoted neural precursor cell proliferation. One important function of lithium appeared to increase inhibitory phosphorylation of GSK-3β, leading to GSK-3β suppression and subsequent NF-AT activation. Moreover, lithium-induced proliferation of neural precursor cells was independent of its role in inositol depletion. These findings not only provide mechanistic insights into the clinical effects of lithium, but also suggest an alternative therapeutic strategy for bipolar disorder and other neural diseases by targeting the non-canonical GSK-3β-NF-AT signaling.

  15. Three Tctn proteins are functionally conserved in the regulation of neural tube patterning and Gli3 processing but not ciliogenesis and Hedgehog signaling in the mouse.

    Science.gov (United States)

    Wang, Chengbing; Li, Jia; Meng, Qing; Wang, Baolin

    2017-10-01

    Tctn1, Tctn2, and Tctn3 are membrane proteins that localize at the transition zone of primary cilia. Tctn1 and Tctn2 mutations have been reported in both humans and mice, but Tctn3 mutations have been reported only in humans. It is also not clear whether the three Tctn proteins are functionally conserved with respect to ciliogenesis and Hedgehog (Hh) signaling. In the present study, we report that loss of Tctn3 gene function in mice results in a decrease in ciliogenesis and Hh signaling. Consistent with this, Tctn3 mutant mice exhibit holoprosencephaly and randomized heart looping and lack the floor plate in the neural tube, the phenotypes similar to those of Tctn1 and Tctn2 mutants. We also show that overexpression of Tctn3, but not Tctn1 or Tctn2, can rescue ciliogenesis in Tctn3 mutant cells. Similarly, replacement of Tctn3 with Tctn1 or Tctn2 in the Tctn3 gene locus results in reduced ciliogenesis and Hh signaling, holoprosencephaly, and randomized heart looping. Surprisingly, however, the neural tube patterning and the proteolytic processing of Gli3 (a transcription regulator for Hh signaling) into a repressor, both of which are usually impaired in ciliary gene mutants, are normal. These results suggest that Tctn1, Tctn2, and Tctn3 are functionally divergent with respect to their role in ciliogenesis and Hh signaling but conserved in neural tube patterning and Gli3 processing. Copyright © 2017 Elsevier Inc. All rights reserved.

  16. SOX1 links the function of neural patterning and Notch signalling in the ventral spinal cord during the neuron-glial fate switch

    Energy Technology Data Exchange (ETDEWEB)

    Genethliou, Nicholas; Panayiotou, Elena [The Cyprus Institute of Neurology and Genetics, Airport Avenue, No. 6, Agios Dometios, 2370 Nicosia (Cyprus); Department of Biological Sciences, University of Cyprus, P.O. Box 20537, 1678 Nicosia (Cyprus); Panayi, Helen; Orford, Michael; Mean, Richard; Lapathitis, George; Gill, Herman; Raoof, Sahir [The Cyprus Institute of Neurology and Genetics, Airport Avenue, No. 6, Agios Dometios, 2370 Nicosia (Cyprus); Gasperi, Rita De; Elder, Gregory [James J. Peters VA Medical Center, Research and Development (3F22), 130 West Kingsbridge Road, Bronx, NY 10468 (United States); Kessaris, Nicoletta; Richardson, William D. [Wolfson Institute for Biomedical Research and Research Department of Cell and Developmental Biology, University College London, Gower Street, London WC1E 6BT (United Kingdom); Malas, Stavros, E-mail: smalas@cing.ac.cy [The Cyprus Institute of Neurology and Genetics, Airport Avenue, No. 6, Agios Dometios, 2370 Nicosia (Cyprus); Department of Biological Sciences, University of Cyprus, P.O. Box 20537, 1678 Nicosia (Cyprus)

    2009-12-25

    During neural development the transition from neurogenesis to gliogenesis, known as the neuron-glial ({Nu}/G) fate switch, requires the coordinated function of patterning factors, pro-glial factors and Notch signalling. How this process is coordinated in the embryonic spinal cord is poorly understood. Here, we demonstrate that during the N/G fate switch in the ventral spinal cord (vSC) SOX1 links the function of neural patterning and Notch signalling. We show that, SOX1 expression in the vSC is regulated by PAX6, NKX2.2 and Notch signalling in a domain-specific manner. We further show that SOX1 regulates the expression of Hes1 and that loss of Sox1 leads to enhanced production of oligodendrocyte precursors from the pMN. Finally, we show that Notch signalling functions upstream of SOX1 during this fate switch and is independently required for the acquisition of the glial fate perse by regulating Nuclear Factor I A expression in a PAX6/SOX1/HES1/HES5-independent manner. These data integrate functional roles of neural patterning factors, Notch signalling and SOX1 during gliogenesis.

  17. Distinct steps of neural induction revealed by Asterix, Obelix and TrkC, genes induced by different signals from the organizer.

    Directory of Open Access Journals (Sweden)

    Sonia Pinho

    2011-04-01

    Full Text Available The amniote organizer (Hensen's node can induce a complete nervous system when grafted into a peripheral region of a host embryo. Although BMP inhibition has been implicated in neural induction, non-neural cells cannot respond to BMP antagonists unless previously exposed to a node graft for at least 5 hours before BMP inhibitors. To define signals and responses during the first 5 hours of node signals, a differential screen was conducted. Here we describe three early response genes: two of them, Asterix and Obelix, encode previously undescribed proteins of unknown function but Obelix appears to be a nuclear RNA-binding protein. The third is TrkC, a neurotrophin receptor. All three genes are induced by a node graft within 4-5 hours but they differ in the extent to which they are inducible by FGF: FGF is both necessary and sufficient to induce Asterix, sufficient but not necessary to induce Obelix and neither sufficient nor necessary for induction of TrkC. These genes are also not induced by retinoic acid, Noggin, Chordin, Dkk1, Cerberus, HGF/SF, Somatostatin or ionomycin-mediated Calcium entry. Comparison of the expression and regulation of these genes with other early neural markers reveals three distinct "epochs", or temporal waves, of gene expression accompanying neural induction by a grafted organizer, which are mirrored by specific stages of normal neural plate development. The results are consistent with neural induction being a cascade of responses elicited by different signals, culminating in the formation of a patterned nervous system.

  18. Neural coding merges sex and habitat chemosensory signals in an insect herbivore.

    Science.gov (United States)

    Trona, Federica; Anfora, Gianfranco; Balkenius, Anna; Bengtsson, Marie; Tasin, Marco; Knight, Alan; Janz, Niklas; Witzgall, Peter; Ignell, Rickard

    2013-06-07

    Understanding the processing of odour mixtures is a focus in olfaction research. Through a neuroethological approach, we demonstrate that different odour types, sex and habitat cues are coded together in an insect herbivore. Stronger flight attraction of codling moth males, Cydia pomonella, to blends of female sex pheromone and plant odour, compared with single compounds, was corroborated by functional imaging of the olfactory centres in the insect brain, the antennal lobes (ALs). The macroglomerular complex (MGC) in the AL, which is dedicated to pheromone perception, showed an enhanced response to blends of pheromone and plant signals, whereas the response in glomeruli surrounding the MGC was suppressed. Intracellular recordings from AL projection neurons that transmit odour information to higher brain centres, confirmed this synergistic interaction in the MGC. These findings underscore that, in nature, sex pheromone and plant odours are perceived as an ensemble. That mating and habitat cues are coded as blends in the MGC of the AL highlights the dual role of plant signals in habitat selection and in premating sexual communication. It suggests that the MGC is a common target for sexual and natural selection in moths, facilitating ecological speciation.

  19. Self-organizing feature map (neural networks) as a tool to select the best indicator of road traffic pollution (soil, leaves or bark of Robinia pseudoacacia L.).

    Science.gov (United States)

    Samecka-Cymerman, A; Stankiewicz, A; Kolon, K; Kempers, A J

    2009-07-01

    Concentrations of the elements Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn were measured in the leaves and bark of Robinia pseudoacacia and the soil in which it grew, in the town of Oleśnica (SW Poland) and at a control site. We selected this town because emission from motor vehicles is practically the only source of air pollution, and it seemed interesting to evaluate its influence on soil and plants. The self-organizing feature map (SOFM) yielded distinct groups of soils and R. pseudoacacia leaves and bark, depending on traffic intensity. Only the map classifying bark samples identified an additional group of highly polluted sites along the main highway from Wrocław to Warszawa. The bark of R. pseudoacacia seems to be a better bioindicator of long-term cumulative traffic pollution in the investigated area, while leaves are good indicators of short-term seasonal accumulation trends.

  20. Ascorbic acid alters cell fate commitment of human neural progenitors in a WNT/β-catenin/ROS signaling dependent manner.

    Science.gov (United States)

    Rharass, Tareck; Lantow, Margareta; Gbankoto, Adam; Weiss, Dieter G; Panáková, Daniela; Lucas, Stéphanie

    2017-10-16

    Improving the neuronal yield from in vitro cultivated neural progenitor cells (NPCs) is an essential challenge in transplantation therapy in neurological disorders. In this regard, Ascorbic acid (AA) is widely used to expand neurogenesis from NPCs in cultures although the mechanisms of its action remain unclear. Neurogenesis from NPCs is regulated by the redox-sensitive WNT/β-catenin signaling pathway. We therefore aimed to investigate how AA interacts with this pathway and potentiates neurogenesis. Effects of 200 μM AA were compared with the pro-neurogenic reagent and WNT/β-catenin signaling agonist lithium chloride (LiCl), and molecules with antioxidant activities i.e. N-acetyl-L-cysteine (NAC) and ruthenium red (RuR), in differentiating neural progenitor ReNcell VM cells. Cells were supplemented with reagents for two periods of treatment: a full period encompassing the whole differentiation process versus an early short period that is restricted to the cell fate commitment stage. Intracellular redox balance and reactive oxygen species (ROS) metabolism were examined by flow cytometry using redox and ROS sensors. Confocal microscopy was performed to assess cell viability, neuronal yield, and levels of two proteins: Nucleoredoxin (NXN) and the WNT/β-catenin signaling component Dishevelled 2 (DVL2). TUBB3 and MYC gene responses were evaluated by quantitative real-time PCR. DVL2-NXN complex dissociation was measured by fluorescence resonance energy transfer (FRET). In contrast to NAC which predictably exhibited an antioxidant effect, AA treatment enhanced ROS metabolism with no cytotoxic induction. Both drugs altered ROS levels only at the early stage of the differentiation as no changes were held beyond the neuronal fate commitment stage. FRET studies showed that AA treatment accelerated the redox-dependent release of the initial pool of DVL2 from its sequestration by NXN, while RuR treatment hampered the dissociation of the two proteins. Accordingly, AA

  1. Traffic analysis and control using image processing

    Science.gov (United States)

    Senthilkumar, K.; Ellappan, Vijayan; Arun, A. R.

    2017-11-01

    This paper shows the work on traffic analysis and control till date. It shows an approach to regulate traffic the use of image processing and MATLAB systems. This concept uses computational images that are to be compared with original images of the street taken in order to determine the traffic level percentage and set the timing for the traffic signal accordingly which are used to reduce the traffic stoppage on traffic lights. They concept proposes to solve real life scenarios in the streets, thus enriching the traffic lights by adding image receivers like HD cameras and image processors. The input is then imported into MATLAB to be used. as a method for calculating the traffic on roads. Their results would be computed in order to adjust the traffic light timings on a particular street, and also with respect to other similar proposals but with the added value of solving a real, big instance.

  2. USP9X deubiquitylating enzyme maintains RAPTOR protein levels, mTORC1 signalling and proliferation in neural progenitors

    OpenAIRE

    Caitlin R. Bridges; Men-Chee Tan; Susitha Premarathne; Devathri Nanayakkara; Bernadette Bellette; Dusan Zencak; Deepti Domingo; Jozef Gecz; Mariyam Murtaza; Jolly, Lachlan A.; Wood, Stephen A.

    2017-01-01

    USP9X, is highly expressed in neural progenitors and, essential for neural development in mice. In humans, mutations in USP9X are associated with neurodevelopmental disorders. To understand USP9X?s role in neural progenitors, we studied the effects of altering its expression in both the human neural progenitor cell line, ReNcell VM, as well as neural stem and progenitor cells derived from Nestin-cre conditionally deleted Usp9x mice. Decreasing USP9X resulted in ReNcell VM cells arresting in G...

  3. PDF-1 neuropeptide signaling modulates a neural circuit for mate-searching behavior in C. elegans.

    Science.gov (United States)

    Barrios, Arantza; Ghosh, Rajarshi; Fang, Chunhui; Emmons, Scott W; Barr, Maureen M

    2012-12-01

    Appetitive behaviors require complex decision making that involves the integration of environmental stimuli and physiological needs. C. elegans mate searching is a male-specific exploratory behavior regulated by two competing needs: food and reproductive appetite. We found that the pigment dispersing factor receptor (PDFR-1) modulates the circuit that encodes the male reproductive drive that promotes male exploration following mate deprivation. PDFR-1 and its ligand, PDF-1, stimulated mate searching in the male, but not in the hermaphrodite. pdf-1 was required in the gender-shared interneuron AIM, and the receptor acted in internal and external environment-sensing neurons of the shared nervous system (URY, PQR and PHA) to produce mate-searching behavior. Thus, the pdf-1 and pdfr-1 pathway functions in non-sex-specific neurons to produce a male-specific, goal-oriented exploratory behavior. Our results indicate that secretin neuropeptidergic signaling is involved in regulating motivational internal states.

  4. Design and measurements of 64-channel ASIC for neural signal recording.

    Science.gov (United States)

    Kmon, P; Zoladz, M; Grybos, P; Szczygiel, R

    2009-01-01

    This paper presents the design and measurements of a low noise multi-channel front-end electronics for recording extra-cellular neuronal signals using microelectrode arrays. The integrated circuit contains 64 readout channels and was fabricated in CMOS 0.18 microm technology. A single readout channel is built of an AC coupling circuit at the input, a low noise preamplifier, a band-pass filter and a second amplifier. In order to reduce the number of output lines, the 64 analog signals from readout channels are multiplexed to a single output by an analog multiplexer. The chip is optimized for low noise and matching performance with the possibility of cut-off frequencies tuning. The low cut-off frequency can be tuned in the 1 Hz-60 Hz range and the high cut-off frequency can be tuned in the 3.5 kHz-15 kHz range. For the nominal gain setting at 44 dB and power dissipation per single channel of 220 microW the equivalent input noise is in the range from 6 microV-11 microV rms depending on the band-pass filter settings. The chip has good uniformity concerning the spread of its electrical parameters from channel to channel. The spread of gain calculated as standard deviation to mean value is about 4.4% and the spread of the low cut-off frequency is on the same level. The chip occupies 5x2.3 mm(2) of silicon area.

  5. Real-Time Corrected Traffic Correlation Model for Traffic Flow Forecasting

    Directory of Open Access Journals (Sweden)

    Hua-pu Lu

    2015-01-01

    Full Text Available This paper focuses on the problems of short-term traffic flow forecasting. The main goal is to put forward traffic correlation model and real-time correction algorithm for traffic flow forecasting. Traffic correlation model is established based on the temporal-spatial-historical correlation characteristic of traffic big data. In order to simplify the traffic correlation model, this paper presents correction coefficients optimization algorithm. Considering multistate characteristic of traffic big data, a dynamic part is added to traffic correlation model. Real-time correction algorithm based on Fuzzy Neural Network is presented to overcome the nonlinear mapping problems. A case study based on a real-world road network in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling methods.

  6. Building the vision, a series of AZTech ITS model deployment success stories for the Phoenix metropolitan area : number five : a strong signal transmitting traffic information via FM subcarrier

    Science.gov (United States)

    1998-01-01

    A key element of AZTech's mission is to make up-to-the-minute traffic information available to virtually any traveler. In pursuit of this goal, AZTech set its sights on obtaining an FM subcarrier that could : transmit a wide variety of traffic-relate...

  7. 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.

  8. When the brain simulates stopping: Neural activity recorded during real and imagined stop-signal tasks.

    Science.gov (United States)

    González-Villar, Alberto J; Bonilla, F Mauricio; Carrillo-de-la-Peña, María T

    2016-10-01

    It has been suggested that mental rehearsal activates brain areas similar to those activated by real performance. Although inhibition is a key function of human behavior, there are no previous reports of brain activity during imagined response cancellation. We analyzed event-related potentials (ERPs) and time-frequency data associated with motor execution and inhibition during real and imagined performance of a stop-signal task. The ERPs characteristic of stop trials-that is, the stop-N2 and stop-P3-were also observed during covert performance of the task. Imagined stop (IS) trials yielded smaller stop-N2 amplitudes than did successful stop (SS) and unsuccessful stop (US) trials, but midfrontal theta power similar to that in SS trials. The stop-P3 amplitude for IS was intermediate between those observed for SS and US. The results may be explained by the absence of error-processing and correction processes during imagined performance. For go trials, real execution was associated with higher mu and beta desynchronization over motor areas, which confirms previous reports of lower motor activation during imagined execution and also with larger P3b amplitudes, probably indicating increased top-down attention to the real task. The similar patterns of activity observed for imagined and real performance suggest that imagination tasks may be useful for training inhibitory processes. Nevertheless, brain activation was generally weaker during mental rehearsal, probably as a result of the reduced engagement of top-down mechanisms and limited error processing.

  9. Notch signaling and proneural genes work together to control the neural building blocks for the initial scaffold in the hypothalamus

    Science.gov (United States)

    Ware, Michelle; Hamdi-Rozé, Houda; Dupé, Valérie

    2014-01-01

    The vertebrate embryonic prosencephalon gives rise to the hypothalamus, which plays essential roles in sensory information processing as well as control of physiological homeostasis and behavior. While patterning of the hypothalamus has received much attention, initial neurogenesis in the developing hypothalamus has mostly been neglected. The first differentiating progenitor cells of the hypothalamus will give rise to neurons that form the nucleus of the tract of the postoptic commissure (nTPOC) and the nucleus of the mammillotegmental tract (nMTT). The formation of these neuronal populations has to be highly controlled both spatially and temporally as these tracts will form part of the ventral longitudinal tract (VLT) and act as a scaffold for later, follower axons. This review will cumulate and summarize the existing data available describing initial neurogenesis in the vertebrate hypothalamus. It is well-known that the Notch signaling pathway through the inhibition of proneural genes is a key regulator of neurogenesis in the vertebrate central nervous system. It has only recently been proposed that loss of Notch signaling in the developing chick embryo causes an increase in the number of neurons in the hypothalamus, highlighting an early function of the Notch pathway during hypothalamus formation. Further analysis in the chick and mouse hypothalamus confirms the expression of Notch components and Ascl1 before the appearance of the first differentiated neurons. Many newly identified proneural target genes were also found to be expressed during neuronal differentiation in the hypothalamus. Given the critical role that hypothalamic neural circuitry plays in maintaining homeostasis, it is particularly important to establish the targets downstream of this Notch/proneural network. PMID:25520625

  10. Cadherin-6B stimulates an epithelial mesenchymal transition and the delamination of cells from the neural ectoderm via LIMK/cofilin mediated non-canonical BMP receptor signaling

    Science.gov (United States)

    Park, Ki-Sook; Gumbiner, Barry M.

    2012-01-01

    We previously provided evidence that cadherin-6B induces de-epithelialization of the neural crest prior to delamination and is required for the overall epithelial mesenchymal transition (EMT). Furthermore, de-epithelialization induced by cadherin-6B was found to be mediated by BMP receptor signaling independent of BMP. We now find that de-epithelialization is mediated by non-canonical BMP signaling through the BMP type II receptor (BMPRII) and not by canonical Smad dependent signaling through BMP Type I receptor. The LIM kinase/cofilin pathway mediates non-canonical BMPRII induced de-epithelialization, in response to either cadherin-6B or BMP. LIMK1 induces de-epithelialization in the neural tube and dominant negative LIMK1 decreases de-epithelialization induced by either cadherin-6B or BMP. Cofilin is the major known LIMK1 target and a S3A phosphorylation deficient mutated cofilin inhibits de-epithelialization induced by cadherin-6B as well as LIMK1. Importantly, LIMK1 as well as cadherin-6B can trigger ectopic delamination when co-expressed with the competence factor SOX9, showing that this cadherin-6B stimulated signaling pathway can mediate the full EMT in the appropriate context. These findings suggest that the de-epithelialization step of the neural crest EMT by cadherin-6B/BMPRII involves regulation of actin dynamics via LIMK/cofilin. PMID:22537493

  11. Neural Substrates of Social Emotion Regulation: A fMRI Study on Imitation and Expressive Suppression to Dynamic Facial Signals

    Directory of Open Access Journals (Sweden)

    Pascal eVrticka

    2013-02-01

    Full Text Available Emotion regulation is crucial for successfully engaging in social interactions. Yet, little is known about the neural mechanisms controlling behavioral responses to emotional expressions perceived in the face of other people, which constitute a key element of interpersonal communication. Here, we investigated brain systems involved in social emotion perception and regulation, using functional magnetic resonance imaging (fMRI in 20 healthy participants who saw dynamic facial expressions of either happiness or sadness, and were asked to either imitate the expression or to suppress any expression on their own face (in addition to a gender judgment control task. fMRI results revealed higher activity in regions associated with emotion (e.g., the insula, motor function (e.g., motor cortex, and theory of mind during imitation. Activity in dorsal cingulate cortex was also increased during imitation, possibly reflecting greater action monitoring or conflict with own feeling states. In addition, premotor regions were more strongly activated during both imitation and suppression, suggesting a recruitment of motor control for both the production and inhibition of emotion expressions. Expressive suppression produced increases in dorsolateral and lateral prefrontal cortex typically related to cognitive control. These results suggest that voluntary imitation and expressive suppression modulate brain responses to emotional signals perceived from faces, by up- and down-regulating activity in distributed subcortical and cortical networks that are particularly involved in emotion, action monitoring, and cognitive control.

  12. Chondroitin sulfate proteoglycans regulate the growth, differentiation and migration of multipotent neural precursor cells through the integrin signaling pathway

    Directory of Open Access Journals (Sweden)

    Lü He-Zuo

    2009-10-01

    Full Text Available Abstract Background Neural precursor cells (NPCs are defined by their ability to proliferate, self-renew, and retain the potential to differentiate into neurons and glia. Deciphering the factors that regulate their behaviors will greatly aid in their use as potential therapeutic agents or targets. Chondroitin sulfate proteoglycans (CSPGs are prominent components of the extracellular matrix (ECM in the central nervous system (CNS and are assumed to play important roles in controlling neuronal differentiation and development. Results In the present study, we demonstrated that CSPGs were constitutively expressed on the NPCs isolated from the E16 rat embryonic brain. When chondroitinase ABC was used to abolish the function of endogenous CSPGs on NPCs, it induced a series of biological responses including the proliferation, differentiation and migration of NPCs, indicating that CSPGs may play a critical role in NPC development and differentiation. Finally, we provided evidence suggesting that integrin signaling pathway may be involved in the effects of CSPGs on NPCs. Conclusion The present study investigating the influence and mechanisms of CSPGs on the differentiation and migration of NPCs should help us to understand the basic biology of NPCs during CNS development and provide new insights into developing new strategies for the treatment of the neurological disorders in the CNS.

  13. The Dlx5-FGF10 signaling cascade controls cranial neural crest and myoblast interaction during oropharyngeal patterning and development.

    Science.gov (United States)

    Sugii, Hideki; Grimaldi, Alexandre; Li, Jingyuan; Parada, Carolina; Vu-Ho, Thach; Feng, Jifan; Jing, Junjun; Yuan, Yuan; Guo, Yuxing; Maeda, Hidefumi; Chai, Yang

    2017-11-01

    Craniofacial development depends on cell-cell interactions, coordinated cellular movement and differentiation under the control of regulatory gene networks, which include the distal-less (Dlx) gene family. However, the functional significance of Dlx5 in patterning the oropharyngeal region has remained unknown. Here, we show that loss of Dlx5 leads to a shortened soft palate and an absence of the levator veli palatini, palatopharyngeus and palatoglossus muscles that are derived from the 4th pharyngeal arch (PA); however, the tensor veli palatini, derived from the 1st PA, is unaffected. Dlx5-positive cranial neural crest (CNC) cells are in direct contact with myoblasts derived from the pharyngeal mesoderm, and Dlx5 disruption leads to altered proliferation and apoptosis of CNC and muscle progenitor cells. Moreover, the FGF10 pathway is downregulated in Dlx5-/- mice, and activation of FGF10 signaling rescues CNC cell proliferation and myogenic differentiation in these mutant mice. Collectively, our results indicate that Dlx5 plays crucial roles in the patterning of the oropharyngeal region and development of muscles derived from the 4th PA mesoderm in the soft palate, likely via interactions between CNC-derived and myogenic progenitor cells. © 2017. Published by The Company of Biologists Ltd.

  14. Thyroid hormone signaling: Contribution to neural function, cognition, and relationship to nicotine

    Science.gov (United States)

    Leach, Prescott T.; Gould, Thomas J.

    2015-01-01

    Cigarette smoking is common despite its adverse effects on health, such as cardiovascular disease and stroke. Understanding the mechanisms that contribute to the addictive properties of nicotine makes it possible to target them to prevent the initiation of smoking behavior and/or increase the chance of successful quit attempts. While highly addictive, nicotine is not generally considered to be as reinforcing as other drugs of abuse. There are likely other mechanisms at work that contribute to the addictive liability of nicotine. Nicotine modulates aspects of the endocrine system, including the thyroid, which is critical for normal cognitive functioning. It is possible that nicotine’s effects on thyroid function may alter learning and memory, and this may underlie some of its addictive potential. Here, we review the literature on thyroid function and cognition, with a focus on how nicotine alters thyroid hormone signaling and the potential impact on cognition. Changes in cognition are a major symptom of nicotine addiction. Current anti-smoking therapies have modest success at best. If some of the cognitive effects of nicotine are mediated through the thyroid hormone system, then thyroid hormone agonists may be novel treatments for smoking cessation therapies. The content of this review is important because it clarifies the relationship between smoking and thyroid function, which has been ill-defined in the past. This review is timely because the reduction in smoking rates we have seen in recent decades, due to public awareness campaigns and public smoking bans, has leveled off in recent years. Therefore, novel treatment approaches are needed to help reduce smoking rates further. PMID:26344666

  15. Self-Organizing Traffic at a Malfunctioning Intersection

    OpenAIRE

    Sujai Kumar; Sugata Mitra

    2006-01-01

    Traffic signals and traffic flow models have been studied extensively in the past and have provided valuable insights on the design of signalling systems, congestion control, and punitive policies. This paper takes a slightly different tack and describes what happens at an intersection where the traffic signals are malfunctioning and stuck in some configuration. By modelling individual vehicles as agents, we were able to replicate the surprisingly organized traffic flow that we observed at a ...

  16. Self-organizing feature map (neural networks) as a tool to select the best indicator of road traffic pollution (soil, leaves or bark of Robinia pseudoacacia L.)

    Energy Technology Data Exchange (ETDEWEB)

    Samecka-Cymerman, A., E-mail: sameckaa@biol.uni.wroc.p [Department of Ecology, Biogeochemistry and Environmental Protection, Wroclaw University, ul. Kanonia 6/8, 50-328 Wroclaw (Poland); Stankiewicz, A.; Kolon, K. [Department of Ecology, Biogeochemistry and Environmental Protection, Wroclaw University, ul. Kanonia 6/8, 50-328 Wroclaw (Poland); Kempers, A.J. [Department of Environmental Sciences, Radboud University of Nijmegen, Toernooiveld, 6525 ED Nijmegen (Netherlands)

    2009-07-15

    Concentrations of the elements Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn were measured in the leaves and bark of Robinia pseudoacacia and the soil in which it grew, in the town of Olesnica (SW Poland) and at a control site. We selected this town because emission from motor vehicles is practically the only source of air pollution, and it seemed interesting to evaluate its influence on soil and plants. The self-organizing feature map (SOFM) yielded distinct groups of soils and R. pseudoacacia leaves and bark, depending on traffic intensity. Only the map classifying bark samples identified an additional group of highly polluted sites along the main highway from Wroclaw to Warszawa. The bark of R. pseudoacacia seems to be a better bioindicator of long-term cumulative traffic pollution in the investigated area, while leaves are good indicators of short-term seasonal accumulation trends. - Once trained, SOFM could be used in the future to recognize types of pollution.

  17. Activation of mTor Signaling by Gene Transduction to Induce Axon Regeneration in the Central Nervous System Following Neural Injury

    Science.gov (United States)

    2017-08-01

    AWARD NUMBER: W81XWH-12-1-0051 TITLE: Activation of mTor Signaling by Gene Transduction to Induce Axon Regeneration in the Central Nervous System ...Central Nervous System Following Neural Injury 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-12-1-0051 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Robert...mature mammalian central nervous system (CNS), unlike the peripheral nervous system (PNS), is incapable of axon regeneration. There are currently two

  18. Introduction to Artificial Neural Networks

    DEFF Research Database (Denmark)

    Larsen, Jan

    1999-01-01

    The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....

  19. Cold atmospheric plasma (CAP), a novel physicochemical source, induces neural differentiation through cross-talk between the specific RONS cascade and Trk/Ras/ERK signaling pathway.

    Science.gov (United States)

    Jang, Ja-Young; Hong, Young June; Lim, Junsup; Choi, Jin Sung; Choi, Eun Ha; Kang, Seongman; Rhim, Hyangshuk

    2018-02-01

    Plasma, formed by ionization of gas molecules or atoms, is the most abundant form of matter and consists of highly reactive physicochemical species. In the physics and chemistry fields, plasma has been extensively studied; however, the exact action mechanisms of plasma on biological systems, including cells and humans, are not well known. Recent evidence suggests that cold atmospheric plasma (CAP), which refers to plasma used in the biomedical field, may regulate diverse cellular processes, including neural differentiation. However, the mechanism by which these physicochemical signals, elicited by reactive oxygen and nitrogen species (RONS), are transmitted to biological system remains elusive. In this study, we elucidated the physicochemical and biological (PCB) connection between the CAP cascade and Trk/Ras/ERK signaling pathway, which resulted in neural differentiation. Excited atomic oxygen in the plasma phase led to the formation of RONS in the PCB network, which then interacted with reactive atoms in the extracellular liquid phase to form nitric oxide (NO). Production of large amounts of superoxide radical (O2-) in the mitochondria of cells exposed to CAP demonstrated that extracellular NO induced the reversible inhibition of mitochondrial complex IV. We also demonstrated that cytosolic hydrogen peroxide, formed by O2- dismutation, act as an intracellular messenger to specifically activate the Trk/Ras/ERK signaling pathway. This study is the first to elucidate the mechanism linking physicochemical signals from the CAP cascade to the intracellular neural differentiation signaling pathway, providing physical, chemical and biological insights into the development of therapeutic techniques to treat neurological diseases. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Evaluation of New Jersey Route 18 OPAC/MIST traffic-control system

    Science.gov (United States)

    1997-01-01

    Conventional traffic-control strategies have limitations in handling unanticipated traffic demands. An adaptive traffic-signal control is expected to mitigate this problem and improve overall system performance. Furthermore, with the increasing needs...

  1. Transforming Growth Factor-Beta Signaling in the Neural Stem Cell Niche: A Therapeutic Target for Huntington's Disease

    Directory of Open Access Journals (Sweden)

    Mahesh Kandasamy

    2011-01-01

    Full Text Available The neural stem cell niches possess the regenerative capacity to generate new functional neurons in the adult brain, suggesting the possibility of endogenous neuronal replacement after injury or disease. Huntington disease (HD is a neurodegenerative disease and characterized by neuronal loss in the basal ganglia, leading to motor, cognitive, and psychological disabilities. Apparently, in order to make use of the neural stem cell niche as a therapeutic concept for repair strategies in HD, it is important to understand the cellular and molecular composition of the neural stem cell niche under such neurodegenerative conditions. This paper mainly discusses the current knowledge on the regulation of the hippocampal neural stem cell niche in the adult brain and by which mechanism it might be compromised in the case of HD.

  2. A multi-channel low-power system-on-chip for single-unit recording and narrowband wireless transmission of neural signal.

    Science.gov (United States)

    Bonfanti, A; Ceravolo, M; Zambra, G; Gusmeroli, R; Spinelli, A S; Lacaita, A L; Angotzi, G N; Baranauskas, G; Fadiga, L

    2010-01-01

    This paper reports a multi-channel neural recording system-on-chip (SoC) with digital data compression and wireless telemetry. The circuit consists of a 16 amplifiers, an analog time division multiplexer, an 8-bit SAR AD converter, a digital signal processor (DSP) and a wireless narrowband 400-MHz binary FSK transmitter. Even though only 16 amplifiers are present in our current die version, the whole system is designed to work with 64 channels demonstrating the feasibility of a digital processing and narrowband wireless transmission of 64 neural recording channels. A digital data compression, based on the detection of action potentials and storage of correspondent waveforms, allows the use of a 1.25-Mbit/s binary FSK wireless transmission. This moderate bit-rate and a low frequency deviation, Manchester-coded modulation are crucial for exploiting a narrowband wireless link and an efficient embeddable antenna. The chip is realized in a 0.35- εm CMOS process with a power consumption of 105 εW per channel (269 εW per channel with an extended transmission range of 4 m) and an area of 3.1 × 2.7 mm(2). The transmitted signal is captured by a digital TV tuner and demodulated by a wideband phase-locked loop (PLL), and then sent to a PC via an FPGA module. The system has been tested for electrical specifications and its functionality verified in in-vivo neural recording experiments.

  3. Neural Differentiation of Human Adipose Tissue-Derived Stem Cells Involves Activation of the Wnt5a/JNK Signalling

    Directory of Open Access Journals (Sweden)

    Sujeong Jang

    2015-01-01

    Full Text Available Stem cells are a powerful resource for cell-based transplantation therapies, but understanding of stem cell differentiation at the molecular level is not clear yet. We hypothesized that the Wnt pathway controls stem cell maintenance and neural differentiation. We have characterized the transcriptional expression of Wnt during the neural differentiation of hADSCs. After neural induction, the expressions of Wnt2, Wnt4, and Wnt11 were decreased, but the expression of Wnt5a was increased compared with primary hADSCs in RT-PCR analysis. In addition, the expression levels of most Fzds and LRP5/6 ligand were decreased, but not Fzd3 and Fzd5. Furthermore, Dvl1 and RYK expression levels were downregulated in NI-hADSCs. There were no changes in the expression of ß-catenin and GSK3ß. Interestingly, Wnt5a expression was highly increased in NI-hADSCs by real time RT-PCR analysis and western blot. Wnt5a level was upregulated after neural differentiation and Wnt3, Dvl2, and Naked1 levels were downregulated. Finally, we found that the JNK expression was increased after neural induction and ERK level was decreased. Thus, this study shows for the first time how a single Wnt5a ligand can activate the neural differentiation pathway through the activation of Wnt5a/JNK pathway by binding Fzd3 and Fzd5 and directing Axin/GSK-3ß in hADSCs.

  4. High-level activation of cyclic AMP signaling attenuates bone morphogenetic protein 2-induced sympathoadrenal lineage development and promotes melanogenesis in neural crest cultures.

    Science.gov (United States)

    Ji, Ming; Andrisani, Ourania M

    2005-06-01

    The intensity of cyclic AMP (cAMP) signaling is a differential instructive signal in neural crest (NC) cell specification. By an unknown mechanism, sympathoadrenal lineage specification is suppressed by high-level activation of cAMP signaling. In NC cultures, high-level activation of cAMP signaling mediates protein kinase A (PKA)-dependent Rap1-B-Raf-ERK1/2 activation, leading to cytoplasmic accumulation of phospho-Smad1, thus terminating bone morphogenetic protein 2 (BMP2)-induced sympathoadrenal cell development. Concurrently, cAMP signaling induces transcription of the melanocyte-determining transcription factor Mitf and melanogenesis. dnACREB and E1A inhibit Mitf expression and melanogenesis, supporting the notion that CREB activation is necessary for melanogenesis. However, constitutively active CREB(DIEDML) without PKA activation is insufficient for Mitf expression and melanogenesis, indicating PKA regulates additional aspects of Mitf transcription. Thus, high-level activation of cAMP signaling plays a dual role in NC cell differentiation: attenuation of BMP2-induced sympathoadrenal cell development and induction of melanogenesis. We conclude the intensity of activation of signal transduction cascades determines cell lineage segregation mechanisms.

  5. Concise Review: Reprogramming, Behind the Scenes: Noncanonical Neural Stem Cell Signaling Pathways Reveal New, Unseen Regulators of Tissue Plasticity With Therapeutic Implications.

    Science.gov (United States)

    Poser, Steven W; Chenoweth, Josh G; Colantuoni, Carlo; Masjkur, Jimmy; Chrousos, George; Bornstein, Stefan R; McKay, Ronald D; Androutsellis-Theotokis, Andreas

    2015-11-01

    Interest is great in the new molecular concepts that explain, at the level of signal transduction, the process of reprogramming. Usually, transcription factors with developmental importance are used, but these approaches give limited information on the signaling networks involved, which could reveal new therapeutic opportunities. Recent findings involving reprogramming by genetic means and soluble factors with well-studied downstream signaling mechanisms, including signal transducer and activator of transcription 3 (STAT3) and hairy and enhancer of split 3 (Hes3), shed new light into the molecular mechanisms that might be involved. We examine the appropriateness of common culture systems and their ability to reveal unusual (noncanonical) signal transduction pathways that actually operate in vivo. We then discuss such novel pathways and their importance in various plastic cell types, culminating in their emerging roles in reprogramming mechanisms. We also discuss a number of reprogramming paradigms (mouse induced pluripotent stem cells, direct conversion to neural stem cells, and in vivo conversion of acinar cells to β-like cells). Specifically for acinar-to-β-cell reprogramming paradigms, we discuss the common view of the underlying mechanism (involving the Janus kinase-STAT pathway that leads to STAT3-tyrosine phosphorylation) and present alternative interpretations that implicate STAT3-serine phosphorylation alone or serine and tyrosine phosphorylation occurring in sequential order. The implications for drug design and therapy are important given that different phosphorylation sites on STAT3 intercept different signaling pathways. We introduce a new molecular perspective in the field of reprogramming with broad implications in basic, biotechnological, and translational research. Reprogramming is a powerful approach to change cell identity, with implications in both basic and applied biology. Most efforts involve the forced expression of key transcription

  6. Adults with high social anhedonia have altered neural connectivity with ventral lateral prefrontal cortex when processing positive social signals

    Directory of Open Access Journals (Sweden)

    Hong eYin

    2015-08-01

    Full Text Available Social anhedonia (SA is a debilitating characteristic of schizophrenia and a vulnerability for developing schizophrenia among people at risk. Prior work (Hooker et al, 2014 has revealed neural deficits in ventral lateral prefrontal cortex (VLPFC during processing of positive emotion in a community sample of people with high social anhedonia. Deficits in VLPFC neural activity are related to worse self-reported schizophrenia-spectrum symptoms and worse mood and behavior after social stress. In the current study, psychophysiological interaction (PPI analysis was applied to investigate the neural mechanisms mediated by VLPFC during emotion processing. PPI analysis revealed that, compared to low SA controls, participants with high SA displayed reduced VLPFC integration, specifically reduced connectivity between VLPFC and premotor cortex, inferior parietal and posterior temporal regions when viewing positive relative to neutral emotion. Across all participants, connectivity between VLPFC and inferior parietal region when viewing positive (versus neutral emotion was significantly correlated with measures of emotion management and attentional control. Additionally connectivity between VLPFC and superior temporal sulcus was related to reward and pleasure anticipation, and connectivity between VLPFC and inferior temporal sulcus correlated with attentional control measure. Our results suggest that impairments to VLPFC mediated neural circuitry underlie the cognitive and emotional deficits.

  7. Green Wave Traffic Optimization - A Survey

    DEFF Research Database (Denmark)

    Warberg, Andreas; Larsen, Jesper; Jørgensen, Rene Munk

    The objective of this survey is to cover the research in the area of adaptive traffic control with emphasis on the applied optimization methods. The problem of optimizing traffic signals can be viewed in various ways, depending on political, economic and ecological goals. The survey highlights so...

  8. Synchronization of Traffic Light Systems for Maximum Efficiency along Jalan Bukit Gambier, Penang, Malaysia

    OpenAIRE

    Ahmad Rafidi M.A.; Abdul Hamid A.H.

    2014-01-01

    The synchronization of traffic light systems is one of the best solutions in order to avoid problematic traffic jams. Traffic timing is a major concern when it comes to traffic management. One of the common causes of traffic jams is because of nonsynchronized traffic light systems. Once a light turns green, traffic begins to move, but by the time the moving traffic reaches the next light, the signal is still red. This will disrupt the continuity of the traffic flow, especially for large main ...

  9. A Traffic Prediction Model for Self-Adapting Routing Overlay Network in Publish/Subscribe System

    Directory of Open Access Journals (Sweden)

    Meng Chi

    2017-01-01

    Full Text Available In large-scale location-based service, an ideal situation is that self-adapting routing strategies use future traffic data as input to generate a topology which could adapt to the changing traffic well. In the paper, we propose a traffic prediction model for the broker in publish/subscribe system, which can predict the traffic of the link in future by neural network. We first introduced our traffic prediction model and then described the model integration. Finally, the experimental results show that our traffic prediction model could predict the traffic of link well.

  10. Implementation of a Computational Model for Information Processing and Signaling from a Biological Neural Network of Neostriatum Nucleus

    Directory of Open Access Journals (Sweden)

    C. Sanchez-Vazquez

    2014-06-01

    Full Text Available Recently, several mathematical models have been developed to study and explain the way information is processed in the brain. The models published account for a myriad of perspectives from single neuron segments to neural networks, and lately, with the use of supercomputing facilities, to the study of whole environments of nuclei interacting for massive stimuli and processing. Some of the most complex neural structures -and also most studied- are basal ganglia nuclei in the brain; amongst which we can find the Neostriatum. Currently, just a few papers about high scale biological-based computational modeling of this region have been published. It has been demonstrated that the Basal Ganglia region contains functions related to learning and decision making based on rules of the action-selection type, which are of particular interest for the machine autonomous-learning field. This knowledge could be clearly transferred between areas of research. The present work proposes a model of information processing, by integrating knowledge generated from widely accepted experiments in both morphology and biophysics, through integrating theories such as the compartmental electrical model, the Rall’s cable equation, and the Hodking-Huxley particle potential regulations, among others. Additionally, the leaky integrator framework is incorporated in an adapted function. This was accomplished through a computational environment prepared for high scale neural simulation which delivers data output equivalent to that from the original model, and that can not only be analyzed as a Bayesian problem, but also successfully compared to the biological specimen.

  11. Traffic intensity monitoring using multiple object detection with traffic surveillance cameras

    Science.gov (United States)

    Hamdan, H. G. Muhammad; Khalifah, O. O.

    2017-11-01

    Object detection and tracking is a field of research that has many applications in the current generation with increasing number of cameras on the streets and lower cost for Internet of Things(IoT). In this paper, a traffic intensity monitoring system is implemented based on the Macroscopic Urban Traffic model is proposed using computer vision as its source. The input of this program is extracted from a traffic surveillance camera which has another program running a neural network classification which can identify and differentiate the vehicle type is implanted. The neural network toolbox is trained with positive and negative input to increase accuracy. The accuracy of the program is compared to other related works done and the trends of the traffic intensity from a road is also calculated. relevant articles in literature searches, great care should be taken in constructing both. Lastly the limitation and the future work is concluded.

  12. Traffic Predictive Control: Case Study and Evaluation

    Science.gov (United States)

    2017-06-26

    This project developed a quantile regression method for predicting future traffic flow at a signalized intersection by combining both historical and real-time data. The algorithm exploits nonlinear correlations in historical measurements and efficien...

  13. An intelligent control system for traffic lights with simulation-based evaluation

    OpenAIRE

    Jin, Junchen; Ma, Xiaoliang; Kosonen, I.

    2017-01-01

    This paper introduces an intelligent control system for traffic signal applications, called Fuzzy Intelligent Traffic Signal (FITS) control. It provides a convenient and economic approach to improve existing traffic light infrastructure. The control system is programmed on an intermediate hardware device capable of receiving messages from signal controller hardware as well as overriding traffic light indications during real-time operations. Signal control and optimization toolboxes are integr...

  14. Efficiency of Roundabouts as Compared to Traffic Light Controlled ...

    African Journals Online (AJOL)

    Bheema

    light especially when the electric power consumption, fuel consumption and emission by the vehicles stopped by red light at the entrance of signalized intersection are taken into consideration. When traffic density exceeds this value, however, it is recommended for the traffic to be regulated with traffic light at a part time basis ...

  15. Species, sex and individual differences in the vasotocin/vasopressin system: relationship to neurochemical signaling in the social behavior neural network.

    Science.gov (United States)

    Albers, H Elliott

    2015-01-01

    Arginine-vasotocin (AVT)/arginine vasopressin (AVP) are members of the AVP/oxytocin (OT) superfamily of peptides that are involved in the regulation of social behavior, social cognition and emotion. Comparative studies have revealed that AVT/AVP and their receptors are found throughout the "social behavior neural network (SBNN)" and display the properties expected from a signaling system that controls social behavior (i.e., species, sex and individual differences and modulation by gonadal hormones and social factors). Neurochemical signaling within the SBNN likely involves a complex combination of synaptic mechanisms that co-release multiple chemical signals (e.g., classical neurotransmitters and AVT/AVP as well as other peptides) and non-synaptic mechanisms (i.e., volume transmission). Crosstalk between AVP/OT peptides and receptors within the SBNN is likely. A better understanding of the functional properties of neurochemical signaling in the SBNN will allow for a more refined examination of the relationships between this peptide system and species, sex and individual differences in sociality. Copyright © 2014 Elsevier Inc. All rights reserved.

  16. Sonic Hedgehog Signaling Mediates Resveratrol to Increase Proliferation of Neural Stem Cells After Oxygen-Glucose Deprivation/Reoxygenation Injury in Vitro

    Directory of Open Access Journals (Sweden)

    Wei Cheng

    2015-03-01

    Full Text Available Background/Aims: There is interest in drugs and rehabilitation methods to enhance neurogenesis and improve neurological function after brain injury or degeneration. Resveratrol may enhance hippocampal neurogenesis and improve hippocampal atrophy in chronic fatigue mice and prenatally stressed rats. However, its effect and mechanism of neurogenesis after stroke is less well understood. Sonic hedgehog (Shh signaling is crucial for neurogenesis in the embryonic and adult brain, but relatively little is known about the role of Shh signaling in resveratrol-enhanced neurogenesis after stroke. Methods: Neural stem cells (NSCs before oxygen-glucose deprivation/reoxygenation (OGD/R in vitro were pretreated with resveratrol with or without cyclopamine. Survival and proliferation of NSCs was assessed by the CCK8 assay and BrdU immunocytochemical staining. The expressions and activity of signaling proteins and mRNAs were detected by immunocytochemistry, Western blotting, and RT-PCR analysis. Results: Resveratrol significantly increased NSCs survival and proliferation in a concentration-dependent manner after OGD/R injury in vitro. At the same time, the expression of Patched-1, Smoothened (Smo, and Gli-1 proteins and mRNAs was upregulated, and Gli-1 entered the nucleus, which was inhibited by cyclopamine, a Smo inhibitor. Conclusion: Shh signaling mediates resveratrol to increase NSCs proliferation after OGD/R injury in vitro.

  17. A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination

    OpenAIRE

    Xiaoke Zhou; Fei Zhu; Quan Liu; Yuchen Fu; Wei Huang

    2014-01-01

    Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors t...

  18. TrafficTurk evaluation.

    Science.gov (United States)

    2014-04-01

    This report summarizes a project undertaken by the University of Illinois on behalf of the Illinois Department of : Transportation to evaluate a smartphone application called TrafficTurk for traffic safety and traffic monitoring : applications. Traff...

  19. Selecting Statistical Characteristics of Brain Signals to Detect Epileptic Seizures using Discrete Wavelet Transform and Perceptron Neural Network

    Directory of Open Access Journals (Sweden)

    Rezvan Abbasi

    2017-08-01

    Full Text Available Electroencephalogram signals (EEG have always been used in medical diagnosis. Evaluation of the statistical characteristics of EEG signals is actually the foundation of all brain signal processing methods. Since the correct prediction of disease status is of utmost importance, the goal is to use those models that have minimum error and maximum reliability. In anautomatic epileptic seizure detection system, we should be able to distinguish between EEG signals before, during and after seizure. Extracting useful characteristics from EEG data can greatly increase the classification accuracy. In this new approach, we first parse EEG signals to sub-bands in different categories with the help of discrete wavelet transform(DWT and then we derive statistical characteristics such as maximum, minimum, average and standard deviation for each sub-band. A multilayer perceptron (MLPneural network was used to assess the different scenarios of healthy and seizure among the collected signal sets. In order to assess the success and effectiveness of the proposed method, the confusion matrix was used and its accuracy was achieved98.33 percent. Due to the limitations and obstacles in analyzing EEG signals, the proposed method can greatly help professionals experimentally and visually in the classification and diagnosis of epileptic seizures.

  20. Multi-scale Quantitative Precipitation Forecasting Using Nonlinear and Nonstationary Teleconnection Signals and Artificial Neural Network Models

    Science.gov (United States)

    Global sea surface temperature (SST) anomalies can affect terrestrial precipitation via ocean-atmosphere interaction known as climate teleconnection. Non-stationary and non-linear characteristics of the ocean-atmosphere system make the identification of the teleconnection signals...

  1. A 400 MHz Wireless Neural Signal Processing IC With 625 $\\times$ On-Chip Data Reduction and Reconfigurable BFSK/QPSK Transmitter Based on Sequential Injection Locking.

    Science.gov (United States)

    Teng, Kok-Hin; Wu, Tong; Liu, Xiayun; Yang, Zhi; Heng, Chun-Huat

    2017-06-01

    An 8-channel wireless neural signal processing IC, which can perform real-time spike detection, alignment, and feature extraction, and wireless data transmission is proposed. A reconfigurable BFSK/QPSK transmitter (TX) at MICS/MedRadio band is incorporated to support different data rate requirement. By using an Exponential Component-Polynomial Component (EC-PC) spike processing unit with an incremental principal component analysis (IPCA) engine, the detection of neural spikes with poor SNR is possible while achieving 625× data reduction. For the TX, a dual-channel at 401 MHz and 403.8 MHz are supported by applying sequential injection locked techniques while attaining phase noise of -102 dBc/Hz at 100 kHz offset. From the measurement, error vector magnitude (EVM) of 4.60%/9.55% with power amplifier (PA) output power of -15 dBm is achieved for the QPSK at 8 Mbps and the BFSK at 12.5 kbps. Fabricated in 65 nm CMOS with an active area of 1 mm 2, the design consumes a total current of 5  ∼ 5.6 mA with a maximum energy efficiency of 0.7 nJ/b.

  2. Possible activation by the green tea amino acid theanine of mammalian target of rapamycin signaling in undifferentiated neural progenitor cells in vitro

    Directory of Open Access Journals (Sweden)

    Takeshi Takarada

    2016-03-01

    Full Text Available We have shown marked promotion of both proliferation and neuronal differentiation in pluripotent P19 cells exposed to the green tea amino acid theanine, which is a good substrate for SLC38A1 responsible for glutamine transport. In this study, we evaluated the activity of the mammalian target of rapamycin (mTOR kinase pathway, which participates in protein translation, cell growth and autophagy in a manner relevant to intracellular glutamine levels, in murine neural progenitor cells exposed to theanine. Exposure to theanine promoted the phosphorylation of mTOR and downstream proteins in neurospheres from embryonic mouse neocortex. Although stable overexpression of SLC38A1 similarly facilitated phosphorylation of mTOR-relevant proteins in undifferentiated P19 cells, theanine failed to additionally accelerate the increased phosphorylation in these stable transfectants. Theanine accelerated the formation of neurospheres from murine embryonic neocortex and adult hippocampus, along with facilitation of both 5-bromo-2’-deoxyuridine incorporation and 3-(4,5-dimethyl-2-thiazolyl-2,5-diphenyl-2H-tetrazolium bromide reduction in embryonic neurospheres. In embryonic neurospheres previously exposed to theanine, a significant increase was seen in the number of cells immunoreactive for a neuronal marker protein after spontaneous differentiation. These results suggest that theanine activates the mTOR signaling pathway for proliferation together with accelerated neurogenesis in murine undifferentiated neural progenitor cells.

  3. Net analyte signal-based simultaneous determination of antazoline and naphazoline using wavelength region selection by experimental design-neural networks.

    Science.gov (United States)

    Hemmateenejad, Bahram; Ghavami, Raoof; Miri, Ramin; Shamsipur, Majtaba

    2006-02-15

    Net analyte signal (NAS)-based multivariate calibration methods were employed for simultaneous determination of anthazoline and naphazoline. The NAS vectors calculated from the absorbance data of the drugs mixture were used as input for classical least squares (CLS), principal component and partial least squares regression PCR and PLS methods. A wavelength selection strategy was used to find the best wavelength region for each drug separately. As a new procedure, we proposed an experimental design-neural network strategy for wavelength region optimization. By use of a full factorial design method, some different wavelength regions were selected by taking into account different spectral parameters including the starting wavelength, the ending wavelength and the wavelength interval. The performance of all the multivariate calibration methods, in all selected wavelength regions for both drugs, was evaluated by calculating a fitness function based on the root mean square error of calibration and validation. A three-layered feed-forward artificial neural network (ANN) model with back-propagation learning algorithm was employed to model the nonlinear relationship between the spectral parameters and fitness of each regression method. From the resulted ANN models, the spectral regions in which lowest fitness could be obtained were chosen. Comparison of the results revealed that the net NAS-PLS resulted in lower prediction error than the other models. The proposed NAS-based calibration method was successfully applied to the simultaneous analyses of anthazoline and naphazoline in a commercial eye drop sample.

  4. Strobe Traffic Lights Warn of Approaching Emergency Vehicles

    Science.gov (United States)

    Bachelder, Aaron

    2004-01-01

    Strobe-enhanced traffic signals have been developed to aid in the preemption of road intersections for emergency vehicles. The strobe-enhanced traffic signals can be incorporated into both new and pre-existing traffic-control systems in which the traffic-signal heads are of a relatively new type based on arrays of light-emitting diodes (LEDs). The strobe-enhanced traffic signals offer a less expensive, less complex alternative to a recently developed system of LED-based warning signs placed next to traffic signals. Because of its visual complexity, the combination of traffic signals and warning signs is potentially confusing to motorists. The strobe-enhanced traffic signals present less visual clutter. In a given traffic-signal head, the strobe-enhanced traffic signal is embedded in the red LED array of the stop signal. Two strobe LED strips one horizontal and one vertical are made capable of operating separately from the rest of the red LED matrix. When no emergency vehicle is approaching, the red LED array functions as a normal stop signal: all the red LEDs are turned on and off together. When the intersection is to be preempted for an approaching emergency vehicle, only the LEDs in one of the strobe strips are lit, and are turned on in a sequence that indicates the direction of approach. For example (see figure), if an emergency vehicle approaches from the right, the strobe LEDs are lit in a sequence moving from right to left. Important to the success of strobe-enhanced traffic signals is conformance to city ordinances and close relation to pre-existing traffic standards. For instance, one key restriction is that new icons must not include arrows, so that motorists will not confuse new icons with conventional arrows that indicate allowed directions of movement. It is also critical that new displays like strobe-enhanced traffic signals be similar to displays used in traffic-control systems in large cities. For example, Charleston, South Carolina uses horizontal

  5. Dysregulation of Wnt-Signaling and a Candidate Set of miRNAs Underlie the Effect of Metformin on Neural Crest Cell Development.

    Science.gov (United States)

    Banerjee, Poulomi; Dutta, Sunit; Pal, Rajarshi

    2016-02-01

    Neural crest cells (NCC) are a population of epithelial cells that arise from the dorsal tube and undergo epithelial-mesenchymal transition (EMT) eventually generating tissues from peripheral nervous system, melanocytes, craniofacial cartilage, and bone. The antidiabetic drug metformin reportedly inhibits EMT in physiological conditions like cancer and fibrosis. We hypothesize that perturbation of EMT may also contribute to developmental disabilities associated with neural crest (NC) development. To understand the molecular network underlying metformin action during NC formation, we first differentiated murine embryonic stem (ES) cells into NCC and characterized them by demonstrating spatiotemporal regulation of key markers. Metformin treatment prompted a delay in delamination of NCC by inhibiting key markers like Sox-1, Sox-9, HNK-1, and p-75. We then revealed that metformin impedes Wnt axis, a major signaling pathway active during NC formation via DVL-3 inhibition and impairment in nuclear translocation of β-catenin. Concomitantly we identified and tested a candidate set of miRNAs that play a crucial role in NC cell fate determination. Further studies involving loss and gain of function confirmed that NCC specifiers like Sox-1 and Sox-9 are direct targets of miR-200 and miR-145, respectively and that they are essentially modulated by metformin. Our in vitro findings were strongly supported by in vivo studies in zebrafish. Given that metformin is a widely used drug, for the first time we demonstrate that it can induce a delayed onset of developmental EMT during NC formation by interfering with canonical Wnt signaling and mysregulation of miR-145 and miR-200. © 2015 AlphaMed Press.

  6. The effect of pulsed electric fields on the electrotactic migration of human neural progenitor cells through the involvement of intracellular calcium signaling.

    Science.gov (United States)

    Hayashi, Hisamitsu; Edin, Fredrik; Li, Hao; Liu, Wei; Rask-Andersen, Helge

    2016-12-01

    Endogenous electric fields (EFs) are required for the physiological control of the central nervous system development. Application of the direct current EFs to neural stem cells has been studied for the possibility of stem cell transplantation as one of the therapies for brain injury. EFs generated within the nervous system are often associated with action potentials and synaptic activity, apparently resulting in a pulsed current in nature. The aim of this study is to investigate the effect of pulsed EF, which can reduce the cytotoxicity, on the migration of human neural progenitor cells (hNPCs). We applied the mono-directional pulsed EF with a strength of 250mV/mm to hNPCs for 6h. The migration distance of the hNPCs exposed to pulsed EF was significantly greater compared with the control not exposed to the EF. Pulsed EFs, however, had less of an effect on the migration of the differentiated hNPCs. There was no significant change in the survival of hNPCs after exposure to the pulsed EF. To investigate the role of Ca 2+ signaling in electrotactic migration of hNPCs, pharmacological inhibition of Ca 2+ channels in the EF-exposed cells revealed that the electrotactic migration of hNPCs exposed to Ca 2+ channel blockers was significantly lower compared to the control group. The findings suggest that the pulsed EF induced migration of hNPCs is partly influenced by intracellular Ca 2+ signaling. Copyright © 2016 Elsevier B.V. All rights reserved.

  7. Fuzzy Logic Based Autonomous Traffic Control System

    Directory of Open Access Journals (Sweden)

    Muhammad ABBAS

    2012-01-01

    Full Text Available The aim of this paper is to design and implement fuzzy logic based traffic light Control system to solve the traffic congestion issues. In this system four input parameters: Arrival, Queue, Pedestrian and Emergency Vehicle and two output parameters: Extension in Green and Pedestrian Signals are used. Using Fuzzy Rule Base, the system extends or terminates the Green Signal according to the Traffic situation at the junction. On the presence of emergency vehicle, the system decides which signal(s should be red and how much an extension should be given to Green Signal for Emergency Vehicle. The system also monitors the density of people and makes decisions accordingly. In order to verify the proposed design algorithm MATLAB simulation is adopted and results obtained show concurrency to the calculated values according to the Mamdani Model of the Fuzzy Control System.

  8. Queueing and traffic

    NARCIS (Netherlands)

    Baër, Niek

    2015-01-01

    Traffic jams are everywhere, some are caused by constructions or accidents but a large portion occurs naturally. These "natural" traffic jams are a result of variable driving speeds combined with a high number of vehicles. To prevent these traffic jams, we must understand traffic in general, and to

  9. Jamitons: Phantom Traffic Jams

    Science.gov (United States)

    Kowszun, Jorj

    2013-01-01

    Traffic on motorways can slow down for no apparent reason. Sudden changes in speed by one or two drivers can create a chain reaction that causes a traffic jam for the vehicles that are following. This kind of phantom traffic jam is called a "jamiton" and the article discusses some of the ways in which traffic engineers produce…

  10. Functional PDF Signaling in the Drosophila Circadian Neural Circuit Is Gated by Ral A-Dependent Modulation.

    Science.gov (United States)

    Klose, Markus; Duvall, Laura; Li, Weihua; Liang, Xitong; Ren, Chi; Steinbach, Joe Henry; Taghert, Paul H

    2016-05-18

    The neuropeptide PDF promotes the normal sequencing of circadian behavioral rhythms in Drosophila, but its signaling mechanisms are not well understood. We report daily rhythmicity in responsiveness to PDF in critical pacemakers called small LNvs. There is a daily change in potency, as great as 10-fold higher, around dawn. The rhythm persists in constant darkness and does not require endogenous ligand (PDF) signaling or rhythmic receptor gene transcription. Furthermore, rhythmic responsiveness reflects the properties of the pacemaker cell type, not the receptor. Dopamine responsiveness also cycles, in phase with that of PDF, in the same pacemakers, but does not cycle in large LNv. The activity of RalA GTPase in s-LNv regulates PDF responsiveness and behavioral locomotor rhythms. Additionally, cell-autonomous PDF signaling reversed the circadian behavioral effects of lowered RalA activity. Thus, RalA activity confers high PDF responsiveness, providing a daily gate around the dawn hours to promote functional PDF signaling. Copyright © 2016 Elsevier Inc. All rights reserved.

  11. A New High-Resolution Direction Finding Architecture Using Photonics and Neural Network Signal Processing for Miniature Air Vehicle Applications

    Science.gov (United States)

    2015-09-01

    Consortium for Robotics and Unmanned Systems Education and Research (CRUSER) 10. SPONSORING / MONITORING AGENCY REPORT NUMBER 11. SUPPLEMENTARY...perceptron MUSIC multiple signal classification MZM Mach-Zehnder modulator NPS Naval Postgraduate School OSNS optimum symmetric number system PCB...our life! Love you always! This work was supported by the Naval Postgraduate School (NPS) Consortium for Robotics and Unmanned Systems Education and

  12. A traffic situation analysis system

    Science.gov (United States)

    Sidla, Oliver; Rosner, Marcin

    2011-01-01

    The observation and monitoring of traffic with smart visions systems for the purpose of improving traffic safety has a big potential. For example embedded vision systems built into vehicles can be used as early warning systems, or stationary camera systems can modify the switching frequency of signals at intersections. Today the automated analysis of traffic situations is still in its infancy - the patterns of vehicle motion and pedestrian flow in an urban environment are too complex to be fully understood by a vision system. We present steps towards such a traffic monitoring system which is designed to detect potentially dangerous traffic situations, especially incidents in which the interaction of pedestrians and vehicles might develop into safety critical encounters. The proposed system is field-tested at a real pedestrian crossing in the City of Vienna for the duration of one year. It consists of a cluster of 3 smart cameras, each of which is built from a very compact PC hardware system in an outdoor capable housing. Two cameras run vehicle detection software including license plate detection and recognition, one camera runs a complex pedestrian detection and tracking module based on the HOG detection principle. As a supplement, all 3 cameras use additional optical flow computation in a low-resolution video stream in order to estimate the motion path and speed of objects. This work describes the foundation for all 3 different object detection modalities (pedestrians, vehi1cles, license plates), and explains the system setup and its design.

  13. Early postnatal amylin treatment enhances hypothalamic leptin signaling and neural development in the selectively bred diet-induced obese rat.

    Science.gov (United States)

    Johnson, Miranda D; Bouret, Sebastien G; Dunn-Meynell, Ambrose A; Boyle, Christina N; Lutz, Thomas A; Levin, Barry E

    2016-12-01

    Selectively bred diet-induced obese (DIO) rats become obese on a high-fat diet and are leptin resistant before becoming obese. Compared with diet-resistant (DR) neonates, DIO neonates have impaired leptin-dependent arcuate (ARC) neuropeptide Y/agouti-related peptide (NPY/AgRP) and α-melanocyte-stimulating hormone (α-MSH; from proopiomelanocortin (POMC) neurons) axon outgrowth to the paraventricular nucleus (PVN). Using phosphorylation of STAT3 (pSTAT3) as a surrogate, we show that reduced DIO ARC leptin signaling develops by postnatal day 7 (P7) and is reduced within POMC but not NPY/AgRP neurons. Since amylin increases leptin signaling in adult rats, we treated DIO neonates with amylin during postnatal hypothalamic development and assessed leptin signaling, leptin-dependent ARC-PVN pathway development, and metabolic changes. DIO neonates treated with amylin from P0-6 and from P0-16 increased ARC leptin signaling and both AgRP and α-MSH ARC-PVN pathway development, but increased only POMC neuron number. Despite ARC-PVN pathway correction, P0-16 amylin-induced reductions in body weight did not persist beyond treatment cessation. Since amylin enhances adult DIO ARC signaling via an IL-6-dependent mechanism, we assessed ARC-PVN pathway competency in IL-6 knockout mice and found that the AgRP, but not the α-MSH, ARC-PVN pathway was reduced. These results suggest that both leptin and amylin are important neurotrophic factors for the postnatal development of the ARC-PVN pathway. Amylin might act as a direct neurotrophic factor in DIO rats to enhance both the number of POMC neurons and their α-MSH ARC-PVN pathway development. This suggests important and selective roles for amylin during ARC hypothalamic development.

  14. Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification

    Directory of Open Access Journals (Sweden)

    Thitaree Tanprasert

    2017-01-01

    Full Text Available This paper proposes an algorithm for real-time driver identification using the combination of unsupervised anomaly detection and neural networks. The proposed algorithm uses nonphysiological signals as input, namely, driving behavior signals from inertial sensors (e.g., accelerometers and geolocation signals from GPS sensors. First anomaly detection is performed to assess if the current driver is whom he/she claims to be. If an anomaly is detected, the algorithm proceeds to find relevant features in the input signals and use neural networks to identify drivers. To assess the proposed algorithm, real-world data are collected from ten drivers who drive different vehicles on several routes in real-world traffic conditions. Driver identification is performed on each of the seven-second-long driving behavior signals and geolocation signals in a streaming manner. It is shown that the proposed algorithm can achieve relatively high accuracy and identify drivers within 13 seconds. The proposed algorithm also outperforms the previously proposed driver identification algorithms. Furthermore, to demonstrate how the proposed algorithm can be deployed in real-world applications, results from real-world data associated with each operation of the proposed algorithm are shown step-by-step.

  15. Interaction of Notch signaling modulator Numb with α-Adaptin regulates endocytosis of Notch pathway components and cell fate determination of neural stem cells.

    Science.gov (United States)

    Song, Yan; Lu, Bingwei

    2012-05-18

    The ability to balance self-renewal and differentiation is a hallmark of stem cells. In Drosophila neural stem cells (NSCs), Numb/Notch (N) signaling plays a key role in this process. However, the molecular and cellular mechanisms underlying Numb function in a stem cell setting remain poorly defined. Here we show that α-Adaptin (α-Ada), a subunit of the endocytic AP-2 complex, interacts with Numb through a new mode of interaction to regulate NSC homeostasis. In α-ada mutants, N pathway component Sanpodo and the N receptor itself exhibited altered trafficking, and N signaling was up-regulated in the intermediate progenitors of type II NSC lineages, leading to their transformation into ectopic NSCs. Surprisingly, although the Ear domain of α-Ada interacts with the C terminus of Numb and is important for α-Ada function in the sensory organ precursor lineage, it was dispensable in the NSCs. Instead, α-Ada could regulate Sanpodo, N trafficking, and NSC homeostasis by interacting with Numb through new domains in both proteins previously not known to mediate their interaction. This interaction could be bypassed when α-Ada was directly fused to the phospho-tyrosine binding domain of Numb. Our results identify a critical role for the AP-2-mediated endocytosis in regulating NSC behavior and reveal a new mechanism by which Numb regulates NSC behavior through N. These findings are likely to have important implications for cancer biology.

  16. Control of Neural Daughter Cell Proliferation by Multi-level Notch/Su(H/E(spl-HLH Signaling.

    Directory of Open Access Journals (Sweden)

    Caroline Bivik

    2016-04-01

    Full Text Available The Notch pathway controls proliferation during development and in adulthood, and is frequently affected in many disorders. However, the genetic sensitivity and multi-layered transcriptional properties of the Notch pathway has made its molecular decoding challenging. Here, we address the complexity of Notch signaling with respect to proliferation, using the developing Drosophila CNS as model. We find that a Notch/Su(H/E(spl-HLH cascade specifically controls daughter, but not progenitor proliferation. Additionally, we find that different E(spl-HLH genes are required in different neuroblast lineages. The Notch/Su(H/E(spl-HLH cascade alters daughter proliferation by regulating four key cell cycle factors: Cyclin E, String/Cdc25, E2f and Dacapo (mammalian p21CIP1/p27KIP1/p57Kip2. ChIP and DamID analysis of Su(H and E(spl-HLH indicates direct transcriptional regulation of the cell cycle genes, and of the Notch pathway itself. These results point to a multi-level signaling model and may help shed light on the dichotomous proliferative role of Notch signaling in many other systems.

  17. Neural signals of selective attention are modulated by subjective preferences and buying decisions in a virtual shopping task.

    Science.gov (United States)

    Goto, Nobuhiko; Mushtaq, Faisal; Shee, Dexter; Lim, Xue Li; Mortazavi, Matin; Watabe, Motoki; Schaefer, Alexandre

    2017-09-01

    We investigated whether well-known neural markers of selective attention to motivationally-relevant stimuli were modulated by variations in subjective preference towards consumer goods in a virtual shopping task. Specifically, participants viewed and rated pictures of various goods on the extent to which they wanted each item, which they could potentially purchase afterwards. Using the event-related potentials (ERP) method, we found that variations in subjective preferences for consumer goods strongly modulated positive slow waves (PSW) from 800 to 3000 milliseconds after stimulus onset. We also found that subjective preferences modulated the N200 and the late positive potential (LPP). In addition, we found that both PSW and LPP were modulated by subsequent buying decisions. Overall, these findings show that well-known brain event-related potentials reflecting selective attention processes can reliably index preferences to consumer goods in a shopping environment. Based on a large body of previous research, we suggest that early ERPs (e.g. the N200) to consumer goods could be indicative of preferences driven by unconditional and automatic processes, whereas later ERPs such as the LPP and the PSW could reflect preferences built upon more elaborative and conscious cognitive processes. Copyright © 2017 Elsevier B.V. All rights reserved.

  18. NOTCH Signaling Is Essential for Maturation, Self-Renewal, and Tri-Differentiation of In Vitro Derived Human Neural Stem Cells.

    Science.gov (United States)

    Venkatesh, Katari; Reddy, L Vinod Kumar; Abbas, Salar; Mullick, Madhubanti; Moghal, Erfath Thanjeem Begum; Balakrishna, Janardhana Papayya; Sen, Dwaipayan

    2017-10-16

    Although neural stem cells (NSCs) have potential applications in treating neurological disorders, much still needs to be understood about the differentiation biology for their successful clinical translation. In this study, we aimed at deriving NSCs from human umbilical cord blood-derived mesenchymal stem cells (hUCB-MSCs) and explored the role of Notch signaling in the differentiation process. The hUCB-MSCs were characterized as per guidelines of the International Society of Cellular Therapy. NSCs were successfully generated from hUCB-MSCs by using epidermal and fibroblast growth factors under serum-free conditions. The expression of NSC markers (Nestin and Musashi-1) in the neurospheres generated from hUCB-MSCs in the presence or absence of N-[N-(3,5-difluorophenacetyl)-l-alanyl]-S-phenylglycine t-butyl ester (DAPT; Notch inhibitor) was immuno-phenotypically characterized by using immunofluorescence. DAPT showed significant (*p process is Notch dependent. These data were further correlated with formation of a reduced average number of neurospheres derived from hUCB-MSCs (2 colonies vs. 11 colonies/field of view) in the presence of DAPT compared with the control (without DAPT). The expression of Notch target genes in NSC cultures (Notch intracellular domain [NICD], HES1, and HES5) was also significantly downregulated after DAPT treatment. In the presence of DAPT, the markers for neuronal (MAP2, NEFH); and glial (GFAP, GLUL, and MBP) lineages were significantly downregulated as seen via immunofluorescence and quantitative polymerase chain reaction, indicating the role of Notch in the tri-differentiation mechanism of NSCs as well. In addition, Notch signaling inhibition induced higher cell death during the lineage commitment of NSCs as measured 3 days (16.9% vs. 8.9%) and 6 days (42.9% vs. 20.8%) postinduction. These results suggest that the efficient derivation of NSCs and their subsequent lineage commitment from hUCB-MSCs requires the Notch signaling pathway.

  19. DETECCIÓN Y RECONOCIMIENTO DE SEÑALES DE TRÁNSITO UTILIZANDO MATCHING DE CHAMFER DETECTION AND RECOGNITION OF TRAFFIC SIGNALS USING MATCHING OF CHAMFER

    Directory of Open Access Journals (Sweden)

    Cristián Arriagada García

    2007-08-01

    available details at first sight etc, in our case a prototype is presented which allows the opportunity to help a car driver to pay attention to the traffic signs on the road, attempting to assist the driver, and at the same time to avoid traffic infractions and accidents. The prototype developed with computer vision techniques, allows the detection and recognition of signposts that are on the road and to inform its nature to the driver through an audible sign or a visual projection. The research was mainly centered on the phases of initial detection; with the objective of taking into account a quick heuristic, taking advantage of the segmentation by color, with their characteristics of invariability of system HSV (Brightness, Saturation, Value [10], and/or initial detection by borders, making use of the improved algorithm of Chamfer [1], finally to detect and recognize the symbols of the sign, using transformation of distance techniques and hierarchical matching of Chamfer[1], conditioned to this kind of application. The prototype in the phase of proof was implemented in Matlab, with the initial purpose of proving the effectiveness of the methods that were used. Once they are proved an OpenCV was used to verify its functioning in real time.

  20. Intelligent traffic control system using PLC

    Science.gov (United States)

    Barz, C.; Todea, C.; Latinovic, T.; Preradovic, D. M.; Deaconu, S.; Berdie, A.

    2016-08-01

    The paper presents the traffic control system controlled through a PLC which takes the signals from different sensors on roads. The global system developed ensures the coordination of four intersections, setting a path that respects coordination type green light, the integration of additional sensors, the implementation of probes radar to inform traffic participants about recommended speed for accessing the green state located in the intersection that will follow to cross.

  1. Automatic Recognition of Landslides Based on Neural Network Analysis of Seismic Signals: An Application to the Monitoring of Stromboli Volcano (Southern Italy)

    Science.gov (United States)

    Esposito, Antonietta M.; D'Auria, Luca; Giudicepietro, Flora; Peluso, Rosario; Martini, Marcello

    2013-11-01

    In the last 9 years, the amount and the quality of geophysical and volcanological observations of Stromboli's' activity have undergone a marked increase. This new information highlighted that the landslides on the Sciara del Fuoco flank are tightly linked to the volcanic activity. Actually, at the beginning of the December 28, 2002, effusive eruption, the seismic monitoring network was less dense than now, and therefore it is not known if there was an increase in the landslide rate before the eruption. Despite this, it is known that a big landslide occurred 2 days after the beginning of the eruption which caused a tsunami (December 30, 2002). More recently, the effusive eruption in February 2007 was preceded by an increase in landslides on the Sciara del Fuoco flank, which were recorded by the seismological monitoring system that had been improved after the 2002-2003 crisis. These episodes led us to believe that monitoring the Sciara del Fuoco flank instability is an important topic, and that landslides might be significant short-term precursors of effusive eruptions at the Stromboli volcano. To automatically detect landslide signals, we have developed a specialized neural algorithm. This can distinguish between landslides and the other types of seismic signals usually recorded at the Stromboli volcano (i.e., explosion quakes and volcanic tremor). The discrimination results show an average performance of 98.67 %. According to the experience of the crisis of 2007, to identify changes that can be considered as precursors of effusive eruptions, we set up an automatic decision-making method based on the neural network responses. This method can operate on a continuous data stream. It calculates a landslide percentage index (LPI) that depends on the number of records that are classified by the net as landslides over a given time interval. We tested the method on February 27, 2007, including the beginning of the effusive phase. The index showed an increase as early as at

  2. Valproic acid inhibits neural progenitor cell death by activation of NF-κB signaling pathway and up-regulation of Bcl-XL

    Directory of Open Access Journals (Sweden)

    Han Seol

    2011-07-01

    Full Text Available Abstract Background At the beginning of neurogenesis, massive brain cell death occurs and more than 50% of cells are eliminated by apoptosis along with neuronal differentiation. However, few studies were conducted so far regarding the regulation of neural progenitor cells (NPCs death during development. Because of the physiological role of cell death during development, aberration of normal apoptotic cell death is detrimental to normal organogenesis. Apoptosis occurs in not only neuron but also in NPCs and neuroblast. When growth and survival signals such as EGF or LIF are removed, apoptosis is activated as well as the induction of differentiation. To investigate the regulation of cell death during developmental stage, it is essential to investigate the regulation of apoptosis of NPCs. Methods Neural progenitor cells were cultured from E14 embryonic brains of Sprague-Dawley rats. For in vivo VPA animal model, pregnant rats were treated with VPA (400 mg/kg S.C. diluted with normal saline at E12. To analyze the cell death, we performed PI staining and PARP and caspase-3 cleavage assay. Expression level of proteins was investigated by Western blot and immunocytochemical assays. The level of mRNA expression was investigated by RT-PCR. Interaction of Bcl-XL gene promoter and NF-κB p65 was investigated by ChIP assay. Results In this study, FACS analysis, PI staining and PARP and caspase-3 cleavage assay showed that VPA protects cultured NPCs from cell death after growth factor withdrawal both in basal and staurosporine- or hydrogen peroxide-stimulated conditions. The protective effect of prenatally injected VPA was also observed in E16 embryonic brain. Treatment of VPA decreased the level of IκBα and increased the nuclear translocation of NF-κB, which subsequently enhanced expression of anti-apoptotic protein Bcl-XL. Conclusion To the best of our knowledge, this is the first report to indicate the reduced death of NPCs by VPA at developmentally

  3. Dopamine-signalled reward predictions generated by competitive excitation and inhibition in a spiking neural network model

    Directory of Open Access Journals (Sweden)

    Paul eChorley

    2011-05-01

    Full Text Available Dopaminergic neurons in the mammalian substantia nigra displaycharacteristic phasic responses to stimuli which reliably predict thereceipt of primary rewards. These responses have been suggested toencode reward prediction-errors similar to those used in reinforcementlearning. Here, we propose a model of dopaminergic activity in whichprediction error signals are generated by the joint action ofshort-latency excitation and long-latency inhibition, in a networkundergoing dopaminergic neuromodulation of both spike-timing dependentsynaptic plasticity and neuronal excitability. In contrast toprevious models, sensitivity to recent events is maintained by theselective modification of specific striatal synapses, efferent tocortical neurons exhibiting stimulus-specific, temporally extendedactivity patterns. Our model shows, in the presence of significantbackground activity, (i a shift in dopaminergic response from rewardto reward predicting stimuli, (ii preservation of a response tounexpected rewards, and (iii a precisely-timed below-baseline dip inactivity observed when expected rewards are omitted.

  4. The impact of command signal power distribution, processing delays, and speed scaling on neurally-controlled devices

    Science.gov (United States)

    Marathe, A. R.; Taylor, D. M.

    2015-08-01

    Objective. Decoding algorithms for brain-machine interfacing (BMI) are typically only optimized to reduce the magnitude of decoding errors. Our goal was to systematically quantify how four characteristics of BMI command signals impact closed-loop performance: (1) error magnitude, (2) distribution of different frequency components in the decoding errors, (3) processing delays, and (4) command gain. Approach. To systematically evaluate these different command features and their interactions, we used a closed-loop BMI simulator where human subjects used their own wrist movements to command the motion of a cursor to targets on a computer screen. Random noise with three different power distributions and four different relative magnitudes was added to the ongoing cursor motion in real time to simulate imperfect decoding. These error characteristics were tested with four different visual feedback delays and two velocity gains. Main results. Participants had significantly more trouble correcting for errors with a larger proportion of low-frequency, slow-time-varying components than they did with jittery, higher-frequency errors, even when the error magnitudes were equivalent. When errors were present, a movement delay often increased the time needed to complete the movement by an order of magnitude more than the delay itself. Scaling down the overall speed of the velocity command can actually speed up target acquisition time when low-frequency errors and delays are present. Significance. This study is the first to systematically evaluate how the combination of these four key command signal features (including the relatively-unexplored error power distribution) and their interactions impact closed-loop performance independent of any specific decoding method. The equations we derive relating closed-loop movement performance to these command characteristics can provide guidance on how best to balance these different factors when designing BMI systems. The equations reported

  5. Open traffic : A toolbox for traffic research

    NARCIS (Netherlands)

    Tamminga, G.F.; Knoppers, P.; Van Lint, J.W.C.

    2014-01-01

    Open Traffic is an open source software project that provides a transport modeling software environment. While most transport model packages offer ready-to-use modules for end-users, Open Traffic provides open access to a modelling environment for the (further) development of methods and algorithms

  6. Traffic management simulation development.

    Science.gov (United States)

    2011-01-03

    Microscopic simulation can provide significant support to traffic management center (TMC) operations. However, traffic simulation applications require data that are expensive and time-consuming to collect. Data collected by TMCs can be used as a prim...

  7. Traffic management simulation development : summary.

    Science.gov (United States)

    2011-01-01

    Increasingly, Florida traffic is monitored electronically by components of the Intelligent Traffic System (ITS), which send data to regional traffic management centers and assist management of traffic flows and incident response using software called...

  8. Adaptive Spatial Filter Based on Similarity Indices to Preserve the Neural Information on EEG Signals during On-Line Processing

    Science.gov (United States)

    Villa-Parra, Ana Cecilia; Bastos-Filho, Teodiano; López-Delis, Alberto; Frizera-Neto, Anselmo; Krishnan, Sridhar

    2017-01-01

    This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG) signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC) between target electrodes and its correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Afterwards, a model based on CCC is applied to provide higher values of weight to those correlated electrodes with lower similarity to the target electrode. The approach was applied to brain computer-interfaces (BCIs) based on Canonical Correlation Analysis (CCA) to recognize 40 targets of steady-state visual evoked potential (SSVEP), providing an accuracy (ACC) of 86.44 ± 2.81%. In addition, also using this approach, features of low frequency were selected in the pre-processing stage of another BCI to recognize gait planning. In this case, the recognition was significantly (p<0.01) improved for most of the subjects (ACC≥74.79%), when compared with other BCIs based on Common Spatial Pattern, Filter Bank-Common Spatial Pattern, and Riemannian Geometry. PMID:29186848

  9. Adaptive Spatial Filter Based on Similarity Indices to Preserve the Neural Information on EEG Signals during On-Line Processing

    Directory of Open Access Journals (Sweden)

    Denis Delisle-Rodriguez

    2017-11-01

    Full Text Available This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC between target electrodes and its correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Afterwards, a model based on CCC is applied to provide higher values of weight to those correlated electrodes with lower similarity to the target electrode. The approach was applied to brain computer-interfaces (BCIs based on Canonical Correlation Analysis (CCA to recognize 40 targets of steady-state visual evoked potential (SSVEP, providing an accuracy (ACC of 86.44 ± 2.81%. In addition, also using this approach, features of low frequency were selected in the pre-processing stage of another BCI to recognize gait planning. In this case, the recognition was significantly ( p < 0.01 improved for most of the subjects ( A C C ≥ 74.79 % , when compared with other BCIs based on Common Spatial Pattern, Filter Bank-Common Spatial Pattern, and Riemannian Geometry.

  10. Subjective safety in traffic.

    NARCIS (Netherlands)

    2012-01-01

    The term ‘subjective safety in traffic’ refers to people feeling unsafe in traffic or, more generally, to anxiety regarding being unsafe in traffic for oneself and/or others. Subjective safety in traffic can lead to road users limiting their mobility and social activities, which is one of the

  11. Trajectory Based Traffic Analysis

    DEFF Research Database (Denmark)

    Krogh, Benjamin Bjerre; Andersen, Ove; Lewis-Kelham, Edwin

    2013-01-01

    We present the INTRA system for interactive path-based traffic analysis. The analyses are developed in collaboration with traffic researchers and provide novel insights into conditions such as congestion, travel-time, choice of route, and traffic-flow. INTRA supports interactive point...

  12. Gaussian traffic everywhere?

    NARCIS (Netherlands)

    van de Meent, R.; Mandjes, M.R.H.; Pras, Aiko

    2006-01-01

    It is often assumed that Internet traffic exhibits Gaussian characteristics, and this assumption has been validated in various studies of real Internet traffic. Less is known, however, about possible boundaries: at what timescales is traffic Gaussian and how much user aggregation is required for

  13. Automatic welding quality classification for the spot welding based on the Hopfield associative memory neural network and Chernoff face description of the electrode displacement signal features

    Science.gov (United States)

    Zhang, Hongjie; Hou, Yanyan; Zhao, Jian; Wang, Lijing; Xi, Tao; Li, Yafeng

    2017-02-01

    To develop an automatic welding quality classification method for the spot welding based on the Chernoff face image created by the electrode displacement signal features, an effective pattern feature extraction method was proposed by which the Chernoff face images were converted to binary ones, and each binary image could be characterized by a binary matrix. According to expression categories on the Chernoff face images, welding quality was classified into five levels and each level just corresponded to a kind of expression. The Hopfield associative memory neural network was used to build a welding quality classifier in which the pattern feature matrices of some weld samples with different welding quality levels were remembered as the stable states. When the pattern feature matrix of a test weld is input into the classifier, it can be converged to the most similar stable state through associative memory, thus, welding quality corresponding to this finally locked stable state can represent the welding quality of the test weld. The classification performance test results show that the proposed method significantly improves the applicability and efficiency of the Chernoff faces technique for spot welding quality evaluation and it is feasible, effective and reliable.

  14. An Evaluation of Hand-Force Prediction Using Artificial Neural-Network Regression Models of Surface EMG Signals for Handwear Devices

    Directory of Open Access Journals (Sweden)

    Masayuki Yokoyama

    2017-01-01

    Full Text Available Hand-force prediction is an important technology for hand-oriented user interface systems. Specifically, surface electromyography (sEMG is a promising technique for hand-force prediction, which requires a sensor with a small design space and low hardware costs. In this study, we applied several artificial neural-network (ANN regression models with different numbers of neurons and hidden layers and evaluated handgrip forces by using a dynamometer. A handwear with dry electrodes on the dorsal interosseous muscles was used for our evaluation. Eleven healthy subjects participated in our experiments. sEMG signals with six different levels of forces from 0 N to 200 N and maximum voluntary contraction (MVC are measured to train and test our ANN regression models. We evaluated three different methods (intrasession, intrasubject, and intersubject evaluation, and our experimental results show a high correlation (0.840, 0.770, and 0.789 each between the predicted forces and observed forces, which are normalized by the MVC for each subject. Our results also reveal that ANNs with deeper layers of up to four hidden layers show fewer errors in intrasession and intrasubject evaluations.

  15. Individual variation in the neural processes of motor decisions in the stop signal task: the influence of novelty seeking and harm avoidance personality traits.

    Science.gov (United States)

    Hu, Jianping; Lee, Dianne; Hu, Sien; Zhang, Sheng; Chao, Herta; Li, Chiang-Shan R

    2016-06-01

    Personality traits contribute to variation in human behavior, including the propensity to take risk. Extant work targeted risk-taking processes with an explicit manipulation of reward, but it remains unclear whether personality traits influence simple decisions such as speeded versus delayed responses during cognitive control. We explored this issue in an fMRI study of the stop signal task, in which participants varied in response time trial by trial, speeding up and risking a stop error or slowing down to avoid errors. Regional brain activations to speeded versus delayed motor responses (risk-taking) were correlated to novelty seeking (NS), harm avoidance (HA) and reward dependence (RD), with age and gender as covariates, in a whole brain regression. At a corrected threshold, the results showed a positive correlation between NS and risk-taking responses in the dorsomedial prefrontal, bilateral orbitofrontal, and frontopolar cortex, and between HA and risk-taking responses in the parahippocampal gyrus and putamen. No regional activations varied with RD. These findings demonstrate that personality traits influence the neural processes of executive control beyond behavioral tasks that involve explicit monetary reward. The results also speak broadly to the importance of characterizing inter-subject variation in studies of cognition and brain functions.

  16. A Sarsa(λ-Based Control Model for Real-Time Traffic Light Coordination

    Directory of Open Access Journals (Sweden)

    Xiaoke Zhou

    2014-01-01

    Full Text Available Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control.

  17. A Sarsa(λ)-based control model for real-time traffic light coordination.

    Science.gov (United States)

    Zhou, Xiaoke; Zhu, Fei; Liu, Quan; Fu, Yuchen; Huang, Wei

    2014-01-01

    Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control.

  18. Signal Processing Algorithms for Down-Stream Traffic in Next Generation 10 Gbit/s Fixed-Grid Passive Optical Networks

    Directory of Open Access Journals (Sweden)

    Rameez Asif

    2014-01-01

    Full Text Available We have analyzed the impact of digital and optical signal processing algorithms, that is, Volterra equalization (VE, digital backpropagation (BP, and optical phase conjugation with nonlinearity module (OPC-NM, in next generation 10 Gbit/s (also referred to as XG DP-QPSK long haul WDM (fixed-grid passive optical network (PON without midspan repeaters over 120 km standard single mode fiber (SMF link for downstream signals. Due to the compensation of optical Kerr effects, the sensitivity penalty is improved by 2 dB by implementing BP algorithm, 1.5 dB by VE algorithm, and 2.69 dB by OPC-NM. Moreover, with the implementation of NL equalization technique, we are able to get the transmission distance of 126.6 km SMF for the 1 : 1024 split ratio at 5 GHz channel spacing in the nonlinear region.

  19. Occupant traffic estimation through structural vibration sensing

    Science.gov (United States)

    Pan, Shijia; Mirshekari, Mostafa; Zhang, Pei; Noh, Hae Young

    2016-04-01

    The number of people passing through different indoor areas is useful in various smart structure applications, including occupancy-based building energy/space management, marketing research, security, etc. Existing approaches to estimate occupant traffic include vision-, sound-, and radio-based (mobile) sensing methods, which have placement limitations (e.g., requirement of line-of-sight, quiet environment, carrying a device all the time). Such limitations make these direct sensing approaches difficult to deploy and maintain. An indirect approach using geophones to measure floor vibration induced by footsteps can be utilized. However, the main challenge lies in distinguishing multiple simultaneous walkers by developing features that can effectively represent the number of mixed signals and characterize the selected features under different traffic conditions. This paper presents a method to monitor multiple persons. Once the vibration signals are obtained, features are extracted to describe the overlapping vibration signals induced by multiple footsteps, which are used for occupancy traffic estimation. In particular, we focus on analysis of the efficiency and limitations of the four selected key features when used for estimating various traffic conditions. We characterize these features with signals collected from controlled impulse load tests as well as from multiple people walking through a real-world sensing area. In our experiments, the system achieves the mean estimation error of +/-0.2 people for different occupant traffic conditions (from one to four) using k-nearest neighbor classifier.

  20. Simulations of highway traffic with various degrees of automation

    Energy Technology Data Exchange (ETDEWEB)

    Doss, E.; Hanebutte, U.; Vitela, J.; Brown-VanHoozer, A.; Ewing, T.; Tentner, A.

    1996-10-01

    A traffic simulator to study highway traffic under various degrees of automation is being developed at Argonne National Laboratory (ANL). The key components of this simulator include a global and a local Expert Drive Mode, a human factor study and a graphical user interface. Further, an Autonomous Intelligent Cruise Control (AICC) which is based on a neural network controller is described and results for a typical driving scenario are given.

  1. Traffic Light Options

    DEFF Research Database (Denmark)

    Jørgensen, Peter Løchte

    2006, and supervisory authorities in many other European countries have implemented similar regulation. Traffic light options are therefore likely to attract the attention of a wider audience of pension fund managers in the future. Focusing on the valuation of the traffic light option we set up a Black......This paper introduces, prices, and analyzes traffic light options. The traffic light option is an innovative structured OTC derivative developed independently by several London-based investment banks to suit the needs of Danish life and pension (L&P) companies, which must comply with the traffic...

  2. Traffic Light Options

    DEFF Research Database (Denmark)

    Jørgensen, Peter Løchte

    2007-01-01

    2006, and supervisory authorities in many other European countries have implemented similar regulation. Traffic light options are therefore likely to attract the attention of a wider audience of pension fund managers in the future. Focusing on the valuation of the traffic light option we set up a Black......This paper introduces, prices, and analyzes traffic light options. The traffic light option is an innovative structured OTC derivative developed independently by several London-based investment banks to suit the needs of Danish life and pension (L&P) companies, which must comply with the traffic...

  3. Caveolin-1 plays a crucial role in inhibiting neuronal differentiation of neural stem/progenitor cells via VEGF signaling-dependent pathway.

    Directory of Open Access Journals (Sweden)

    Yue Li

    Full Text Available In the present study, we aim to elucidate the roles of caveolin-1(Cav-1, a 22 kDa protein in plasma membrane invaginations, in modulating neuronal differentiation of neural progenitor cells (NPCs. In the hippocampal dentate gyrus, we found that Cav-1 knockout mice revealed remarkably higher levels of vascular endothelial growth factor (VEGF and the more abundant formation of newborn neurons than wild type mice. We then studied the potential mechanisms of Cav-1 in modulating VEGF signaling and neuronal differentiation in isolated cultured NPCs under normoxic and hypoxic conditions. Hypoxic embryonic rat NPCs were exposed to 1% O₂ for 24 h and then switched to 21% O₂ for 1, 3, 7 and 14 days whereas normoxic NPCs were continuously cultured with 21% O₂. Compared with normoxic NPCs, hypoxic NPCs had down-regulated expression of Cav-1 and up-regulated VEGF expression and p44/42MAPK phosphorylation, and enhanced neuronal differentiation. We further studied the roles of Cav-1 in inhibiting neuronal differentiation by using Cav-1 scaffolding domain peptide and Cav-1-specific small interfering RNA. In both normoxic and hypoxic NPCs, Cav-1 peptide markedly down-regulated the expressions of VEGF and flk1, decreased the phosphorylations of p44/42MAPK, Akt and Stat3, and inhibited neuronal differentiation, whereas the knockdown of Cav-1 promoted the expression of VEGF, phosphorylations of p44/42MAPK, Akt and Stat3, and stimulated neuronal differentiation. Moreover, the enhanced phosphorylations of p44/42MAPK, Akt and Stat3, and neuronal differentiation were abolished by co-treatment of VEGF inhibitor V1. These results provide strong evidence to prove that Cav-1 can inhibit neuronal differentiation via down-regulations of VEGF, p44/42MAPK, Akt and Stat3 signaling pathways, and that VEGF signaling is a crucial target of Cav-1. The hypoxia-induced down-regulation of Cav-1 contributes to enhanced neuronal differentiation in NPCs.

  4. Remotely Accessed Vehicle Traffic Management System

    Science.gov (United States)

    Al-Alawi, Raida

    2010-06-01

    The ever increasing number of vehicles in most metropolitan cities around the world and the limitation in altering the transportation infrastructure, led to serious traffic congestion and an increase in the travelling time. In this work we exploit the emergence of novel technologies such as the internet, to design an intelligent Traffic Management System (TMS) that can remotely monitor and control a network of traffic light controllers located at different sites. The system is based on utilizing Embedded Web Servers (EWS) technology to design a web-based TMS. The EWS located at each intersection uses IP technology for communicating remotely with a Central Traffic Management Unit (CTMU) located at the traffic department authority. Friendly GUI software installed at the CTMU will be able to monitor the sequence of operation of the traffic lights and the presence of traffic at each intersection as well as remotely controlling the operation of the signals. The system has been validated by constructing a prototype that resembles the real application.

  5. A Marine Traffic Flow Model

    Directory of Open Access Journals (Sweden)

    Tsz Leung Yip

    2013-03-01

    Full Text Available A model is developed for studying marine traffic flow through classical traffic flow theories, which can provide us with a better understanding of the phenomenon of traffic flow of ships. On one hand, marine traffic has its special features and is fundamentally different from highway, air and pedestrian traffic. The existing traffic models cannot be simply extended to marine traffic without addressing marine traffic features. On the other hand, existing literature on marine traffic focuses on one ship or two ships but does not address the issues in marine traffic flow.

  6. Control of Networked Traffic Flow Distribution - A Stochastic Distribution System Perspective

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Hong [Pacific Northwest National Laboratory (PNNL); Aziz, H M Abdul [ORNL; Young, Stan [National Renewable Energy Laboratory (NREL); Patil, Sagar [Pacific Northwest National Laboratory (PNNL)

    2017-10-01

    Networked traffic flow is a common scenario for urban transportation, where the distribution of vehicle queues either at controlled intersections or highway segments reflect the smoothness of the traffic flow in the network. At signalized intersections, the traffic queues are controlled by traffic signal control settings and effective traffic lights control would realize both smooth traffic flow and minimize fuel consumption. Funded by the Energy Efficient Mobility Systems (EEMS) program of the Vehicle Technologies Office of the US Department of Energy, we performed a preliminary investigation on the modelling and control framework in context of urban network of signalized intersections. In specific, we developed a recursive input-output traffic queueing models. The queue formation can be modeled as a stochastic process where the number of vehicles entering each intersection is a random number. Further, we proposed a preliminary B-Spline stochastic model for a one-way single-lane corridor traffic system based on theory of stochastic distribution control.. It has been shown that the developed stochastic model would provide the optimal probability density function (PDF) of the traffic queueing length as a dynamic function of the traffic signal setting parameters. Based upon such a stochastic distribution model, we have proposed a preliminary closed loop framework on stochastic distribution control for the traffic queueing system to make the traffic queueing length PDF follow a target PDF that potentially realizes the smooth traffic flow distribution in a concerned corridor.

  7. Cumulative Interarrival Time Distributions of Freeway Entrance Ramp Traffic for Traffic Simulations

    Directory of Open Access Journals (Sweden)

    Erdinç Öner

    2013-02-01

    Full Text Available Cumulative interarrival time (IAT distributions for signalized and non-signalized freeway entrance ramps were developed to be used in digital computer traffic simulation models. The data from four different non-signalized entrance ramps (three ramps with a single lane, one ramp with two lanes and two different signalized entrance ramps (both with a single lane were used for developing the cumulative IAT distributions. The cumulative IAT distributions for the signalized and non-signalized entrance ramps were compared with each other and with the cumulative IAT distributions of the lanes for freeways. The comparative results showed that the cumulative IAT distributions for non-signalized entrance ramps are very close to the leftmost lane of a 3-lane freeway where the maximum absolute difference between the cumulative IAT distribution of the leftmost lane of a 3-lane freeway and the entrance ramps cumulative IAT distribution was 3%. The cumulative IAT distribution for the signalized entrance ramps was found to be different from the non-signalized entrance ramp cumulative IAT distribution. The approximated cumulative IAT distributions for signalized and non-signalized entrance ramp traffic for any hourly traffic volume from a few vehicles/hour up to 2,500 vehicles/hour can be obtained at http://www.ohio.edu/orite/research/uitds.cfm.

  8. Impacts of Traffic Noise and Traffic Volume on Birds of Roadside Habitats

    Directory of Open Access Journals (Sweden)

    Kirsten M. Parris

    2009-06-01

    Full Text Available Roadside habitats are important for a range of taxa including plants, insects, mammals, and birds, particularly in developed countries in which large expanses of native vegetation have been cleared for agriculture or urban development. Although roadside vegetation may provide suitable habitat for many species, resident animals can be exposed to high levels of traffic noise, visual disturbance from passing vehicles, and the risk of collision with cars and trucks. Traffic noise can reduce the distance over which acoustic signals such as song can be detected, an effect known as acoustic interference or masking. Studies from the northern hemisphere show that the singing behavior of birds changes in the presence of traffic noise. We investigated the impact of traffic noise and traffic volume on two species of birds, the Grey Shrike-thrush (Colluricincla harmonica and the Grey Fantail (Rhipidura fuliginosa, at 58 roadside sites on the Mornington Peninsula, southeastern Australia. The lower singing Grey Shrike-thrush sang at a higher frequency in the presence of traffic noise, with a predicted increase in dominant frequency of 5.8 Hz/dB of traffic noise, and a total effect size of 209 Hz. In contrast, the higher singing Grey Fantail did not appear to change its song in traffic noise. The probability of detecting each species on a visit to a site declined substantially with increasing traffic noise and traffic volume, with several lines of evidence supporting a larger effect of traffic noise. Traffic noise could hamper detection of song by conspecifics, making it more difficult for birds to establish and maintain territories, attract mates and maintain pair bonds, and possibly leading to reduced breeding success in noisy roadside habitats. Closing key roads during the breeding season is a potential, but untested, management strategy to protect threatened bird species from traffic noise and collision with vehicles at the time of year when they are most

  9. Synchronization of Traffic Light Systems for Maximum Efficiency along Jalan Bukit Gambier, Penang, Malaysia

    Directory of Open Access Journals (Sweden)

    Ahmad Rafidi M.A.

    2014-01-01

    Full Text Available The synchronization of traffic light systems is one of the best solutions in order to avoid problematic traffic jams. Traffic timing is a major concern when it comes to traffic management. One of the common causes of traffic jams is because of nonsynchronized traffic light systems. Once a light turns green, traffic begins to move, but by the time the moving traffic reaches the next light, the signal is still red. This will disrupt the continuity of the traffic flow, especially for large main roads. The smooth flow of traffic on main routes is important to clear dense traffic in a given time. This study examined the density of vehicles on Jalan Bukit Gambier and also the traffic timing was documented in order to plan out proper re-timing for traffic lights along the studied road. The outcomes of this study support the hypothesis that retiming traffic lights to create a synchronized traffic light system for main roads will greatly improve traffic flow.

  10. Costs of traffic injuries

    DEFF Research Database (Denmark)

    Kruse, Marie

    2015-01-01

    OBJECTIVE: The aim of this study was to analyse the socioeconomic costs of traffic injuries in Denmark, notably the healthcare costs and the productivity costs related to traffic injuries, in a bottom-up, register-based perspective. METHOD: Traffic injury victims were identified using national...... emergency room data and police records. Victims were matched with five controls per case by means of propensity score, nearest-neighbour matching. In the cohort, consisting of the 52 526 individuals that experienced a traffic injury in 2000 and 262 630 matched controls, attributable healthcare costs were...... assessed using Danish national healthcare registers. Productivity costs were computed using duration analysis (Cox regression models). In a subanalysis, cost per severe traffic injury was computed for the 12 995 individuals that experienced a severe injury. RESULTS: The socioeconomic cost of a traffic...

  11. Anomaly based intrusion detection for a biometric identification system using neural networks

    CSIR Research Space (South Africa)

    Mgabile, T

    2012-10-01

    Full Text Available detection technique that analyses the fingerprint biometric network traffic for evidence of intrusion. The neural network algorithm that imitates the way a human brain works is used in this study to classify normal traffic and learn the correct traffic...

  12. Multi-Layer Traffic Steering

    DEFF Research Database (Denmark)

    Fotiadis, Panagiotis; Polignano, Michele; Gimenez, Lucas Chavarria

    2013-01-01

    This paper investigates the potentials of traffic steering in the Radio Resource Control (RRC) Idle state by evaluating the Absolute Priorities (AP) framework in a multilayer Long Term Evolution (LTE) macrocell scenario. Frequency priorities are broadcast on the system information and RRC Idle...... signaling. The priority adjustment is based on both the Composite Available Capacity (CAC) and the radio conditions of the candidate layers. Compared to broadcast AP, the proposed scheme achieves better load balancing performance and improves network capacity, given that the User Equipment (UE) inactivity...

  13. Adaptive traffic control systems for urban networks

    Directory of Open Access Journals (Sweden)

    Radivojević Danilo

    2017-01-01

    Full Text Available Adaptive traffic control systems represent complex, but powerful tool for improvement of traffic flow conditions in locations or zones where applied. Many traffic agencies, especially those that have a large number of signalized intersections with high variability of the traffic demand, choose to apply some of the adaptive traffic control systems. However, those systems are manufactured and offered by multiple vendors (companies that are competing for the market share. Due to that fact, besides the information available from the vendors themselves, or the information from different studies conducted on different continents, very limited amount of information is available about the details how those systems are operating. The reason for that is the protecting of the intellectual property from plagiarism. The primary goal of this paper is to make a brief analysis of the functionalities, characteristics, abilities and results of the most recognized, but also less known adaptive traffic control systems to the professional public and other persons with interest in this subject.

  14. Detecting Distributed Network Traffic Anomaly with Network-Wide Correlation Analysis

    Science.gov (United States)

    Zonglin, Li; Guangmin, Hu; Xingmiao, Yao; Dan, Yang

    2008-12-01

    Distributed network traffic anomaly refers to a traffic abnormal behavior involving many links of a network and caused by the same source (e.g., DDoS attack, worm propagation). The anomaly transiting in a single link might be unnoticeable and hard to detect, while the anomalous aggregation from many links can be prevailing, and does more harm to the networks. Aiming at the similar features of distributed traffic anomaly on many links, this paper proposes a network-wide detection method by performing anomalous correlation analysis of traffic signals' instantaneous parameters. In our method, traffic signals' instantaneous parameters are firstly computed, and their network-wide anomalous space is then extracted via traffic prediction. Finally, an anomaly is detected by a global correlation coefficient of anomalous space. Our evaluation using Abilene traffic traces demonstrates the excellent performance of this approach for distributed traffic anomaly detection.

  15. Detecting Distributed Network Traffic Anomaly with Network-Wide Correlation Analysis

    Directory of Open Access Journals (Sweden)

    Yang Dan

    2008-12-01

    Full Text Available Distributed network traffic anomaly refers to a traffic abnormal behavior involving many links of a network and caused by the same source (e.g., DDoS attack, worm propagation. The anomaly transiting in a single link might be unnoticeable and hard to detect, while the anomalous aggregation from many links can be prevailing, and does more harm to the networks. Aiming at the similar features of distributed traffic anomaly on many links, this paper proposes a network-wide detection method by performing anomalous correlation analysis of traffic signals' instantaneous parameters. In our method, traffic signals' instantaneous parameters are firstly computed, and their network-wide anomalous space is then extracted via traffic prediction. Finally, an anomaly is detected by a global correlation coefficient of anomalous space. Our evaluation using Abilene traffic traces demonstrates the excellent performance of this approach for distributed traffic anomaly detection.

  16. Controlled Traffic Farming

    OpenAIRE

    Controlled Traffic Farming Europe

    2011-01-01

    Metadata only record Controlled Traffic Farming (CTF) is a farming method used to reduce soil compaction, decrease inputs, and improve soil structure when coupled with reduced-till or no-till practices. This practices utilizes permanent traffic/wheel zones to limit soil compaction to a specific area. This website provides practical information on CTF, case studies, workshops, and links to additional resources.

  17. Visualization of Traffic Accidents

    Science.gov (United States)

    Wang, Jie; Shen, Yuzhong; Khattak, Asad

    2010-01-01

    Traffic accidents have tremendous impact on society. Annually approximately 6.4 million vehicle accidents are reported by police in the US and nearly half of them result in catastrophic injuries. Visualizations of traffic accidents using geographic information systems (GIS) greatly facilitate handling and analysis of traffic accidents in many aspects. Environmental Systems Research Institute (ESRI), Inc. is the world leader in GIS research and development. ArcGIS, a software package developed by ESRI, has the capabilities to display events associated with a road network, such as accident locations, and pavement quality. But when event locations related to a road network are processed, the existing algorithm used by ArcGIS does not utilize all the information related to the routes of the road network and produces erroneous visualization results of event locations. This software bug causes serious problems for applications in which accurate location information is critical for emergency responses, such as traffic accidents. This paper aims to address this problem and proposes an improved method that utilizes all relevant information of traffic accidents, namely, route number, direction, and mile post, and extracts correct event locations for accurate traffic accident visualization and analysis. The proposed method generates a new shape file for traffic accidents and displays them on top of the existing road network in ArcGIS. Visualization of traffic accidents along Hampton Roads Bridge Tunnel is included to demonstrate the effectiveness of the proposed method.

  18. Road Traffic in China

    NARCIS (Netherlands)

    Jie, L.; Van Zuylen, H.J.

    2014-01-01

    Traffic is tightly related to the social and economic development in a country. In China the development of the economy has been very fast in the past 30 years and this is still continuing. The transport infrastructure shows a similar pattern, while traffic is also rapidly growing. In urban areas

  19. Assessing the Structural, Driver and Economic Impacts of Traffic Pole Mounted Wind Power Generator and Solar Panel Hybrid System

    Science.gov (United States)

    2012-06-01

    This project evaluates the physical and economic feasibility of using existing traffic infrastructure to mount wind power : generators. Some possible places to mount a light weight wind generator and solar panel hybrid system are: i) Traffic : signal...

  20. Lock Traffic Signal Stations - USACE IENC

    Data.gov (United States)

    Department of Homeland Security — These inland electronic Navigational charts (IENCs) were developed from available data used in maintenance of Navigation channels. Users of these IENCs should be...

  1. Delay-feedback control strategy for reducing CO2 emission of traffic flow system

    Science.gov (United States)

    Zhang, Li-Dong; Zhu, Wen-Xing

    2015-06-01

    To study the signal control strategy for reducing traffic emission theoretically, we first presented a kind of discrete traffic flow model with relative speed term based on traditional coupled map car-following model. In the model, the relative speed difference between two successive running cars is incorporated into following vehicle's acceleration running equation. Then we analyzed its stability condition with discrete control system stability theory. Third, we designed a delay-feedback controller to suppress traffic jam and decrease traffic emission based on modern controller theory. Last, numerical simulations are made to support our theoretical results, including the comparison of models' stability analysis, the influence of model type and signal control on CO2 emissions. The results show that the temporal behavior of our model is superior to other models, and the traffic signal controller has good effect on traffic jam suppression and traffic CO2 emission, which fully supports the theoretical conclusions.

  2. Toward Intelligent Traffic Light Control with Quality-of-Service Provisioning

    OpenAIRE

    Miao, Lei; Xu, Lijian

    2017-01-01

    Today's fixed-cycle traffic signaling is highly suboptimal and aggravates traffic congestion and waste of energy in urban areas. In addition, it offers no quality-of-service guarantee and makes travel time prediction extremely hard. While existing traffic light control research primarily focuses on improving the average wait time of cars, we study in this paper how traffic light scheduling affects the worst-case wait time. In particular, we derive the time a car spends at an intersection in t...

  3. Traffic planning for non-homogeneous traffic

    Indian Academy of Sciences (India)

    2Civil and Architectural Engineering Department, Illinois Institute of. Technology, Illinois, Chicago ..... parametric test, i.e., the Wilcoxon signed-rank test, compared between observed and derived densities. ..... istics related to freeway design, vehicle performance, and the traffic stream on passenger-car equivalents for heavy ...

  4. A study on hearing threshold profile in traffic police personnel

    Directory of Open Access Journals (Sweden)

    Shelke BN, Aundhkar VG, Adgaonkar BD, Somwanshi SD, Gavkare AM, Ghuge SH

    2013-10-01

    Full Text Available Introduction: Noise is one of the causes of preventable sensori-neural loss. The traffic police personnel (TPP busy in controlling traffic at heavy traffic junctions suffer from the ill effects of noise and air pollution. Aim and objectives: The objective of this study was to assess the hearing threshold at various frequencies of the traffic police persons exposed to the vehicular noise and comparison with controls not exposed to noise. Material and methods: Thirty TPP and thirty controls were evaluated by clinical methods and subjected to the Pure Tone Audiometry (PTA in ENT department. Audiogram recorded by using conventional techniques in both ears. RESULTS: There was a significant difference in the hearing thresholds at frequency 2000 Hz, 4000 Hz and 8000 Hz of right and left ear between the two groups. Conclusion: This study concludes an increased risk of noise induced hearing loss (NIHL for the environmental noise exposed subjects.

  5. Comsat's TDMA traffic terminal

    Science.gov (United States)

    Benjamin, M. C.; Bogaert, W. M.

    1985-06-01

    Comsat has installed two traffic terminals in the Etam earth-station and is currently installing a third in the new Roaring Creek earth-station to access the Intelsat TDMA network. This paper describes the Comsat TDMA traffic terminal equipment from the supergroup interface to the antenna. Comsat's 1: N redundancy approach for terrestrial interface equipment and DSI unit back-up is described as well as electrical path length, amplitude and group delay equalization techniques, special on-line RF monitoring and failure reporting facilities and the operation and maintenance center which can operate and perform diagnostic testing on up to four traffic terminals from a central location.

  6. VBR video traffic models

    CERN Document Server

    Tanwir, Savera

    2014-01-01

    There has been a phenomenal growth in video applications over the past few years. An accurate traffic model of Variable Bit Rate (VBR) video is necessary for performance evaluation of a network design and for generating synthetic traffic that can be used for benchmarking a network. A large number of models for VBR video traffic have been proposed in the literature for different types of video in the past 20 years. Here, the authors have classified and surveyed these models and have also evaluated the models for H.264 AVC and MVC encoded video and discussed their findings.

  7. Neurally based measurement and evaluation of environmental noise

    CERN Document Server

    Soeta, Yoshiharu

    2015-01-01

    This book deals with methods of measurement and evaluation of environmental noise based on an auditory neural and brain-oriented model. The model consists of the autocorrelation function (ACF) and the interaural cross-correlation function (IACF) mechanisms for signals arriving at the two ear entrances. Even when the sound pressure level of a noise is only about 35 dBA, people may feel annoyed due to the aspects of sound quality. These aspects can be formulated by the factors extracted from the ACF and IACF. Several examples of measuring environmental noise—from outdoor noise such as that of aircraft, traffic, and trains, and indoor noise such as caused by floor impact, toilets, and air-conditioning—are demonstrated. According to the noise measurement and evaluation, applications for sound design are discussed. This book provides an excellent resource for students, researchers, and practitioners in a wide range of fields, such as the automotive, railway, and electronics industries, and soundscape, architec...

  8. On Movement of Emergency Services amidst Urban Traffic

    Directory of Open Access Journals (Sweden)

    Manoj Bode

    2015-12-01

    Full Text Available Managing traffic in urban areas is a complex affair. The same becomes more challenging when one needs to take into account the prioritized movement of emergency vehicles along with the normal flow of traffic. Although, mechanisms have been proposed to model intelligent traffic management systems, a concentrated effort to facilitate the movement of emergency services amongst urban traffic is yet to be formalized. This paper proposes a distributed multi-agent based mechanism to create partial green corridors for the movement of emergency service vehicles such as ambulances, fire brigade and police vans, amidst urban traffic. The proposed approach makes se of a digital network of traffic signal nodes equipped with traffic sensors and an agent framework to autonomously extend, maintain and manage partial green corridors for such emergency vehicles. The approach was emulated using Tartarus, an agent framework over a LAN. The results gathered under varying traffic conditions and also several emergency vehicles, validate the performance of this approach and its effects on the movement of normal traffic. Comparisons with the non-prioritized and full green corridor approaches indicate that the proposed partial corridor approach outperforms the rest.

  9. Application of a computational neural network to optimize the fluorescence signal from a receptor-ligand interaction on a microfluidic chip.

    Science.gov (United States)

    Ortega, Maria; Hanrahan, Grady; Arceo, Marilyn; Gomez, Frank A

    2015-02-01

    We describe the use of a computational neural network platform to optimize the fluorescence upon binding 5-carboxyfluorescein-d-Ala-d-Ala-d-Ala (5-FAM(DA)3 ) (1) to the antibiotic teicoplanin covalently attached to a glass slide. A three-level response surface experimental design was used as the first stage of investigation. Subsequently, three defined experimental parameters were examined by the neural network approach: (i) the concentration of teicoplanin used to derivatize a glass platform on the microfluidic device, (ii) the time required for the immobilization of teicoplanin on the platform, and (iii) the length of time 1 is allowed to equilibrate with teicoplanin in the microfluidic channel. Optimal neural structure provided a best fit model, both for the training set (r(2) = 0.961) and test set (r(2) = 0.934) data. Model simulated results were experimentally validated with excellent agreement (% difference) between experimental and predicted fluorescence shown, thus demonstrating efficiency of the neural network approach. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  10. The role of CXC chemokine ligand (CXCL)12-CXC chemokine receptor (CXCR)4 signalling in the migration of neural stem cells towards a brain tumour

    NARCIS (Netherlands)

    van der Meulen, A. A. E.; Biber, K.; Lukovac, S.; Balasubramaniyan, V.; den Dunnen, W. F. A.; Boddeke, H. W. G. M.; Mooij, J. J. A.

    2009-01-01

    Aims: It has been shown that neural stem cells (NSCs) migrate towards areas of brain injury or brain tumours and that NSCs have the capacity to track infiltrating tumour cells. The possible mechanism behind the migratory behaviour of NSCs is not yet completely understood. As chemokines are involved

  11. Allegheny County Traffic Counts

    Data.gov (United States)

    Allegheny County / City of Pittsburgh / Western PA Regional Data Center — Traffic sensors at over 1,200 locations in Allegheny County collect vehicle counts for the Pennsylvania Department of Transportation. Data included in the Health...

  12. Non-Traffic Citations

    Data.gov (United States)

    Allegheny County / City of Pittsburgh / Western PA Regional Data Center — Non-traffic citations (NTCs, also known as "summary offenses") document low-level criminal offenses where a law enforcement officer or other authorized official...

  13. Driver behavior in traffic.

    Science.gov (United States)

    2012-02-01

    Existing traffic analysis and management tools do not model the ability of drivers to recognize their environment and respond to it with behaviors that vary according to the encountered driving situation. The small body of literature on characterizin...

  14. Neural network based system for equipment surveillance

    Science.gov (United States)

    Vilim, R.B.; Gross, K.C.; Wegerich, S.W.

    1998-04-28

    A method and system are disclosed for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process. 33 figs.

  15. Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

    Directory of Open Access Journals (Sweden)

    Haiyang Yu

    2017-06-01

    Full Text Available Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs, for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs and long short-term memory (LSTM neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

  16. Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

    Science.gov (United States)

    Yu, Haiyang; Wu, Zhihai; Wang, Shuqin; Wang, Yunpeng; Ma, Xiaolei

    2017-01-01

    Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction. PMID:28672867

  17. Salient Feature Selection Using Feed-Forward Neural Networks and Signal-to-Noise Ratios with a Focus Toward Network Threat Detection and Classification

    Science.gov (United States)

    2014-03-27

    primary output of Fullstats is the ARFF file format, intended for use with the WEKA Java-based data mining software developed at the University of Waikato... WEKA software has many analysis tools included in it, analysis for this research was to be done using the neural net tool in MATLAB, which cannot read...Accessed 15 November 2013]. [63] "Snort," 2013. [Online]. Available: http://www.snort.org/. [Accessed 04 January 2014]. [64] " WEKA ," 2013

  18. Traffic pollution and countermeasures of urban traffic environment

    Science.gov (United States)

    He, Yuhong; Zheng, Chaocheng

    2018-01-01

    Background: Traffic environment has become a serious social problem in China currently, therefore, urban traffic environment governance is the requirement to solve this issue because as an important place in people's social life, urban traffic environment shows a strong city's energy. Objective: Based on analysis on social function of city traffic environment and its influence of traffic on urban environment in this paper, the goal to establish a healthy urban traffic environment must be included under the aim of sustainable development eternally and feasible measures were put forward afterwards. Method, result, conclusion and possible applications.

  19. Insulin receptor signaling in cones

    National Research Council Canada - National Science Library

    Rajala, Ammaji; Dighe, Radhika; Agbaga, Martin-Paul; Anderson, Robert E; Rajala, Raju V S

    2013-01-01

    .... To date there are no studies on the insulin receptor signaling in cones; however, mRNA levels of IR signaling proteins are significantly higher in cone-dominant neural retina leucine zipper (Nrl...

  20. Traffic camera system development

    Science.gov (United States)

    Hori, Toshi

    1997-04-01

    The intelligent transportation system has generated a strong need for the development of intelligent camera systems to meet the requirements of sophisticated applications, such as electronic toll collection (ETC), traffic violation detection and automatic parking lot control. In order to achieve the highest levels of accuracy in detection, these cameras must have high speed electronic shutters, high resolution, high frame rate, and communication capabilities. A progressive scan interline transfer CCD camera, with its high speed electronic shutter and resolution capabilities, provides the basic functions to meet the requirements of a traffic camera system. Unlike most industrial video imaging applications, traffic cameras must deal with harsh environmental conditions and an extremely wide range of light. Optical character recognition is a critical function of a modern traffic camera system, with detection and accuracy heavily dependent on the camera function. In order to operate under demanding conditions, communication and functional optimization is implemented to control cameras from a roadside computer. The camera operates with a shutter speed faster than 1/2000 sec. to capture highway traffic both day and night. Consequently camera gain, pedestal level, shutter speed and gamma functions are controlled by a look-up table containing various parameters based on environmental conditions, particularly lighting. Lighting conditions are studied carefully, to focus only on the critical license plate surface. A unique light sensor permits accurate reading under a variety of conditions, such as a sunny day, evening, twilight, storms, etc. These camera systems are being deployed successfully in major ETC projects throughout the world.