WorldWideScience

Sample records for underlying neural structures

  1. Tracting the neural basis of music: Deficient structural connectivity underlying acquired amusia.

    Science.gov (United States)

    Sihvonen, Aleksi J; Ripollés, Pablo; Särkämö, Teppo; Leo, Vera; Rodríguez-Fornells, Antoni; Saunavaara, Jani; Parkkola, Riitta; Soinila, Seppo

    2017-12-01

    Acquired amusia provides a unique opportunity to investigate the fundamental neural architectures of musical processing due to the transition from a functioning to defective music processing system. Yet, the white matter (WM) deficits in amusia remain systematically unexplored. To evaluate which WM structures form the neural basis for acquired amusia and its recovery, we studied 42 stroke patients longitudinally at acute, 3-month, and 6-month post-stroke stages using DTI [tract-based spatial statistics (TBSS) and deterministic tractography (DT)] and the Scale and Rhythm subtests of the Montreal Battery of Evaluation of Amusia (MBEA). Non-recovered amusia was associated with structural damage and subsequent degeneration in multiple WM tracts including the right inferior fronto-occipital fasciculus (IFOF), arcuate fasciculus (AF), inferior longitudinal fasciculus (ILF), uncinate fasciculus (UF), and frontal aslant tract (FAT), as well as in the corpus callosum (CC) and its posterior part (tapetum). In a linear regression analysis, the volume of the right IFOF was the main predictor of MBEA performance across time. Overall, our results provide a comprehensive picture of the large-scale deficits in intra- and interhemispheric structural connectivity underlying amusia, and conversely highlight which pathways are crucial for normal music perception. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. Revisiting the Neural Basis of Acquired Amusia: Lesion Patterns and Structural Changes Underlying Amusia Recovery

    Science.gov (United States)

    Sihvonen, Aleksi J.; Ripollés, Pablo; Rodríguez-Fornells, Antoni; Soinila, Seppo; Särkämö, Teppo

    2017-01-01

    Although, acquired amusia is a common deficit following stroke, relatively little is still known about its precise neural basis, let alone to its recovery. Recently, we performed a voxel-based lesion-symptom mapping (VLSM) and morphometry (VBM) study which revealed a right lateralized lesion pattern, and longitudinal gray matter volume (GMV) and white matter volume (WMV) changes that were specifically associated with acquired amusia after stroke. In the present study, using a larger sample of stroke patients (N = 90), we aimed to replicate and extend the previous structural findings as well as to determine the lesion patterns and volumetric changes associated with amusia recovery. Structural MRIs were acquired at acute and 6-month post-stroke stages. Music perception was behaviorally assessed at acute and 3-month post-stroke stages using the Scale and Rhythm subtests of the Montreal Battery of Evaluation of Amusia (MBEA). Using these scores, the patients were classified as non-amusic, recovered amusic, and non-recovered amusic. The results of the acute stage VLSM analyses and the longitudinal VBM analyses converged to show that more severe and persistent (non-recovered) amusia was associated with an extensive pattern of lesions and GMV/WMV decrease in right temporal, frontal, parietal, striatal, and limbic areas. In contrast, less severe and transient (recovered) amusia was linked to lesions specifically in left inferior frontal gyrus as well as to a GMV decrease in right parietal areas. Separate continuous analyses of MBEA Scale and Rhythm scores showed extensively overlapping lesion pattern in right temporal, frontal, and subcortical structures as well as in the right insula. Interestingly, the recovered pitch amusia was related to smaller GMV decreases in the temporoparietal junction whereas the recovered rhythm amusia was associated to smaller GMV decreases in the inferior temporal pole. Overall, the results provide a more comprehensive picture of the lesions

  3. Revisiting the Neural Basis of Acquired Amusia: Lesion Patterns and Structural Changes Underlying Amusia Recovery

    Directory of Open Access Journals (Sweden)

    Aleksi J. Sihvonen

    2017-07-01

    Full Text Available Although, acquired amusia is a common deficit following stroke, relatively little is still known about its precise neural basis, let alone to its recovery. Recently, we performed a voxel-based lesion-symptom mapping (VLSM and morphometry (VBM study which revealed a right lateralized lesion pattern, and longitudinal gray matter volume (GMV and white matter volume (WMV changes that were specifically associated with acquired amusia after stroke. In the present study, using a larger sample of stroke patients (N = 90, we aimed to replicate and extend the previous structural findings as well as to determine the lesion patterns and volumetric changes associated with amusia recovery. Structural MRIs were acquired at acute and 6-month post-stroke stages. Music perception was behaviorally assessed at acute and 3-month post-stroke stages using the Scale and Rhythm subtests of the Montreal Battery of Evaluation of Amusia (MBEA. Using these scores, the patients were classified as non-amusic, recovered amusic, and non-recovered amusic. The results of the acute stage VLSM analyses and the longitudinal VBM analyses converged to show that more severe and persistent (non-recovered amusia was associated with an extensive pattern of lesions and GMV/WMV decrease in right temporal, frontal, parietal, striatal, and limbic areas. In contrast, less severe and transient (recovered amusia was linked to lesions specifically in left inferior frontal gyrus as well as to a GMV decrease in right parietal areas. Separate continuous analyses of MBEA Scale and Rhythm scores showed extensively overlapping lesion pattern in right temporal, frontal, and subcortical structures as well as in the right insula. Interestingly, the recovered pitch amusia was related to smaller GMV decreases in the temporoparietal junction whereas the recovered rhythm amusia was associated to smaller GMV decreases in the inferior temporal pole. Overall, the results provide a more comprehensive picture of

  4. Revisiting the Neural Basis of Acquired Amusia: Lesion Patterns and Structural Changes Underlying Amusia Recovery.

    Science.gov (United States)

    Sihvonen, Aleksi J; Ripollés, Pablo; Rodríguez-Fornells, Antoni; Soinila, Seppo; Särkämö, Teppo

    2017-01-01

    Although, acquired amusia is a common deficit following stroke, relatively little is still known about its precise neural basis, let alone to its recovery. Recently, we performed a voxel-based lesion-symptom mapping (VLSM) and morphometry (VBM) study which revealed a right lateralized lesion pattern, and longitudinal gray matter volume (GMV) and white matter volume (WMV) changes that were specifically associated with acquired amusia after stroke. In the present study, using a larger sample of stroke patients ( N = 90), we aimed to replicate and extend the previous structural findings as well as to determine the lesion patterns and volumetric changes associated with amusia recovery. Structural MRIs were acquired at acute and 6-month post-stroke stages. Music perception was behaviorally assessed at acute and 3-month post-stroke stages using the Scale and Rhythm subtests of the Montreal Battery of Evaluation of Amusia (MBEA). Using these scores, the patients were classified as non-amusic, recovered amusic, and non-recovered amusic. The results of the acute stage VLSM analyses and the longitudinal VBM analyses converged to show that more severe and persistent (non-recovered) amusia was associated with an extensive pattern of lesions and GMV/WMV decrease in right temporal, frontal, parietal, striatal, and limbic areas. In contrast, less severe and transient (recovered) amusia was linked to lesions specifically in left inferior frontal gyrus as well as to a GMV decrease in right parietal areas. Separate continuous analyses of MBEA Scale and Rhythm scores showed extensively overlapping lesion pattern in right temporal, frontal, and subcortical structures as well as in the right insula. Interestingly, the recovered pitch amusia was related to smaller GMV decreases in the temporoparietal junction whereas the recovered rhythm amusia was associated to smaller GMV decreases in the inferior temporal pole. Overall, the results provide a more comprehensive picture of the lesions

  5. Structured Pyramidal Neural Networks.

    Science.gov (United States)

    Soares, Alessandra M; Fernandes, Bruno J T; Bastos-Filho, Carmelo J A

    2017-02-09

    The Pyramidal Neural Networks (PNN) are an example of a successful recently proposed model inspired by the human visual system and deep learning theory. PNNs are applied to computer vision and based on the concept of receptive fields. This paper proposes a variation of PNN, named here as Structured Pyramidal Neural Network (SPNN). SPNN has self-adaptive variable receptive fields, while the original PNNs rely on the same size for the fields of all neurons, which limits the model since it is not possible to put more computing resources in a particular region of the image. Another limitation of the original approach is the need to define values for a reasonable number of parameters, which can turn difficult the application of PNNs in contexts in which the user does not have experience. On the other hand, SPNN has a fewer number of parameters. Its structure is determined using a novel method with Delaunay Triangulation and k-means clustering. SPNN achieved better results than PNNs and similar performance when compared to Convolutional Neural Network (CNN) and Support Vector Machine (SVM), but using lower memory capacity and processing time.

  6. Deciphering the Cognitive and Neural Mechanisms Underlying ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Deciphering the Cognitive and Neural Mechanisms Underlying Auditory Learning. This project seeks to understand the brain mechanisms necessary for people to learn to perceive sounds. Neural circuits and learning. The research team will test people with and without musical training to evaluate their capacity to learn ...

  7. Neural dynamics underlying emotional transmissions between individuals.

    Science.gov (United States)

    Golland, Yulia; Levit-Binnun, Nava; Hendler, Talma; Lerner, Yulia

    2017-08-01

    Emotional experiences are frequently shaped by the emotional responses of co-present others. Research has shown that people constantly monitor and adapt to the incoming social-emotional signals, even without face-to-face interaction. And yet, the neural processes underlying such emotional transmissions have not been directly studied. Here, we investigated how the human brain processes emotional cues which arrive from another, co-attending individual. We presented continuous emotional feedback to participants who viewed a movie in the scanner. Participants in the social group (but not in the control group) believed that the feedback was coming from another person who was co-viewing the same movie. We found that social-emotional feedback significantly affected the neural dynamics both in the core affect and in the medial pre-frontal regions. Specifically, the response time-courses in those regions exhibited increased similarity across recipients and increased neural alignment with the timeline of the feedback in the social compared with control group. Taken in conjunction with previous research, this study suggests that emotional cues from others shape the neural dynamics across the whole neural continuum of emotional processing in the brain. Moreover, it demonstrates that interpersonal neural alignment can serve as a neural mechanism through which affective information is conveyed between individuals. © The Author (2017). Published by Oxford University Press.

  8. Neural dynamics underlying emotional transmissions between individuals

    OpenAIRE

    Golland, Yulia; Levit-Binnun, Nava; Hendler, Talma; Lerner, Yulia

    2017-01-01

    Abstract Emotional experiences are frequently shaped by the emotional responses of co-present others. Research has shown that people constantly monitor and adapt to the incoming social–emotional signals, even without face-to-face interaction. And yet, the neural processes underlying such emotional transmissions have not been directly studied. Here, we investigated how the human brain processes emotional cues which arrive from another, co-attending individual. We presented continuous emotional...

  9. Structured neural networks for pattern recognition.

    Science.gov (United States)

    Murino, V

    1998-01-01

    This paper proposes a novel approach for the design of structures of neural networks for pattern recognition. The basic idea lies in subdividing the whole classification problem in smaller and simpler problems at different levels, each managed by appropriate components of a complex neural architecture. Three neural structures are presented and applied in a surveillance system aimed at monitoring a railway waiting room classifying potential dangerous situations. Each architecture is composed by nodes, which are actual multilayer perceptrons trained to discriminate between subsets of classes until a complete separation among the classes is achieved. This approach showed better performances with respect to a classical statistical classification procedures and to a single neural network.

  10. Neural Networks for protein Structure Prediction

    DEFF Research Database (Denmark)

    Bohr, Henrik

    1998-01-01

    This is a review about neural network applications in bioinformatics. Especially the applications to protein structure prediction, e.g. prediction of secondary structures, prediction of surface structure, fold class recognition and prediction of the 3-dimensional structure of protein backbones...

  11. Neural Population Dynamics Underlying Motor Learning Transfer.

    Science.gov (United States)

    Vyas, Saurabh; Even-Chen, Nir; Stavisky, Sergey D; Ryu, Stephen I; Nuyujukian, Paul; Shenoy, Krishna V

    2018-03-07

    Covert motor learning can sometimes transfer to overt behavior. We investigated the neural mechanism underlying transfer by constructing a two-context paradigm. Subjects performed cursor movements either overtly using arm movements, or covertly via a brain-machine interface that moves the cursor based on motor cortical activity (in lieu of arm movement). These tasks helped evaluate whether and how cortical changes resulting from "covert rehearsal" affect overt performance. We found that covert learning indeed transfers to overt performance and is accompanied by systematic population-level changes in motor preparatory activity. Current models of motor cortical function ascribe motor preparation to achieving initial conditions favorable for subsequent movement-period neural dynamics. We found that covert and overt contexts share these initial conditions, and covert rehearsal manipulates them in a manner that persists across context changes, thus facilitating overt motor learning. This transfer learning mechanism might provide new insights into other covert processes like mental rehearsal. Copyright © 2018 Elsevier Inc. All rights reserved.

  12. Neural correlates underlying micrographia in Parkinson's disease.

    Science.gov (United States)

    Wu, Tao; Zhang, Jiarong; Hallett, Mark; Feng, Tao; Hou, Yanan; Chan, Piu

    2016-01-01

    Micrographia is a common symptom in Parkinson's disease, which manifests as either a consistent or progressive reduction in the size of handwriting or both. Neural correlates underlying micrographia remain unclear. We used functional magnetic resonance imaging to investigate micrographia-related neural activity and connectivity modulations. In addition, the effect of attention and dopaminergic administration on micrographia was examined. We found that consistent micrographia was associated with decreased activity and connectivity in the basal ganglia motor circuit; while progressive micrographia was related to the dysfunction of basal ganglia motor circuit together with disconnections between the rostral supplementary motor area, rostral cingulate motor area and cerebellum. Attention significantly improved both consistent and progressive micrographia, accompanied by recruitment of anterior putamen and dorsolateral prefrontal cortex. Levodopa improved consistent micrographia accompanied by increased activity and connectivity in the basal ganglia motor circuit, but had no effect on progressive micrographia. Our findings suggest that consistent micrographia is related to dysfunction of the basal ganglia motor circuit; while dysfunction of the basal ganglia motor circuit and disconnection between the rostral supplementary motor area, rostral cingulate motor area and cerebellum likely contributes to progressive micrographia. Attention improves both types of micrographia by recruiting additional brain networks. Levodopa improves consistent micrographia by restoring the function of the basal ganglia motor circuit, but does not improve progressive micrographia, probably because of failure to repair the disconnected networks. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  13. Two distinct neural mechanisms underlying indirect reciprocity.

    Science.gov (United States)

    Watanabe, Takamitsu; Takezawa, Masanori; Nakawake, Yo; Kunimatsu, Akira; Yamasue, Hidenori; Nakamura, Mitsuhiro; Miyashita, Yasushi; Masuda, Naoki

    2014-03-18

    Cooperation is a hallmark of human society. Humans often cooperate with strangers even if they will not meet each other again. This so-called indirect reciprocity enables large-scale cooperation among nonkin and can occur based on a reputation mechanism or as a succession of pay-it-forward behavior. Here, we provide the functional and anatomical neural evidence for two distinct mechanisms governing the two types of indirect reciprocity. Cooperation occurring as reputation-based reciprocity specifically recruited the precuneus, a region associated with self-centered cognition. During such cooperative behavior, the precuneus was functionally connected with the caudate, a region linking rewards to behavior. Furthermore, the precuneus of a cooperative subject had a strong resting-state functional connectivity (rsFC) with the caudate and a large gray matter volume. In contrast, pay-it-forward reciprocity recruited the anterior insula (AI), a brain region associated with affective empathy. The AI was functionally connected with the caudate during cooperation occurring as pay-it-forward reciprocity, and its gray matter volume and rsFC with the caudate predicted the tendency of such cooperation. The revealed difference is consistent with the existing results of evolutionary game theory: although reputation-based indirect reciprocity robustly evolves as a self-interested behavior in theory, pay-it-forward indirect reciprocity does not on its own. The present study provides neural mechanisms underlying indirect reciprocity and suggests that pay-it-forward reciprocity may not occur as myopic profit maximization but elicit emotional rewards.

  14. Parallel and interrelated neural systems underlying adaptive navigation.

    Science.gov (United States)

    Mizumori, Sheri J Y; Canfield, James G; Yeshenko, Oksana

    2005-06-01

    The ability to process in parallel multiple forms of sensory information, and link sensory-sensory associations to behavior, presumably allows for the opportunistic use of the most reliable and predictive sensory modalities in diverse behavioral contexts. Evolutionary considerations indicate that such processing may represent a fundamental operating principle underlying complex sensory associations and sensory-motor integration. Here, we suggest that animal navigation is a particularly useful model of such opportunistic use of sensory and motor information because it is possible to study directly the effects of memory on neural system functions. First, comparative evidence for parallel processing across multiple brain structures during navigation is provided from the literatures on fish and rodent navigation. Then, based on neurophysiological evidence of coordinated, multiregional processing, we provide a neurobiological explanation of learning and memory effects on neural circuitry mediating navigation.

  15. Neural correlates underlying musical semantic memory.

    Science.gov (United States)

    Groussard, M; Viader, F; Landeau, B; Desgranges, B; Eustache, F; Platel, H

    2009-07-01

    Numerous functional imaging studies have examined the neural basis of semantic memory mainly using verbal and visuospatial materials. Musical material also allows an original way to explore semantic memory processes. We used PET imaging to determine the neural substrates that underlie musical semantic memory using different tasks and stimuli. The results of three PET studies revealed a greater involvement of the anterior part of the temporal lobe. Concerning clinical observations and our neuroimaging data, the musical lexicon (and most widely musical semantic memory) appears to be sustained by a temporo-prefrontal cerebral network involving right and left cerebral regions.

  16. Hierarchical Neural Network Structures for Phoneme Recognition

    CERN Document Server

    Vasquez, Daniel; Minker, Wolfgang

    2013-01-01

    In this book, hierarchical structures based on neural networks are investigated for automatic speech recognition. These structures are evaluated on the phoneme recognition task where a  Hybrid Hidden Markov Model/Artificial Neural Network paradigm is used. The baseline hierarchical scheme consists of two levels each which is based on a Multilayered Perceptron. Additionally, the output of the first level serves as a second level input. The computational speed of the phoneme recognizer can be substantially increased by removing redundant information still contained at the first level output. Several techniques based on temporal and phonetic criteria have been investigated to remove this redundant information. The computational time could be reduced by 57% whilst keeping the system accuracy comparable to the baseline hierarchical approach.

  17. Review of the Neural Oscillations Underlying Meditation

    Directory of Open Access Journals (Sweden)

    Darrin J. Lee

    2018-03-01

    Full Text Available Objective: Meditation is one type of mental training that has been shown to produce many cognitive benefits. Meditation practice is associated with improvement in concentration and reduction of stress, depression, and anxiety symptoms. Furthermore, different forms of meditation training are now being used as interventions for a variety of psychological and somatic illnesses. These benefits are thought to occur as a result of neurophysiologic changes. The most commonly studied specific meditation practices are focused attention (FA, open-monitoring (OM, as well as transcendental meditation (TM, and loving-kindness (LK meditation. In this review, we compare the neural oscillatory patterns during these forms of meditation.Method: We performed a systematic review of neural oscillations during FA, OM, TM, and LK meditation practices, comparing meditators to meditation-naïve adults.Results: FA, OM, TM, and LK meditation are associated with global increases in oscillatory activity in meditators compared to meditation-naïve adults, with larger changes occurring as the length of meditation training increases. While FA and OM are related to increases in anterior theta activity, only FA is associated with changes in posterior theta oscillations. Alpha activity increases in posterior brain regions during both FA and OM. In anterior regions, FA shows a bilateral increase in alpha power, while OM shows a decrease only in left-sided power. Gamma activity in these meditation practices is similar in frontal regions, but increases are variable in parietal and occipital regions.Conclusions: The current literature suggests distinct differences in neural oscillatory activity among FA, OM, TM, and LK meditation practices. Further characterizing these oscillatory changes may better elucidate the cognitive and therapeutic effects of specific meditation practices, and potentially lead to the development of novel neuromodulation targets to take advantage of their

  18. Ontogeny of neural circuits underlying spatial memory in the rat

    Directory of Open Access Journals (Sweden)

    James Alexander Ainge

    2012-03-01

    Full Text Available Spatial memory is a well characterised psychological function in both humans and rodents. The combined computations of a network of systems including place cells in the hippocampus, grid cells in the medial entorhinal cortex and head direction cells found in numerous structures in the brain have been suggested to form the neural instantiation of the cognitive map as first described by Tolman in 1948. However, while our understanding of the neural mechanisms underlying spatial representations in adults is relatively sophisticated, we know substantially less about how this network develops in young animals. In this article we review studies examining the developmental timescale that these systems follow. Electrophysiological recordings from very young rats show that directional information is at adult levels at the outset of navigational experience. The systems supporting allocentric memory, however, take longer to mature. This is consistent with behavioural studies of young rats which show that spatial memory based on head direction develops very early but that allocentric spatial memory takes longer to mature. We go on to report new data demonstrating that memory for associations between objects and their spatial locations is slower to develop than memory for objects alone. This is again consistent with previous reports suggesting that adult like spatial representations have a protracted development in rats and also suggests that the systems involved in processing non-spatial stimuli come online earlier.

  19. Neural mechanisms and models underlying joint action.

    Science.gov (United States)

    Chersi, Fabian

    2011-06-01

    Humans, in particular, and to a lesser extent also other species of animals, possess the impressive capability of smoothly coordinating their actions with those of others. The great amount of work done in recent years in neuroscience has provided new insights into the processes involved in joint action, intention understanding, and task sharing. In particular, the discovery of mirror neurons, which fire both when animals execute actions and when they observe the same actions done by other individuals, has shed light on the intimate relationship between perception and action elucidating the direct contribution of motor knowledge to action understanding. Up to date, however, a detailed description of the neural processes involved in these phenomena is still mostly lacking. Building upon data from single neuron recordings in monkeys observing the actions of a demonstrator and then executing the same or a complementary action, this paper describes the functioning of a biologically constraint neural network model of the motor and mirror systems during joint action. In this model, motor sequences are encoded as independent neuronal chains that represent concatenations of elementary motor acts leading to a specific goal. Action execution and recognition are achieved through the propagation of activity within specific chains. Due to the dual property of mirror neurons, the same architecture is capable of smoothly integrating and switching between observed and self-generated action sequences, thus allowing to evaluate multiple hypotheses simultaneously, understand actions done by others, and to respond in an appropriate way.

  20. Neural Partial Differentiation for Aircraft Parameter Estimation Under Turbulent Atmospheric Conditions

    Science.gov (United States)

    Kuttieri, R. A.; Sinha, M.

    2012-07-01

    An approach based on neural partial differentiation is suggested for aircraft parameter estimation using the flight data gathered under turbulent atmospheric conditions. The classical methods such as output error and equation error methods suffer from severe convergence issues; resulting in biased, inaccurate, and inconsistent estimates. Though filter error method yields better estimates while dealing with the flight data having process noise, it has few demerits like computational overheads and it allows estimation of a single set of process noise distribution matrix. The proposed neural method does not face any such problem of the classical methods. Moreover, the neural method does not require parameter initialization and a priori knowledge of the model structure. The neural network maps the aircraft state and control variables into the output variables corresponding to aerodynamic forces and moments. The parameter estimation, pertaining to lateral-directional motion, of the research aircraft de Havilland DHC-2 with simulated process noise, is presented. The results obtained using the neural partial differentiation are compared with the nominal values given in literature and with the classical methods. The neural method yields the aerodynamic derivatives very close to the nominal values and having quite low standard deviation. The neural methodology is also validated by comparing actual output variables with the neural predicted and neural reconstructed variables.

  1. Identification of Non-Linear Structures using Recurrent Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

    Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

  2. Identification of Non-Linear Structures using Recurrent Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

    1995-01-01

    Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

  3. Antagonistic neural networks underlying differentiated leadership roles

    Science.gov (United States)

    Boyatzis, Richard E.; Rochford, Kylie; Jack, Anthony I.

    2014-01-01

    The emergence of two distinct leadership roles, the task leader and the socio-emotional leader, has been documented in the leadership literature since the 1950s. Recent research in neuroscience suggests that the division between task-oriented and socio-emotional-oriented roles derives from a fundamental feature of our neurobiology: an antagonistic relationship between two large-scale cortical networks – the task-positive network (TPN) and the default mode network (DMN). Neural activity in TPN tends to inhibit activity in the DMN, and vice versa. The TPN is important for problem solving, focusing of attention, making decisions, and control of action. The DMN plays a central role in emotional self-awareness, social cognition, and ethical decision making. It is also strongly linked to creativity and openness to new ideas. Because activation of the TPN tends to suppress activity in the DMN, an over-emphasis on task-oriented leadership may prove deleterious to social and emotional aspects of leadership. Similarly, an overemphasis on the DMN would result in difficulty focusing attention, making decisions, and solving known problems. In this paper, we will review major streams of theory and research on leadership roles in the context of recent findings from neuroscience and psychology. We conclude by suggesting that emerging research challenges the assumption that role differentiation is both natural and necessary, in particular when openness to new ideas, people, emotions, and ethical concerns are important to success. PMID:24624074

  4. Antagonistic Neural Networks Underlying Differentiated Leadership Roles

    Directory of Open Access Journals (Sweden)

    Richard Eleftherios Boyatzis

    2014-03-01

    Full Text Available The emergence of two distinct leadership roles, the task leader and the socio-emotional leader, has been documented in the leadership literature since the 1950’s. Recent research in neuroscience suggests that the division between task oriented and socio-emotional oriented roles derives from a fundamental feature of our neurobiology: an antagonistic relationship between two large-scale cortical networks -- the Task Positive Network (TPN and the Default Mode Network (DMN. Neural activity in TPN tends to inhibit activity in the DMN, and vice versa. The TPN is important for problem solving, focusing of attention, making decisions, and control of action. The DMN plays a central role in emotional self-awareness, social cognition, and ethical decision making. It is also strongly linked to creativity and openness to new ideas. Because activation of the TPN tends to suppress activity in the DMN, an over-emphasis on task oriented leadership may prove deleterious to social and emotional aspects of leadership. Similarly, an overemphasis on the DMN would result in difficulty focusing attention, making decisions and solving known problems. In this paper, we will review major streams of theory and research on leadership roles in the context of recent findings from neuroscience and psychology. We conclude by suggesting that emerging research challenges the assumption that role differentiation is both natural and necessary, in particular when openness to new ideas, people, emotions, and ethical concerns are important to success.

  5. Antagonistic neural networks underlying differentiated leadership roles.

    Science.gov (United States)

    Boyatzis, Richard E; Rochford, Kylie; Jack, Anthony I

    2014-01-01

    The emergence of two distinct leadership roles, the task leader and the socio-emotional leader, has been documented in the leadership literature since the 1950s. Recent research in neuroscience suggests that the division between task-oriented and socio-emotional-oriented roles derives from a fundamental feature of our neurobiology: an antagonistic relationship between two large-scale cortical networks - the task-positive network (TPN) and the default mode network (DMN). Neural activity in TPN tends to inhibit activity in the DMN, and vice versa. The TPN is important for problem solving, focusing of attention, making decisions, and control of action. The DMN plays a central role in emotional self-awareness, social cognition, and ethical decision making. It is also strongly linked to creativity and openness to new ideas. Because activation of the TPN tends to suppress activity in the DMN, an over-emphasis on task-oriented leadership may prove deleterious to social and emotional aspects of leadership. Similarly, an overemphasis on the DMN would result in difficulty focusing attention, making decisions, and solving known problems. In this paper, we will review major streams of theory and research on leadership roles in the context of recent findings from neuroscience and psychology. We conclude by suggesting that emerging research challenges the assumption that role differentiation is both natural and necessary, in particular when openness to new ideas, people, emotions, and ethical concerns are important to success.

  6. Neural Circuitry and Plasticity Mechanisms Underlying Delay Eyeblink Conditioning

    Science.gov (United States)

    Freeman, John H.; Steinmetz, Adam B.

    2011-01-01

    Pavlovian eyeblink conditioning has been used extensively as a model system for examining the neural mechanisms underlying associative learning. Delay eyeblink conditioning depends on the intermediate cerebellum ipsilateral to the conditioned eye. Evidence favors a two-site plasticity model within the cerebellum with long-term depression of…

  7. Neural mechanisms underlying context-dependent shifts in risk preferences

    NARCIS (Netherlands)

    Losecaat Vermeer, A.B.; Boksem, M.A.S.; Sanfey, A.G.

    2014-01-01

    Studies of risky decision-making have demonstrated that humans typically prefer risky options after incurring a financial loss, while generally preferring safer options after a monetary gain. Here, we examined the neural processes underlying these inconsistent risk preferences by investigating the

  8. Preserving neural function under extreme scaling.

    Directory of Open Access Journals (Sweden)

    Hermann Cuntz

    Full Text Available Important brain functions need to be conserved throughout organisms of extremely varying sizes. Here we study the scaling properties of an essential component of computation in the brain: the single neuron. We compare morphology and signal propagation of a uniquely identifiable interneuron, the HS cell, in the blowfly (Calliphora with its exact counterpart in the fruit fly (Drosophila which is about four times smaller in each dimension. Anatomical features of the HS cell scale isometrically and minimise wiring costs but, by themselves, do not scale to preserve the electrotonic behaviour. However, the membrane properties are set to conserve dendritic as well as axonal delays and attenuation as well as dendritic integration of visual information. In conclusion, the electrotonic structure of a neuron, the HS cell in this case, is surprisingly stable over a wide range of morphological scales.

  9. The structural neural substrate of subjective happiness.

    Science.gov (United States)

    Sato, Wataru; Kochiyama, Takanori; Uono, Shota; Kubota, Yasutaka; Sawada, Reiko; Yoshimura, Sayaka; Toichi, Motomi

    2015-11-20

    Happiness is a subjective experience that is an ultimate goal for humans. Psychological studies have shown that subjective happiness can be measured reliably and consists of emotional and cognitive components. However, the neural substrates of subjective happiness remain unclear. To investigate this issue, we used structural magnetic resonance imaging and questionnaires that assessed subjective happiness, the intensity of positive and negative emotional experiences, and purpose in life. We found a positive relationship between the subjective happiness score and gray matter volume in the right precuneus. Moreover, the same region showed an association with the combined positive and negative emotional intensity and purpose in life scores. Our findings suggest that the precuneus mediates subjective happiness by integrating the emotional and cognitive components of happiness.

  10. Neural Global Pattern Similarity Underlies True and False Memories.

    Science.gov (United States)

    Ye, Zhifang; Zhu, Bi; Zhuang, Liping; Lu, Zhonglin; Chen, Chuansheng; Xue, Gui

    2016-06-22

    The neural processes giving rise to human memory strength signals remain poorly understood. Inspired by formal computational models that posit a central role of global matching in memory strength, we tested a novel hypothesis that the strengths of both true and false memories arise from the global similarity of an item's neural activation pattern during retrieval to that of all the studied items during encoding (i.e., the encoding-retrieval neural global pattern similarity [ER-nGPS]). We revealed multiple ER-nGPS signals that carried distinct information and contributed differentially to true and false memories: Whereas the ER-nGPS in the parietal regions reflected semantic similarity and was scaled with the recognition strengths of both true and false memories, ER-nGPS in the visual cortex contributed solely to true memory. Moreover, ER-nGPS differences between the parietal and visual cortices were correlated with frontal monitoring processes. By combining computational and neuroimaging approaches, our results advance a mechanistic understanding of memory strength in recognition. What neural processes give rise to memory strength signals, and lead to our conscious feelings of familiarity? Using fMRI, we found that the memory strength of a given item depends not only on how it was encoded during learning, but also on the similarity of its neural representation with other studied items. The global neural matching signal, mainly in the parietal lobule, could account for the memory strengths of both studied and unstudied items. Interestingly, a different global matching signal, originated from the visual cortex, could distinguish true from false memories. The findings reveal multiple neural mechanisms underlying the memory strengths of events registered in the brain. Copyright © 2016 the authors 0270-6474/16/366792-11$15.00/0.

  11. Neural mechanisms underlying morphine withdrawal in addicted patients: a review

    Directory of Open Access Journals (Sweden)

    Nima Babhadiashar

    2015-06-01

    Full Text Available Morphine is one of the most potent alkaloid in opium, which has substantial medical uses and needs and it is the first active principle purified from herbal source. Morphine has commonly been used for relief of moderate to severe pain as it acts directly on the central nervous system; nonetheless, its chronic abuse increases tolerance and physical dependence, which is commonly known as opiate addiction. Morphine withdrawal syndrome is physiological and behavioral symptoms that stem from prolonged exposure to morphine. A majority of brain regions are hypofunctional over prolonged abstinence and acute morphine withdrawal. Furthermore, several neural mechanisms are likely to contribute to morphine withdrawal. The present review summarizes the literature pertaining to neural mechanisms underlying morphine withdrawal. Despite the fact that morphine withdrawal is a complex process, it is suggested that neural mechanisms play key roles in morphine withdrawal.

  12. Neural Schematics as a unified formal graphical representation of large-scale Neural Network Structures

    Directory of Open Access Journals (Sweden)

    Matthias eEhrlich

    2013-10-01

    Full Text Available One of the major outcomes of neuroscientific research are models of Neural Network Structures. Descriptions of these models usually consist of a non-standardized mixture of text, figures, and other means of visual information communication in print media. However, as neuroscience is an interdisciplinary domain by nature, a standardized way of consistently representing models of Neural Network Structures is required. While generic descriptions of such models in textual form have recently been developed, a formalized way of schematically expressing them does not exist to date. Hence, in this paper we present Neural Schematics as a concept inspired by similar approaches from other disciplines for a generic two dimensional representation of said structures. After introducing Neural Network Structures in general, a set of current visualizations of models of Neural Network Structures is reviewed and analyzed for what information they convey and how their elements are rendered. This analysis then allows for the definition of general items and symbols to consistently represent these models as Neural Schematics on a two dimensional plane. We will illustrate the possibilities an agreed upon standard can yield on sampled diagrams transformed into Neural Schematics and an example application for the design and modeling of large-scale Neural Network Structures.

  13. Combining neural networks for protein secondary structure prediction

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric

    1995-01-01

    In this paper structured neural networks are applied to the problem of predicting the secondary structure of proteins. A hierarchical approach is used where specialized neural networks are designed for each structural class and then combined using another neural network. The submodels are designed...... by using a priori knowledge of the mapping between protein building blocks and the secondary structure and by using weight sharing. Since none of the individual networks have more than 600 adjustable weights over-fitting is avoided. When ensembles of specialized experts are combined the performance...... is better than most secondary structure prediction methods based on single sequences even though this model contains much fewer parameters...

  14. Automated Modeling of Microwave Structures by Enhanced Neural Networks

    Directory of Open Access Journals (Sweden)

    Z. Raida

    2006-12-01

    Full Text Available The paper describes the methodology of the automated creation of neural models of microwave structures. During the creation process, artificial neural networks are trained using the combination of the particle swarm optimization and the quasi-Newton method to avoid critical training problems of the conventional neural nets. In the paper, neural networks are used to approximate the behavior of a planar microwave filter (moment method, Zeland IE3D. In order to evaluate the efficiency of neural modeling, global optimizations are performed using numerical models and neural ones. Both approaches are compared from the viewpoint of CPU-time demands and the accuracy. Considering conclusions, methodological recommendations for including neural networks to the microwave design are formulated.

  15. Neural processing of reward magnitude under varying attentional demands.

    Science.gov (United States)

    Stoppel, Christian Michael; Boehler, Carsten Nicolas; Strumpf, Hendrik; Heinze, Hans-Jochen; Hopf, Jens-Max; Schoenfeld, Mircea Ariel

    2011-04-06

    Central to the organization of behavior is the ability to represent the magnitude of a prospective reward and the costs related to obtaining it. Therein, reward-related neural activations are discounted in dependence of the effort required to resolve a given task. Varying attentional demands of the task might however affect reward-related neural activations. Here we employed fMRI to investigate the neural representation of expected values during a monetary incentive delay task with varying attentional demands. Following a cue, indicating at the same time the difficulty (hard/easy) and the reward magnitude (high/low) of the upcoming trial, subjects performed an attention task and subsequently received feedback about their monetary reward. Consistent with previous results, activity in anterior-cingulate, insular/orbitofrontal and mesolimbic regions co-varied with the anticipated reward-magnitude, but also with the attentional requirements of the task. These activations occurred contingent on action-execution and resembled the response time pattern of the subjects. In contrast, cue-related activations, signaling the forthcoming task-requirements, were only observed within attentional control structures. These results suggest that anticipated reward-magnitude and task-related attentional demands are concurrently processed in partially overlapping neural networks of anterior-cingulate, insular/orbitofrontal, and mesolimbic regions. Copyright © 2011 Elsevier B.V. All rights reserved.

  16. Modeling Broadband Microwave Structures by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    V. Otevrel

    2004-06-01

    Full Text Available The paper describes the exploitation of feed-forward neural networksand recurrent neural networks for replacing full-wave numerical modelsof microwave structures in complex microwave design tools. Building aneural model, attention is turned to the modeling accuracy and to theefficiency of building a model. Dealing with the accuracy, we describea method of increasing it by successive completing a training set.Neural models are mutually compared in order to highlight theiradvantages and disadvantages. As a reference model for comparisons,approximations based on standard cubic splines are used. Neural modelsare used to replace both the time-domain numeric models and thefrequency-domain ones.

  17. Neural processes underlying cultural differences in cognitive persistence.

    Science.gov (United States)

    Telzer, Eva H; Qu, Yang; Lin, Lynda C

    2017-08-01

    Self-improvement motivation, which occurs when individuals seek to improve upon their competence by gaining new knowledge and improving upon their skills, is critical for cognitive, social, and educational adjustment. While many studies have delineated the neural mechanisms supporting extrinsic motivation induced by monetary rewards, less work has examined the neural processes that support intrinsically motivated behaviors, such as self-improvement motivation. Because cultural groups traditionally vary in terms of their self-improvement motivation, we examined cultural differences in the behavioral and neural processes underlying motivated behaviors during cognitive persistence in the absence of extrinsic rewards. In Study 1, 71 American (47 females, M=19.68 years) and 68 Chinese (38 females, M=19.37 years) students completed a behavioral cognitive control task that required cognitive persistence across time. In Study 2, 14 American and 15 Chinese students completed the same cognitive persistence task during an fMRI scan. Across both studies, American students showed significant declines in cognitive performance across time, whereas Chinese participants demonstrated effective cognitive persistence. These behavioral effects were explained by cultural differences in self-improvement motivation and paralleled by increasing activation and functional coupling between the inferior frontal gyrus (IFG) and ventral striatum (VS) across the task among Chinese participants, neural activation and coupling that remained low in American participants. These findings suggest a potential neural mechanism by which the VS and IFG work in concert to promote cognitive persistence in the absence of extrinsic rewards. Thus, frontostriatal circuitry may be a neurobiological signal representing intrinsic motivation for self-improvement that serves an adaptive function, increasing Chinese students' motivation to engage in cognitive persistence. Copyright © 2017 Elsevier Inc. All rights

  18. Linking neural and symbolic representation and processing of conceptual structures

    NARCIS (Netherlands)

    van der Velde, Frank; Forth, Jamie; Nazareth, Deniece S.; Wiggins, Geraint A.

    2017-01-01

    We compare and discuss representations in two cognitive architectures aimed at representing and processing complex conceptual (sentence-like) structures. First is the Neural Blackboard Architecture (NBA), which aims to account for representation and processing of complex and combinatorial conceptual

  19. Neural changes underlying early stages of L2 vocabulary acquisition.

    Science.gov (United States)

    Pu, He; Holcomb, Phillip J; Midgley, Katherine J

    2016-11-01

    Research has shown neural changes following second language (L2) acquisition after weeks or months of instruction. But are such changes detectable even earlier than previously shown? The present study examines the electrophysiological changes underlying the earliest stages of second language vocabulary acquisition by recording event-related potentials (ERPs) within the first week of learning. Adult native English speakers with no previous Spanish experience completed less than four hours of Spanish vocabulary training, with pre- and post-training ERPs recorded to a backward translation task. Results indicate that beginning L2 learners show rapid neural changes following learning, manifested in changes to the N400 - an ERP component sensitive to lexicosemantic processing and degree of L2 proficiency. Specifically, learners in early stages of L2 acquisition show growth in N400 amplitude to L2 words following learning as well as a backward translation N400 priming effect that was absent pre-training. These results were shown within days of minimal L2 training, suggesting that the neural changes captured during adult second language acquisition are more rapid than previously shown. Such findings are consistent with models of early stages of bilingualism in adult learners of L2 ( e.g. Kroll and Stewart's RHM) and reinforce the use of ERP measures to assess L2 learning.

  20. Neural mechanisms underlying melodic perception and memory for pitch.

    Science.gov (United States)

    Zatorre, R J; Evans, A C; Meyer, E

    1994-04-01

    The neural correlates of music perception were studied by measuring cerebral blood flow (CBF) changes with positron emission tomography (PET). Twelve volunteers were scanned using the bolus water method under four separate conditions: (1) listening to a sequence of noise bursts, (2) listening to unfamiliar tonal melodies, (3) comparing the pitch of the first two notes of the same set of melodies, and (4) comparing the pitch of the first and last notes of the melodies. The latter two conditions were designed to investigate short-term pitch retention under low or high memory load, respectively. Subtraction of the obtained PET images, superimposed on matched MRI scans, provides anatomical localization of CBF changes associated with specific cognitive functions. Listening to melodies, relative to acoustically matched noise sequences, resulted in CBF increases in the right superior temporal and right occipital cortices. Pitch judgments of the first two notes of each melody, relative to passive listening to the same stimuli, resulted in right frontal-lobe activation. Analysis of the high memory load condition relative to passive listening revealed the participation of a number of cortical and subcortical regions, notably in the right frontal and right temporal lobes, as well as in parietal and insular cortex. Both pitch judgment conditions also revealed CBF decreases within the left primary auditory cortex. We conclude that specialized neural systems in the right superior temporal cortex participate in perceptual analysis of melodies; pitch comparisons are effected via a neural network that includes right prefrontal cortex, but active retention of pitch involves the interaction of right temporal and frontal cortices.

  1. Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

    Science.gov (United States)

    Jorgensen, Charles C.

    1997-01-01

    A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.

  2. Linking Neural and Symbolic Representation and Processing of Conceptual Structures

    Directory of Open Access Journals (Sweden)

    Frank van der Velde

    2017-08-01

    Full Text Available We compare and discuss representations in two cognitive architectures aimed at representing and processing complex conceptual (sentence-like structures. First is the Neural Blackboard Architecture (NBA, which aims to account for representation and processing of complex and combinatorial conceptual structures in the brain. Second is IDyOT (Information Dynamics of Thinking, which derives sentence-like structures by learning statistical sequential regularities over a suitable corpus. Although IDyOT is designed at a level more abstract than the neural, so it is a model of cognitive function, rather than neural processing, there are strong similarities between the composite structures developed in IDyOT and the NBA. We hypothesize that these similarities form the basis of a combined architecture in which the individual strengths of each architecture are integrated. We outline and discuss the characteristics of this combined architecture, emphasizing the representation and processing of conceptual structures.

  3. Adaptive neural network motion control for aircraft under uncertainty conditions

    Science.gov (United States)

    Efremov, A. V.; Tiaglik, M. S.; Tiumentsev, Yu V.

    2018-02-01

    We need to provide motion control of modern and advanced aircraft under diverse uncertainty conditions. This problem can be solved by using adaptive control laws. We carry out an analysis of the capabilities of these laws for such adaptive systems as MRAC (Model Reference Adaptive Control) and MPC (Model Predictive Control). In the case of a nonlinear control object, the most efficient solution to the adaptive control problem is the use of neural network technologies. These technologies are suitable for the development of both a control object model and a control law for the object. The approximate nature of the ANN model was taken into account by introducing additional compensating feedback into the control system. The capabilities of adaptive control laws under uncertainty in the source data are considered. We also conduct simulations to assess the contribution of adaptivity to the behavior of the system.

  4. Hearing loss impacts neural alpha oscillations under adverse listening conditions

    Directory of Open Access Journals (Sweden)

    Eline Borch Petersen

    2015-02-01

    Full Text Available Degradations in external, acoustic stimulation have long been suspected to increase the load on working memory. One neural signature of working memory load is enhanced power of alpha oscillations (6 ‒ 12 Hz. However, it is unknown to what extent common internal, auditory degradation, that is, hearing impairment, affects the neural mechanisms of working memory when audibility has been ensured via amplification. Using an adapted auditory Sternberg paradigm, we varied the orthogonal factors memory load and background noise level, while the electroencephalogram (EEG was recorded. In each trial, participants were presented with 2, 4, or 6 spoken digits embedded in one of three different levels of background noise. After a stimulus-free delay interval, participants indicated whether a probe digit had appeared in the sequence of digits. Participants were healthy older adults (62 – 86 years, with normal to moderately impaired hearing. Importantly, the background noise levels were individually adjusted and participants were wearing hearing aids to equalize audibility across participants. Irrespective of hearing loss, behavioral performance improved with lower memory load and also with lower levels of background noise. Interestingly, the alpha power in the stimulus-free delay interval was dependent on the interplay between task demands (memory load and noise level and hearing loss; while alpha power increased with hearing loss during low and intermediate levels of memory load and background noise, it dropped for participants with the relatively most severe hearing loss under the highest memory load and background noise level. These findings suggest that adaptive neural mechanisms for coping with adverse listening conditions break down for higher degrees of hearing loss, even when adequate hearing aid amplification is in place.

  5. Microscopic neural image registration based on the structure of mitochondria

    Science.gov (United States)

    Cao, Huiwen; Han, Hua; Rao, Qiang; Xiao, Chi; Chen, Xi

    2017-02-01

    Microscopic image registration is a key component of the neural structure reconstruction with serial sections of neural tissue. The goal of microscopic neural image registration is to recover the 3D continuity and geometrical properties of specimen. During image registration, various distortions need to be corrected, including image rotation, translation, tissue deformation et.al, which come from the procedure of sample cutting, staining and imaging. Furthermore, there is only certain similarity between adjacent sections, and the degree of similarity depends on local structure of the tissue and the thickness of the sections. These factors make the microscopic neural image registration a challenging problem. To tackle the difficulty of corresponding landmarks extraction, we introduce a novel image registration method for Scanning Electron Microscopy (SEM) images of serial neural tissue sections based on the structure of mitochondria. The ellipsoidal shape of mitochondria ensures that the same mitochondria has similar shape between adjacent sections, and its characteristic of broad distribution in the neural tissue guarantees that landmarks based on the mitochondria distributed widely in the image. The proposed image registration method contains three parts: landmarks extraction between adjacent sections, corresponding landmarks matching and image deformation based on the correspondences. We demonstrate the performance of our method with SEM images of drosophila brain.

  6. Neural correlates underlying micrographia in Parkinson’s disease

    Science.gov (United States)

    Zhang, Jiarong; Hallett, Mark; Feng, Tao; Hou, Yanan; Chan, Piu

    2016-01-01

    Micrographia is a common symptom in Parkinson’s disease, which manifests as either a consistent or progressive reduction in the size of handwriting or both. Neural correlates underlying micrographia remain unclear. We used functional magnetic resonance imaging to investigate micrographia-related neural activity and connectivity modulations. In addition, the effect of attention and dopaminergic administration on micrographia was examined. We found that consistent micrographia was associated with decreased activity and connectivity in the basal ganglia motor circuit; while progressive micrographia was related to the dysfunction of basal ganglia motor circuit together with disconnections between the rostral supplementary motor area, rostral cingulate motor area and cerebellum. Attention significantly improved both consistent and progressive micrographia, accompanied by recruitment of anterior putamen and dorsolateral prefrontal cortex. Levodopa improved consistent micrographia accompanied by increased activity and connectivity in the basal ganglia motor circuit, but had no effect on progressive micrographia. Our findings suggest that consistent micrographia is related to dysfunction of the basal ganglia motor circuit; while dysfunction of the basal ganglia motor circuit and disconnection between the rostral supplementary motor area, rostral cingulate motor area and cerebellum likely contributes to progressive micrographia. Attention improves both types of micrographia by recruiting additional brain networks. Levodopa improves consistent micrographia by restoring the function of the basal ganglia motor circuit, but does not improve progressive micrographia, probably because of failure to repair the disconnected networks. PMID:26525918

  7. Neural basis of increased costly norm enforcement under adversity.

    Science.gov (United States)

    Wu, Yan; Yu, Hongbo; Shen, Bo; Yu, Rongjun; Zhou, Zhiheng; Zhang, Guoping; Jiang, Yushi; Zhou, Xiaolin

    2014-12-01

    Humans are willing to punish norm violations even at a substantial personal cost. Using fMRI and a variant of the ultimatum game and functional magnetic resonance imaging, we investigated how the brain differentially responds to fairness in loss and gain domains. Participants (responders) received offers from anonymous partners indicating a division of an amount of monetary gain or loss. If they accept, both get their shares according to the division; if they reject, both get nothing or lose the entire stake. We used a computational model to derive perceived fairness of offers and participant-specific inequity aversion. Behaviorally, participants were more likely to reject unfair offers in the loss (vs gain) domain. Neurally, the positive correlation between fairness and activation in ventral striatum was reduced, whereas the negative correlations between fairness and activations in dorsolateral prefrontal cortex were enhanced in the loss domain. Moreover, rejection-related dorsal striatum activation was higher in the loss domain. Furthermore, the gain-loss domain modulates costly punishment only when unfair behavior was directed toward the participants and not when it was directed toward others. These findings provide neural and computational accounts of increased costly norm enforcement under adversity and advanced our understanding of the context-dependent nature of fairness preference. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  8. Neural-fitted TD-leaf learning for playing Othello with structured neural networks.

    Science.gov (United States)

    van den Dries, Sjoerd; Wiering, Marco A

    2012-11-01

    This paper describes a methodology for quickly learning to play games at a strong level. The methodology consists of a novel combination of three techniques, and a variety of experiments on the game of Othello demonstrates their usefulness. First, structures or topologies in neural network connectivity patterns are used to decrease the number of learning parameters and to deal more effectively with the structural credit assignment problem, which is to change individual network weights based on the obtained feedback. Furthermore, the structured neural networks are trained with the novel neural-fitted temporal difference (TD) learning algorithm to create a system that can exploit most of the training experiences and enhance learning speed and performance. Finally, we use the neural-fitted TD-leaf algorithm to learn more effectively when look-ahead search is performed by the game-playing program. Our extensive experimental study clearly indicates that the proposed method outperforms linear networks and fully connected neural networks or evaluation functions evolved with evolutionary algorithms.

  9. Neural entrainment to the rhythmic structure of music.

    Science.gov (United States)

    Tierney, Adam; Kraus, Nina

    2015-02-01

    The neural resonance theory of musical meter explains musical beat tracking as the result of entrainment of neural oscillations to the beat frequency and its higher harmonics. This theory has gained empirical support from experiments using simple, abstract stimuli. However, to date there has been no empirical evidence for a role of neural entrainment in the perception of the beat of ecologically valid music. Here we presented participants with a single pop song with a superimposed bassoon sound. This stimulus was either lined up with the beat of the music or shifted away from the beat by 25% of the average interbeat interval. Both conditions elicited a neural response at the beat frequency. However, although the on-the-beat condition elicited a clear response at the first harmonic of the beat, this frequency was absent in the neural response to the off-the-beat condition. These results support a role for neural entrainment in tracking the metrical structure of real music and show that neural meter tracking can be disrupted by the presentation of contradictory rhythmic cues.

  10. Neural-Fitted TD-Leaf Learning for Playing Othello With Structured Neural Networks

    NARCIS (Netherlands)

    van den Dries, Sjoerd; Wiering, Marco A.

    2012-01-01

    This paper describes a methodology for quickly learning to play games at a strong level. The methodology consists of a novel combination of three techniques, and a variety of experiments on the game of Othello demonstrates their usefulness. First, structures or topologies in neural network

  11. Neural mechanisms underlying cognitive inflexibility in Parkinson's disease.

    Science.gov (United States)

    Lange, Florian; Seer, Caroline; Loens, Sebastian; Wegner, Florian; Schrader, Christoph; Dressler, Dirk; Dengler, Reinhard; Kopp, Bruno

    2016-12-01

    Cognitive inflexibility is a hallmark of executive dysfunction in Parkinson's disease (PD). This deficit consistently manifests itself in a PD-related increase in the number of perseverative errors committed on the Wisconsin Card Sorting Test (WCST). However, the neural processes underlying perseverative WCST performance in PD are still largely unknown. The present study is the first to investigate the event-related potential (ERP) correlates of cognitive inflexibility on the WCST in PD patients. Thirty-two PD patients and 35 matched control participants completed a computerized version of the WCST while the electroencephalogram (EEG) was recorded. Behavioral results revealed the expected increase in perseverative errors in patients with PD. ERP analysis focused on two established indicators of executive processes: the fronto-central P3a as an index of attentional orienting and the sustained parietal positivity (SPP) as an index of set-shifting processes. In comparison to controls, P3a amplitudes were significantly attenuated in PD patients. Regression analysis further revealed that P3a and SPP amplitudes interactively contributed to the prediction of perseverative errors in PD patients: The number of perseverative errors was only increased when both ERP amplitudes were attenuated. Notably, the two ERP markers of executive processes accounted for more than 40% of the variance in perseverative errors in PD patients. We conclude that cognitive inflexibility in PD occurs when the neural bases of multiple executive processes are affected by the pathophysiology of PD. The combined measurement of P3a and SPP might yield an electrophysiological marker of cognitive inflexibility in PD. Copyright © 2016 Elsevier Ltd. All rights reserved.

  12. Neural processes underlying the orienting of attention without awareness.

    Science.gov (United States)

    Giattino, Charles M; Alam, Zaynah M; Woldorff, Marty G

    2017-07-22

    Despite long being of interest to both philosophers and scientists, the relationship between attention and perceptual awareness is not well understood, especially to what extent they are even dissociable. Previous studies have shown that stimuli of which we are unaware can orient spatial attention and affect behavior. Yet, relatively little is understood about the neural processes underlying such unconscious orienting of attention, and how they compare to conscious orienting. To directly compare the cascade of attentional processes with and without awareness of the orienting stimulus, we employed a spatial-cueing paradigm and used object-substitution masking to manipulate subjects' awareness of the cues. We recorded EEG during the task, from which we extracted hallmark event-related-potential (ERP) indices of attention. Behaviorally, there was a 61 ms validity effect (invalidly minus validly cued target RTs) on cue-aware trials. On cue-unaware trials, subjects also had a robust validity effect of 20 ms, despite being unaware of the cue. An N2pc to the cue, a hallmark ERP index of the lateralized orienting of attention, was observed for cue-aware but not cue-unaware trials, despite the latter showing a clear behavioral validity effect. Finally, the P1 sensory-ERP response to the targets was larger when validly versus invalidly cued, even when subjects were unaware of the preceding cue, demonstrating enhanced sensory processing of targets following subliminal cues. These results suggest that subliminal stimuli can orient attention and lead to subsequent enhancements to both stimulus sensory processing and behavior, but through different neural mechanisms (such as via a subcortical pathway) than stimuli we perceive. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Neural Mechanisms Underlying Hyperphagia in Prader-Willi Syndrome

    Science.gov (United States)

    Holsen, Laura M.; Zarcone, Jennifer R.; Brooks, William M.; Butler, Merlin G.; Thompson, Travis I.; Ahluwalia, Jasjit S.; Nollen, Nicole L.; Savage, Cary R.

    2006-01-01

    Objective Prader-Willi syndrome (PWS) is a genetic disorder associated with developmental delay, obesity, and obsessive behavior related to food consumption. The most striking symptom of PWS is hyperphagia; as such, PWS may provide important insights into factors leading to overeating and obesity in the general population. We used functional magnetic resonance imaging to study the neural mechanisms underlying responses to visual food stimuli, before and after eating, in individuals with PWS and a healthy weight control (HWC) group. Research Methods and Procedures Participants were scanned once before (pre-meal) and once after (post-meal) eating a standardized meal. Pictures of food, animals, and blurred control images were presented in a block design format during acquisition of functional magnetic resonance imaging data. Results Statistical contrasts in the HWC group showed greater activation to food pictures in the pre-meal condition compared with the post-meal condition in the amygdala, orbitofrontal cortex, medial prefrontal cortex (medial PFC), and frontal operculum. In comparison, the PWS group exhibited greater activation to food pictures in the post-meal condition compared with the pre-meal condition in the orbitofrontal cortex, medial PFC, insula, hippocampus, and parahippocampal gyrus. Between-group contrasts in the pre- and post-meal conditions confirmed group differences, with the PWS group showing greater activation than the HWC group after the meal in food motivation networks. Discussion Results point to distinct neural mechanisms associated with hyperphagia in PWS. After eating a meal, the PWS group showed hyperfunction in limbic and para-limbic regions that drive eating behavior (e.g., the amygdala) and in regions that suppress food intake (e.g., the medial PFC). PMID:16861608

  14. Neural mechanisms underlying hyperphagia in Prader-Willi syndrome.

    Science.gov (United States)

    Holsen, Laura M; Zarcone, Jennifer R; Brooks, William M; Butler, Merlin G; Thompson, Travis I; Ahluwalia, Jasjit S; Nollen, Nicole L; Savage, Cary R

    2006-06-01

    Prader-Willi syndrome (PWS) is a genetic disorder associated with developmental delay, obesity, and obsessive behavior related to food consumption. The most striking symptom of PWS is hyperphagia; as such, PWS may provide important insights into factors leading to overeating and obesity in the general population. We used functional magnetic resonance imaging to study the neural mechanisms underlying responses to visual food stimuli, before and after eating, in individuals with PWS and a healthy weight control (HWC) group. Participants were scanned once before (pre-meal) and once after (post-meal) eating a standardized meal. Pictures of food, animals, and blurred control images were presented in a block design format during acquisition of functional magnetic resonance imaging data. Statistical contrasts in the HWC group showed greater activation to food pictures in the pre-meal condition compared with the post-meal condition in the amygdala, orbitofrontal cortex, medial prefrontal cortex (medial PFC), and frontal operculum. In comparison, the PWS group exhibited greater activation to food pictures in the post-meal condition compared with the pre-meal condition in the orbitofrontal cortex, medial PFC, insula, hippocampus, and parahippocampal gyrus. Between-group contrasts in the pre- and post-meal conditions confirmed group differences, with the PWS group showing greater activation than the HWC group after the meal in food motivation networks. Results point to distinct neural mechanisms associated with hyperphagia in PWS. After eating a meal, the PWS group showed hyperfunction in limbic and paralimbic regions that drive eating behavior (e.g., the amygdala) and in regions that suppress food intake (e.g., the medial PFC).

  15. Structural Damage Assessment under Uncertainty

    Science.gov (United States)

    Lopez Martinez, Israel

    Structural damage assessment has applications in the majority of engineering structures and mechanical systems ranging from aerospace vehicles to manufacturing equipment. The primary goals of any structural damage assessment and health monitoring systems are to ascertain the condition of a structure and to provide an evaluation of changes as a function of time as well as providing an early-warning of an unsafe condition. There are many structural heath monitoring and assessment techniques developed for research using numerical simulations and scaled structural experiments. However, the transition from research to real-world structures has been rather slow. One major reason for this slow-progress is the existence of uncertainty in every step of the damage assessment process. This dissertation research involved the experimental and numerical investigation of uncertainty in vibration-based structural health monitoring and development of robust detection and localization methods. The basic premise of vibration-based structural health monitoring is that changes in structural characteristics, such as stiffness, mass and damping, will affect the global vibration response of the structure. The diagnostic performance of vibration-based monitoring system is affected by uncertainty sources such as measurement errors, environmental disturbances and parametric modeling uncertainties. To address diagnostic errors due to irreducible uncertainty, a pattern recognition framework for damage detection has been developed to be used for continuous monitoring of structures. The robust damage detection approach developed is based on the ensemble of dimensional reduction algorithms for improved damage-sensitive feature extraction. For damage localization, the determination of an experimental structural model was performed based on output-only modal analysis. An experimental model correlation technique is developed in which the discrepancies between the undamaged and damaged modal data are

  16. A loop-based neural architecture for structured behavior encoding and decoding.

    Science.gov (United States)

    Gisiger, Thomas; Boukadoum, Mounir

    2018-02-01

    We present a new type of artificial neural network that generalizes on anatomical and dynamical aspects of the mammal brain. Its main novelty lies in its topological structure which is built as an array of interacting elementary motifs shaped like loops. These loops come in various types and can implement functions such as gating, inhibitory or executive control, or encoding of task elements to name a few. Each loop features two sets of neurons and a control region, linked together by non-recurrent projections. The two neural sets do the bulk of the loop's computations while the control unit specifies the timing and the conditions under which the computations implemented by the loop are to be performed. By functionally linking many such loops together, a neural network is obtained that may perform complex cognitive computations. To demonstrate the potential offered by such a system, we present two neural network simulations. The first illustrates the structure and dynamics of a single loop implementing a simple gating mechanism. The second simulation shows how connecting four loops in series can produce neural activity patterns that are sufficient to pass a simplified delayed-response task. We also show that this network reproduces electrophysiological measurements gathered in various regions of the brain of monkeys performing similar tasks. We also demonstrate connections between this type of neural network and recurrent or long short-term memory network models, and suggest ways to generalize them for future artificial intelligence research. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Combinatorial structures and processing in neural blackboard architectures

    NARCIS (Netherlands)

    van der Velde, Frank; van der Velde, Frank; de Kamps, Marc; Besold, Tarek R.; d'Avila Garcez, Artur; Marcus, Gary F.; Miikkulainen, Risto

    2015-01-01

    We discuss and illustrate Neural Blackboard Architectures (NBAs) as the basis for variable binding and combinatorial processing the brain. We focus on the NBA for sentence structure. NBAs are based on the notion that conceptual representations are in situ, hence cannot be copied or transported.

  18. Genetic dyslexia risk variant is related to neural connectivity patterns underlying phonological awareness in children.

    Science.gov (United States)

    Skeide, Michael A; Kirsten, Holger; Kraft, Indra; Schaadt, Gesa; Müller, Bent; Neef, Nicole; Brauer, Jens; Wilcke, Arndt; Emmrich, Frank; Boltze, Johannes; Friederici, Angela D

    2015-09-01

    Phonological awareness is the best-validated predictor of reading and spelling skill and therefore highly relevant for developmental dyslexia. Prior imaging genetics studies link several dyslexia risk genes to either brain-functional or brain-structural factors of phonological deficits. However, coherent evidence for genetic associations with both functional and structural neural phenotypes underlying variation in phonological awareness has not yet been provided. Here we demonstrate that rs11100040, a reported modifier of SLC2A3, is related to the functional connectivity of left fronto-temporal phonological processing areas at resting state in a sample of 9- to 12-year-old children. Furthermore, we provide evidence that rs11100040 is related to the fractional anisotropy of the arcuate fasciculus, which forms the structural connection between these areas. This structural connectivity phenotype is associated with phonological awareness, which is in turn associated with the individual retrospective risk scores in an early dyslexia screening as well as to spelling. These results suggest a link between a dyslexia risk genotype and a functional as well as a structural neural phenotype, which is associated with a phonological awareness phenotype. The present study goes beyond previous work by integrating genetic, brain-functional and brain-structural aspects of phonological awareness within a single approach. These combined findings might be another step towards a multimodal biomarker for developmental dyslexia. Copyright © 2015 Elsevier Inc. All rights reserved.

  19. Discriminative training of self-structuring hidden control neural models

    DEFF Research Database (Denmark)

    Sørensen, Helge Bjarup Dissing; Hartmann, Uwe; Hunnerup, Preben

    1995-01-01

    This paper presents a new training algorithm for self-structuring hidden control neural (SHC) models. The SHC models were trained non-discriminatively for speech recognition applications. Better recognition performance can generally be achieved, if discriminative training is applied instead. Thus...... we developed a discriminative training algorithm for SHC models, where each SHC model for a specific speech pattern is trained with utterances of the pattern to be recognized and with other utterances. The discriminative training of SHC neural models has been tested on the TIDIGITS database...

  20. Structural and functional neural correlates of music perception.

    Science.gov (United States)

    Limb, Charles J

    2006-04-01

    This review article highlights state-of-the-art functional neuroimaging studies and demonstrates the novel use of music as a tool for the study of human auditory brain structure and function. Music is a unique auditory stimulus with properties that make it a compelling tool with which to study both human behavior and, more specifically, the neural elements involved in the processing of sound. Functional neuroimaging techniques represent a modern and powerful method of investigation into neural structure and functional correlates in the living organism. These methods have demonstrated a close relationship between the neural processing of music and language, both syntactically and semantically. Greater neural activity and increased volume of gray matter in Heschl's gyrus has been associated with musical aptitude. Activation of Broca's area, a region traditionally considered to subserve language, is important in interpreting whether a note is on or off key. The planum temporale shows asymmetries that are associated with the phenomenon of perfect pitch. Functional imaging studies have also demonstrated activation of primitive emotional centers such as ventral striatum, midbrain, amygdala, orbitofrontal cortex, and ventral medial prefrontal cortex in listeners of moving musical passages. In addition, studies of melody and rhythm perception have elucidated mechanisms of hemispheric specialization. These studies show the power of music and functional neuroimaging to provide singularly useful tools for the study of brain structure and function.

  1. Neural mechanisms underlying the induction and relief of perceptual curiosity

    Directory of Open Access Journals (Sweden)

    Marieke eJepma

    2012-02-01

    Full Text Available Curiosity is one of the most basic biological drives in both animals and humans, and has been identified as a key motive for learning and discovery. Despite the importance of curiosity and related behaviors, the topic has been largely neglected in human neuroscience; hence little is known about the neurobiological mechanisms underlying curiosity. We used functional magnetic resonance imaging (fMRI to investigate what happens in our brain during the induction and subsequent relief of perceptual curiosity. Our core findings were that (i the induction of perceptual curiosity, through the presentation of ambiguous visual input, activated the anterior insula and anterior cingulate cortex, brain regions sensitive to conflict and arousal; (ii the relief of perceptual curiosity, through visual disambiguation, activated regions of the striatum that have been related to reward processing; and (iii the relief of perceptual curiosity was associated with hippocampal activation and enhanced incidental memory. These findings provide the first demonstration of the neural basis of human perceptual curiosity. Our results provide neurobiological support for a classic psychological theory of curiosity, which holds that curiosity is an aversive condition of increased arousal whose termination is rewarding and facilitates memory.

  2. Neural mechanism underlying autobiographical memory modulated by remoteness and emotion

    Science.gov (United States)

    Ge, Ruiyang; Fu, Yan; Wang, DaHua; Yao, Li; Long, Zhiying

    2012-03-01

    Autobiographical memory is the ability to recollect past events from one's own life. Both emotional tone and memory remoteness can influence autobiographical memory retrieval along the time axis of one's life. Although numerous studies have been performed to investigate brain regions involved in retrieving processes of autobiographical memory, the effect of emotional tone and memory age on autobiographical memory retrieval remains to be clarified. Moreover, whether the involvement of hippocampus in consolidation of autobiographical events is time dependent or independent has been controversial. In this study, we investigated the effect of memory remoteness (factor1: recent and remote) and emotional valence (factor2: positive and negative) on neural correlates underlying autobiographical memory by using functional magnetic resonance imaging (fMRI) technique. Although all four conditions activated some common regions known as "core" regions in autobiographical memory retrieval, there are some other regions showing significantly different activation for recent versus remote and positive versus negative memories. In particular, we found that bilateral hippocampal regions were activated in the four conditions regardless of memory remoteness and emotional valence. Thus, our study confirmed some findings of previous studies and provided further evidence to support the multi-trace theory which believes that the role of hippocampus involved in autobiographical memory retrieval is time-independent and permanent in memory consolidation.

  3. Analytic Treatment of Deep Neural Networks Under Additive Gaussian Noise

    KAUST Repository

    Alfadly, Modar M.

    2018-04-12

    Despite the impressive performance of deep neural networks (DNNs) on numerous vision tasks, they still exhibit yet-to-understand uncouth behaviours. One puzzling behaviour is the reaction of DNNs to various noise attacks, where it has been shown that there exist small adversarial noise that can result in a severe degradation in the performance of DNNs. To rigorously treat this, we derive exact analytic expressions for the first and second moments (mean and variance) of a small piecewise linear (PL) network with a single rectified linear unit (ReLU) layer subject to general Gaussian input. We experimentally show that these expressions are tight under simple linearizations of deeper PL-DNNs, especially popular architectures in the literature (e.g. LeNet and AlexNet). Extensive experiments on image classification show that these expressions can be used to study the behaviour of the output mean of the logits for each class, the inter-class confusion and the pixel-level spatial noise sensitivity of the network. Moreover, we show how these expressions can be used to systematically construct targeted and non-targeted adversarial attacks. Then, we proposed a special estimator DNN, named mixture of linearizations (MoL), and derived the analytic expressions for its output mean and variance, as well. We employed these expressions to train the model to be particularly robust against Gaussian attacks without the need for data augmentation. Upon training this network on a loss that is consolidated with the derived output probabilistic moments, the network is not only robust under very high variance Gaussian attacks but is also as robust as networks that are trained with 20 fold data augmentation.

  4. Hearing loss impacts neural alpha oscillations under adverse listening conditions

    OpenAIRE

    Petersen, Eline B.; Wöstmann, Malte; Obleser, Jonas; Stenfelt, Stefan; Lunner, Thomas

    2015-01-01

    Degradations in external, acoustic stimulation have long been suspected to increase the load on working memory (WM). One neural signature of WM load is enhanced power of alpha oscillations (6–12 Hz). However, it is unknown to what extent common internal, auditory degradation, that is, hearing impairment, affects the neural mechanisms of WM when audibility has been ensured via amplification. Using an adapted auditory Sternberg paradigm, we varied the orthogonal factors memory load and backgrou...

  5. Neural correlates underlying true and false associative memories.

    Science.gov (United States)

    Dennis, Nancy A; Johnson, Christina E; Peterson, Kristina M

    2014-07-01

    Despite the fact that associative memory studies produce a large number of false memories, neuroimaging analyses utilizing this paradigm typically focus only on neural activity mediating successful retrieval. The current study sought to expand on this prior research by examining the neural basis of both true and false associative memories. Though associative false memories are substantially different than those found in semantic or perceptual false memory paradigms, results suggest that associative false memories are mediated by similar neural mechanisms. Specifically, we found increased frontal activity that likely represents enhanced monitoring and evaluation compared to that needed for true memories and correct rejections. Results also indicated that true, and not false associative memories, are mediated by neural activity in the MTL, specifically the hippocampus. Finally, while activity in early visual cortex distinguished true from false memories, a lack of neural differences between hits and correct rejections failed to support previous findings suggesting that activity in early visual cortex represents sensory reactivation of encoding-related processing. Copyright © 2014 Elsevier Inc. All rights reserved.

  6. Identifying the Neural Correlates Underlying Social Pain: Implications for Developmental Processes

    Science.gov (United States)

    Eisenberger, Naomi I.

    2006-01-01

    Although the need for social connection is critical for early social development as well as for psychological well-being throughout the lifespan, relatively little is known about the neural processes involved in maintaining social connections. The following review summarizes what is known regarding the neural correlates underlying feeling of…

  7. Delineating Neural Structures of Developmental Human Brains with Diffusion Tensor Imaging

    Directory of Open Access Journals (Sweden)

    Hao Huang

    2010-01-01

    Full Text Available The human brain anatomy is characterized by dramatic structural changes during fetal development. It is extraordinarily complex and yet its origin is a simple tubular structure. Revealing detailed anatomy at different stages of brain development not only aids in understanding this highly ordered process, but also provides clues to detect abnormalities caused by genetic or environmental factors. However, anatomical studies of human brain development during the fetal period are surprisingly scarce and histology-based atlases have become available only recently. Diffusion tensor imaging (DTI measures water diffusion to delineate the underlying neural structures. The high contrasts derived from DTI can be used to establish the brain atlas. With DTI tractography, coherent neural structures, such as white matter tracts, can be three-dimensionally reconstructed. The primary eigenvector of the diffusion tensor can be further explored to characterize microstructures in the cerebral wall of the developmental brains. In this mini-review, the application of DTI in order to reveal the structures of developmental fetal brains has been reviewed in the above-mentioned aspects. The fetal brain DTI provides a unique insight for delineating the neural structures in both macroscopic and microscopic levels. The resultant DTI database will provide structural guidance for the developmental study of human fetal brains in basic neuroscience, and reference standards for diagnostic radiology of premature newborns.

  8. Neural mechanisms underlying probalistic category learning in normal aging.

    NARCIS (Netherlands)

    Fera, F.; Weickert, T.W.; Goldberg, T.E.; Tessitore, A.; Hariri, A.; Das, S.; Lee, S.; Zoltick, B.; Meeter, M.; Gluck, M.A.; Weinberger, D.A.; Matta, V.S.

    2005-01-01

    Probabilistic category learning engages neural circuitry that includes the prefrontal cortex and caudate nucleus, two regions that show prominent changes with normal aging. However, the specific contributions of these brain regions are uncertain, and the effects of normal aging have not been

  9. Neural suppression of irrelevant information underlies optimal working memory performance.

    Science.gov (United States)

    Zanto, Theodore P; Gazzaley, Adam

    2009-03-11

    Our ability to focus attention on task-relevant information and ignore distractions is reflected by differential enhancement and suppression of neural activity in sensory cortex (i.e., top-down modulation). Such selective, goal-directed modulation of activity may be intimately related to memory, such that the focus of attention biases the likelihood of successfully maintaining relevant information by limiting interference from irrelevant stimuli. Despite recent studies elucidating the mechanistic overlap between attention and memory, the relationship between top-down modulation of visual processing during working memory (WM) encoding, and subsequent recognition performance has not yet been established. Here, we provide neurophysiological evidence in healthy, young adults that top-down modulation of early visual processing (performance, such that the likelihood of successfully remembering relevant information is associated with limiting interference from irrelevant stimuli. The consequences of a failure to ignore distractors on recognition performance was replicated for two types of feature-based memory, motion direction and color. Moreover, attention to irrelevant stimuli was reflected neurally during the WM maintenance period as an increased memory load. These results suggest that neural enhancement of relevant information is not the primary determinant of high-level performance, but rather optimal WM performance is dependent on effectively filtering irrelevant information through neural suppression to prevent overloading a limited memory capacity.

  10. Neural mechanisms underlying neurooptometric rehabilitation following traumatic brain injury

    Directory of Open Access Journals (Sweden)

    Hudac CM

    2012-01-01

    Full Text Available Caitlin M Hudac1, Srinivas Kota1, James L Nedrow2, Dennis L Molfese1,31Department of Psychology, University of Nebraska-Lincoln, 2Oculi Vision Rehabilitation, 3Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, Lincoln, NEAbstract: Mild to severe traumatic brain injuries have lasting effects on everyday functioning. Issues relating to sensory problems are often overlooked or not addressed until well after the onset of the injury. In particular, vision problems related to ambient vision and the magnocellular pathway often result in posttrauma vision syndrome or visual midline shift syndrome. Symptoms from these syndromes are not restricted to the visual domain. Patients commonly experience proprioceptive, kinesthetic, vestibular, cognitive, and language problems. Neurooptometric rehabilitation often entails the use of corrective lenses, prisms, and binasal occlusion to accommodate the unstable magnocellular system. However, little is known regarding the neural mechanisms engaged during neurooptometric rehabilitation, nor how these mechanisms impact other domains. Event-related potentials from noninvasive electrophysiological recordings can be used to assess rehabilitation progress in patients. In this case report, high-density visual event-related potentials were recorded from one patient with posttrauma vision syndrome and secondary visual midline shift syndrome during a pattern reversal task, both with and without prisms. Results indicate that two factors occurring during the end portion of the P148 component (168–256 milliseconds poststimulus onset map onto two separate neural systems that were engaged with and without neurooptometric rehabilitation. Without prisms, neural sources within somatosensory, language, and executive brain regions engage inefficient magnocellular system processing. However, when corrective prisms were worn, primary visual areas were appropriately engaged. The impact of using early

  11. Attractor in Circular Structure of Oscillatory Generalized Neural Elements

    Directory of Open Access Journals (Sweden)

    E. V. Konovalov

    2014-01-01

    Full Text Available A perspective model of a neuron cell — the generalized neural element (GNE is studied in this article. The model has an universal character. It combines properties of a neuron-oscillator and a neuron-detector. In this structure cyclic sequential pulse generation elements of the ring are studied. A nonlinear mapping for mismatches between pulses of neighboring elements is constructed. We prove the existence of a fixed point of this mapping (threshold value of mismatches and its stability in a small neighborhood of the fixed point. In doing so the existence of a stable oscillatory mode of neural activity (attractor of a certain type is proved. The parameters of the attractor (threshold values of mismatches can be controlled in advance, due to the choice of synaptic weights of the links in the ring.

  12. Child Abuse, Neural Structure, and Adolescent Psychopathology: A Longitudinal Study.

    Science.gov (United States)

    Busso, Daniel S; McLaughlin, Katie A; Brueck, Stephanie; Peverill, Matthew; Gold, Andrea L; Sheridan, Margaret A

    2017-04-01

    Child abuse exerts a deleterious impact on a broad array of mental health outcomes. However, the neurobiological mechanisms that mediate this association remain poorly characterized. Here, we use a longitudinal design to prospectively identify neural mediators of the association between child abuse and psychiatric disorders in a community sample of adolescents. Structural magnetic resonance imaging (MRI) data and assessments of mental health were acquired for 51 adolescents (aged 13-20; M=16.96; SD=1.51), 19 of whom were exposed to physical or sexual abuse. Participants were assessed for abuse exposure (time 1), participated in MRI scanning and a diagnostic structured interview (time 2), and 2 years later were followed-up to assess psychopathology (time 3). We examined associations between child abuse and neural structure, and identified whether abuse-related differences in neural structure prospectively predicted psychiatric symptoms. Abuse was associated with reduced cortical thickness in medial and lateral prefrontal and temporal lobe regions. Thickness of the left and right parahippocampal gyrus predicted antisocial behavior symptoms, and thickness of the middle temporal gyrus predicted symptoms of generalized anxiety disorder. Thickness of the left parahippocampal gyrus mediated the longitudinal association of abuse with antisocial behavior. Child abuse is associated with widespread disruptions in cortical structure, and these disruptions are selectively associated with increased vulnerability to internalizing and externalizing psychopathology. Identifying predictive biomarkers of vulnerability following childhood maltreatment may uncover neurodevelopmental mechanisms linking environmental experience with the onset of psychopathology. Copyright © 2017 American Academy of Child and Adolescent Psychiatry. All rights reserved.

  13. Age-related neural correlates of cognitive task performance under increased postural load

    NARCIS (Netherlands)

    Van Impe, A; Bruijn, S M; Coxon, J P; Wenderoth, N; Sunaert, S; Duysens, J; Swinnen, S P

    2013-01-01

    Behavioral studies suggest that postural control requires increased cognitive control and visuospatial processing with aging. Consequently, performance can decline when concurrently performing a postural and a demanding cognitive task. We aimed to identify the neural substrate underlying this

  14. The neural sociometer: brain mechanisms underlying state self-esteem.

    Science.gov (United States)

    Eisenberger, Naomi I; Inagaki, Tristen K; Muscatell, Keely A; Byrne Haltom, Kate E; Leary, Mark R

    2011-11-01

    On the basis of the importance of social connection for survival, humans may have evolved a "sociometer"-a mechanism that translates perceptions of rejection or acceptance into state self-esteem. Here, we explored the neural underpinnings of the sociometer by examining whether neural regions responsive to rejection or acceptance were associated with state self-esteem. Participants underwent fMRI while viewing feedback words ("interesting," "boring") ostensibly chosen by another individual (confederate) to describe the participant's previously recorded interview. Participants rated their state self-esteem in response to each feedback word. Results demonstrated that greater activity in rejection-related neural regions (dorsal ACC, anterior insula) and mentalizing regions was associated with lower-state self-esteem. Additionally, participants whose self-esteem decreased from prescan to postscan versus those whose self-esteem did not showed greater medial prefrontal cortical activity, previously associated with self-referential processing, in response to negative feedback. Together, the results inform our understanding of the origin and nature of our feelings about ourselves.

  15. The functional and structural neural basis of individual differences in loss aversion.

    Science.gov (United States)

    Canessa, Nicola; Crespi, Chiara; Motterlini, Matteo; Baud-Bovy, Gabriel; Chierchia, Gabriele; Pantaleo, Giuseppe; Tettamanti, Marco; Cappa, Stefano F

    2013-09-04

    Decision making under risk entails the anticipation of prospective outcomes, typically leading to the greater sensitivity to losses than gains known as loss aversion. Previous studies on the neural bases of choice-outcome anticipation and loss aversion provided inconsistent results, showing either bidirectional mesolimbic responses of activation for gains and deactivation for losses, or a specific amygdala involvement in processing losses. Here we focused on loss aversion with the aim to address interindividual differences in the neural bases of choice-outcome anticipation. Fifty-six healthy human participants accepted or rejected 104 mixed gambles offering equal (50%) chances of gaining or losing different amounts of money while their brain activity was measured with functional magnetic resonance imaging (fMRI). We report both bidirectional and gain/loss-specific responses while evaluating risky gambles, with amygdala and posterior insula specifically tracking the magnitude of potential losses. At the individual level, loss aversion was reflected both in limbic fMRI responses and in gray matter volume in a structural amygdala-thalamus-striatum network, in which the volume of the "output" centromedial amygdala nuclei mediating avoidance behavior was negatively correlated with monetary performance. We conclude that outcome anticipation and ensuing loss aversion involve multiple neural systems, showing functional and structural individual variability directly related to the actual financial outcomes of choices. By supporting the simultaneous involvement of both appetitive and aversive processing in economic decision making, these results contribute to the interpretation of existing inconsistencies on the neural bases of anticipating choice outcomes.

  16. Deep Convolutional Neural Networks: Structure, Feature Extraction and Training

    Directory of Open Access Journals (Sweden)

    Namatēvs Ivars

    2017-12-01

    Full Text Available Deep convolutional neural networks (CNNs are aimed at processing data that have a known network like topology. They are widely used to recognise objects in images and diagnose patterns in time series data as well as in sensor data classification. The aim of the paper is to present theoretical and practical aspects of deep CNNs in terms of convolution operation, typical layers and basic methods to be used for training and learning. Some practical applications are included for signal and image classification. Finally, the present paper describes the proposed block structure of CNN for classifying crucial features from 3D sensor data.

  17. The neural bases underlying social risk perception in purchase decisions.

    Science.gov (United States)

    Yokoyama, Ryoichi; Nozawa, Takayuki; Sugiura, Motoaki; Yomogida, Yukihito; Takeuchi, Hikaru; Akimoto, Yoritaka; Shibuya, Satoru; Kawashima, Ryuta

    2014-05-01

    Social considerations significantly influence daily purchase decisions, and the perception of social risk (i.e., the anticipated disapproval of others) is crucial in dissuading consumers from making purchases. However, the neural basis for consumers' perception of social risk remains undiscovered, and this novel study clarifies the relevant neural processes. A total of 26 volunteers were scanned while they evaluated purchase intention of products (purchase intention task) and their anticipation of others' disapproval for possessing a product (social risk task), using functional magnetic resonance imaging (fMRI). The fMRI data from the purchase intention task was used to identify the brain region associated with perception of social risk during purchase decision making by using subjective social risk ratings for a parametric modulation analysis. Furthermore, we aimed to explore if there was a difference between participants' purchase decisions and their explicit evaluations of social risk, with reference to the neural activity associated with social risk perception. For this, subjective social risk ratings were used for a parametric modulation analysis on fMRI data from the social risk task. Analysis of the purchase intention task revealed a significant positive correlation between ratings of social risk and activity in the anterior insula, an area of the brain that is known as part of the emotion-related network. Analysis of the social risk task revealed a significant positive correlation between ratings of social risk and activity in the temporal parietal junction and the medial prefrontal cortex, which are known as theory-of-mind regions. Our results suggest that the anterior insula processes consumers' social risk implicitly to prompt consumers not to buy socially unacceptable products, whereas ToM-related regions process such risk explicitly in considering the anticipated disapproval of others. These findings may prove helpful in understanding the mental

  18. Structural neural substrates of reading the mind in the eyes

    Directory of Open Access Journals (Sweden)

    Wataru eSato

    2016-04-01

    Full Text Available The ability to read the minds of others in their eyes plays an important role in human adaptation to social environments. Behavioral studies have resulted in the development of a test to measure this ability (Reading the Mind in the Eyes Test, revised version; Eyes Test, and have demonstrated that this ability is consistent over time. Although functional neuroimaging studies revealed brain activation while performing the Eyes Test, the structural neural substrates supporting consistent performance on the Eyes Test remain unclear. In this study we assessed the Eyes Test and analyzed structural magnetic resonance images using voxel-based morphometry in healthy participants. Test performance was positively associated with the gray matter volumes of the dorsomedial prefrontal cortex, inferior parietal lobule (temporoparietal junction, and precuneus in the left hemisphere. These results suggest that the fronto-temporoparietal network structures support the consistent ability to read the mind in the eyes.

  19. Concrete structures under projectile impact

    CERN Document Server

    Fang, Qin

    2017-01-01

    In this book, the authors present their theoretical, experimental and numerical investigations into concrete structures subjected to projectile and aircraft impacts in recent years. Innovative approaches to analyze the rigid, mass abrasive and eroding projectile penetration and perforation are proposed. Damage and failure analyses of nuclear power plant containments impacted by large commercial aircrafts are numerically and experimentally analyzed. Ultra-high performance concrete materials and structures against the projectile impact are developed and their capacities of resisting projectile impact are evaluated. This book is written for the researchers, engineers and graduate students in the fields of protective structures and terminal ballistics.

  20. PREDICTION OF SITE RESPONSE SPECTRUM UNDER EARTHQUAKE VIBRATION USING AN OPTIMIZED DEVELOPED ARTIFICIAL NEURAL NETWORK MODEL

    Directory of Open Access Journals (Sweden)

    Reza Esmaeilabadi

    2016-06-01

    Full Text Available Site response spectrum is one of the key factors to determine the maximum acceleration and displacement, as well as structure behavior analysis during earthquake vibrations. The main objective of this paper is to develop an optimized model based on artificial neural network (ANN using five different training algorithms to predict nonlinear site response spectrum subjected to Silakhor earthquake vibrations is. The model output was tested for a specified area in west of Iran. The performance and quality of optimized model under all training algorithms have been examined by various statistical, analytical and graph analyses criteria as well as a comparison with numerical methods. The observed adaptabilities in results indicate a feasible and satisfactory engineering alternative method for predicting the analysis of nonlinear site response.

  1. Is Artificial Neural Network Suitable for Damage Level Determination of Rc- Structures?

    OpenAIRE

    Baltacıoğlu, A. K.; Öztürk, B.; Civalek, Ö.; Akgöz, B.

    2010-01-01

    In the present study, an artificial neural network (ANN) application is introduced for estimation of damage level of reinforced concrete structures. Back-propagation learning algorithm is adopted. A typical neural network architecture is proposed and some conclusions are presented. Applicability of artificial neural network (ANN) for the assessment of earthquake related damage is investigated

  2. Neural mechanisms underlying social conformity in an ultimatum game

    Directory of Open Access Journals (Sweden)

    Zhenyu eWei

    2013-12-01

    Full Text Available When individuals’ actions are incongruent with those of the group they belong to, they may change their initial behavior in order to conform to the group norm. This phenomenon is known as social conformity. In the present study, we used event-related functional magnetic resonance imaging (fMRI to investigate brain activity in response to group opinion during an ultimatum game. Results showed that participants changed their choices when these choices conflicted with the normative opinion of the group they were members of, especially in conditions of unfair treatment. The fMRI data revealed that a conflict with group norms activated the brain regions involved in norm violations and behavioral adjustment. Furthermore, in the reject-unfair condition, we observed that a conflict with group norms activated the medial frontal gyrus. These findings contribute to recent research examining neural mechanisms involved in detecting violations of social norms, and provide information regarding the neural representation of conformity behavior in an economic game.

  3. The behavioral and neural mechanisms underlying the tracking of expertise.

    Science.gov (United States)

    Boorman, Erie D; O'Doherty, John P; Adolphs, Ralph; Rangel, Antonio

    2013-12-18

    Evaluating the abilities of others is fundamental for successful economic and social behavior. We investigated the computational and neurobiological basis of ability tracking by designing an fMRI task that required participants to use and update estimates of both people and algorithms' expertise through observation of their predictions. Behaviorally, we find a model-based algorithm characterized subject predictions better than several alternative models. Notably, when the agent's prediction was concordant rather than discordant with the subject's own likely prediction, participants credited people more than algorithms for correct predictions and penalized them less for incorrect predictions. Neurally, many components of the mentalizing network-medial prefrontal cortex, anterior cingulate gyrus, temporoparietal junction, and precuneus-represented or updated expertise beliefs about both people and algorithms. Moreover, activity in lateral orbitofrontal and medial prefrontal cortex reflected behavioral differences in learning about people and algorithms. These findings provide basic insights into the neural basis of social learning. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

  4. Structured information in sparse-code metric neural networks

    Science.gov (United States)

    Dominguez, David; González, Mario; Rodríguez, Francisco B.; Serrano, Eduardo; Erichsen, R.; Theumann, W. K.

    2012-02-01

    Sparse-code networks have retrieval abilities which are strongly dependent on the firing threshold for the neurons. If the connections are spatially uniform, the macroscopic properties of the network can be measured by the overlap between neurons and learned patterns, and by the global activity. However, for nonuniform networks, for instance small-world networks, the neurons can retrieve fragments of patterns without performing global retrieval. Local overlaps are needed to describe the network. We characterize the structure type of the neural states using a parameter that is related to fluctuations of the local overlaps, with distinction between bump and block phases. Simulation of neural dynamics shows a competition between localized (bump), structured (block) and global retrieval. When the network topology randomness increases, the phase-diagram shows a transition from local to global retrieval. Furthermore, the local phase splits into a bump phase for low activity and a block phase for high activity. A theoretical approach solves the asymptotic limit of the model, and confirms the simulation results which predicts the change of stability from bumps to blocks when the storage ratio increases.

  5. Group Membership Modulates the Neural Circuitry Underlying Third Party Punishment.

    Science.gov (United States)

    Morese, Rosalba; Rabellino, Daniela; Sambataro, Fabio; Perussia, Felice; Valentini, Maria Consuelo; Bara, Bruno G; Bosco, Francesca M

    2016-01-01

    This research aims to explore the neural correlates involved in altruistic punishment, parochial altruism and anti-social punishment, using the Third-Party Punishment (TPP) game. In particular, this study considered these punishment behaviors in in-group vs. out-group game settings, to compare how people behave with members of their own national group and with members of another national group. The results showed that participants act altruistically to protect in-group members. This study indicates that norm violation in in-group (but not in out-group) settings results in increased activity in the medial prefrontal cortex and temporo-parietal junction, brain regions involved in the mentalizing network, as the third-party attempts to understand or justify in-group members' behavior. Finally, exploratory analysis during anti-social punishment behavior showed brain activation recruitment of the ventromedial prefrontal cortex, an area associated with altered regulation of emotions.

  6. Simulation of morphologically structured photo-thermal neural stimulation

    Science.gov (United States)

    Weissler, Y.; Farah, N.; Shoham, S.

    2017-10-01

    Objective. Rational design of next-generation techniques for photo-thermal excitation requires the development of tools capable of modeling the effects of spatially- and temporally-dependent temperature distribution on cellular neuronal structures. Approach. We present a new computer simulation tool for predicting the effects of arbitrary spatiotemporally-structured photo-thermal stimulation on 3D, morphologically realistic neurons. The new simulation tool is based on interfacing two generic platforms, NEURON and MATLAB and is therefore suited for capturing different kinds of stimuli and neural models. Main results. Simulation results are validated using photo-absorber induced neuro-thermal stimulation (PAINTS) empirical results, and advanced features are explored. Significance. The new simulation tool could have an important role in understanding and investigating complex optical stimulation at the single-cell and network levels.

  7. Neural networks for harmonic structure in music perception and action.

    Science.gov (United States)

    Bianco, R; Novembre, G; Keller, P E; Kim, Seung-Goo; Scharf, F; Friederici, A D; Villringer, A; Sammler, D

    2016-11-15

    The ability to predict upcoming structured events based on long-term knowledge and contextual priors is a fundamental principle of human cognition. Tonal music triggers predictive processes based on structural properties of harmony, i.e., regularities defining the arrangement of chords into well-formed musical sequences. While the neural architecture of structure-based predictions during music perception is well described, little is known about the neural networks for analogous predictions in musical actions and how they relate to auditory perception. To fill this gap, expert pianists were presented with harmonically congruent or incongruent chord progressions, either as musical actions (photos of a hand playing chords) that they were required to watch and imitate without sound, or in an auditory format that they listened to without playing. By combining task-based functional magnetic resonance imaging (fMRI) with functional connectivity at rest, we identified distinct sub-regions in right inferior frontal gyrus (rIFG) interconnected with parietal and temporal areas for processing action and audio sequences, respectively. We argue that the differential contribution of parietal and temporal areas is tied to motoric and auditory long-term representations of harmonic regularities that dynamically interact with computations in rIFG. Parsing of the structural dependencies in rIFG is co-determined by both stimulus- or task-demands. In line with contemporary models of prefrontal cortex organization and dual stream models of visual-spatial and auditory processing, we show that the processing of musical harmony is a network capacity with dissociated dorsal and ventral motor and auditory circuits, which both provide the infrastructure for predictive mechanisms optimising action and perception performance. Copyright © 2016 Elsevier Inc. All rights reserved.

  8. Neural Network Enhanced Structure Determination of Osteoporosis, Immune System, and Radiation Repair Proteins, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed innovation will utilize self learning neural network technology to determine the structure of osteoporosis, immune system disease, and excess radiation...

  9. A modular structure to accident identification using neural networks

    International Nuclear Information System (INIS)

    Duque Estrada, Cassius Rodrigo

    2005-01-01

    This work uses the accident identification method based on Artificial Neural Networks (ANN) as basic blocks of a modular structure, allowing the inclusion of new accidents to be identified without modifying the ANN already trained. This structure comprises several modules for accident identification and one module for analysis. Each identification module follows the structure of the basic block. The identification modules are responsible for the recognition of an accident belonging to the specific set of events for which it were trained. The analysis module processes the output from the identification module to determine the system response. In order to test this structure it was proposed a transient identification problem comprising fifty accidents distributed in five identification modules. The results have demonstrated that the accident identification method used as basic block of a modular structure allows the inclusion of new sets of accidents, or variations of a same accident, without modifying the ANN already trained. For this, it is enough to include into the system an specific module for this new set of accidents. (author)

  10. The neural underpinnings of music listening under different attention conditions.

    Science.gov (United States)

    Jäncke, Lutz; Leipold, Simon; Burkhard, Anja

    2018-05-02

    Most studies examining the neural underpinnings of music listening have no specific instruction on how to process the presented musical pieces. In this study, we explicitly manipulated the participants' focus of attention while they listened to the musical pieces. We used an ecologically valid experimental setting by presenting the musical stimuli simultaneously with naturalistic film sequences. In one condition, the participants were instructed to focus their attention on the musical piece (attentive listening), whereas in the second condition, the participants directed their attention to the film sequence (passive listening). We used two instrumental musical pieces: an electronic pop song, which was a major hit at the time of testing, and a classical musical piece. During music presentation, we measured electroencephalographic oscillations and responses from the autonomic nervous system (heart rate and high-frequency heart rate variability). During passive listening to the pop song, we found strong event-related synchronizations in all analyzed frequency bands (theta, lower alpha, upper alpha, lower beta, and upper beta). The neurophysiological responses during attentive listening to the pop song were similar to those of the classical musical piece during both listening conditions. Thus, the focus of attention had a strong influence on the neurophysiological responses to the pop song, but not on the responses to the classical musical piece. The electroencephalographic responses during passive listening to the pop song are interpreted as a neurophysiological and psychological state typically observed when the participants are 'drawn into the music'.

  11. Neural correlates underlying change in state self-esteem.

    Science.gov (United States)

    Kawamichi, Hiroaki; Sugawara, Sho K; Hamano, Yuki H; Kitada, Ryo; Nakagawa, Eri; Kochiyama, Takanori; Sadato, Norihiro

    2018-01-29

    State self-esteem, the momentary feeling of self-worth, functions as a sociometer involved in maintenance of interpersonal relations. How others' appraisal is subjectively interpreted to change state self-esteem is unknown, and the neural underpinnings of this process remain to be elucidated. We hypothesized that changes in state self-esteem are represented by the mentalizing network, which is modulated by interactions with regions involved in the subjective interpretation of others' appraisal. To test this hypothesis, we conducted task-based and resting-state fMRI. Participants were repeatedly presented with their reputations, and then rated their pleasantness and reported their state self-esteem. To evaluate the individual sensitivity of the change in state self-esteem based on pleasantness (i.e., the subjective interpretation of reputation), we calculated evaluation sensitivity as the rate of change in state self-esteem per unit pleasantness. Evaluation sensitivity varied across participants, and was positively correlated with precuneus activity evoked by reputation rating. Resting-state fMRI revealed that evaluation sensitivity was positively correlated with functional connectivity of the precuneus with areas activated by negative reputation, but negatively correlated with areas activated by positive reputation. Thus, the precuneus, as the part of the mentalizing system, serves as a gateway for translating the subjective interpretation of reputation into state self-esteem.

  12. Mapping Common Aphasia Assessments to Underlying Cognitive Processes and Their Neural Substrates.

    Science.gov (United States)

    Lacey, Elizabeth H; Skipper-Kallal, Laura M; Xing, Shihui; Fama, Mackenzie E; Turkeltaub, Peter E

    2017-05-01

    Understanding the relationships between clinical tests, the processes they measure, and the brain networks underlying them, is critical in order for clinicians to move beyond aphasia syndrome classification toward specification of individual language process impairments. To understand the cognitive, language, and neuroanatomical factors underlying scores of commonly used aphasia tests. Twenty-five behavioral tests were administered to a group of 38 chronic left hemisphere stroke survivors and a high-resolution magnetic resonance image was obtained. Test scores were entered into a principal components analysis to extract the latent variables (factors) measured by the tests. Multivariate lesion-symptom mapping was used to localize lesions associated with the factor scores. The principal components analysis yielded 4 dissociable factors, which we labeled Word Finding/Fluency, Comprehension, Phonology/Working Memory Capacity, and Executive Function. While many tests loaded onto the factors in predictable ways, some relied heavily on factors not commonly associated with the tests. Lesion symptom mapping demonstrated discrete brain structures associated with each factor, including frontal, temporal, and parietal areas extending beyond the classical language network. Specific functions mapped onto brain anatomy largely in correspondence with modern neural models of language processing. An extensive clinical aphasia assessment identifies 4 independent language functions, relying on discrete parts of the left middle cerebral artery territory. A better understanding of the processes underlying cognitive tests and the link between lesion and behavior may lead to improved aphasia diagnosis, and may yield treatments better targeted to an individual's specific pattern of deficits and preserved abilities.

  13. Dissociable neural processes underlying risky decisions for self versus other

    Directory of Open Access Journals (Sweden)

    Daehyun eJung

    2013-03-01

    Full Text Available Previous neuroimaging studies on decision making have mainly focused on decisions on behalf of oneself. Considering that people often make decisions on behalf of others, it is intriguing that there is little neurobiological evidence on how decisions for others differ from those for self. Thus, the present study focused on the direct comparison between risky decisions for self and those for other using functional magnetic resonance imaging (fMRI. Participants (N = 23 were asked to perform a gambling task for themselves (decision-for-self condition or for another person (decision-for-other condition while in the scanner. Their task was to choose between a low-risk option (i.e., win or lose 10 points and a high-risk option (i.e., win or lose 90 points. The winning probabilities of each option varied from 17% to 83%. Compared to choices for others, choices for self were more risk-averse at lower winning probability and more risk-seeking at higher winning probability, perhaps due to stronger affective process during risky decision for self compared to other. The brain activation pattern changed according to the target of the decision, such that reward-related regions were more active in the decision-for-self condition than in the decision-for-other condition, whereas brain regions related to the theory of mind (ToM showed greater activation in the decision-for-other condition than in the decision-for-self condition. A parametric modulation analysis reflecting each individual’s decision model revealed that activation of the amygdala and the dorsomedial prefrontal cortex (DMPFC were associated with value computation for self and for other, respectively, during a risky financial decision. The present study suggests that decisions for self and other may recruit fundamentally distinctive neural processes, which can be mainly characterized by dominant affective/impulsive and cognitive/regulatory processes, respectively.

  14. Cortical Neural Activity Predicts Sensory Acuity Under Optogenetic Manipulation.

    Science.gov (United States)

    Briguglio, John J; Aizenberg, Mark; Balasubramanian, Vijay; Geffen, Maria N

    2018-02-21

    Excitatory and inhibitory neurons in the mammalian sensory cortex form interconnected circuits that control cortical stimulus selectivity and sensory acuity. Theoretical studies have predicted that suppression of inhibition in such excitatory-inhibitory networks can lead to either an increase or, paradoxically, a decrease in excitatory neuronal firing, with consequent effects on stimulus selectivity. We tested whether modulation of inhibition or excitation in the auditory cortex of male mice could evoke such a variety of effects in tone-evoked responses and in behavioral frequency discrimination acuity. We found that, indeed, the effects of optogenetic manipulation on stimulus selectivity and behavior varied in both magnitude and sign across subjects, possibly reflecting differences in circuitry or expression of optogenetic factors. Changes in neural population responses consistently predicted behavioral changes for individuals separately, including improvement and impairment in acuity. This correlation between cortical and behavioral change demonstrates that, despite the complex and varied effects that these manipulations can have on neuronal dynamics, the resulting changes in cortical activity account for accompanying changes in behavioral acuity. SIGNIFICANCE STATEMENT Excitatory and inhibitory interactions determine stimulus specificity and tuning in sensory cortex, thereby controlling perceptual discrimination acuity. Modeling has predicted that suppressing the activity of inhibitory neurons can lead to increased or, paradoxically, decreased excitatory activity depending on the architecture of the network. Here, we capitalized on differences between subjects to test whether suppressing/activating inhibition and excitation can in fact exhibit such paradoxical effects for both stimulus sensitivity and behavioral discriminability. Indeed, the same optogenetic manipulation in the auditory cortex of different mice could improve or impair frequency discrimination

  15. Resolution of Singularities Introduced by Hierarchical Structure in Deep Neural Networks.

    Science.gov (United States)

    Nitta, Tohru

    2017-10-01

    We present a theoretical analysis of singular points of artificial deep neural networks, resulting in providing deep neural network models having no critical points introduced by a hierarchical structure. It is considered that such deep neural network models have good nature for gradient-based optimization. First, we show that there exist a large number of critical points introduced by a hierarchical structure in deep neural networks as straight lines, depending on the number of hidden layers and the number of hidden neurons. Second, we derive a sufficient condition for deep neural networks having no critical points introduced by a hierarchical structure, which can be applied to general deep neural networks. It is also shown that the existence of critical points introduced by a hierarchical structure is determined by the rank and the regularity of weight matrices for a specific class of deep neural networks. Finally, two kinds of implementation methods of the sufficient conditions to have no critical points are provided. One is a learning algorithm that can avoid critical points introduced by the hierarchical structure during learning (called avoidant learning algorithm). The other is a neural network that does not have some critical points introduced by the hierarchical structure as an inherent property (called avoidant neural network).

  16. Imaging Neuronal Populations in Behaving Rodents: Paradigms for Studying Neural Circuits Underlying Behavior in the Mammalian Cortex

    Science.gov (United States)

    Andermann, Mark L.; Keck, Tara; Xu, Ning-Long; Ziv, Yaniv

    2013-01-01

    Understanding the neural correlates of behavior in the mammalian cortex requires measurements of activity in awake, behaving animals. Rodents have emerged as a powerful model for dissecting the cortical circuits underlying behavior attributable to the convergence of several methods. Genetically encoded calcium indicators combined with viral-mediated or transgenic tools enable chronic monitoring of calcium signals in neuronal populations and subcellular structures of identified cell types. Stable one- and two-photon imaging of neuronal activity in awake, behaving animals is now possible using new behavioral paradigms in head-fixed animals, or using novel miniature head-mounted microscopes in freely moving animals. This mini-symposium will highlight recent applications of these methods for studying sensorimotor integration, decision making, learning, and memory in cortical and subcortical brain areas. We will outline future prospects and challenges for identifying the neural underpinnings of task-dependent behavior using cellular imaging in rodents. PMID:24198355

  17. The Energy Coding of a Structural Neural Network Based on the Hodgkin-Huxley Model.

    Science.gov (United States)

    Zhu, Zhenyu; Wang, Rubin; Zhu, Fengyun

    2018-01-01

    Based on the Hodgkin-Huxley model, the present study established a fully connected structural neural network to simulate the neural activity and energy consumption of the network by neural energy coding theory. The numerical simulation result showed that the periodicity of the network energy distribution was positively correlated to the number of neurons and coupling strength, but negatively correlated to signal transmitting delay. Moreover, a relationship was established between the energy distribution feature and the synchronous oscillation of the neural network, which showed that when the proportion of negative energy in power consumption curve was high, the synchronous oscillation of the neural network was apparent. In addition, comparison with the simulation result of structural neural network based on the Wang-Zhang biophysical model of neurons showed that both models were essentially consistent.

  18. Exploiting the 1/f structure of neural signals for the design of integrated neural amplifiers.

    Science.gov (United States)

    Venkatraman, Subramaniam; Patten, Craig; Carmena, Jose M

    2009-01-01

    Neural amplifiers require a large time-constant high-pass filter at approximately 1Hz to reject large DC offsets while amplifying low frequency neural signals. This high pass filter is typically realized using large area capacitors and teraohm resistances which makes integration difficult. In this paper, we present a novel topology for a neural amplifier which exploits the (1/f)(n) power spectra of local field potentials (LFP). Using a high-pass filter at approximately 100Hz, we pre-filter the LFP before amplification. Post digitization, we can recover the LFP signal by building the inverse of the high pass filter in software. We built an array of neural amplifiers based on this principle and tested it on rats chronically implanted with microelectrode arrays. We found that we could recover the initial LFP signal and the power spectral information over time with correlation coefficient greater than 0.94.

  19. Potential neural mechanisms underlying the effectiveness of early intervention for children with autism spectrum disorder

    Science.gov (United States)

    Sullivan, Katherine; Stone, Wendy L.; Dawson, Geraldine

    2014-01-01

    Although evidence supports the efficacy of early intervention for improving outcomes for children with autism spectrum disorder (ASD), the mechanisms underlying their effectiveness remain poorly understood. This paper reviews the research literature on the neural bases of the early core deficits in ASD and proposes three key features of early intervention related to the neural mechanisms that may contribute to its effectiveness in improving deficit areas. These features include (1) the early onset of intensive intervention which capitalizes on the experience-expectant plasticity of the immature brain, (2) the use of treatment strategies that address core deficits in social motivation through an emphasis on positive social engagement and arousal modulation, and (3) promotion of complex neural networks and connectivity through thematic, multi-sensory and multi-domain teaching approaches. Understanding the mechanisms of effective early intervention will enable us to identify common or foundational active ingredients for promoting optimal outcomes in children with ASD. PMID:25108609

  20. Studies of Neuronal Gene Regulation Controlling the Molecular Mechanisms Underlying Neural Plasticity.

    Science.gov (United States)

    Fukuchi, Mamoru

    2017-01-01

    The regulation of the development and function of the nervous system is not preprogramed but responds to environmental stimuli to change neural development and function flexibly. This neural plasticity is a characteristic property of the nervous system. For example, strong synaptic activation evoked by environmental stimuli leads to changes in synaptic functions (known as synaptic plasticity). Long-lasting synaptic plasticity is one of the molecular mechanisms underlying long-term learning and memory. Since discovering the role of the transcription factor cAMP-response element-binding protein in learning and memory, it has been widely accepted that gene regulation in neurons contributes to long-lasting changes in neural functions. However, it remains unclear how synaptic activation is converted into gene regulation that results in long-lasting neural functions like long-term memory. We continue to address this question. This review introduces our recent findings on the gene regulation of brain-derived neurotrophic factor and discusses how regulation of the gene participates in long-lasting changes in neural functions.

  1. Neural network model for evaluation of seedling vigour under clinostated conditions

    Science.gov (United States)

    Zaidi, M.; Murase, H.

    A hierarchical neural net can be applied to simulate nonlinear phenomena found in biological systems. The learning process of the hierarchical neural net can be used as an algorithm for nonlinear multivariate analysis. The non- invasive technique for monitoring the plant's growth stage is one part of the required technology of the bio-response feedback control system. The stage of a plant's growth can be identified or quantified by measuring physical indices. Automated monitoring is also necessary in the clinostat experiment and neural networks are used for the calibration of lettuce plant growth. A back propagation neural network was trained to evaluate the plant growth in terms of plant growth characteristics, with a network consisting of 4, 8 and 1 processing units in the input, hidden and output layers, respectively. Sixteen sets of training data were used. The training was terminated after 800 times of iterative calculations at the RMS error value equal to 3.35x10-3 . Four sets of validation data were used to calculate the prediction error. The ability of the neural network models to predict the required information is very accurate. As a result, there is potential for the present technique to be applied to seedling vigour evaluating system under the clinostated conditions.

  2. Acoustic leak monitoring with neural networks at complicated structures

    International Nuclear Information System (INIS)

    Hessel, G.; Schmitt, W.; Weiss, F.P.

    1996-01-01

    Acoustic leak monitoring of the primary circuit in nuclear power plants has been used for a number of years. At complicated three-dimensional structures the well-known acoustic methods based on the attenuation differences or the propagation time differences fail for localizing the leak because, due to reflection and modes with different sound velocities, there are multiple propagation paths. To render possible leak localization at such topologies, the neural network approach was combined with acoustic methods. Results are presented from experiments with simulated leaks at a soviet-type pressurized VVER-44O reactor vessel head in the former Greifswald nuclear power station. As features for characterizing the occurrence and the location of a leak, RMS values of acoustic emission sensors as well as coherence values and auto power spectra of microphone signals were used. Three-layer perception networks were found to be best suited for leak localization because they provide a correct classification, an excellent separability, and an advantageous capability of generalization. Perception networks with two continuously valued outputs corresponding to the coordinates of the leak position are useful for classifying even positions which were not offered during training. (authors)

  3. Behavior identification of distinct neighborhoods in large structures using neural networks

    Science.gov (United States)

    Fu, Xiaoyun; Bartlett, James P.

    2001-03-01

    This paper presents a new method to identify the vibration model for large smart structures. A large structure typically has low frequency vibrations, unique distributed dynamic characteristics, and no simple control model. Study of composite aircraft fuselage vibration suppression required the identification of a distributed parameter control method. Artificial neural networks were used to identify structural vibration characteristics in distinct neighborhoods. Neural networks were trained and compared using a plate model.

  4. Colloidal Aggregate Structure under Shear by USANS

    Science.gov (United States)

    Chatterjee, Tirtha; van Dyk, Antony K.; Ginzburg, Valeriy V.; Nakatani, Alan I.

    2015-03-01

    Paints are complex formulations of polymeric binders, inorganic pigments, dispersants, surfactants, colorants, rheology modifiers, and other additives. A commercially successful paint exhibits a desired viscosity profile over a wide shear rate range from 10-5 s-1 for settling to >104 s-1 for rolling, and spray applications. Understanding paint formulation structure is critical as it governs the paint viscosity profile. However, probing paint formulation structure under shear is a challenging task due to the formulation complexity containing structures with different hierarchical length scales and their alterations under the influence of an external flow field. In this work mesoscale structures of paint formulations under shear are investigated using Ultra Small-Angle Neutron Scattering (rheo-USANS). Contrast match conditions were utilized to independently probe the structure of latex binder particle aggregates and the TiO2 pigment particle aggregates. Rheo-USANS data revealed that the aggregates are fractal in nature and their self-similarity dimensions and correlations lengths depend on the chemistry of the binder particles, the type of rheology modifier present and the shear stress imposed upon the formulation. These results can be explained in the framework of diffusion and reaction limited transient aggregates structure evolution under simple shear.

  5. The role of automaticity and attention in neural processes underlying empathy for happiness, sadness, and anxiety

    Directory of Open Access Journals (Sweden)

    Sylvia A. Morelli

    2013-05-01

    Full Text Available Although many studies have examined the neural basis of experiencing empathy, relatively little is known about how empathic processes are affected by different attentional conditions. Thus, we examined whether instructions to empathize might amplify responses in empathy-related regions and whether cognitive load would diminish the involvement of these regions. 32 participants completed a functional magnetic resonance imaging session assessing empathic responses to individuals experiencing happy, sad, and anxious events. Stimuli were presented under three conditions: watching naturally, while instructed to empathize, and under cognitive load. Across analyses, we found evidence for a core set of neural regions that support empathic processes (dorsomedial prefrontal cortex, DMPFC; medial prefrontal cortex, MPFC; temporoparietal junction, TPJ; amygdala; ventral anterior insula, AI; septal area, SA. Two key regions – the ventral AI and SA – were consistently active across all attentional conditions, suggesting that they are automatically engaged during empathy. In addition, watching versus empathizing with targets was not markedly different and instead led to similar subjective and neural responses to others’ emotional experiences. In contrast, cognitive load reduced the subjective experience of empathy and diminished neural responses in several regions related to empathy (DMPFC, MPFC, TPJ, amygdala and social cognition. The current results reveal how attention impacts empathic processes and provides insight into how empathy may unfold in everyday interactions.

  6. Interactive extraction of neural structures with user-guided morphological diffusion

    KAUST Repository

    Yong Wan,

    2012-10-01

    Extracting neural structures with their fine details from confocal volumes is essential to quantitative analysis in neurobiology research. Despite the abundance of various segmentation methods and tools, for complex neural structures, both manual and semi-automatic methods are ine ective either in full 3D or when user interactions are restricted to 2D slices. Novel interaction techniques and fast algorithms are demanded by neurobiologists to interactively and intuitively extract neural structures from confocal data. In this paper, we present such an algorithm-technique combination, which lets users interactively select desired structures from visualization results instead of 2D slices. By integrating the segmentation functions with a confocal visualization tool neurobiologists can easily extract complex neural structures within their typical visualization workflow.

  7. Water Demand Under Alternative Price Structures

    OpenAIRE

    Sheila Olmstead; W. Michael Hanemann; Robert N. Stavins

    2007-01-01

    We estimate the price elasticity of water demand with household-level data, structurally modeling the piecewise-linear budget constraints imposed by increasing-block pricing. We develop a mathematical expression for the unconditional price elasticity of demand under increasing-block prices and compare conditional and unconditional elasticities analytically and empirically. We test the hypothesis that price elasticity may depend on price structure, beyond technical differences in elasticity co...

  8. Efficient Bayesian inference under the structured coalescent.

    Science.gov (United States)

    Vaughan, Timothy G; Kühnert, Denise; Popinga, Alex; Welch, David; Drummond, Alexei J

    2014-08-15

    Population structure significantly affects evolutionary dynamics. Such structure may be due to spatial segregation, but may also reflect any other gene-flow-limiting aspect of a model. In combination with the structured coalescent, this fact can be used to inform phylogenetic tree reconstruction, as well as to infer parameters such as migration rates and subpopulation sizes from annotated sequence data. However, conducting Bayesian inference under the structured coalescent is impeded by the difficulty of constructing Markov Chain Monte Carlo (MCMC) sampling algorithms (samplers) capable of efficiently exploring the state space. In this article, we present a new MCMC sampler capable of sampling from posterior distributions over structured trees: timed phylogenetic trees in which lineages are associated with the distinct subpopulation in which they lie. The sampler includes a set of MCMC proposal functions that offer significant mixing improvements over a previously published method. Furthermore, its implementation as a BEAST 2 package ensures maximum flexibility with respect to model and prior specification. We demonstrate the usefulness of this new sampler by using it to infer migration rates and effective population sizes of H3N2 influenza between New Zealand, New York and Hong Kong from publicly available hemagglutinin (HA) gene sequences under the structured coalescent. The sampler has been implemented as a publicly available BEAST 2 package that is distributed under version 3 of the GNU General Public License at http://compevol.github.io/MultiTypeTree. © The Author 2014. Published by Oxford University Press.

  9. Neural correlates underlying mental calculation in abacus experts: a functional magnetic resonance imaging study.

    Science.gov (United States)

    Hanakawa, Takashi; Honda, Manabu; Okada, Tomohisa; Fukuyama, Hidenao; Shibasaki, Hiroshi

    2003-06-01

    Experts of abacus operation demonstrate extraordinary ability in mental calculation. There is psychological evidence that abacus experts utilize a mental image of an abacus to remember and manipulate large numbers in solving problems; however, the neural correlates underlying this expertise are unknown. Using functional magnetic resonance imaging, we compared the neural correlates associated with three mental-operation tasks (numeral, spatial, verbal) among six experts in abacus operations and eight nonexperts. In general, there was more involvement of neural correlates for visuospatial processing (e.g., right premotor and parietal areas) for abacus experts during the numeral mental-operation task. Activity of these areas and the fusiform cortex was correlated with the size of numerals used in the numeral mental-operation task. Particularly, the posterior superior parietal cortex revealed significantly enhanced activity for experts compared with controls during the numeral mental-operation task. Comparison with the other mental-operation tasks indicated that activity in the posterior superior parietal cortex was relatively specific to computation in 2-dimensional space. In conclusion, mental calculation of abacus experts is likely associated with enhanced involvement of the neural resources for visuospatial information processing in 2-dimensional space.

  10. Anger under control: neural correlates of frustration as a function of trait aggression.

    Science.gov (United States)

    Pawliczek, Christina M; Derntl, Birgit; Kellermann, Thilo; Gur, Ruben C; Schneider, Frank; Habel, Ute

    2013-01-01

    Antisocial behavior and aggression are prominent symptoms in several psychiatric disorders including antisocial personality disorder. An established precursor to aggression is a frustrating event, which can elicit anger or exasperation, thereby prompting aggressive responses. While some studies have investigated the neural correlates of frustration and aggression, examination of their relation to trait aggression in healthy populations are rare. Based on a screening of 550 males, we formed two extreme groups, one including individuals reporting high (n=21) and one reporting low (n=18) trait aggression. Using functional magnetic resonance imaging (fMRI) at 3T, all participants were put through a frustration task comprising unsolvable anagrams of German nouns. Despite similar behavioral performance, males with high trait aggression reported higher ratings of negative affect and anger after the frustration task. Moreover, they showed relatively decreased activation in the frontal brain regions and the dorsal anterior cingulate cortex (dACC) as well as relatively less amygdala activation in response to frustration. Our findings indicate distinct frontal and limbic processing mechanisms following frustration modulated by trait aggression. In response to a frustrating event, HA individuals show some of the personality characteristics and neural processing patterns observed in abnormally aggressive populations. Highlighting the impact of aggressive traits on the behavioral and neural responses to frustration in non-psychiatric extreme groups can facilitate further characterization of neural dysfunctions underlying psychiatric disorders that involve abnormal frustration processing and aggression.

  11. Anger under control: neural correlates of frustration as a function of trait aggression.

    Directory of Open Access Journals (Sweden)

    Christina M Pawliczek

    Full Text Available Antisocial behavior and aggression are prominent symptoms in several psychiatric disorders including antisocial personality disorder. An established precursor to aggression is a frustrating event, which can elicit anger or exasperation, thereby prompting aggressive responses. While some studies have investigated the neural correlates of frustration and aggression, examination of their relation to trait aggression in healthy populations are rare. Based on a screening of 550 males, we formed two extreme groups, one including individuals reporting high (n=21 and one reporting low (n=18 trait aggression. Using functional magnetic resonance imaging (fMRI at 3T, all participants were put through a frustration task comprising unsolvable anagrams of German nouns. Despite similar behavioral performance, males with high trait aggression reported higher ratings of negative affect and anger after the frustration task. Moreover, they showed relatively decreased activation in the frontal brain regions and the dorsal anterior cingulate cortex (dACC as well as relatively less amygdala activation in response to frustration. Our findings indicate distinct frontal and limbic processing mechanisms following frustration modulated by trait aggression. In response to a frustrating event, HA individuals show some of the personality characteristics and neural processing patterns observed in abnormally aggressive populations. Highlighting the impact of aggressive traits on the behavioral and neural responses to frustration in non-psychiatric extreme groups can facilitate further characterization of neural dysfunctions underlying psychiatric disorders that involve abnormal frustration processing and aggression.

  12. Anger under Control: Neural Correlates of Frustration as a Function of Trait Aggression

    Science.gov (United States)

    Pawliczek, Christina M.; Derntl, Birgit; Kellermann, Thilo; Gur, Ruben C.; Schneider, Frank; Habel, Ute

    2013-01-01

    Antisocial behavior and aggression are prominent symptoms in several psychiatric disorders including antisocial personality disorder. An established precursor to aggression is a frustrating event, which can elicit anger or exasperation, thereby prompting aggressive responses. While some studies have investigated the neural correlates of frustration and aggression, examination of their relation to trait aggression in healthy populations are rare. Based on a screening of 550 males, we formed two extreme groups, one including individuals reporting high (n=21) and one reporting low (n=18) trait aggression. Using functional magnetic resonance imaging (fMRI) at 3T, all participants were put through a frustration task comprising unsolvable anagrams of German nouns. Despite similar behavioral performance, males with high trait aggression reported higher ratings of negative affect and anger after the frustration task. Moreover, they showed relatively decreased activation in the frontal brain regions and the dorsal anterior cingulate cortex (dACC) as well as relatively less amygdala activation in response to frustration. Our findings indicate distinct frontal and limbic processing mechanisms following frustration modulated by trait aggression. In response to a frustrating event, HA individuals show some of the personality characteristics and neural processing patterns observed in abnormally aggressive populations. Highlighting the impact of aggressive traits on the behavioral and neural responses to frustration in non-psychiatric extreme groups can facilitate further characterization of neural dysfunctions underlying psychiatric disorders that involve abnormal frustration processing and aggression. PMID:24205247

  13. Vibration Based Damage Assessment of a Civil Engineering Structures using a Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Rytter, A.

    In this paper the possibility of using a Multilayer Perceptron (MLP) network trained with the Backpropagation Algorith as a non-destructive damage assessment technique to locate and quantify a damage in Civil Engineering structures is investigated. Since artificial neural networks are proving...... to be an effective tool for pattern recognition, the basic idea is to train a neural network with simulated values of modal parameters in order to recognize the behaviour of the damaged as well as the undamaged structure. Subjecting this trained neural network to measured modal parameters should imply information...

  14. Dimensions of childhood adversity have distinct associations with neural systems underlying executive functioning.

    Science.gov (United States)

    Sheridan, Margaret A; Peverill, Matthew; Finn, Amy S; McLaughlin, Katie A

    2017-12-01

    Childhood adversity is associated with increased risk for psychopathology. Neurodevelopmental pathways underlying this risk remain poorly understood. A recent conceptual model posits that childhood adversity can be deconstructed into at least two underlying dimensions, deprivation and threat, that are associated with distinct neurocognitive consequences. This model argues that deprivation (i.e., a lack of cognitive stimulation and learning opportunities) is associated with poor executive function (EF), whereas threat is not. We examine this hypothesis in two studies measuring EF at multiple levels: performance on EF tasks, neural recruitment during EF, and problems with EF in daily life. In Study 1, deprivation (low parental education and child neglect) was associated with greater parent-reported problems with EF in adolescents (N = 169; 13-17 years) after adjustment for levels of threat (community violence and abuse), which were unrelated to EF. In Study 2, low parental education was associated with poor working memory (WM) performance and inefficient neural recruitment in the parietal and prefrontal cortex during high WM load among adolescents (N = 51, 13-20 years) after adjusting for abuse, which was unrelated to WM task performance and neural recruitment during WM. These findings constitute strong preliminary evidence for a novel model of the neurodevelopmental consequences of childhood adversity.

  15. Suppression of anomalous synchronization and nonstationary behavior of neural network under small-world topology

    Science.gov (United States)

    Boaretto, B. R. R.; Budzinski, R. C.; Prado, T. L.; Kurths, J.; Lopes, S. R.

    2018-05-01

    It is known that neural networks under small-world topology can present anomalous synchronization and nonstationary behavior for weak coupling regimes. Here, we propose methods to suppress the anomalous synchronization and also to diminish the nonstationary behavior occurring in weakly coupled neural network under small-world topology. We consider a network of 2000 thermally sensitive identical neurons, based on the model of Hodgkin-Huxley in a small-world topology, with the probability of adding non local connection equal to p = 0 . 001. Based on experimental protocols to suppress anomalous synchronization, as well as nonstationary behavior of the neural network dynamics, we make use of (i) external stimulus (pulsed current); (ii) biologic parameters changing (neuron membrane conductance changes); and (iii) body temperature changes. Quantification analysis to evaluate phase synchronization makes use of the Kuramoto's order parameter, while recurrence quantification analysis, particularly the determinism, computed over the easily accessible mean field of network, the local field potential (LFP), is used to evaluate nonstationary states. We show that the methods proposed can control the anomalous synchronization and nonstationarity occurring for weak coupling parameter without any effect on the individual neuron dynamics, neither in the expected asymptotic synchronized states occurring for large values of the coupling parameter.

  16. Regional cerebral glucose metabolic changes in oculopalatal myoclonus: implication for neural pathways, underlying the disorder

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Sang Soo; Moon, So Young; Kim, Ji Soo; Kim, Sang Eun [College of Medicine, Seoul National University, Seoul (Korea, Republic of)

    2004-07-01

    Palatal myoclonus (PM) is characterized by rhythmic involuntary jerky movements of the soft palate of the throat. When associated with eye movements, it is called oculopalatal myoclonus (OPM). Ordinary PM is characterized by hypertrophic olivary degeneration, a trans-synaptic degeneration following loss of neuronal input to the inferior olivary nucleus due to an interruption of the Guillain-Mollaret triangle usually by a hemorrhage. However, the neural pathways underlying the disorder are uncertain. In an attempt to understand the pathologic neural pathways, we examined the metabolic correlates of this tremulous condition. Brain FDG PET scans were acquired in 8 patients with OPM (age, 49.9{+-}4.6 y: all males: 7 with pontine hemorrhage, 1 with diffuse brainstem infarction) and age-matched 50 healthy males (age, 50.7{+-} 9.0) and the regional glucose metabolism compared using SPM99. For group analysis, the hemispheres containing lesions were assigned to the right side of the brain. Patients with OPM had significant hypometabolism in the ipsilateral (to the lesion) brainstem and superior temporal and parahippocampal gyri (P < 0.05 corrected, k = 100). By contrast, there was significant hypermetabolism in the contralateral middle and inferior temporal gyri, thalamus, middle frontal gyrus and precuneus (P < 0.05 corrected, k=l00). Our data demonstrate the distinct metabolic changes between several ipsilateral and contralateral brain regions (hypometabolism vs. hypermetabolism) in patients with OPM. This may provide clues for understanding the neural pathways underlying the disorder.

  17. Ambiguity resolution in a Neural Blackboard Architecture for sentence structure

    NARCIS (Netherlands)

    van der Velde, Frank; van der Velde, Frank; de Kamps, Marc; Besold, Tarek R.; Kühnberger, Kai-Uwe

    2015-01-01

    We simulate two examples of ambiguity resolution found in human language processing in a neural blackboard architecture for sentence representation and processing. The architecture also accounts for a related garden path effect. The architecture represents and processes sentences in terms of

  18. Monitoring Scientific Developments from a Dynamic Perspective: Self-Organized Structuring To Map Neural Network Research.

    Science.gov (United States)

    Noyons, E. C. M.; van Raan, A. F. J.

    1998-01-01

    Using bibliometric mapping techniques, authors developed a methodology of self-organized structuring of scientific fields which was applied to neural network research. Explores the evolution of a data generated field structure by monitoring the interrelationships between subfields, the internal structure of subfields, and the dynamic features of…

  19. Application of structured support vector machine backpropagation to a convolutional neural network for human pose estimation.

    Science.gov (United States)

    Witoonchart, Peerajak; Chongstitvatana, Prabhas

    2017-08-01

    In this study, for the first time, we show how to formulate a structured support vector machine (SSVM) as two layers in a convolutional neural network, where the top layer is a loss augmented inference layer and the bottom layer is the normal convolutional layer. We show that a deformable part model can be learned with the proposed structured SVM neural network by backpropagating the error of the deformable part model to the convolutional neural network. The forward propagation calculates the loss augmented inference and the backpropagation calculates the gradient from the loss augmented inference layer to the convolutional layer. Thus, we obtain a new type of convolutional neural network called an Structured SVM convolutional neural network, which we applied to the human pose estimation problem. This new neural network can be used as the final layers in deep learning. Our method jointly learns the structural model parameters and the appearance model parameters. We implemented our method as a new layer in the existing Caffe library. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Thermomechanics of composite structures under high temperatures

    CERN Document Server

    Dimitrienko, Yu I

    2016-01-01

    This pioneering book presents new models for the thermomechanical behavior of composite materials and structures taking into account internal physico-chemical transformations such as thermodecomposition, sublimation and melting at high temperatures (up to 3000 K). It is of great importance for the design of new thermostable materials and for the investigation of reliability and fire safety of composite structures. It also supports the investigation of interaction of composites with laser irradiation and the design of heat-shield systems. Structural methods are presented for calculating the effective mechanical and thermal properties of matrices, fibres and unidirectional, reinforced by dispersed particles and textile composites, in terms of properties of their constituent phases. Useful calculation methods are developed for characteristics such as the rate of thermomechanical erosion of composites under high-speed flow and the heat deformation of composites with account of chemical shrinkage. The author expan...

  1. Fatigue in Steel Structures under Random Loading

    DEFF Research Database (Denmark)

    Agerskov, Henning

    1999-01-01

    test results. Both the fracture mechanics analysis and the fatigue test results indicate that Miner's rule, which is normally used in the design against fatigue in steel structures, may give results, which are unconservative, and that the validity of the results obtained from Miner's rule will depend......Fatigue damage accumulation in steel structures under random loading is studied. The fatigue life of welded joints has been determined both experimentally and from a fracture mechanics analysis. In the experimental part of the investigation, fatigue test series have been carried through on various...... types of welded plate test specimens and full-scale offshore tubular joints. The materials that have been used are either conventional structural steel with a yield stress of ~ 360-410 MPa or high-strength steel with a yield stress of ~ 810-1010 MPa. The fatigue tests and the fracture mechanics analyses...

  2. The research on structural damage identification using rough set and integrated neural network

    Science.gov (United States)

    Li, Juelong; Li, Hairui; Xing, Jianchun; Yang, Qiliang

    2013-09-01

    A huge amount of information and identification accuracy in large civil engineering structural damage identification has not been addressed yet. To efficiently solve this problem, a new damage identification method based on rough set and integrated neural network is first proposed. In brief, rough set was used to reduce attributes so as to decrease spatial dimensions of data and extract effective features. And then the reduced attributes will be put into the sub-neural network. The sub-neural network can give the preliminary diagnosis from different aspects of damage. The decision fusion network will give the final damage identification results. The identification examples show that this method can simplify the redundant information to reduce the neural network model, making full use of the range of information to effectively improve the accuracy of structural damage identification.

  3. On the induction of temporal structure by recurrent neural networks

    OpenAIRE

    Shertil, MS

    2014-01-01

    Language acquisition is one of the core problems in artificial intelligence (AI) and it is generally accepted that any successful AI account of the mind will stand or fall depending on its ability to model human language. Simple Recurrent Networks (SRNs) are a class of so-called artificial neural networks that have a long history in language modelling via learning to predict the next word in a sentence. However, SRNs have also been shown to suffer from catastrophic forgetting, lack of syntact...

  4. An object recognition using structured light and neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Byeong Gab; Kim, Dong Gi; Kang, E Sok [Chungnam National Univ., Taejon (Korea, Republic of); Yoon, Ji Sup [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    1997-12-31

    This paper presents a 3D image processing which uses neural networks to combine a 2D vision camera and a laser slit beam. A laser slit beam from laser source is slitted by a set of cylindrical lenses and the line image of the networks allow to get the 3D image parameters such as the size, the position and the orientation from the line image without knowing the camera intrinsic parameters. (author). 7 refs., 3 tabs., 5 figs.

  5. Neural mechanisms underlying transcranial direct current stimulation in aphasia: A feasibility study.

    Directory of Open Access Journals (Sweden)

    Lena eUlm

    2015-10-01

    Full Text Available Little is known about the neural mechanisms by which transcranial direct current stimulation (tDCS impacts on language processing in post-stroke aphasia. This was addressed in a proof-of-principle study that explored the effects of tDCS application in aphasia during simultaneous functional magnetic resonance imaging (fMRI. We employed a single subject, cross-over, sham-tDCS controlled design and the stimulation was administered to an individualized perilesional stimulation site that was identified by a baseline fMRI scan and a picture naming task. Peak activity during the baseline scan was located in the spared left inferior frontal gyrus (IFG and this area was stimulated during a subsequent cross-over phase. tDCS was successfully administered to the target region and anodal- vs. sham-tDCS resulted in selectively increased activity at the stimulation site. Our results thus demonstrate that it is feasible to precisely target an individualized stimulation site in aphasia patients during simultaneous fMRI which allows assessing the neural mechanisms underlying tDCS application. The functional imaging results of this case report highlight one possible mechanism that may have contributed to beneficial behavioural stimulation effects in previous clinical tDCS trials in aphasia. In the future, this approach will allow identifying distinct patterns of stimulation effects on neural processing in larger cohorts of patients. This may ultimately yield information about the variability of tDCS-effects on brain functions in aphasia.

  6. The impact of abacus training on working memory and underlying neural correlates in young adults.

    Science.gov (United States)

    Dong, Shanshan; Wang, Chunjie; Xie, Ye; Hu, Yuzheng; Weng, Jian; Chen, Feiyan

    2016-09-22

    Abacus-based mental calculation (AMC) activates the frontoparietal areas largely overlapping with the working memory (WM) network. Given the critical role of WM in cognition, how to improve WM capability has attracted intensive attention in past years. However, it is still unclear whether WM could be enhanced by AMC training. The current research thus explored the impact of AMC training on verbal and visuospatial WM, as well as the underlying neural basis. Participants were randomly assigned to an abacus group and a control group. Their verbal WM was evaluated by digit/letter memory span (DMS/LMS) tests, and visuospatial WM was assessed by a visuospatial n-back task. Neural activity during the n-back task was examined using functional MRI. Our results showed reliable improvements of both verbal and visuospatial WM in the abacus group after 20-day AMC training but not in the control. In addition, the n-back task-induced activations in the right frontoparietal circuitry and left occipitotemporal junction (OTJ) declined as a result of training. Notably, the decreases in activity were positively correlated with performance gains across trained participants. These results suggest AMC training not only improves calculating skills but also have the potential to promote individuals' WM capabilities, which is associated with the functional plasticity of the common neural substrates. Copyright © 2016 IBRO. All rights reserved.

  7. Spatially Nonlinear Interdependence of Alpha-Oscillatory Neural Networks under Chan Meditation

    Directory of Open Access Journals (Sweden)

    Pei-Chen Lo

    2013-01-01

    Full Text Available This paper reports the results of our investigation of the effects of Chan meditation on brain electrophysiological behaviors from the viewpoint of spatially nonlinear interdependence among regional neural networks. Particular emphasis is laid on the alpha-dominated EEG (electroencephalograph. Continuous-time wavelet transform was adopted to detect the epochs containing substantial alpha activities. Nonlinear interdependence quantified by similarity index S(X∣Y, the influence of source signal Y on sink signal X, was applied to the nonlinear dynamical model in phase space reconstructed from multichannel EEG. Experimental group involved ten experienced Chan-Meditation practitioners, while control group included ten healthy subjects within the same age range, yet, without any meditation experience. Nonlinear interdependence among various cortical regions was explored for five local neural-network regions, frontal, posterior, right-temporal, left-temporal, and central regions. In the experimental group, the inter-regional interaction was evaluated for the brain dynamics under three different stages, at rest (stage R, pre-meditation background recording, in Chan meditation (stage M, and the unique Chakra-focusing practice (stage C. Experimental group exhibits stronger interactions among various local neural networks at stages M and C compared with those at stage R. The intergroup comparison demonstrates that Chan-meditation brain possesses better cortical inter-regional interactions than the resting brain of control group.

  8. Neural Systems Underlying Perceptual Adjustment to Non-Standard Speech Tokens.

    Science.gov (United States)

    Myers, Emily B; Mesite, Laura M

    2014-10-01

    It has long been noted that listeners use top-down information from context to guide perception of speech sounds. A recent line of work employing a phenomenon termed 'perceptual learning for speech' shows that listeners use top-down information to not only resolve the identity of perceptually ambiguous speech sounds, but also to adjust perceptual boundaries in subsequent processing of speech from the same talker. Even so, the neural mechanisms that underlie this process are not well understood. Of particular interest is whether this type of adjustment comes about because of a retuning of sensitivities to phonetic category structure early in the neural processing stream or whether the boundary shift results from decision-related or attentional mechanisms further downstream. In the current study, neural activation was measured using fMRI as participants categorized speech sounds that were perceptually shifted as a result of exposure to these sounds in lexically-unambiguous contexts. Sensitivity to lexically-mediated shifts in phonetic categorization emerged in right hemisphere frontal and middle temporal regions, suggesting that the perceptual learning for speech phenomenon relies on the adjustment of perceptual criteria downstream from primary auditory cortex. By the end of the session, this same sensitivity was seen in left superior temporal areas, which suggests that a rapidly-adapting system may be accompanied by more slowly evolving shifts in regions of the brain related to phonetic processing.

  9. Beam Structure Damage Identification Based on BP Neural Network and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Bo Yan

    2014-01-01

    Full Text Available It is not easy to find marine cracks of structures by directly manual testing. When the cracks of important components are extended under extreme offshore environment, the whole structure would lose efficacy, endanger the staff’s safety, and course a significant economic loss and marine environment pollution. Thus, early discovery of structure cracks is very important. In this paper, a beam structure damage identification model based on intelligent algorithm is firstly proposed to identify partial cracks in supported beams on ocean platform. In order to obtain the replacement mode and strain mode of the beams, the paper takes simple supported beam with single crack and double cracks as an example. The results show that the difference curves of strain mode change drastically only on the injured part and different degrees of injury would result in different mutation degrees of difference curve more or less. While the model based on support vector machine (SVM and BP neural network can identify cracks of supported beam intelligently, the methods can discern injured degrees of sound condition, single crack, and double cracks. Furthermore, the two methods are compared. The results show that the two methods presented in the paper have a preferable identification precision and adaptation. And damage identification based on support vector machine (SVM has smaller error results.

  10. Neural network remodeling underlying motor map reorganization induced by rehabilitative training after ischemic stroke.

    Science.gov (United States)

    Okabe, Naohiko; Shiromoto, Takashi; Himi, Naoyuki; Lu, Feng; Maruyama-Nakamura, Emi; Narita, Kazuhiko; Iwachidou, Nobuhisa; Yagita, Yoshiki; Miyamoto, Osamu

    2016-12-17

    Motor map reorganization is believed to be one mechanism underlying rehabilitation-induced functional recovery. Although the ipsilesional secondary motor area has been known to reorganize motor maps and contribute to rehabilitation-induced functional recovery, it is unknown how the secondary motor area is reorganized by rehabilitative training. In the present study, using skilled forelimb reaching tasks, we investigated neural network remodeling in the rat rostral forelimb area (RFA) of the secondary motor area during 4weeks of rehabilitative training. Following photothrombotic stroke in the caudal forelimb area (CFA), rehabilitative training led to task-specific recovery and motor map reorganization in the RFA. A second injury to the RFA resulted in reappearance of motor deficits. Further, when both the CFA and RFA were destroyed simultaneously, rehabilitative training no longer improved task-specific recovery. In neural tracer studies, although rehabilitative training did not alter neural projection to the RFA from other brain areas, rehabilitative training increased neural projection from the RFA to the lower spinal cord, which innervates the muscles in the forelimb. Double retrograde tracer studies revealed that rehabilitative training increased the neurons projecting from the RFA to both the upper cervical cord, which innervates the muscles in the neck, trunk, and part of the proximal forelimb, and the lower cervical cord. These results suggest that neurons projecting to the upper cervical cord provide new connections to the denervated forelimb area of the spinal cord, and these new connections may contribute to rehabilitation-induced task-specific recovery and motor map reorganization in the secondary motor area. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

  11. STABILITY OF UNDERWATER STRUCTURE UNDER WAVE ATTACK

    Directory of Open Access Journals (Sweden)

    C. Paotonan

    2012-02-01

    Full Text Available Geotube is, among others, a type of coastal structure that is increasingly accepted for coastal protection especially underwater breakwater. Besides its relatively low cost, it has other advantages such as flexibility, ease of construction and the fact that it can be filled with local sand material. Similar to all other coastal structures, it should also be stable under wave attack. A simple theoretical approach based on linear wave was adopted to estimate the stability of such structure. The theoretical solution was then compared with an experimental study. The experimental study was conducted at the Hydraulics and Hydrology Laboratory of Universitas Gadjah Mada. However, instead of a real geotube, PVC pipe was used where the weight of the PVC was varied by adjusting the volume of sand in the pipe. The result indicated that the agreement between the theoretical solution and the experiment was encouraging. The analytical solution may be utilized to predict underwater pipe stability under wave attack with certain degree of accuracy.

  12. Neural substrates underlying motor skill learning in chronic hemiparetic stroke patients

    Directory of Open Access Journals (Sweden)

    Stephanie eLefebvre

    2015-06-01

    Full Text Available Motor skill learning is critical in post-stroke motor recovery, but little is known about its underlying neural substrates. Recently, using a new visuomotor skill learning paradigm involving a speed/accuracy trade-off in healthy individuals we identified three subpopulations based on their behavioral trajectories: fitters (in whom improvement in speed or accuracy coincided with deterioration in the other parameter, shifters (in whom speed and/or accuracy improved without degradation of the other parameter, and non-learners. We aimed to identify the neural substrates underlying the first stages of motor skill learning in chronic hemiparetic stroke patients and to determine whether specific neural substrates were recruited in shifters versus fitters. During functional magnetic resonance imaging (fMRI, 23 patients learned the visuomotor skill with their paretic upper limb. In the whole-group analysis, correlation between activation and motor skill learning was restricted to the dorsal prefrontal cortex of the damaged hemisphere (DLPFCdamh: r=-0.82 and the dorsal premotor cortex (PMddamh: r=0.70; the correlations was much lesser (-0.160.25 in the other regions of interest. In a subgroup analysis, significant activation was restricted to bilateral posterior parietal cortices of the fitters and did not correlate with motor skill learning. Conversely, in shifters significant activation occurred in the primary sensorimotor cortexdamh and supplementary motor areadamh and in bilateral PMd where activation changes correlated significantly with motor skill learning (r=0.91. Finally, resting-state activity acquired before learning showed a higher functional connectivity in the salience network of shifters compared with fitters (qFDR<0.05. These data suggest a neuroplastic compensatory reorganization of brain activity underlying the first stages of motor skill learning with the paretic upper limb in chronic hemiparetic stroke patients, with a key role of

  13. At-risk for pathological gambling: imaging neural reward processing under chronic dopamine agonists.

    Science.gov (United States)

    Abler, Birgit; Hahlbrock, Roman; Unrath, Alexander; Grön, Georg; Kassubek, Jan

    2009-09-01

    Treatment with dopamine receptor agonists has been associated with impulse control disorders and pathological gambling (PG) secondary to medication in previously unaffected patients with Parkinson's disease or restless legs syndrome (RLS). In a within-subjects design, we investigated the underlying neurobiology in RLS patients using functional magnetic resonance imaging. We scanned 12 female RLS patients without a history of PG. All patients were scanned twice: once whilst taking their regular medication with low dose dopamine receptor agonists and once after a washout phase interval. They performed an established gambling game task involving expectation and receipt or omission of monetary rewards at different levels of probabilities. Upon expectation of rewards, reliable ventral striatal activation was detected only when patients were on, but not when patients were off medication. Upon receipt or omission of rewards, the observed ventral striatal signal under medication differed markedly from its predicted pattern which by contrast was apparent when patients were off medication. Orbitofrontal activation was not affected by medication. Chronic dopamine receptor agonist medication changed the neural signalling of reward expectation predisposing the dopaminergic reward system to mediate an increased appetitive drive. Even without manifest PG, chronic medication with dopamine receptor agonists led to markedly changed neural processing of negative consequences probably mediating dysfunctional learning of contingencies. Intact orbitofrontal functioning, potentially moderating impulse control, may explain why none of the patients actually developed PG. Our results support the notion of a general medication effect in patients under dopamine receptor agonists in terms of a sensitization towards impulse control disorders.

  14. Experimental study and artificial neural network modeling of tartrazine removal by photocatalytic process under solar light.

    Science.gov (United States)

    Sebti, Aicha; Souahi, Fatiha; Mohellebi, Faroudja; Igoud, Sadek

    2017-07-01

    This research focuses on the application of an artificial neural network (ANN) to predict the removal efficiency of tartrazine from simulated wastewater using a photocatalytic process under solar illumination. A program is developed in Matlab software to optimize the neural network architecture and select the suitable combination of training algorithm, activation function and hidden neurons number. The experimental results of a batch reactor operated under different conditions of pH, TiO 2 concentration, initial organic pollutant concentration and solar radiation intensity are used to train, validate and test the networks. While negligible mineralization is demonstrated, the experimental results show that under sunlight irradiation, 85% of tartrazine is removed after 300 min using only 0.3 g/L of TiO 2 powder. Therefore, irradiation time is prolonged and almost 66% of total organic carbon is reduced after 15 hours. ANN 5-8-1 with Bayesian regulation back-propagation algorithm and hyperbolic tangent sigmoid transfer function is found to be able to predict the response with high accuracy. In addition, the connection weights approach is used to assess the importance contribution of each input variable on the ANN model response. Among the five experimental parameters, the irradiation time has the greatest effect on the removal efficiency of tartrazine.

  15. Bridging the Gap: Towards a Cell-Type Specific Understanding of Neural Circuits Underlying Fear Behaviors

    Science.gov (United States)

    McCullough, KM; Morrison, FG; Ressler, KJ

    2016-01-01

    Fear and anxiety-related disorders are remarkably common and debilitating, and are often characterized by dysregulated fear responses. Rodent models of fear learning and memory have taken great strides towards elucidating the specific neuronal circuitries underlying the learning of fear responses. The present review addresses recent research utilizing optogenetic approaches to parse circuitries underlying fear behaviors. It also highlights the powerful advances made when optogenetic techniques are utilized in a genetically defined, cell-type specific, manner. The application of next-generation genetic and sequencing approaches in a cell-type specific context will be essential for a mechanistic understanding of the neural circuitry underlying fear behavior and for the rational design of targeted, circuit specific, pharmacologic interventions for the treatment and prevention of fear-related disorders. PMID:27470092

  16. Strength of concrete structures under dynamic loading

    Science.gov (United States)

    Kumpyak, O. G.; Galyautdinov, Z. R.; Kokorin, D. N.

    2016-01-01

    The use of elastic supports is one the efficient methods of decreasing the dynamic loading. The paper describes the influence of elastic supports on the stress-strain state of steel concrete structures exposed to one-time dynamic loading resulting in failure. Oblique bending beams on elastic supports and their elastic, elastoplastic, and elastoplastic consolidation behavior are considered in this paper. For numerical calculations the developed computer program is used based on the finite element method. Research findings prove high efficiency of elastic supports under dynamic loading conditions. The most effective behavior of elastic supports is demonstrated at the elastoplastic stage. A good agreement is observed between the theoretical and experimental results.

  17. A Neural Network Model of the Structure and Dynamics of Human Personality

    Science.gov (United States)

    Read, Stephen J.; Monroe, Brian M.; Brownstein, Aaron L.; Yang, Yu; Chopra, Gurveen; Miller, Lynn C.

    2010-01-01

    We present a neural network model that aims to bridge the historical gap between dynamic and structural approaches to personality. The model integrates work on the structure of the trait lexicon, the neurobiology of personality, temperament, goal-based models of personality, and an evolutionary analysis of motives. It is organized in terms of two…

  18. Abnormal neural activation patterns underlying working memory impairment in chronic phencyclidine-treated mice.

    Directory of Open Access Journals (Sweden)

    Yosefu Arime

    Full Text Available Working memory impairment is a hallmark feature of schizophrenia and is thought be caused by dysfunctions in the prefrontal cortex (PFC and associated brain regions. However, the neural circuit anomalies underlying this impairment are poorly understood. The aim of this study is to assess working memory performance in the chronic phencyclidine (PCP mouse model of schizophrenia, and to identify the neural substrates of working memory. To address this issue, we conducted the following experiments for mice after withdrawal from chronic administration (14 days of either saline or PCP (10 mg/kg: (1 a discrete paired-trial variable-delay task in T-maze to assess working memory, and (2 brain-wide c-Fos mapping to identify activated brain regions relevant to this task performance either 90 min or 0 min after the completion of the task, with each time point examined under working memory effort and basal conditions. Correct responses in the test phase of the task were significantly reduced across delays (5, 15, and 30 s in chronic PCP-treated mice compared with chronic saline-treated controls, suggesting delay-independent impairments in working memory in the PCP group. In layer 2-3 of the prelimbic cortex, the number of working memory effort-elicited c-Fos+ cells was significantly higher in the chronic PCP group than in the chronic saline group. The main effect of working memory effort relative to basal conditions was to induce significantly increased c-Fos+ cells in the other layers of prelimbic cortex and the anterior cingulate and infralimbic cortex regardless of the different chronic regimens. Conversely, this working memory effort had a negative effect (fewer c-Fos+ cells in the ventral hippocampus. These results shed light on some putative neural networks relevant to working memory impairments in mice chronically treated with PCP, and emphasize the importance of the layer 2-3 of the prelimbic cortex of the PFC.

  19. Concrete structures under impact and impulsive loading

    International Nuclear Information System (INIS)

    Plauk, G.

    1982-05-01

    This book contains papers contributed to the RILEM/CEB/IABSE/IASS-Interassociation Symposium on 'Concrete Structures under Impact and Impulsive Loading'. The essential aim of this symposium is to provide an international forum for the exchange of information on existing and current research relating to impact problems as well as to identify areas to which further research activities should be directed. The subject of the symposium is far ranging. Fifty five papers were proposed and arranged in six technical sessions, a task which sometimes posed difficulties for the Organization Committee and the Advisory Group, because some of the papers touched several topics and were difficult to integrate. However, we are confident that these minor difficulties were solved to the satisfaction of everyone involved. Each session of the symposium is devoted to a major subject area and introduced by a distinguished Introductory Reporter. The large international attendance, some 21 countries are represented, and the large number of excellent papers will certainly produce a lively discussion after each session and thus help to further close the gaps in our knowledge about the behaviour of structures and materials under impact and impulsive loading. (orig./RW)

  20. Puzzle Pieces: Neural Structure and Function in Prader-Willi Syndrome

    Directory of Open Access Journals (Sweden)

    Katherine E. Manning

    2015-12-01

    Full Text Available Prader-Willi syndrome (PWS is a neurodevelopmental disorder of genomic imprinting, presenting with a behavioural phenotype encompassing hyperphagia, intellectual disability, social and behavioural difficulties, and propensity to psychiatric illness. Research has tended to focus on the cognitive and behavioural investigation of these features, and, with the exception of eating behaviour, the neural physiology is currently less well understood. A systematic review was undertaken to explore findings relating to neural structure and function in PWS, using search terms designed to encompass all published articles concerning both in vivo and post-mortem studies of neural structure and function in PWS. This supported the general paucity of research in this area, with many articles reporting case studies and qualitative descriptions or focusing solely on the overeating behaviour, although a number of systematic investigations were also identified. Research to date implicates a combination of subcortical and higher order structures in PWS, including those involved in processing reward, motivation, affect and higher order cognitive functions, with both anatomical and functional investigations indicating abnormalities. It appears likely that PWS involves aberrant activity across distributed neural networks. The characterisation of neural structure and function warrants both replication and further systematic study.

  1. Puzzle Pieces: Neural Structure and Function in Prader-Willi Syndrome

    Science.gov (United States)

    Manning, Katherine E.; Holland, Anthony J.

    2015-01-01

    Prader-Willi syndrome (PWS) is a neurodevelopmental disorder of genomic imprinting, presenting with a behavioural phenotype encompassing hyperphagia, intellectual disability, social and behavioural difficulties, and propensity to psychiatric illness. Research has tended to focus on the cognitive and behavioural investigation of these features, and, with the exception of eating behaviour, the neural physiology is currently less well understood. A systematic review was undertaken to explore findings relating to neural structure and function in PWS, using search terms designed to encompass all published articles concerning both in vivo and post-mortem studies of neural structure and function in PWS. This supported the general paucity of research in this area, with many articles reporting case studies and qualitative descriptions or focusing solely on the overeating behaviour, although a number of systematic investigations were also identified. Research to date implicates a combination of subcortical and higher order structures in PWS, including those involved in processing reward, motivation, affect and higher order cognitive functions, with both anatomical and functional investigations indicating abnormalities. It appears likely that PWS involves aberrant activity across distributed neural networks. The characterisation of neural structure and function warrants both replication and further systematic study. PMID:28943631

  2. Neural sensitivity to statistical regularities as a fundamental biological process that underlies auditory learning: the role of musical practice.

    Science.gov (United States)

    François, Clément; Schön, Daniele

    2014-02-01

    There is increasing evidence that humans and other nonhuman mammals are sensitive to the statistical structure of auditory input. Indeed, neural sensitivity to statistical regularities seems to be a fundamental biological property underlying auditory learning. In the case of speech, statistical regularities play a crucial role in the acquisition of several linguistic features, from phonotactic to more complex rules such as morphosyntactic rules. Interestingly, a similar sensitivity has been shown with non-speech streams: sequences of sounds changing in frequency or timbre can be segmented on the sole basis of conditional probabilities between adjacent sounds. We recently ran a set of cross-sectional and longitudinal experiments showing that merging music and speech information in song facilitates stream segmentation and, further, that musical practice enhances sensitivity to statistical regularities in speech at both neural and behavioral levels. Based on recent findings showing the involvement of a fronto-temporal network in speech segmentation, we defend the idea that enhanced auditory learning observed in musicians originates via at least three distinct pathways: enhanced low-level auditory processing, enhanced phono-articulatory mapping via the left Inferior Frontal Gyrus and Pre-Motor cortex and increased functional connectivity within the audio-motor network. Finally, we discuss how these data predict a beneficial use of music for optimizing speech acquisition in both normal and impaired populations. Copyright © 2013 Elsevier B.V. All rights reserved.

  3. Structural behavior of supercritical fluids under confinement

    Science.gov (United States)

    Ghosh, Kanka; Krishnamurthy, C. V.

    2018-01-01

    The existence of the Frenkel line in the supercritical regime of a Lennard-Jones (LJ) fluid shown through molecular dynamics (MD) simulations initially and later corroborated by experiments on argon opens up possibilities of understanding the structure and dynamics of supercritical fluids in general and of the Frenkel line in particular. The location of the Frenkel line, which demarcates two distinct physical states, liquidlike and gaslike within the supercritical regime, has been established through MD simulations of the velocity autocorrelation (VACF) and radial distribution function (RDF). We, in this article, explore the changes in the structural features of supercritical LJ fluid under partial confinement using atomistic walls. The study is carried out across the Frenkel line through a series of MD simulations considering a set of thermodynamics states in the supercritical regime (P =5000 bar, 240 K ≤T ≤1500 K ) of argon well above the critical point. Confinement is partial, with atomistic walls located normal to z and extending to "infinity" along the x and y directions. In the "liquidlike" regime of the supercritical phase, particles are found to be distributed in distinct layers along the z axis with layer spacing less than one atomic diameter and the lateral RDF showing amorphous-like structure for specific spacings (packing frustration) and non-amorphous-like structure for other spacings. Increasing the rigidity of the atomistic walls is found to lead to stronger layering and increased structural order. For confinement with reflective walls, layers are found to form with one atomic diameter spacing and the lateral RDF showing close-packed structure for the smaller confinements. Translational order parameter and excess entropy assessment confirms the ordering taking place for atomistic wall and reflective wall confinements. In the "gaslike" regime of the supercritical phase, particle distribution along the spacing and the lateral RDF exhibit features

  4. Revealing the Structural Neural Circuitry of Attention Deficit Hyperactivity Disorder With Diffusion MRI: Implications for Future Diagnosis and Treatment.

    Science.gov (United States)

    Adisetiyo, Vitria

    2018-04-01

    Rates of attention deficit hyperactivity disorder (ADHD) diagnosis and psychostimulant prescriptions continue to rise, yet there are no clear diagnostic tests or biomarkers for the disorder. The purpose of this article is to highlight the role of diffusion MRI in bolstering a neurobiologic conceptualization of ADHD and how this holds promise for optimizing future diagnosis. Diffusion MRI is a powerful neuroimaging tool for noninvasive assessment of the structural neural circuitry underlying brain function and behavior. Though the modality is still in its infancy, diffusion MRI studies are showing neural network disruption in ADHD consistent with findings from other imaging modalities. Given the mounting evidence of brain-behavior correlates in ADHD, it is likely that imaging-based biomarkers will one day be incorporated into clinical diagnosis and treatment evaluation. Until then, diffusion MRI findings serve to validate ADHD as a brain-based disorder with immediate public health implications for individuals with ADHD.

  5. Neural systems underlying aversive conditioning in humans with primary and secondary reinforcers

    Directory of Open Access Journals (Sweden)

    Mauricio R Delgado

    2011-05-01

    Full Text Available Money is a secondary reinforcer commonly used across a range of disciplines in experimental paradigms investigating reward learning and decision-making. The effectiveness of monetary reinforcers during aversive learning and its neural basis, however, remains a topic of debate. Specifically, it is unclear if the initial acquisition of aversive representations of monetary losses depends on similar neural systems as more traditional aversive conditioning that involves primary reinforcers. This study contrasts the efficacy of a biologically defined primary reinforcer (shock and a socially defined secondary reinforcer (money during aversive learning and its associated neural circuitry. During a two-part experiment, participants first played a gambling game where wins and losses were based on performance to gain an experimental bank. Participants were then exposed to two separate aversive conditioning sessions. In one session, a primary reinforcer (mild shock served as an unconditioned stimulus (US and was paired with one of two colored squares, the conditioned stimuli (CS+ and CS-, respectively. In another session, a secondary reinforcer (loss of money served as the US and was paired with one of two different CS. Skin conductance responses were greater for CS+ compared to CS- trials irrespective of type of reinforcer. Neuroimaging results revealed that the striatum, a region typically linked with reward-related processing, was found to be involved in the acquisition of aversive conditioned response irrespective of reinforcer type. In contrast, the amygdala was involved during aversive conditioning with primary reinforcers, as suggested by both an exploratory fMRI analysis and a follow-up case study with a patient with bilateral amygdala damage. Taken together, these results suggest that learning about potential monetary losses may depend on reinforcement learning related systems, rather than on typical structures involved in more biologically based

  6. Structural modifications of spinels under radiation

    International Nuclear Information System (INIS)

    Quentin, A.

    2010-12-01

    This work is devoted to the study of spinel structure materials under radiation. For that purpose, samples of polycrystalline ZnAl 2 O 4 and monocrystalline MgAl 2 O 4 were irradiated by different heavy ions with different energies. Samples of ZnAl 2 O 4 were studied par electron transmission microscopy, and by grazing incidence X-Ray diffraction and Rietveld analysis. Samples of MgAl 2 O 4 were studied by optical spectroscopy. Most of the results concern amorphization and crystalline structure modification of ZnAl 2 O 4 especially the inversion. We were able to determine a stopping power threshold for amorphization, between 11 keV/nm and 12 keV/nm, and also the amorphization process, which is a multiple impacts process. We studied the evolution of the amorphous phase by TEM and showed a nano-patterning phenomenon. Concerning the inversion, we determined that it did happen by a single impact process, and the saturation value did not reach the random cation distribution value. Inversion and amorphization have different, but close, stopping power threshold. However, amorphization seems to be conditioned by a pre-damage of the material which consists in inversion. (author)

  7. Neural and Behavioral Correlates of Alcohol-Induced Aggression Under Provocation.

    Science.gov (United States)

    Gan, Gabriela; Sterzer, Philipp; Marxen, Michael; Zimmermann, Ulrich S; Smolka, Michael N

    2015-12-01

    Although alcohol consumption is linked to increased aggression, its neural correlates have not directly been studied in humans so far. Based on a comprehensive neurobiological model of alcohol-induced aggression, we hypothesized that alcohol-induced aggression would go along with increased amygdala and ventral striatum reactivity and impaired functioning of the prefrontal cortex (PFC) under alcohol. We measured neural and behavioral correlates of alcohol-induced aggression in a provoking vs non-provoking condition with a variant of the Taylor aggression paradigm (TAP) allowing to differentiate between reactive (provoked) and proactive (unprovoked) aggression. In a placebo-controlled cross-over design with moderate alcohol intoxication (~0.6 g/kg), 35 young healthy adults performed the TAP during functional magnetic resonance imaging (fMRI). Analyses revealed that provoking vs non-provoking conditions and alcohol vs placebo increased aggression and decreased brain responses in the anterior cingulate cortex/dorso-medial PFC (provokingalcoholalcohol specifically increased proactive (unprovoked) but not reactive (provoked) aggression (alcohol × provocation interaction). However, investigation of inter-individual differences revealed (1) that pronounced alcohol-induced proactive aggression was linked to higher levels of aggression under placebo, and (2) that pronounced alcohol-induced reactive aggression was related to increased amygdala and ventral striatum reactivity under alcohol, providing evidence for their role in human alcohol-induced reactive aggression. Our findings suggest that in healthy young adults a liability for alcohol-induced aggression in a non-provoking context might depend on overall high levels of aggression, but on alcohol-induced increased striatal and amygdala reactivity when triggered by provocation.

  8. A neural network underlying intentional emotional facial expression in neurodegenerative disease

    Directory of Open Access Journals (Sweden)

    Kelly A. Gola

    2017-01-01

    Full Text Available Intentional facial expression of emotion is critical to healthy social interactions. Patients with neurodegenerative disease, particularly those with right temporal or prefrontal atrophy, show dramatic socioemotional impairment. This was an exploratory study examining the neural and behavioral correlates of intentional facial expression of emotion in neurodegenerative disease patients and healthy controls. One hundred and thirty three participants (45 Alzheimer's disease, 16 behavioral variant frontotemporal dementia, 8 non-fluent primary progressive aphasia, 10 progressive supranuclear palsy, 11 right-temporal frontotemporal dementia, 9 semantic variant primary progressive aphasia patients and 34 healthy controls were video recorded while imitating static images of emotional faces and producing emotional expressions based on verbal command; the accuracy of their expression was rated by blinded raters. Participants also underwent face-to-face socioemotional testing and informants described participants' typical socioemotional behavior. Patients' performance on emotion expression tasks was correlated with gray matter volume using voxel-based morphometry (VBM across the entire sample. We found that intentional emotional imitation scores were related to fundamental socioemotional deficits; patients with known socioemotional deficits performed worse than controls on intentional emotion imitation; and intentional emotional expression predicted caregiver ratings of empathy and interpersonal warmth. Whole brain VBMs revealed a rightward cortical atrophy pattern homologous to the left lateralized speech production network was associated with intentional emotional imitation deficits. Results point to a possible neural mechanisms underlying complex socioemotional communication deficits in neurodegenerative disease patients.

  9. A neural network underlying intentional emotional facial expression in neurodegenerative disease.

    Science.gov (United States)

    Gola, Kelly A; Shany-Ur, Tal; Pressman, Peter; Sulman, Isa; Galeana, Eduardo; Paulsen, Hillary; Nguyen, Lauren; Wu, Teresa; Adhimoolam, Babu; Poorzand, Pardis; Miller, Bruce L; Rankin, Katherine P

    2017-01-01

    Intentional facial expression of emotion is critical to healthy social interactions. Patients with neurodegenerative disease, particularly those with right temporal or prefrontal atrophy, show dramatic socioemotional impairment. This was an exploratory study examining the neural and behavioral correlates of intentional facial expression of emotion in neurodegenerative disease patients and healthy controls. One hundred and thirty three participants (45 Alzheimer's disease, 16 behavioral variant frontotemporal dementia, 8 non-fluent primary progressive aphasia, 10 progressive supranuclear palsy, 11 right-temporal frontotemporal dementia, 9 semantic variant primary progressive aphasia patients and 34 healthy controls) were video recorded while imitating static images of emotional faces and producing emotional expressions based on verbal command; the accuracy of their expression was rated by blinded raters. Participants also underwent face-to-face socioemotional testing and informants described participants' typical socioemotional behavior. Patients' performance on emotion expression tasks was correlated with gray matter volume using voxel-based morphometry (VBM) across the entire sample. We found that intentional emotional imitation scores were related to fundamental socioemotional deficits; patients with known socioemotional deficits performed worse than controls on intentional emotion imitation; and intentional emotional expression predicted caregiver ratings of empathy and interpersonal warmth. Whole brain VBMs revealed a rightward cortical atrophy pattern homologous to the left lateralized speech production network was associated with intentional emotional imitation deficits. Results point to a possible neural mechanisms underlying complex socioemotional communication deficits in neurodegenerative disease patients.

  10. Improved ultrasonic differentiation model for structural coal types based on neural network

    Energy Technology Data Exchange (ETDEWEB)

    Zi-jian Tian; Fu-zhong Wang; Tao Li; Shan-shan Bai [China University of Mining & Technology, Beijing (China). School of Electromechanical and Information Engineering

    2009-03-15

    In order to solve the difficulty of detailed recognition of subdivisions of structural coal types, a differentiation model that combines BP neural network with an ultrasonic reflection method is proposed. Structural coal types are recognized based on a suitable consideration of ultrasonic speed, an ultrasonic attenuation coefficient, characteristics of ultrasonic transmission and other parameters relating to structural coal types. We have focused on a computational model of ultrasonic speed, attenuation coefficient in coal and differentiation algorithm of structural coal types based on a BP neural network. Experiments demonstrate that the model can distinguish structural coal types effectively. It is important for the improved ultrasonic differentiation model to predict coal and gas outbursts. 12 refs., 1 fig., 5 tabs.

  11. The manipulative skill: Cognitive devices and their neural correlates underlying Machiavellian's decision making.

    Science.gov (United States)

    Bereczkei, Tamas

    2015-10-01

    Until now, Machiavellianism has mainly been studied in personality and social psychological framework, and little attention has been paid to the underlying cognitive and neural equipment. In light of recent findings, Machiavellian social skills are not limited to emotion regulation and "cold-mindedness" as many authors have recently stated, but linked to specific cognitive abilities. Although Machiavellians appear to have a relatively poor mindreading ability and emotional intelligence, they can efficiently exploit others which is likely to come from their flexible problem solving processes in changing environmental circumstances. The author proposed that Machiavellians have specialized cognitive domains of decision making, such as monitoring others' behavior, task orientation, reward seeking, inhibition of cooperative feelings, and choosing victims. He related the relevant aspects of cognitive functions to their neurological substrates, and argued why they make Machiavellians so successful in interpersonal relationships. Copyright © 2015 Elsevier Inc. All rights reserved.

  12. Neural mechanisms underlying paradoxical performance for monetary incentives are driven by loss aversion.

    Science.gov (United States)

    Chib, Vikram S; De Martino, Benedetto; Shimojo, Shinsuke; O'Doherty, John P

    2012-05-10

    Employers often make payment contingent on performance in order to motivate workers. We used fMRI with a novel incentivized skill task to examine the neural processes underlying behavioral responses to performance-based pay. We found that individuals' performance increased with increasing incentives; however, very high incentive levels led to the paradoxical consequence of worse performance. Between initial incentive presentation and task execution, striatal activity rapidly switched between activation and deactivation in response to increasing incentives. Critically, decrements in performance and striatal deactivations were directly predicted by an independent measure of behavioral loss aversion. These results suggest that incentives associated with successful task performance are initially encoded as a potential gain; however, when actually performing a task, individuals encode the potential loss that would arise from failure. Copyright © 2012 Elsevier Inc. All rights reserved.

  13. Neural connectivity patterns underlying symbolic number processing indicate mathematical achievement in children.

    Science.gov (United States)

    Park, Joonkoo; Li, Rosa; Brannon, Elizabeth M

    2014-03-01

    In early childhood, humans learn culturally specific symbols for number that allow them entry into the world of complex numerical thinking. Yet little is known about how the brain supports the development of the uniquely human symbolic number system. Here, we use functional magnetic resonance imaging along with an effective connectivity analysis to investigate the neural substrates for symbolic number processing in young children. We hypothesized that, as children solidify the mapping between symbols and underlying magnitudes, important developmental changes occur in the neural communication between the right parietal region, important for the representation of non-symbolic numerical magnitudes, and other brain regions known to be critical for processing numerical symbols. To test this hypothesis, we scanned children between 4 and 6 years of age while they performed a magnitude comparison task with Arabic numerals (numerical, symbolic), dot arrays (numerical, non-symbolic), and lines (non-numerical). We then identified the right parietal seed region that showed greater blood-oxygen-level-dependent signal in the numerical versus the non-numerical conditions. A psychophysiological interaction method was used to find patterns of effective connectivity arising from this parietal seed region specific to symbolic compared to non-symbolic number processing. Two brain regions, the left supramarginal gyrus and the right precentral gyrus, showed significant effective connectivity from the right parietal cortex. Moreover, the degree of this effective connectivity to the left supramarginal gyrus was correlated with age, and the degree of the connectivity to the right precentral gyrus predicted performance on a standardized symbolic math test. These findings suggest that effective connectivity underlying symbolic number processing may be critical as children master the associations between numerical symbols and magnitudes, and that these connectivity patterns may serve as an

  14. Study Under AC Stimulation on Excitement Properties of Weighted Small-World Biological Neural Networks with Side-Restrain Mechanism

    International Nuclear Information System (INIS)

    Yuan Wujie; Luo Xiaoshu; Jiang Pinqun

    2007-01-01

    In this paper, we propose a new model of weighted small-world biological neural networks based on biophysical Hodgkin-Huxley neurons with side-restrain mechanism. Then we study excitement properties of the model under alternating current (AC) stimulation. The study shows that the excitement properties in the networks are preferably consistent with the behavior properties of a brain nervous system under different AC stimuli, such as refractory period and the brain neural excitement response induced by different intensities of noise and coupling. The results of the study have reference worthiness for the brain nerve electrophysiology and epistemological science.

  15. Explicitly integrating parameter, input, and structure uncertainties into Bayesian Neural Networks for probabilistic hydrologic forecasting

    KAUST Repository

    Zhang, Xuesong

    2011-11-01

    Estimating uncertainty of hydrologic forecasting is valuable to water resources and other relevant decision making processes. Recently, Bayesian Neural Networks (BNNs) have been proved powerful tools for quantifying uncertainty of streamflow forecasting. In this study, we propose a Markov Chain Monte Carlo (MCMC) framework (BNN-PIS) to incorporate the uncertainties associated with parameters, inputs, and structures into BNNs. This framework allows the structure of the neural networks to change by removing or adding connections between neurons and enables scaling of input data by using rainfall multipliers. The results show that the new BNNs outperform BNNs that only consider uncertainties associated with parameters and model structures. Critical evaluation of posterior distribution of neural network weights, number of effective connections, rainfall multipliers, and hyper-parameters shows that the assumptions held in our BNNs are not well supported. Further understanding of characteristics of and interactions among different uncertainty sources is expected to enhance the application of neural networks for uncertainty analysis of hydrologic forecasting. © 2011 Elsevier B.V.

  16. Knowledge base and neural network approach for protein secondary structure prediction.

    Science.gov (United States)

    Patel, Maulika S; Mazumdar, Himanshu S

    2014-11-21

    Protein structure prediction is of great relevance given the abundant genomic and proteomic data generated by the genome sequencing projects. Protein secondary structure prediction is addressed as a sub task in determining the protein tertiary structure and function. In this paper, a novel algorithm, KB-PROSSP-NN, which is a combination of knowledge base and modeling of the exceptions in the knowledge base using neural networks for protein secondary structure prediction (PSSP), is proposed. The knowledge base is derived from a proteomic sequence-structure database and consists of the statistics of association between the 5-residue words and corresponding secondary structure. The predicted results obtained using knowledge base are refined with a Backpropogation neural network algorithm. Neural net models the exceptions of the knowledge base. The Q3 accuracy of 90% and 82% is achieved on the RS126 and CB396 test sets respectively which suggest improvement over existing state of art methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

  17. Magnetic structures of erbium under high pressure

    DEFF Research Database (Denmark)

    Kawano, S.; Lebech, B.; Achiwa, N.

    1993-01-01

    Neutron diffraction studies of the magnetic structures of erbium metal at 4.5 K and 11.5 kbar hydrostatic pressure have revealed that the transition to a conical structure at low temperatures is suppressed and that the cycloidal structure, with modulation vector Q congruent-to (2/7 2pi/c)c persists...

  18. Electronic structure of Ca, Sr, and Ba under pressure.

    Science.gov (United States)

    Animalu, A. O. E.; Heine, V.; Vasvari, B.

    1967-01-01

    Electronic band structure calculations phase of Ca, Sr and Ba over wide range of atomic volumes under pressure electronic band structure calculations for fcc phase of Ca, Sr and Ba over wide range of atomic volumes under pressure electronic band structure calculations for fcc phase of Ca, Sr and Ba over wide range of atomic volumes under pressure

  19. Wave transmission at low-crested structures using neural networks

    NARCIS (Netherlands)

    Van Oosten, R.P.; Peixó Marco, J.; Van der Meer, J.W.; Van Gent, M.; Verhagen, H.J.

    2006-01-01

    The European Union funded project DELOS was focused on wave transmission and an extensive database on low-crested rubble mound structures was generated. During DELOS, new empirical wave transmission formulae were derived. These formulae still showed a considerable scatter due to a limited number of

  20. Learning Orthographic Structure with Sequential Generative Neural Networks

    Science.gov (United States)

    Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

    2016-01-01

    Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in…

  1. Neural networks underlying language and social cognition during self-other processing in Autism spectrum disorders.

    Science.gov (United States)

    Kana, Rajesh K; Sartin, Emma B; Stevens, Carl; Deshpande, Hrishikesh D; Klein, Christopher; Klinger, Mark R; Klinger, Laura Grofer

    2017-07-28

    The social communication impairments defining autism spectrum disorders (ASD) may be built upon core deficits in perspective-taking, language processing, and self-other representation. Self-referential processing entails the ability to incorporate self-awareness, self-judgment, and self-memory in information processing. Very few studies have examined the neural bases of integrating self-other representation and semantic processing in individuals with ASD. The main objective of this functional MRI study is to examine the role of language and social brain networks in self-other processing in young adults with ASD. Nineteen high-functioning male adults with ASD and 19 age-sex-and-IQ-matched typically developing (TD) control participants made "yes" or "no" judgments of whether an adjective, presented visually, described them (self) or their favorite teacher (other). Both ASD and TD participants showed significantly increased activity in the medial prefrontal cortex (MPFC) during self and other processing relative to letter search. Analyses of group differences revealed significantly reduced activity in left inferior frontal gyrus (LIFG), and left inferior parietal lobule (LIPL) in ASD participants, relative to TD controls. ASD participants also showed significantly weaker functional connectivity of the anterior cingulate cortex (ACC) with several brain areas while processing self-related words. The LIFG and IPL are important regions functionally at the intersection of language and social roles; reduced recruitment of these regions in ASD participants may suggest poor level of semantic and social processing. In addition, poor connectivity of the ACC may suggest the difficulty in meeting the linguistic and social demands of this task in ASD. Overall, this study provides new evidence of the altered recruitment of the neural networks underlying language and social cognition in ASD. Published by Elsevier Ltd.

  2. Neural Mechanisms Underlying Affective Theory of Mind in Violent Antisocial Personality Disorder and/or Schizophrenia.

    Science.gov (United States)

    Schiffer, Boris; Pawliczek, Christina; Müller, Bernhard W; Wiltfang, Jens; Brüne, Martin; Forsting, Michael; Gizewski, Elke R; Leygraf, Norbert; Hodgins, Sheilagh

    2017-10-21

    Among violent offenders with schizophrenia, there are 2 sub-groups, one with and one without, conduct disorder (CD) and antisocial personality disorder (ASPD), who differ as to treatment response and alterations of brain structure. The present study aimed to determine whether the 2 groups also differ in Theory of Mind and neural activations subsuming this task. Five groups of men were compared: 3 groups of violent offenders-schizophrenia plus CD/ASPD, schizophrenia with no history of antisocial behavior prior to illness onset, and CD/ASPD with no severe mental illness-and 2 groups of non-offenders, one with schizophrenia and one without (H). Participants completed diagnostic interviews, the Psychopathy Checklist Screening Version Interview, the Interpersonal Reactivity Index, authorized access to clinical and criminal files, and underwent functional magnetic resonance imaging while completing an adapted version of the Reading-the-Mind-in-the-Eyes Task (RMET). Relative to H, nonviolent and violent men with schizophrenia and not CD/ASPD performed more poorly on the RMET, while violent offenders with CD/ASPD, both those with and without schizophrenia, performed similarly. The 2 groups of violent offenders with CD/ASPD, both those with and without schizophrenia, relative to the other groups, displayed higher levels of activation in a network of prefrontal and temporal-parietal regions and reduced activation in the amygdala. Relative to men without CD/ASPD, both groups of violent offenders with CD/ASPD displayed a distinct pattern of neural responses during emotional/mental state attribution pointing to distinct and comparatively successful processing of social information. © The Author 2017. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

  3. Computational Assessment of Neural Probe and Brain Tissue Interface under Transient Motion

    Directory of Open Access Journals (Sweden)

    Michael Polanco

    2016-06-01

    Full Text Available The functional longevity of a neural probe is dependent upon its ability to minimize injury risk during the insertion and recording period in vivo, which could be related to motion-related strain between the probe and surrounding tissue. A series of finite element analyses was conducted to study the extent of the strain induced within the brain in an area around a neural probe. This study focuses on the transient behavior of neural probe and brain tissue interface with a viscoelastic model. Different stages of the interface from initial insertion of neural probe to full bonding of the probe by astro-glial sheath formation are simulated utilizing analytical tools to investigate the effects of relative motion between the neural probe and the brain while friction coefficients and kinematic frequencies are varied. The analyses can provide an in-depth look at the quantitative benefits behind using soft materials for neural probes.

  4. Bayesian Inference using Neural Net Likelihood Models for Protein Secondary Structure Prediction

    Directory of Open Access Journals (Sweden)

    Seong-Gon Kim

    2011-06-01

    Full Text Available Several techniques such as Neural Networks, Genetic Algorithms, Decision Trees and other statistical or heuristic methods have been used to approach the complex non-linear task of predicting Alpha-helicies, Beta-sheets and Turns of a proteins secondary structure in the past. This project introduces a new machine learning method by using an offline trained Multilayered Perceptrons (MLP as the likelihood models within a Bayesian Inference framework to predict secondary structures proteins. Varying window sizes are used to extract neighboring amino acid information and passed back and forth between the Neural Net models and the Bayesian Inference process until there is a convergence of the posterior secondary structure probability.

  5. Training verb argument structure production in agrammatic aphasia: Behavioral and neural recovery patterns

    Science.gov (United States)

    Thompson, Cynthia K.; Riley, Ellyn A.; den Ouden, Dirk-Bart; Meltzer-Asscher, Aya; Lukic, Sladjana

    2013-01-01

    Introduction Neuroimaging and lesion studies indicate a left hemisphere network for verb and verb argument structure processing, involving both frontal and temporoparietal brain regions. Although their verb comprehension is generally unimpaired, it is well known that individuals with agrammatic aphasia often present with verb production deficits, characterized by an argument structure complexity hierarchy, indicating faulty access to argument structure representations for production and integration into syntactic contexts. Recovery of verb processing in agrammatism, however, has received little attention and no studies have examined the neural mechanisms associated with improved verb and argument structure processing. In the present study we trained agrammatic individuals on verbs with complex argument structure in sentence contexts and examined generalization to verbs with less complex argument structure. The neural substrates of improved verb production were examined using functional magnetic resonance imaging (fMRI). Methods Eight individuals with chronic agrammatic aphasia participated in the study (four experimental and four control participants). Production of three-argument verbs in active sentences was trained using a sentence generation task emphasizing the verb’s argument structure and the thematic roles of sentential noun phrases. Before and after training, production of trained and untrained verbs was tested in naming and sentence production and fMRI scans were obtained, using an action naming task. Results Significant pre- to post-training improvement in trained and untrained (one- and two-argument) verbs was found for treated, but not control, participants, with between-group differences found for verb naming, production of verbs in sentences, and production of argument structure. fMRI activation derived from post-treatment compared to pre-treatment scans revealed upregulation in cortical regions implicated for verb and argument structure processing

  6. Training verb argument structure production in agrammatic aphasia: behavioral and neural recovery patterns.

    Science.gov (United States)

    Thompson, Cynthia K; Riley, Ellyn A; den Ouden, Dirk-Bart; Meltzer-Asscher, Aya; Lukic, Sladjana

    2013-10-01

    Neuroimaging and lesion studies indicate a left hemisphere network for verb and verb argument structure processing, involving both frontal and temporoparietal brain regions. Although their verb comprehension is generally unimpaired, it is well known that individuals with agrammatic aphasia often present with verb production deficits, characterized by an argument structure complexity hierarchy, indicating faulty access to argument structure representations for production and integration into syntactic contexts. Recovery of verb processing in agrammatism, however, has received little attention and no studies have examined the neural mechanisms associated with improved verb and argument structure processing. In the present study we trained agrammatic individuals on verbs with complex argument structure in sentence contexts and examined generalization to verbs with less complex argument structure. The neural substrates of improved verb production were examined using functional magnetic resonance imaging (fMRI). Eight individuals with chronic agrammatic aphasia participated in the study (four experimental and four control participants). Production of three-argument verbs in active sentences was trained using a sentence generation task emphasizing the verb's argument structure and the thematic roles of sentential noun phrases. Before and after training, production of trained and untrained verbs was tested in naming and sentence production and fMRI scans were obtained, using an action naming task. Significant pre- to post-training improvement in trained and untrained (one- and two-argument) verbs was found for treated, but not control, participants, with between-group differences found for verb naming, production of verbs in sentences, and production of argument structure. fMRI activation derived from post-treatment compared to pre-treatment scans revealed upregulation in cortical regions implicated for verb and argument structure processing in healthy controls. Training

  7. Neural correlates of expectation of musical termination structure or cadence.

    Science.gov (United States)

    Hoshi-Shiba, Reiko; Furukawa, Kiyoshi; Okanoya, Kazuo

    2014-07-09

    Music is an essential communication tool that can convey emotional information. Segmentation of a sound stream of music at event boundaries is necessary for identification and extraction of musical context and features. To investigate recognition of music termination structure, or cadence, we composed music sequences with two types of dominant-tonic termination structures and presented them to participants and analyzed their segment recognition and brain activities using EEG. The results revealed that a sense of termination was caused by listening to a tonic chord. Frontal area positivity at 380-480 ms was elicited by a dominant chord in cadence type I with a stronger sense of termination. These activities possibly reflected the expectation of the next tonic chord in the cadence.

  8. Computations Underlying Social Hierarchy Learning: Distinct Neural Mechanisms for Updating and Representing Self-Relevant Information.

    Science.gov (United States)

    Kumaran, Dharshan; Banino, Andrea; Blundell, Charles; Hassabis, Demis; Dayan, Peter

    2016-12-07

    Knowledge about social hierarchies organizes human behavior, yet we understand little about the underlying computations. Here we show that a Bayesian inference scheme, which tracks the power of individuals, better captures behavioral and neural data compared with a reinforcement learning model inspired by rating systems used in games such as chess. We provide evidence that the medial prefrontal cortex (MPFC) selectively mediates the updating of knowledge about one's own hierarchy, as opposed to that of another individual, a process that underpinned successful performance and involved functional interactions with the amygdala and hippocampus. In contrast, we observed domain-general coding of rank in the amygdala and hippocampus, even when the task did not require it. Our findings reveal the computations underlying a core aspect of social cognition and provide new evidence that self-relevant information may indeed be afforded a unique representational status in the brain. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  9. Modulating conscious movement intention by noninvasive brain stimulation and the underlying neural mechanisms.

    Science.gov (United States)

    Douglas, Zachary H; Maniscalco, Brian; Hallett, Mark; Wassermann, Eric M; He, Biyu J

    2015-05-06

    Conscious intention is a fundamental aspect of the human experience. Despite long-standing interest in the basis and implications of intention, its underlying neurobiological mechanisms remain poorly understood. Using high-definition transcranial DC stimulation (tDCS), we observed that enhancing spontaneous neuronal excitability in both the angular gyrus and the primary motor cortex caused the reported time of conscious movement intention to be ∼60-70 ms earlier. Slow brain waves recorded ∼2-3 s before movement onset, as well as hundreds of milliseconds after movement onset, independently correlated with the modulation of conscious intention by brain stimulation. These brain activities together accounted for 81% of interindividual variability in the modulation of movement intention by brain stimulation. A computational model using coupled leaky integrator units with biophysically plausible assumptions about the effect of tDCS captured the effects of stimulation on both neural activity and behavior. These results reveal a temporally extended brain process underlying conscious movement intention that spans seconds around movement commencement. Copyright © 2015 Douglas et al.

  10. Response of masonry structure under impact load

    International Nuclear Information System (INIS)

    Makovicka, D.

    1993-01-01

    The paper deals with interaction of a short gaseous impact wave with a plate structure. Analyses of dynamic bending, depending on the parameters of the structure and the impact wave (i.e. the stress and displacement field produced by the resulting incident and reflected wave) have been made by FEM. The calculated data was based on the real material properties of this structure. Pressures greater than computed limit pressures result in the failure of the structure. The calculated and experimental data are compared. (author)

  11. Data processing at the SSC with structured neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Lackner, K.S.; Sandberg, V.D.; Sharp, D.H.

    1990-01-01

    SSC detectors will place extreme demands on data processing systems. One must reduce the data flux to manageable proportions at the earliest possible stage. This observations has led us to emphasize low-level data processing and track reconstruction. We report progress in three areas: A network compiler has been designed which generates programmable and trainable tree-structured nets; algorithms for track reconstruction have been designed and implemented in such nets; exploratory studies have been made on the use of such nets to carry out low-level processing on hardware components of a central detector. 6 refs., 1 fig.

  12. Optimizing the De-Noise Neural Network Model for GPS Time-Series Monitoring of Structures

    Directory of Open Access Journals (Sweden)

    Mosbeh R. Kaloop

    2015-09-01

    Full Text Available The Global Positioning System (GPS is recently used widely in structures and other applications. Notwithstanding, the GPS accuracy still suffers from the errors afflicting the measurements, particularly the short-period displacement of structural components. Previously, the multi filter method is utilized to remove the displacement errors. This paper aims at using a novel application for the neural network prediction models to improve the GPS monitoring time series data. Four prediction models for the learning algorithms are applied and used with neural network solutions: back-propagation, Cascade-forward back-propagation, adaptive filter and extended Kalman filter, to estimate which model can be recommended. The noise simulation and bridge’s short-period GPS of the monitoring displacement component of one Hz sampling frequency are used to validate the four models and the previous method. The results show that the Adaptive neural networks filter is suggested for de-noising the observations, specifically for the GPS displacement components of structures. Also, this model is expected to have significant influence on the design of structures in the low frequency responses and measurements’ contents.

  13. Prediction of Optimal Design and Deflection of Space Structures Using Neural Networks

    Directory of Open Access Journals (Sweden)

    Reza Kamyab Moghadas

    2012-01-01

    Full Text Available The main aim of the present work is to determine the optimal design and maximum deflection of double layer grids spending low computational cost using neural networks. The design variables of the optimization problem are cross-sectional area of the elements as well as the length of the span and height of the structures. In this paper, a number of double layer grids with various random values of length and height are selected and optimized by simultaneous perturbation stochastic approximation algorithm. Then, radial basis function (RBF and generalized regression (GR neural networks are trained to predict the optimal design and maximum deflection of the structures. The numerical results demonstrate the efficiency of the proposed methodology.

  14. Forecasting the term structure of crude oil futures prices with neural networks

    Czech Academy of Sciences Publication Activity Database

    Baruník, Jozef; Malinská, B.

    2016-01-01

    Roč. 164, č. 1 (2016), s. 366-379 ISSN 0306-2619 R&D Projects: GA ČR(CZ) GBP402/12/G097 Institutional support: RVO:67985556 Keywords : Term structure * Nelson–Siegel model * Dynamic neural networks * Crude oil futures Subject RIV: AH - Economics Impact factor: 7.182, year: 2016 http://library.utia.cas.cz/separaty/2016/E/barunik-0453168.pdf

  15. Functional and structural neural alterations in Internet gaming disorder: A systematic review and meta-analysis.

    Science.gov (United States)

    Yao, Yuan-Wei; Liu, Lu; Ma, Shan-Shan; Shi, Xin-Hui; Zhou, Nan; Zhang, Jin-Tao; Potenza, Marc N

    2017-12-01

    This meta-analytic study aimed to identify the common and specific neural alterations in Internet gaming disorder (IGD) across different domains and modalities. Two separate meta-analyses for functional neural activation and gray-matter volume were conducted. Sub-meta-analyses for the domains of reward, cold-executive, and hot-executive functions were also performed, respectively. IGD subjects, compared with healthy controls, showed: (1) hyperactivation in the anterior and posterior cingulate cortices, caudate, posterior inferior frontal gyrus (IFG), which were mainly associated with studies measuring reward and cold-executive functions; and, (2) hypoactivation in the anterior IFG in relation to hot-executive function, the posterior insula, somatomotor and somatosensory cortices in relation to reward function. Furthermore, IGD subjects showed reduced gray-matter volume in the anterior cingulate, orbitofrontal, dorsolateral prefrontal, and premotor cortices. These findings suggest that IGD is associated with both functional and structural neural alterations in fronto-striatal and fronto-cingulate regions. Moreover, multi-domain assessments capture different aspects of neural alterations in IGD, which may be helpful for developing effective interventions targeting specific functions. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. The insula: a critical neural substrate for craving and drug seeking under conflict and risk

    Science.gov (United States)

    Naqvi, Nasir H.; Gaznick, Natassia; Tranel, Daniel; Bechara, Antoine

    2014-01-01

    Drug addiction is characterized by the inability to control drug use when it results in negative consequences or conflicts with more adaptive goals. Our previous work showed that damage to the insula disrupted addiction to cigarette smoking—the first time that the insula was shown to be a critical neural substrate for addiction. Here, we review those findings, as well as more recent studies that corroborate and extend them, demonstrating the role of the insula in (1) incentive motivational processes that drive addictive behavior, (2) control processes that moderate or inhibit addictive behavior, and (3) interoceptive processes that represent bodily states associated with drug use. We then describe a theoretical framework that attempts to integrate these seemingly disparate findings. In this framework, the insula functions in the recall of interoceptive drug effects during craving and drug seeking under specific conditions where drug taking is perceived as risky and/or where there is conflict between drug taking and more adaptive goals. We describe this framework in an evolutionary context and discuss its implications for understanding the mechanisms of behavior change in addiction treatments. PMID:24690001

  17. Neural and computational processes underlying dynamic changes in self-esteem

    Science.gov (United States)

    Rutledge, Robb B; Moutoussis, Michael; Dolan, Raymond J

    2017-01-01

    Self-esteem is shaped by the appraisals we receive from others. Here, we characterize neural and computational mechanisms underlying this form of social influence. We introduce a computational model that captures fluctuations in self-esteem engendered by prediction errors that quantify the difference between expected and received social feedback. Using functional MRI, we show these social prediction errors correlate with activity in ventral striatum/subgenual anterior cingulate cortex, while updates in self-esteem resulting from these errors co-varied with activity in ventromedial prefrontal cortex (vmPFC). We linked computational parameters to psychiatric symptoms using canonical correlation analysis to identify an ‘interpersonal vulnerability’ dimension. Vulnerability modulated the expression of prediction error responses in anterior insula and insula-vmPFC connectivity during self-esteem updates. Our findings indicate that updating of self-evaluative beliefs relies on learning mechanisms akin to those used in learning about others. Enhanced insula-vmPFC connectivity during updating of those beliefs may represent a marker for psychiatric vulnerability. PMID:29061228

  18. Neural correlates underlying naloxone-induced amelioration of sexual behavior deterioration due to an alarm pheromone

    Directory of Open Access Journals (Sweden)

    Tatsuya eKobayashi

    2015-02-01

    Full Text Available Sexual behavior is suppressed by various types of stressors. We previously demonstrated that an alarm pheromone released by stressed male Wistar rats is a stressor to other rats, increases the number of mounts needed for ejaculation, and decreases the hit rate (described as the number of intromissions/sum of the mounts and intromissions. This deterioration in sexual behavior was ameliorated by pretreatment with the opioid receptor antagonist naloxone. However, the neural mechanism underlying this remains to be elucidated. Here, we examined Fos expression in 31 brain regions of pheromone-exposed rats and naloxone-pretreated pheromone-exposed rats 60 min after 10 intromissions. As previously reported, the alarm pheromone increased the number of mounts and decreased the hit rate. In addition, Fos expression was increases in the anterior medial division, anterior lateral division and posterior division of the bed nucleus of the stria terminalis, parvocellular part of the paraventricular nucleus of the hypothalamus, arcuate nucleus, dorsolateral and ventrolateral periaqueductal gray, and nucleus paragigantocellularis. Fos expression decreased in the magnocellular part of the paraventricular nucleus of the hypothalamus. Pretreatment with naloxone blocked the pheromone-induced changes in Fos expression in the magnocellular part of the paraventricular nucleus of the hypothalamus, ventrolateral periaqueductal gray, and nucleus paragigantocellularis. Based on these results, we hypothesize that the alarm pheromone deteriorated sexual behavior by activating the ventrolateral periaqueductal gray-nucleus paragigantocellularis cluster and suppressing the magnocellular part of the paraventricular nucleus of the hypothalamus via the opioidergic pathway.

  19. Feline Neural Progenitor Cells I: Long-Term Expansion under Defined Culture Conditions

    Directory of Open Access Journals (Sweden)

    Jing Yang

    2012-01-01

    Full Text Available Neural progenitor cells (NPCs of feline origin (cNPCs have demonstrated utility in transplantation experiments, yet are difficult to grow in culture beyond the 1 month time frame. Here we use an enriched, serum-free base medium (Ultraculture and report the successful long-term propagation of these cells. Primary cultures were derived from fetal brain tissue and passaged in DMEM/F12-based or Ultraculture-based proliferation media, both in the presence of EGF + bFGF. Cells in standard DMEM/F12-based medium ceased to proliferate by 1-month, whereas the cells in the Ultraculture-based medium continued to grow for at least 5 months (end of study with no evidence of senescence. The Ultraculture-based cultures expressed lower levels of progenitor and lineage-associated markers under proliferation conditions but retained multipotency as evidenced by the ability to differentiate into neurons and glia following growth factor removal in the presence of FBS. Importantly, later passage cNPCs did not develop chromosomal aberrations.

  20. Ear Detection under Uncontrolled Conditions with Multiple Scale Faster Region-Based Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Yi Zhang

    2017-04-01

    Full Text Available Ear detection is an important step in ear recognition approaches. Most existing ear detection techniques are based on manually designing features or shallow learning algorithms. However, researchers found that the pose variation, occlusion, and imaging conditions provide a great challenge to the traditional ear detection methods under uncontrolled conditions. This paper proposes an efficient technique involving Multiple Scale Faster Region-based Convolutional Neural Networks (Faster R-CNN to detect ears from 2D profile images in natural images automatically. Firstly, three regions of different scales are detected to infer the information about the ear location context within the image. Then an ear region filtering approach is proposed to extract the correct ear region and eliminate the false positives automatically. In an experiment with a test set of 200 web images (with variable photographic conditions, 98% of ears were accurately detected. Experiments were likewise conducted on the Collection J2 of University of Notre Dame Biometrics Database (UND-J2 and University of Beira Interior Ear dataset (UBEAR, which contain large occlusion, scale, and pose variations. Detection rates of 100% and 98.22%, respectively, demonstrate the effectiveness of the proposed approach.

  1. The insula: a critical neural substrate for craving and drug seeking under conflict and risk.

    Science.gov (United States)

    Naqvi, Nasir H; Gaznick, Natassia; Tranel, Daniel; Bechara, Antoine

    2014-05-01

    Drug addiction is characterized by the inability to control drug use when it results in negative consequences or conflicts with more adaptive goals. Our previous work showed that damage to the insula disrupted addiction to cigarette smoking-the first time that the insula was shown to be a critical neural substrate for addiction. Here, we review those findings, as well as more recent studies that corroborate and extend them, demonstrating the role of the insula in (1) incentive motivational processes that drive addictive behavior, (2) control processes that moderate or inhibit addictive behavior, and (3) interoceptive processes that represent bodily states associated with drug use. We then describe a theoretical framework that attempts to integrate these seemingly disparate findings. In this framework, the insula functions in the recall of interoceptive drug effects during craving and drug seeking under specific conditions where drug taking is perceived as risky and/or where there is conflict between drug taking and more adaptive goals. We describe this framework in an evolutionary context and discuss its implications for understanding the mechanisms of behavior change in addiction treatments. © 2014 New York Academy of Sciences.

  2. Neural mechanisms underlying valence inferences to sound: The role of the right angular gyrus.

    Science.gov (United States)

    Bravo, Fernando; Cross, Ian; Hawkins, Sarah; Gonzalez, Nadia; Docampo, Jorge; Bruno, Claudio; Stamatakis, Emmanuel Andreas

    2017-07-28

    We frequently infer others' intentions based on non-verbal auditory cues. Although the brain underpinnings of social cognition have been extensively studied, no empirical work has yet examined the impact of musical structure manipulation on the neural processing of emotional valence during mental state inferences. We used a novel sound-based theory-of-mind paradigm in which participants categorized stimuli of different sensory dissonance level in terms of positive/negative valence. Whilst consistent with previous studies which propose facilitated encoding of consonances, our results demonstrated that distinct levels of consonance/dissonance elicited differential influences on the right angular gyrus, an area implicated in mental state attribution and attention reorienting processes. Functional and effective connectivity analyses further showed that consonances modulated a specific inhibitory interaction from associative memory to mental state attribution substrates. Following evidence suggesting that individuals with autism may process social affective cues differently, we assessed the relationship between participants' task performance and self-reported autistic traits in clinically typical adults. Higher scores on the social cognition scales of the AQ were associated with deficits in recognising positive valence in consonant sound cues. These findings are discussed with respect to Bayesian perspectives on autistic perception, which highlight a functional failure to optimize precision in relation to prior beliefs. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Neural circuit remodeling and structural plasticity in the cortex during chronic pain.

    Science.gov (United States)

    Kim, Woojin; Kim, Sun Kwang

    2016-01-01

    Damage in the periphery or spinal cord induces maladaptive plastic changes along the somatosensory nervous system from the periphery to the cortex, often leading to chronic pain. Although the role of neural circuit remodeling and structural synaptic plasticity in the 'pain matrix' cortices in chronic pain has been thought as a secondary epiphenomenon to altered nociceptive signaling in the spinal cord, progress in whole brain imaging studies on human patients and animal models has suggested a possibility that plastic changes in cortical neural circuits may actively contribute to chronic pain symptoms. Furthermore, recent development in two-photon microscopy and fluorescence labeling techniques have enabled us to longitudinally trace the structural and functional changes in local circuits, single neurons and even individual synapses in the brain of living animals. These technical advances has started to reveal that cortical structural remodeling following tissue or nerve damage could rapidly occur within days, which are temporally correlated with functional plasticity of cortical circuits as well as the development and maintenance of chronic pain behavior, thereby modifying the previous concept that it takes much longer periods (e.g. months or years). In this review, we discuss the relation of neural circuit plasticity in the 'pain matrix' cortices, such as the anterior cingulate cortex, prefrontal cortex and primary somatosensory cortex, with chronic pain. We also introduce how to apply long-term in vivo two-photon imaging approaches for the study of pathophysiological mechanisms of chronic pain.

  4. Flexible deep brain neural probes based on a parylene tube structure

    Science.gov (United States)

    Zhao, Zhiguo; Kim, Eric; Luo, Hao; Zhang, Jinsheng; Xu, Yong

    2018-01-01

    Most microfabricated neural probes have limited shank length, which prevents them from reaching many deep brain structures. This paper reports deep brain neural probes with ultra-long penetrating shanks based on a simple but novel parylene tube structure. The mechanical strength of the parylene tube shank is temporarily enhanced during implantation by inserting a metal wire. The metal wire can be removed after implantation, making the implanted probe very flexible and thus minimizing the stress caused by micromotions of brain tissues. Optogenetic stimulation and chemical delivery capabilities can be potentially integrated by taking advantage of the tube structure. Single-shank prototypes with a shank length of 18.2 mm have been developed. The microfabrication process comprises of deep reactive ion etching (DRIE) of silicon, parylene conformal coating/refilling, and XeF2 isotropic silicon etching. In addition to bench-top insertion characterization, the functionality of developed probes has been preliminarily demonstrated by implanting into the amygdala of a rat and recording neural signals.

  5. Unified neural field theory of brain dynamics underlying oscillations in Parkinson's disease and generalized epilepsies.

    Science.gov (United States)

    Müller, E J; van Albada, S J; Kim, J W; Robinson, P A

    2017-09-07

    The mechanisms underlying pathologically synchronized neural oscillations in Parkinson's disease (PD) and generalized epilepsies are explored in parallel via a physiologically-based neural field model of the corticothalamic-basal ganglia (CTBG) system. The basal ganglia (BG) are approximated as a single effective population and their roles in the modulation of oscillatory dynamics of the corticothalamic (CT) system and vice versa are analyzed. In addition to normal EEG rhythms, enhanced activity around 4 Hz and 20 Hz exists in the model, consistent with the characteristic frequencies observed in PD. These rhythms result from resonances in loops formed between the BG and CT populations, analogous to those that underlie epileptic oscillations in a previous CT model, and which are still present in the combined CTBG system. Dopamine depletion is argued to weaken the dampening of these loop resonances in PD, and network connections then explain the significant coherence observed between BG, thalamic, and cortical population activity around 4-8 Hz and 20 Hz. Parallels between the afferent and efferent connection sites of the thalamic reticular nucleus (TRN) and BG predict low dopamine to correspond to a reduced likelihood of tonic-clonic (grand mal) seizures, which agrees with experimental findings. Furthermore, the model predicts an increased likelihood of absence (petit mal) seizure resulting from pathologically low dopamine levels in accordance with experimental observations. Suppression of absence seizure activity is demonstrated when afferent and efferent BG connections to the CT system are strengthened, which is consistent with other CTBG modeling studies. The BG are demonstrated to have a suppressive effect on activity of the CTBG system near tonic-clonic seizure states, which provides insight into the reported efficacy of current treatments in BG circuits. Sleep states of the TRN are also found to suppress pathological PD activity in accordance with

  6. Musical intervention enhances infants’ neural processing of temporal structure in music and speech

    Science.gov (United States)

    Zhao, T. Christina; Kuhl, Patricia K.

    2016-01-01

    Individuals with music training in early childhood show enhanced processing of musical sounds, an effect that generalizes to speech processing. However, the conclusions drawn from previous studies are limited due to the possible confounds of predisposition and other factors affecting musicians and nonmusicians. We used a randomized design to test the effects of a laboratory-controlled music intervention on young infants’ neural processing of music and speech. Nine-month-old infants were randomly assigned to music (intervention) or play (control) activities for 12 sessions. The intervention targeted temporal structure learning using triple meter in music (e.g., waltz), which is difficult for infants, and it incorporated key characteristics of typical infant music classes to maximize learning (e.g., multimodal, social, and repetitive experiences). Controls had similar multimodal, social, repetitive play, but without music. Upon completion, infants’ neural processing of temporal structure was tested in both music (tones in triple meter) and speech (foreign syllable structure). Infants’ neural processing was quantified by the mismatch response (MMR) measured with a traditional oddball paradigm using magnetoencephalography (MEG). The intervention group exhibited significantly larger MMRs in response to music temporal structure violations in both auditory and prefrontal cortical regions. Identical results were obtained for temporal structure changes in speech. The intervention thus enhanced temporal structure processing not only in music, but also in speech, at 9 mo of age. We argue that the intervention enhanced infants’ ability to extract temporal structure information and to predict future events in time, a skill affecting both music and speech processing. PMID:27114512

  7. Musical intervention enhances infants' neural processing of temporal structure in music and speech.

    Science.gov (United States)

    Zhao, T Christina; Kuhl, Patricia K

    2016-05-10

    Individuals with music training in early childhood show enhanced processing of musical sounds, an effect that generalizes to speech processing. However, the conclusions drawn from previous studies are limited due to the possible confounds of predisposition and other factors affecting musicians and nonmusicians. We used a randomized design to test the effects of a laboratory-controlled music intervention on young infants' neural processing of music and speech. Nine-month-old infants were randomly assigned to music (intervention) or play (control) activities for 12 sessions. The intervention targeted temporal structure learning using triple meter in music (e.g., waltz), which is difficult for infants, and it incorporated key characteristics of typical infant music classes to maximize learning (e.g., multimodal, social, and repetitive experiences). Controls had similar multimodal, social, repetitive play, but without music. Upon completion, infants' neural processing of temporal structure was tested in both music (tones in triple meter) and speech (foreign syllable structure). Infants' neural processing was quantified by the mismatch response (MMR) measured with a traditional oddball paradigm using magnetoencephalography (MEG). The intervention group exhibited significantly larger MMRs in response to music temporal structure violations in both auditory and prefrontal cortical regions. Identical results were obtained for temporal structure changes in speech. The intervention thus enhanced temporal structure processing not only in music, but also in speech, at 9 mo of age. We argue that the intervention enhanced infants' ability to extract temporal structure information and to predict future events in time, a skill affecting both music and speech processing.

  8. Structure of polymer chains under confinement

    Indian Academy of Sciences (India)

    cluded volume interactions (so-called regime of “semi-dilute cigars”). For confined charged polymers, a peak is observed whose intensity increases with molecular weight and the asymptotic 1/q scattering region is extended compared to the bulk. We infer that the chains are sufficiently extended, under the influence of ...

  9. Artificial neural networks aided conceptual stage design of water harvesting structures

    Directory of Open Access Journals (Sweden)

    Vinay Chandwani

    2016-09-01

    Full Text Available The paper presents artificial neural networks (ANNs based methodology for ascertaining the structural parameters of water harvesting structures (WHS at the conceptual stage of design. The ANN is trained using exemplar patterns generated using an in-house MSExcel based design program, to draw a functional relationship between the five inputs design parameters namely, peak flood discharge, safe bearing capacity of strata, length of structure, height of structure and silt factor and four outputs namely, top width, bottom width, foundation depth and flood lift representing the structural parameters of WHS. The results of the study show that, the structural parameters of the WHS predicted using ANN model are in close agreement with the actual field parameters. The versatility of ANN to map complex or complex unknown relationships has been proven in the study. A parametric sensitivity study is also performed to assess the most significant design parameter. The study holistically presents a neural network based decision support tool that can be used to accurately estimate the major design parameters of the WHS at the conceptual stage of design in quick time, aiding the engineer-in-charge to conveniently forecast the budget requirements and minimize the labor involved during the subsequent phases of analysis and design.

  10. Concurrent Structural Fatigue Damage Prognosis Under Uncertainty

    Science.gov (United States)

    2014-04-30

    same experiment is carried on AISI 4340 steel. AISI 4340 steel is a heat treatable, low alloy steel containing nickel, chromium and molybdenum. The...but after the unstable crack growth after the overload, it is 82 83 hard to measure the crack growth per cycle which is smaller than 20...structural and macro materials level. The extension to include material microstructure effect for the fatigue prognosis needs further investigations

  11. Robustness Assessment of Building Structures under Explosion

    Directory of Open Access Journals (Sweden)

    Mark Waggoner

    2012-12-01

    Full Text Available Over the past decade, much research has focused on the behaviour of structures following the failure of a key structural component. Particular attention has been given to sudden column loss, though questions remain as to whether this event-independent scenario is relevant to actual extreme events such as explosion. Few studies have been conducted to assess the performance of floor slabs above a failed column, and the computational tools used have not been validated against experimental results. The research program presented in this paper investigates the adequacy of sudden column loss as an idealisation of local damage caused by realistic explosion events, and extends prior work by combining the development of accurate computational models with large-scale testing of a typical floor system in a prototypical steel-framed structure. The floor system consists of corrugated decking topped by a lightly reinforced concrete slab that is connected to the floor beams through shear studs. The design is consistent with typical building practices in the US. The first test has been completed, and subsequent tests are currently being planned. This paper addresses the importance of robustness design for localized damage and includes a detailed description regarding how the research program advances the current state of knowledge for assessing robustness of compositely constructed steel-framed buildings.

  12. Materials and structures under shock and impact

    CERN Document Server

    Bailly, Patrice

    2013-01-01

    In risk studies, engineers often have to consider the consequences of an accident leading to a shock on a construction. This can concern the impact of a ground vehicle or aircraft, or the effects of an explosion on an industrial site.This book presents a didactic approach starting with the theoretical elements of the mechanics of materials and structures, in order to develop their applications in the cases of shocks and impacts. The latter are studied on a local scale at first. They lead to stresses and strains in the form of waves propagating through the material, this movement then extending

  13. Self-awareness in neurodegenerative disease relies on neural structures mediating reward-driven attention.

    Science.gov (United States)

    Shany-Ur, Tal; Lin, Nancy; Rosen, Howard J; Sollberger, Marc; Miller, Bruce L; Rankin, Katherine P

    2014-08-01

    versus exaggerating deficits, overestimation and underestimation scores were analysed separately, controlling for age, sex, total intracranial volume and extent of actual functional decline. Atrophy related to overestimating one's functioning included bilateral, right greater than left frontal and subcortical regions, including dorsal superior and middle frontal gyri, lateral and medial orbitofrontal gyri, right anterior insula, putamen, thalamus, and caudate, and midbrain and pons. Thus, our patients' tendency to under-represent their functional decline was related to degeneration of domain-general dorsal frontal regions involved in attention, as well as orbitofrontal and subcortical regions likely involved in assigning a reward value to self-related processing and maintaining accurate self-knowledge. The anatomic correlates of underestimation (right rostral anterior cingulate cortex, uncorrected significance level) were distinct from overestimation and had a substantially smaller effect size. This suggests that underestimation or 'tarnishing' may be influenced by non-structural neurobiological and sociocultural factors, and should not be considered to be on a continuum with overestimation or 'polishing' of functional capacity, which appears to be more directly mediated by neural circuit dysfunction. © The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  14. Elements of an algorithm for optimizing a parameter-structural neural network

    Directory of Open Access Journals (Sweden)

    Mrówczyńska Maria

    2016-06-01

    Full Text Available The field of processing information provided by measurement results is one of the most important components of geodetic technologies. The dynamic development of this field improves classic algorithms for numerical calculations in the aspect of analytical solutions that are difficult to achieve. Algorithms based on artificial intelligence in the form of artificial neural networks, including the topology of connections between neurons have become an important instrument connected to the problem of processing and modelling processes. This concept results from the integration of neural networks and parameter optimization methods and makes it possible to avoid the necessity to arbitrarily define the structure of a network. This kind of extension of the training process is exemplified by the algorithm called the Group Method of Data Handling (GMDH, which belongs to the class of evolutionary algorithms. The article presents a GMDH type network, used for modelling deformations of the geometrical axis of a steel chimney during its operation.

  15. Temporal entrainment of cognitive functions: musical mnemonics induce brain plasticity and oscillatory synchrony in neural networks underlying memory.

    Science.gov (United States)

    Thaut, Michael H; Peterson, David A; McIntosh, Gerald C

    2005-12-01

    In a series of experiments, we have begun to investigate the effect of music as a mnemonic device on learning and memory and the underlying plasticity of oscillatory neural networks. We used verbal learning and memory tests (standardized word lists, AVLT) in conjunction with electroencephalographic analysis to determine differences between verbal learning in either a spoken or musical (verbal materials as song lyrics) modality. In healthy adults, learning in both the spoken and music condition was associated with significant increases in oscillatory synchrony across all frequency bands. A significant difference between the spoken and music condition emerged in the cortical topography of the learning-related synchronization. When using EEG measures as predictors during learning for subsequent successful memory recall, significantly increased coherence (phase-locked synchronization) within and between oscillatory brain networks emerged for music in alpha and gamma bands. In a similar study with multiple sclerosis patients, superior learning and memory was shown in the music condition when controlled for word order recall, and subjects were instructed to sing back the word lists. Also, the music condition was associated with a significant power increase in the low-alpha band in bilateral frontal networks, indicating increased neuronal synchronization. Musical learning may access compensatory pathways for memory functions during compromised PFC functions associated with learning and recall. Music learning may also confer a neurophysiological advantage through the stronger synchronization of the neuronal cell assemblies underlying verbal learning and memory. Collectively our data provide evidence that melodic-rhythmic templates as temporal structures in music may drive internal rhythm formation in recurrent cortical networks involved in learning and memory.

  16. Factor structure underlying components of allostatic load.

    Directory of Open Access Journals (Sweden)

    Jeanne M McCaffery

    Full Text Available Allostatic load is a commonly used metric of health risk based on the hypothesis that recurrent exposure to environmental demands (e.g., stress engenders a progressive dysregulation of multiple physiological systems. Prominent indicators of response to environmental challenges, such as stress-related hormones, sympatho-vagal balance, or inflammatory cytokines, comprise primary allostatic mediators. Secondary mediators reflect ensuing biological alterations that accumulate over time and confer risk for clinical disease but overlap substantially with a second metric of health risk, the metabolic syndrome. Whether allostatic load mediators covary and thus warrant treatment as a unitary construct remains to be established and, in particular, the relation of allostatic load parameters to the metabolic syndrome requires elucidation. Here, we employ confirmatory factor analysis to test: 1 whether a single common factor underlies variation in physiological systems associated with allostatic load; and 2 whether allostatic load parameters continue to load on a single common factor if a second factor representing the metabolic syndrome is also modeled. Participants were 645 adults from Allegheny County, PA (30-54 years old, 82% non-Hispanic white, 52% female who were free of confounding medications. Model fitting supported a single, second-order factor underlying variance in the allostatic load components available in this study (metabolic, inflammatory and vagal measures. Further, this common factor reflecting covariation among allostatic load components persisted when a latent factor representing metabolic syndrome facets was conjointly modeled. Overall, this study provides novel evidence that the modeled allostatic load components do share common variance as hypothesized. Moreover, the common variance suggests the existence of statistical coherence above and beyond that attributable to the metabolic syndrome.

  17. Neural network underlying ictal pouting ("chapeau de gendarme") in frontal lobe epilepsy.

    Science.gov (United States)

    Souirti, Zouhayr; Landré, Elisabeth; Mellerio, Charles; Devaux, Bertrand; Chassoux, Francine

    2014-08-01

    In order to determine the anatomical neural network underlying ictal pouting (IP), with the mouth turned down like a "chapeau de gendarme", in frontal lobe epilepsy (FLE), we reviewed the video-EEG recordings of 36 patients with FLE who became seizure-free after surgery. We selected the cases presenting IP, defined as a symmetrical and sustained (>5s) lowering of labial commissures with contraction of chin, mimicking an expression of fear, disgust, or menace. Ictal pouting was identified in 11 patients (8 males; 16-48 years old). We analyzed the clinical semiology, imaging, and electrophysiological data associated with IP, including FDG-PET in 10 and SEEG in 9 cases. In 37 analyzed seizures (2-7/patient), IP was an early symptom, occurring during the first 10s in 9 cases. The main associated features consisted of fear, anguish, vegetative disturbances, behavioral disorders (sudden agitation, insults, and fighting), tonic posturing, and complex motor activities. The epileptogenic zone assessed by SEEG involved the mesial frontal areas, especially the anterior cingulate cortex (ACC) in 8 patients, whereas lateral frontal onset with an early spread to the ACC was seen in the other patient. Ictal pouting associated with emotional changes and hypermotor behavior had high localizing value for rostroventral "affective" ACC, whereas less intense facial expressions were related to the dorsal "cognitive" ACC. Fluorodeoxyglucose positron emission tomography demonstrated the involvement of both the ACC and lateral cortex including the anterior insula in all cases. We propose that IP is sustained by reciprocal mesial and lateral frontal interactions involved in emotional and cognitive processes, in which the ACC plays a pivotal role. Copyright © 2014 Elsevier Inc. All rights reserved.

  18. Neural mechanisms underlying migrating motor complex formation in mouse isolated colon

    Science.gov (United States)

    Brierley, Stuart M; Nichols, Kim; Grasby, Dallas J; Waterman, Sally A

    2001-01-01

    Little is known about the intrinsic enteric reflex pathways associated with migrating motor complex (MMC) formation. Acetylcholine (ACh) mediates the rapid component of the MMC, however a non-cholinergic component also exists. The present study investigated the possible role of endogenous tachykinins (TKs) in the formation of colonic MMCs and the relative roles of excitatory and inhibitory pathways.MMCs were recorded from the circular muscle at four sites (proximal, proximal-mid, mid-distal and distal) along the mouse colon using force transducers.The tachykinin (NK1 and NK2) receptor antagonists SR-140 333 (250 nM) and SR-48 968 (250 nM) reduced the amplitude of MMCs at all recording sites, preferentially abolishing the long duration contraction. Residual MMCs were abolished by the subsequent addition of atropine (1 μM).The neuronal nitric oxide synthase inhibitor, Nωnitro-L-arginine (L-NOARG, 100 μM), increased MMC amplitude in the distal region, whilst reducing the amplitude in the proximal region. In preparations where MMCs did not migrate to the distal colon, addition of L-NOARG resulted in the formation of MMCs. Subsequent addition of apamin (250 nM) or suramin (100 μM) further increased MMC amplitude in the distal region, whilst suramin increased MMC amplitude in the mid-distal region. Apamin but not suramin reduced MMC amplitude in the proximal region. Subsequent addition of SR-140 333 and SR-48 968 reduced MMC amplitude at all sites. Residual MMCs were abolished by atropine (1 μM).In conclusion, TKs, ACh, nitric oxide (NO) and ATP are involved in the neural mechanisms underlying the formation of MMCs in the mouse colon. Tachykinins mediate the long duration component of the MMC via NK1 and NK2 receptors. Inhibitory pathways may be involved in determining whether MMCs are formed. PMID:11159701

  19. Neural correlates of erotic stimulation under different levels of female sexual hormones.

    Directory of Open Access Journals (Sweden)

    Birgit Abler

    Full Text Available Previous studies have demonstrated variable influences of sexual hormonal states on female brain activation and the necessity to control for these in neuroimaging studies. However, systematic investigations of these influences, particularly those of hormonal contraceptives as compared to the physiological menstrual cycle are scarce. In the present study, we investigated the hormonal modulation of neural correlates of erotic processing in a group of females under hormonal contraceptives (C group; N = 12, and a different group of females (nC group; N = 12 not taking contraceptives during their mid-follicular and mid-luteal phases of the cycle. We used functional magnetic resonance imaging to measure hemodynamic responses as an estimate of brain activation during three different experimental conditions of visual erotic stimulation: dynamic videos, static erotic pictures, and expectation of erotic pictures. Plasma estrogen and progesterone levels were assessed in all subjects. No strong hormonally modulating effect was detected upon more direct and explicit stimulation (viewing of videos or pictures with significant activations in cortical and subcortical brain regions previously linked to erotic stimulation consistent across hormonal levels and stimulation type. Upon less direct and less explicit stimulation (expectation, activation patterns varied between the different hormonal conditions with various, predominantly frontal brain regions showing significant within- or between-group differences. Activation in the precentral gyrus during the follicular phase in the nC group was found elevated compared to the C group and positively correlated with estrogen levels. From the results we conclude that effects of hormonal influences on brain activation during erotic stimulation are weak if stimulation is direct and explicit but that female sexual hormones may modulate more subtle aspects of sexual arousal and behaviour as involved in sexual

  20. Neural activity changes underlying the working memory deficit in alpha-CaMKII heterozygous knockout mice

    Directory of Open Access Journals (Sweden)

    Naoki Matsuo

    2009-09-01

    Full Text Available The alpha-isoform of calcium/calmodulin-dependent protein kinase II (α-CaMKII is expressed abundantly in the forebrain and is considered to have an essential role in synaptic plasticity and cognitive function. Previously, we reported that mice heterozygous for a null mutation of α-CaMKII (α-CaMKII+/- have profoundly dysregulated behaviors including a severe working memory deficit, which is an endophenotype of schizophrenia and other psychiatric disorders. In addition, we found that almost all the neurons in the dentate gyrus (DG of the mutant mice failed to mature at molecular, morphological and electrophysiological levels. In the present study, to identify the brain substrates of the working memory deficit in the mutant mice, we examined the expression of the immediate early genes (IEGs, c-Fos and Arc, in the brain after a working memory version of the eight-arm radial maze test. c-Fos expression was abolished almost completely in the DG and was reduced significantly in neurons in the CA1 and CA3 areas of the hippocampus, central amygdala, and medial prefrontal cortex (mPFC. However, c-Fos expression was intact in the entorhinal and visual cortices. Immunohistochemical studies using arc promoter driven dVenus transgenic mice demonstrated that arc gene activation after the working memory task occurred in mature, but not immature neurons in the DG of wild-type mice. These results suggest crucial insights for the neural circuits underlying spatial mnemonic processing during a working memory task and suggest the involvement of α-CaMKII in the proper maturation and integration of DG neurons into these circuits.

  1. Neural systems supporting linguistic structure, linguistic experience, and symbolic communication in sign language and gesture.

    Science.gov (United States)

    Newman, Aaron J; Supalla, Ted; Fernandez, Nina; Newport, Elissa L; Bavelier, Daphne

    2015-09-15

    Sign languages used by deaf communities around the world possess the same structural and organizational properties as spoken languages: In particular, they are richly expressive and also tightly grammatically constrained. They therefore offer the opportunity to investigate the extent to which the neural organization for language is modality independent, as well as to identify ways in which modality influences this organization. The fact that sign languages share the visual-manual modality with a nonlinguistic symbolic communicative system-gesture-further allows us to investigate where the boundaries lie between language and symbolic communication more generally. In the present study, we had three goals: to investigate the neural processing of linguistic structure in American Sign Language (using verbs of motion classifier constructions, which may lie at the boundary between language and gesture); to determine whether we could dissociate the brain systems involved in deriving meaning from symbolic communication (including both language and gesture) from those specifically engaged by linguistically structured content (sign language); and to assess whether sign language experience influences the neural systems used for understanding nonlinguistic gesture. The results demonstrated that even sign language constructions that appear on the surface to be similar to gesture are processed within the left-lateralized frontal-temporal network used for spoken languages-supporting claims that these constructions are linguistically structured. Moreover, although nonsigners engage regions involved in human action perception to process communicative, symbolic gestures, signers instead engage parts of the language-processing network-demonstrating an influence of experience on the perception of nonlinguistic stimuli.

  2. Adaptive nonlinear polynomial neural networks for control of boundary layer/structural interaction

    Science.gov (United States)

    Parker, B. Eugene, Jr.; Cellucci, Richard L.; Abbott, Dean W.; Barron, Roger L.; Jordan, Paul R., III; Poor, H. Vincent

    1993-01-01

    The acoustic pressures developed in a boundary layer can interact with an aircraft panel to induce significant vibration in the panel. Such vibration is undesirable due to the aerodynamic drag and structure-borne cabin noises that result. The overall objective of this work is to develop effective and practical feedback control strategies for actively reducing this flow-induced structural vibration. This report describes the results of initial evaluations using polynomial, neural network-based, feedback control to reduce flow induced vibration in aircraft panels due to turbulent boundary layer/structural interaction. Computer simulations are used to develop and analyze feedback control strategies to reduce vibration in a beam as a first step. The key differences between this work and that going on elsewhere are as follows: that turbulent and transitional boundary layers represent broadband excitation and thus present a more complex stochastic control scenario than that of narrow band (e.g., laminar boundary layer) excitation; and secondly, that the proposed controller structures are adaptive nonlinear infinite impulse response (IIR) polynomial neural network, as opposed to the traditional adaptive linear finite impulse response (FIR) filters used in most studies to date. The controllers implemented in this study achieved vibration attenuation of 27 to 60 dB depending on the type of boundary layer established by laminar, turbulent, and intermittent laminar-to-turbulent transitional flows. Application of multi-input, multi-output, adaptive, nonlinear feedback control of vibration in aircraft panels based on polynomial neural networks appears to be feasible today. Plans are outlined for Phase 2 of this study, which will include extending the theoretical investigation conducted in Phase 2 and verifying the results in a series of laboratory experiments involving both bum and plate models.

  3. Neural mechanisms underlying subsequent memory for personal beliefs:An fMRI study.

    Science.gov (United States)

    Wing, Erik A; Iyengar, Vijeth; Hess, Thomas M; LaBar, Kevin S; Huettel, Scott A; Cabeza, Roberto

    2018-04-01

    Many fMRI studies have examined the neural mechanisms supporting emotional memory for stimuli that generate emotion rather automatically (e.g., a picture of a dangerous animal or of appetizing food). However, far fewer studies have examined how memory is influenced by emotion related to social and political issues (e.g., a proposal for large changes in taxation policy), which clearly vary across individuals. In order to investigate the neural substrates of affective and mnemonic processes associated with personal opinions, we employed an fMRI task wherein participants rated the intensity of agreement/disagreement to sociopolitical belief statements paired with neural face pictures. Following the rating phase, participants performed an associative recognition test in which they distinguished identical versus recombined face-statement pairs. The study yielded three main findings: behaviorally, the intensity of agreement ratings was linked to greater subjective emotional arousal as well as enhanced high-confidence subsequent memory. Neurally, statements that elicited strong (vs. weak) agreement or disagreement were associated with greater activation of the amygdala. Finally, a subsequent memory analysis showed that the behavioral memory advantage for statements generating stronger ratings was dependent on the medial prefrontal cortex (mPFC). Together, these results both underscore consistencies in neural systems supporting emotional arousal and suggest a modulation of arousal-related encoding mechanisms when emotion is contingent on referencing personal beliefs.

  4. Applications of Artificial Neural Networks in Structural Engineering with Emphasis on Continuum Models

    Science.gov (United States)

    Kapania, Rakesh K.; Liu, Youhua

    1998-01-01

    The use of continuum models for the analysis of discrete built-up complex aerospace structures is an attractive idea especially at the conceptual and preliminary design stages. But the diversity of available continuum models and hard-to-use qualities of these models have prevented them from finding wide applications. In this regard, Artificial Neural Networks (ANN or NN) may have a great potential as these networks are universal approximators that can realize any continuous mapping, and can provide general mechanisms for building models from data whose input-output relationship can be highly nonlinear. The ultimate aim of the present work is to be able to build high fidelity continuum models for complex aerospace structures using the ANN. As a first step, the concepts and features of ANN are familiarized through the MATLAB NN Toolbox by simulating some representative mapping examples, including some problems in structural engineering. Then some further aspects and lessons learned about the NN training are discussed, including the performances of Feed-Forward and Radial Basis Function NN when dealing with noise-polluted data and the technique of cross-validation. Finally, as an example of using NN in continuum models, a lattice structure with repeating cells is represented by a continuum beam whose properties are provided by neural networks.

  5. Localization and identification of structural nonlinearities using cascaded optimization and neural networks

    Science.gov (United States)

    Koyuncu, A.; Cigeroglu, E.; Özgüven, H. N.

    2017-10-01

    In this study, a new approach is proposed for identification of structural nonlinearities by employing cascaded optimization and neural networks. Linear finite element model of the system and frequency response functions measured at arbitrary locations of the system are used in this approach. Using the finite element model, a training data set is created, which appropriately spans the possible nonlinear configurations space of the system. A classification neural network trained on these data sets then localizes and determines the types of all nonlinearities associated with the nonlinear degrees of freedom in the system. A new training data set spanning the parametric space associated with the determined nonlinearities is created to facilitate parametric identification. Utilizing this data set, initially, a feed forward regression neural network is trained, which parametrically identifies the classified nonlinearities. Then, the results obtained are further improved by carrying out an optimization which uses network identified values as starting points. Unlike identification methods available in literature, the proposed approach does not require data collection from the degrees of freedoms where nonlinear elements are attached, and furthermore, it is sufficiently accurate even in the presence of measurement noise. The application of the proposed approach is demonstrated on an example system with nonlinear elements and on a real life experimental setup with a local nonlinearity.

  6. The neural basis of loss aversion in decision-making under risk.

    Science.gov (United States)

    Tom, Sabrina M; Fox, Craig R; Trepel, Christopher; Poldrack, Russell A

    2007-01-26

    People typically exhibit greater sensitivity to losses than to equivalent gains when making decisions. We investigated neural correlates of loss aversion while individuals decided whether to accept or reject gambles that offered a 50/50 chance of gaining or losing money. A broad set of areas (including midbrain dopaminergic regions and their targets) showed increasing activity as potential gains increased. Potential losses were represented by decreasing activity in several of these same gain-sensitive areas. Finally, individual differences in behavioral loss aversion were predicted by a measure of neural loss aversion in several regions, including the ventral striatum and prefrontal cortex.

  7. Empirical Analysis of Farm Credit Risk under the Structure Model

    Science.gov (United States)

    Yan, Yan

    2009-01-01

    The study measures farm credit risk by using farm records collected by Farm Business Farm Management (FBFM) during the period 1995-2004. The study addresses the following questions: (1) whether farm's financial position is fully described by the structure model, (2) what are the determinants of farm capital structure under the structure model, (3)…

  8. Neural network modeling to evaluate the dynamic flow stress of high strength armor steels under high strain rate compression

    Directory of Open Access Journals (Sweden)

    Ravindranadh Bobbili

    2014-12-01

    Full Text Available An artificial neural network (ANN constitutive model is developed for high strength armor steel tempered at 500 °C, 600 °C and 650 °C based on high strain rate data generated from split Hopkinson pressure bar (SHPB experiments. A new neural network configuration consisting of both training and validation is effectively employed to predict flow stress. Tempering temperature, strain rate and strain are considered as inputs, whereas flow stress is taken as output of the neural network. A comparative study on Johnson–Cook (J–C model and neural network model is performed. It was observed that the developed neural network model could predict flow stress under various strain rates and tempering temperatures. The experimental stress–strain data obtained from high strain rate compression tests using SHPB, over a range of tempering temperatures (500–650 °C, strains (0.05–0.2 and strain rates (1000–5500/s are employed to formulate J–C model to predict the high strain rate deformation behavior of high strength armor steels. The J-C model and the back-propagation ANN model were developed to predict the high strain rate deformation behavior of high strength armor steel and their predictability is evaluated in terms of correlation coefficient (R and average absolute relative error (AARE. R and AARE for the J–C model are found to be 0.7461 and 27.624%, respectively, while R and AARE for the ANN model are 0.9995 and 2.58%, respectively. It was observed that the predictions by ANN model are in consistence with the experimental data for all tempering temperatures.

  9. Developmental Pathway Genes and Neural Plasticity Underlying Emotional Learning and Stress-Related Disorders

    Science.gov (United States)

    Maheau, Marissa E.; Ressler, Kerry J.

    2017-01-01

    The manipulation of neural plasticity as a means of intervening in the onset and progression of stress-related disorders retains its appeal for many researchers, despite our limited success in translating such interventions from the laboratory to the clinic. Given the challenges of identifying individual genetic variants that confer increased risk…

  10. Optogenetic dissection of neural circuit underlying locomotory decision-making in Caenorhabditis Elegans

    Science.gov (United States)

    Kocabas, Askin; Guo, Zengcai; Ramanathan, Sharad

    2011-03-01

    Despite the knowledge of the physical connectivity of the entire nervous system of C.elegans, we know little about how neuronal dynamics results in decision-making. Detailed understanding of functional and dynamic relations of the neural circuitry requires spatiotemporal control of the neuronal activity. Recent discoveries of light gated ion channels have allowed temporal optical control of neural activity. However, excitation of a specific neuron from among many expressing the channel has been a challenge. By combining optogenetic tools, micro mirror array technology and fast real time image processing, we have developed a technique to activate specific single or multiple neurons in an intact crawling animal while tracking its behavior. Using this setup we traced the neural pathway controlling the gradual turning of the animal during the locomotion. We found that the activity of a specific neuronal circuit that receives inputs from sensory neurons is coordinated with head movement. This coordination allows the animal to turn left or right based on the variation of sensory stimulus during head movement. By directly modulating the activity of the neural circuit, we can force the animal to turn in a specific direction independent of sensory stimuli. Human Frontier Science Program.

  11. Multifractal analysis of information processing in hippocampal neural ensembles during working memory under Δ⁹-tetrahydrocannabinol administration.

    Science.gov (United States)

    Fetterhoff, Dustin; Opris, Ioan; Simpson, Sean L; Deadwyler, Sam A; Hampson, Robert E; Kraft, Robert A

    2015-04-15

    Multifractal analysis quantifies the time-scale-invariant properties in data by describing the structure of variability over time. By applying this analysis to hippocampal interspike interval sequences recorded during performance of a working memory task, a measure of long-range temporal correlations and multifractal dynamics can reveal single neuron correlates of information processing. Wavelet leaders-based multifractal analysis (WLMA) was applied to hippocampal interspike intervals recorded during a working memory task. WLMA can be used to identify neurons likely to exhibit information processing relevant to operation of brain-computer interfaces and nonlinear neuronal models. Neurons involved in memory processing ("Functional Cell Types" or FCTs) showed a greater degree of multifractal firing properties than neurons without task-relevant firing characteristics. In addition, previously unidentified FCTs were revealed because multifractal analysis suggested further functional classification. The cannabinoid type-1 receptor (CB1R) partial agonist, tetrahydrocannabinol (THC), selectively reduced multifractal dynamics in FCT neurons compared to non-FCT neurons. WLMA is an objective tool for quantifying the memory-correlated complexity represented by FCTs that reveals additional information compared to classification of FCTs using traditional z-scores to identify neuronal correlates of behavioral events. z-Score-based FCT classification provides limited information about the dynamical range of neuronal activity characterized by WLMA. Increased complexity, as measured with multifractal analysis, may be a marker of functional involvement in memory processing. The level of multifractal attributes can be used to differentially emphasize neural signals to improve computational models and algorithms underlying brain-computer interfaces. Copyright © 2014 Elsevier B.V. All rights reserved.

  12. 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.

  13. Thermal behavior of spatial structures under solar irradiation

    International Nuclear Information System (INIS)

    Liu, Hongbo; Liao, Xiangwei; Chen, Zhihua; Zhang, Qian

    2015-01-01

    The temperature, particularly the non-uniform temperature under solar irradiation, is the main load for large-span steel structures. Due the shortage of in-site temperature test in previous studies, an in-site test was conducted on the large-span steel structures under solar irradiation, which was covered by glass roof and light roof, to gain insight into the temperature distribution of steel members under glass roof or light roof. A numerical method also was presented and verified to forecast the temperature of steel member under glass roof or light roof. Based on the on-site measurement and numerical analyses conducted, the following conclusions were obtained: 1) a remarkable temperature difference exists between the steel member under glass roof and that under light roof, 2) solar irradiation has a significant effect on the temperature distribution and thermal behavior of large-span spatial structures, 3) negative thermal load is the controlling factor for member stress, and the positive thermal load is the controlling factor for nodal displacement. - Highlights: • Temperature was measured for a steel structures under glass roof and light roof. • Temperature simulation method was presented and verified. • The thermal behavior of steel structures under glass or light roof was presented

  14. Structural Analysis of Three-dimensional Human Neural Tissue derived from Induced Pluripotent Stem Cells

    DEFF Research Database (Denmark)

    Terrence Brooks, Patrick; Rasmussen, Mikkel Aabech; Hyttel, Poul

    2016-01-01

    Objective: The present study aimed at establishing a method for production of a three-dimensional (3D) human neural tissue derived from induced pluripotent stem cells (iPSCs) and analyzing the outcome by a combination of tissue ultrastructure and expression of neural markers. Methods: A two......-step cell culture procedure was implemented by subjecting human iPSCs to a 3D scaffoldbased neural differentiation protocol. First, neural fate-inducing small molecules were used to create a neuroepithelial monolayer. Second, the monolayer was trypsinized into single cells and seeded into a porous...... polystyrene scaffold and further cultured to produce a 3D neural tissue. The neural tissue was characterized by a combination of immunohistochemistry and transmission electron microscopy (TEM). Results: iPSCs developed into a 3D neural tissue expressing markers for neural progenitor cells, early neural...

  15. Crystal structure of the Ig1 domain of the neural cell adhesion molecule NCAM2 displays domain swapping

    DEFF Research Database (Denmark)

    Rasmussen, Kim Krighaar; Kulahin, Nikolaj; Kristensen, Ole

    2008-01-01

    The crystal structure of the first immunoglobulin (Ig1) domain of neural cell adhesion molecule 2 (NCAM2/OCAM/RNCAM) is presented at a resolution of 2.7 A. NCAM2 is a member of the immunoglobulin superfamily of cell adhesion molecules (IgCAMs). In the structure, two Ig domains interact by domain...

  16. Cortical Neural Synchronization Underlies Primary Visual Consciousness of Qualia: Evidence from Event-Related Potentials

    OpenAIRE

    Babiloni, Claudio; Marzano, Nicola; Soricelli, Andrea; Cordone, Susanna; Mill?n-Calenti, Jos? Carlos; Del Percio, Claudio; Buj?n, Ana

    2016-01-01

    This article reviews three experiments on event-related potentials (ERPs) testing the hypothesis that primary visual consciousness (stimulus self-report) is related to enhanced cortical neural synchronization as a function of stimulus features. ERP peak latency and sources were compared between “seen” trials and “not seen” trials, respectively related and unrelated to the primary visual consciousness. Three salient features of visual stimuli were considered (visuospatial, emotional face expre...

  17. Anger under Control: Neural Correlates of Frustration as a Function of Trait Aggression

    OpenAIRE

    Pawliczek, Christina M.; Derntl, Birgit; Kellermann, Thilo; Gur, Ruben C.; Schneider, Frank; Habel, Ute

    2013-01-01

    Antisocial behavior and aggression are prominent symptoms in several psychiatric disorders including antisocial personality disorder. An established precursor to aggression is a frustrating event, which can elicit anger or exasperation, thereby prompting aggressive responses. While some studies have investigated the neural correlates of frustration and aggression, examination of their relation to trait aggression in healthy populations are rare. Based on a screening of 550 males, we formed tw...

  18. Strong geomagnetic activity forecast by neural networks under dominant southern orientation of the interplanetary magnetic field

    Czech Academy of Sciences Publication Activity Database

    Valach, F.; Bochníček, Josef; Hejda, Pavel; Revallo, M.

    2014-01-01

    Roč. 53, č. 4 (2014), s. 589-598 ISSN 0273-1177 R&D Projects: GA AV ČR(CZ) IAA300120608; GA MŠk OC09070 Institutional support: RVO:67985530 Keywords : geomagnetic activity * interplanetary magnetic field * artificial neural network * ejection of coronal mass * X-ray flares Subject RIV: DE - Earth Magnetism, Geodesy, Geography Impact factor: 1.358, year: 2014

  19. Engagement of neural circuits underlying 2D spatial navigation in a rodent virtual reality system

    OpenAIRE

    Aronov, Dmitriy; Tank, David W.

    2014-01-01

    Virtual reality (VR) enables precise control of an animal’s environment and otherwise impossible experimental manipulations. Neural activity in navigating rodents has been studied on virtual linear tracks. However, the spatial navigation system’s engagement in complete two-dimensional environments has not been shown. We describe a VR setup for rats, including control software and a large-scale electrophysiology system, which supports 2D navigation by allowing animals to rotate and walk in any...

  20. Neural mechanisms underlying sound-induced visual motion perception: An fMRI study.

    Science.gov (United States)

    Hidaka, Souta; Higuchi, Satomi; Teramoto, Wataru; Sugita, Yoichi

    2017-07-01

    Studies of crossmodal interactions in motion perception have reported activation in several brain areas, including those related to motion processing and/or sensory association, in response to multimodal (e.g., visual and auditory) stimuli that were both in motion. Recent studies have demonstrated that sounds can trigger illusory visual apparent motion to static visual stimuli (sound-induced visual motion: SIVM): A visual stimulus blinking at a fixed location is perceived to be moving laterally when an alternating left-right sound is also present. Here, we investigated brain activity related to the perception of SIVM using a 7T functional magnetic resonance imaging technique. Specifically, we focused on the patterns of neural activities in SIVM and visually induced visual apparent motion (VIVM). We observed shared activations in the middle occipital area (V5/hMT), which is thought to be involved in visual motion processing, for SIVM and VIVM. Moreover, as compared to VIVM, SIVM resulted in greater activation in the superior temporal area and dominant functional connectivity between the V5/hMT area and the areas related to auditory and crossmodal motion processing. These findings indicate that similar but partially different neural mechanisms could be involved in auditory-induced and visually-induced motion perception, and neural signals in auditory, visual, and, crossmodal motion processing areas closely and directly interact in the perception of SIVM. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. Fractionating the neural correlates of individual working memory components underlying arithmetic problem solving skills in children.

    Science.gov (United States)

    Metcalfe, Arron W S; Ashkenazi, Sarit; Rosenberg-Lee, Miriam; Menon, Vinod

    2013-10-01

    Baddeley and Hitch's multi-component working memory (WM) model has played an enduring and influential role in our understanding of cognitive abilities. Very little is known, however, about the neural basis of this multi-component WM model and the differential role each component plays in mediating arithmetic problem solving abilities in children. Here, we investigate the neural basis of the central executive (CE), phonological (PL) and visuo-spatial (VS) components of WM during a demanding mental arithmetic task in 7-9 year old children (N=74). The VS component was the strongest predictor of math ability in children and was associated with increased arithmetic complexity-related responses in left dorsolateral and right ventrolateral prefrontal cortices as well as bilateral intra-parietal sulcus and supramarginal gyrus in posterior parietal cortex. Critically, VS, CE and PL abilities were associated with largely distinct patterns of brain response. Overlap between VS and CE components was observed in left supramarginal gyrus and no overlap was observed between VS and PL components. Our findings point to a central role of visuo-spatial WM during arithmetic problem-solving in young grade-school children and highlight the usefulness of the multi-component Baddeley and Hitch WM model in fractionating the neural correlates of arithmetic problem solving during development. Copyright © 2013 Elsevier Ltd. All rights reserved.

  2. Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Panagiotis G. Asteris

    2016-01-01

    Full Text Available The fundamental period is one of the most critical parameters for the seismic design of structures. There are several literature approaches for its estimation which often conflict with each other, making their use questionable. Furthermore, the majority of these approaches do not take into account the presence of infill walls into the structure despite the fact that infill walls increase the stiffness and mass of structure leading to significant changes in the fundamental period. In the present paper, artificial neural networks (ANNs are used to predict the fundamental period of infilled reinforced concrete (RC structures. For the training and the validation of the ANN, a large data set is used based on a detailed investigation of the parameters that affect the fundamental period of RC structures. The comparison of the predicted values with analytical ones indicates the potential of using ANNs for the prediction of the fundamental period of infilled RC frame structures taking into account the crucial parameters that influence its value.

  3. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

    Science.gov (United States)

    Abdeljaber, Osama; Avci, Onur; Kiranyaz, Serkan; Gabbouj, Moncef; Inman, Daniel J.

    2017-02-01

    Structural health monitoring (SHM) and vibration-based structural damage detection have been a continuous interest for civil, mechanical and aerospace engineers over the decades. Early and meticulous damage detection has always been one of the principal objectives of SHM applications. The performance of a classical damage detection system predominantly depends on the choice of the features and the classifier. While the fixed and hand-crafted features may either be a sub-optimal choice for a particular structure or fail to achieve the same level of performance on another structure, they usually require a large computation power which may hinder their usage for real-time structural damage detection. This paper presents a novel, fast and accurate structural damage detection system using 1D Convolutional Neural Networks (CNNs) that has an inherent adaptive design to fuse both feature extraction and classification blocks into a single and compact learning body. The proposed method performs vibration-based damage detection and localization of the damage in real-time. The advantage of this approach is its ability to extract optimal damage-sensitive features automatically from the raw acceleration signals. Large-scale experiments conducted on a grandstand simulator revealed an outstanding performance and verified the computational efficiency of the proposed real-time damage detection method.

  4. Structural neural correlates of multitasking: A voxel-based morphometry study.

    Science.gov (United States)

    Zhang, Rui-Ting; Yang, Tian-Xiao; Wang, Yi; Sui, Yuxiu; Yao, Jingjing; Zhang, Chen-Yuan; Cheung, Eric F C; Chan, Raymond C K

    2016-12-01

    Multitasking refers to the ability to organize assorted tasks efficiently in a short period of time, which plays an important role in daily life. However, the structural neural correlates of multitasking performance remain unclear. The present study aimed at exploring the brain regions associated with multitasking performance using global correlation analysis. Twenty-six healthy participants first underwent structural brain scans and then performed the modified Six Element Test, which required participants to attempt six subtasks in 10 min while obeying a specific rule. Voxel-based morphometry of the whole brain was used to detect the structural correlates of multitasking ability. Grey matter volume of the anterior cingulate cortex (ACC) was positively correlated with the overall performance and time monitoring in multitasking. In addition, white matter volume of the anterior thalamic radiation (ATR) was also positively correlated with time monitoring during multitasking. Other related brain regions associated with multitasking included the superior frontal gyrus, the inferior occipital gyrus, the lingual gyrus, and the inferior longitudinal fasciculus. No significant correlation was found between grey matter volume of the prefrontal cortex (Brodmann Area 10) and multitasking performance. Using a global correlation analysis to examine various aspects of multitasking performance, this study provided new insights into the structural neural correlates of multitasking ability. In particular, the ACC was identified as an important brain region that played both a general and a specific time-monitoring role in multitasking, extending the role of the ACC from lesioned populations to healthy populations. The present findings also support the view that the ATR may influence multitasking performance by affecting time-monitoring abilities. © 2016 The Institute of Psychology, Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

  5. Cortical Neural Synchronization Underlies Primary Visual Consciousness of Qualia: Evidence from Event-Related Potentials.

    Science.gov (United States)

    Babiloni, Claudio; Marzano, Nicola; Soricelli, Andrea; Cordone, Susanna; Millán-Calenti, José Carlos; Del Percio, Claudio; Buján, Ana

    2016-01-01

    This article reviews three experiments on event-related potentials (ERPs) testing the hypothesis that primary visual consciousness (stimulus self-report) is related to enhanced cortical neural synchronization as a function of stimulus features. ERP peak latency and sources were compared between "seen" trials and "not seen" trials, respectively related and unrelated to the primary visual consciousness. Three salient features of visual stimuli were considered (visuospatial, emotional face expression, and written words). Results showed the typical visual ERP components in both "seen" and "not seen" trials. There was no statistical difference in the ERP peak latencies between the "seen" and "not seen" trials, suggesting a similar timing of the cortical neural synchronization regardless the primary visual consciousness. In contrast, ERP sources showed differences between "seen" and "not seen" trials. For the visuospatial stimuli, the primary consciousness was related to higher activity in dorsal occipital and parietal sources at about 400 ms post-stimulus. For the emotional face expressions, there was greater activity in parietal and frontal sources at about 180 ms post-stimulus. For the written letters, there was higher activity in occipital, parietal and temporal sources at about 230 ms post-stimulus. These results hint that primary visual consciousness is associated with an enhanced cortical neural synchronization having entirely different spatiotemporal characteristics as a function of the features of the visual stimuli and possibly, the relative qualia (i.e., visuospatial, face expression, and words). In this framework, the dorsal visual stream may be synchronized in association with the primary consciousness of visuospatial and emotional face contents. Analogously, both dorsal and ventral visual streams may be synchronized in association with the primary consciousness of linguistic contents. In this line of reasoning, the ensemble of the cortical neural networks

  6. Neural Correlates of Choking Under Pressure: Athletes High in Sports Anxiety Monitor Errors More When Performance Is Being Evaluated.

    Science.gov (United States)

    Masaki, Hiroaki; Maruo, Yuya; Meyer, Alexandria; Hajcak, Greg

    2017-01-01

    We investigated the relationship between performance-related anxiety and the neural response to errors. Using the sport anxiety scale, we selected university athletes high in sports anxiety and low in sports anxiety. The two groups performed a spatial Stroop task while their performance was being evaluated by an experimenter and also during a control (i.e., no evaluation) condition. The error-related negativity was significantly larger during the evaluation than control condition among athletes who reported high performance-related anxiety. These results suggest that performance evaluation may make errors particularly aversive or salient for individuals who fail to perform well under pressure.

  7. Exponential synchronization of delayed neutral-type neural networks with Lévy noise under non-Lipschitz condition

    Science.gov (United States)

    Ma, Shuo; Kang, Yanmei

    2018-04-01

    In this paper, the exponential synchronization of stochastic neutral-type neural networks with time-varying delay and Lévy noise under non-Lipschitz condition is investigated for the first time. Using the general Itô's formula and the nonnegative semi-martingale convergence theorem, we derive general sufficient conditions of two kinds of exponential synchronization for the drive system and the response system with adaptive control. Numerical examples are presented to verify the effectiveness of the proposed criteria.

  8. Robust Finite-Time Stabilization of Fractional-Order Neural Networks With Discontinuous and Continuous Activation Functions Under Uncertainty.

    Science.gov (United States)

    Ding, Zhixia; Zeng, Zhigang; Wang, Leimin

    2017-03-10

    This paper is concerned with robust finite-time stabilization for a class of fractional-order neural networks (FNNs) with two types of activation functions (i.e., discontinuous and continuous activation function) under uncertainty. It is worth noting that there exist few results about FNNs with discontinuous activation functions, which is mainly because classical solutions and theories of differential equations cannot be applied in this case. Especially, there is no relevant finite-time stabilization research for such system, and this paper makes up for the gap. The existence of global solution under the framework of Filippov for such system is guaranteed by limiting discontinuous activation functions. According to set-valued analysis and Kakutani's fixed point theorem, we obtain the existence of equilibrium point. In particular, based on differential inclusion theory and fractional Lyapunov stability theory, several new sufficient conditions are given to ensure finite-time stabilization via a novel discontinuous controller, and the upper bound of the settling time for stabilization is estimated. In addition, we analyze the finite-time stabilization of FNNs with Lipschitz-continuous activation functions under uncertainty. The results of this paper improve corresponding ones of integer-order neural networks with discontinuous and continuous activation functions. Finally, three numerical examples are given to show the effectiveness of the theoretical results.

  9. Artificial-neural-network-based classification of mammographic microcalcifications using image structure features

    Science.gov (United States)

    Dhawan, Atam P.; Chitre, Yateen S.; Moskowitz, Myron

    1993-07-01

    Mammography associated with clinical breast examination and self-breast examination is the only effective and viable method for mass breast screening. It is however, difficult to distinguish between benign and malignant microcalcifications associated with breast cancer. Most of the techniques used in the computerized analysis of mammographic microcalcifications segment the digitized gray-level image into regions representing microcalcifications. We present a second-order gray-level histogram based feature extraction approach to extract microcalcification features. These features, called image structure features, are computed from the second-order gray-level histogram statistics, and do not require segmentation of the original image into binary regions. Several image structure features were computed for 100 cases of `difficult to diagnose' microcalcification cases with known biopsy results. These features were analyzed in a correlation study which provided a set of five best image structure features. A feedforward backpropagation neural network was used to classify mammographic microcalcifications using the image structure features. The network was trained on 10 cases of mammographic microcalcifications and tested on additional 85 `difficult-to-diagnose' microcalcifications cases using the selected image structure features. The trained network yielded good results for classification of `difficult-to- diagnose' microcalcifications into benign and malignant categories.

  10. A new approach to the structural features of the Aegean Sea: Cellular neural network

    Science.gov (United States)

    Aydogan, Davut; Elmas, Ali; Albora, A. Muhittin; Ucan, Osman N.

    2005-03-01

    In this study, structural features in the Aegean Sea were investigated by application of Cellular Neural Network (CNN) and Cross-Correlation methods to the gravity anomaly map. CNN is a stochastic image processing technique, which is based on template optimization using neighbourhood relationships of pixels, and probabilistic properties of two-Dimensional (2-D) input data. The performance of CNN can be evaluated by various interesting real applications in geophysics such as edge detection, data enhancement and separation of regional/residual potential anomaly maps. In this study, CNN is used in edge detection of geological bodies closer to the surface, which are masked by other structures with various depths and dimensions. CNN was first tested for (prismatic) synthetic examples and satisfactory results were obtained. Subsequently, CNN/Cross-Correlation maps and bathymetric features were evaluated together to obtain a new structural map for most of the Aegean Sea. In our structural map, the locations of the faults and basins are generally in accordance with the previous maps from restricted areas based on seismic data. In the southern and southeastern parts of the Aegean Sea, E-W trending faults cut NE-SW trending basins and faults, similar to on-shore Western Anatolia. Also, in the western, central and northern parts of the Aegean Sea, all of these structures are truncated by NE-trending faults.

  11. CNNdel: Calling Structural Variations on Low Coverage Data Based on Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Jing Wang

    2017-01-01

    Full Text Available Many structural variations (SVs detection methods have been proposed due to the popularization of next-generation sequencing (NGS. These SV calling methods use different SV-property-dependent features; however, they all suffer from poor accuracy when running on low coverage sequences. The union of results from these tools achieves fairly high sensitivity but still produces low accuracy on low coverage sequence data. That is, these methods contain many false positives. In this paper, we present CNNdel, an approach for calling deletions from paired-end reads. CNNdel gathers SV candidates reported by multiple tools and then extracts features from aligned BAM files at the positions of candidates. With labeled feature-expressed candidates as a training set, CNNdel trains convolutional neural networks (CNNs to distinguish true unlabeled candidates from false ones. Results show that CNNdel works well with NGS reads from 26 low coverage genomes of the 1000 Genomes Project. The paper demonstrates that convolutional neural networks can automatically assign the priority of SV features and reduce the false positives efficaciously.

  12. Temporal neural mechanisms underlying conscious access to different levels of facial stimulus contents.

    Science.gov (United States)

    Hsu, Shen-Mou; Yang, Yu-Fang

    2018-04-01

    An important issue facing the empirical study of consciousness concerns how the contents of incoming stimuli gain access to conscious processing. According to classic theories, facial stimuli are processed in a hierarchical manner. However, it remains unclear how the brain determines which level of stimulus content is consciously accessible when facing an incoming facial stimulus. Accordingly, with a magnetoencephalography technique, this study aims to investigate the temporal dynamics of the neural mechanism mediating which level of stimulus content is consciously accessible. Participants were instructed to view masked target faces at threshold so that, according to behavioral responses, their perceptual awareness alternated from consciously accessing facial identity in some trials to being able to consciously access facial configuration features but not facial identity in other trials. Conscious access at these two levels of facial contents were associated with a series of differential neural events. Before target presentation, different patterns of phase angle adjustment were observed between the two types of conscious access. This effect was followed by stronger phase clustering for awareness of facial identity immediately during stimulus presentation. After target onset, conscious access to facial identity, as opposed to facial configural features, was able to elicit more robust late positivity. In conclusion, we suggest that the stages of neural events, ranging from prestimulus to stimulus-related activities, may operate in combination to determine which level of stimulus contents is consciously accessed. Conscious access may thus be better construed as comprising various forms that depend on the level of stimulus contents accessed. NEW & NOTEWORTHY The present study investigates how the brain determines which level of stimulus contents is consciously accessible when facing an incoming facial stimulus. Using magnetoencephalography, we show that prestimulus

  13. Modulation of neural circuits underlying temporal production by facial expressions of pain.

    Directory of Open Access Journals (Sweden)

    Daniela Ballotta

    Full Text Available According to the Scalar Expectancy Theory, humans are equipped with a biological internal clock, possibly modulated by attention and arousal. Both emotions and pain are arousing and can absorb attentional resources, thus causing distortions of temporal perception. The aims of the present single-event fMRI study were to investigate: a whether observation of facial expressions of pain interferes with time production; and b the neural network subserving this kind of temporal distortions. Thirty healthy volunteers took part in the study. Subjects were asked to perform a temporal production task and a concurrent gender discrimination task, while viewing faces of unknown people with either pain-related or neutral expressions. Behavioural data showed temporal underestimation (i.e., longer produced intervals during implicit pain expression processing; this was accompanied by increased activity of right middle temporal gyrus, a region known to be active during the perception of emotional and painful faces. Psycho-Physiological Interaction analyses showed that: 1 the activity of middle temporal gyrus was positively related to that of areas previously reported to play a role in timing: left primary motor cortex, middle cingulate cortex, supplementary motor area, right anterior insula, inferior frontal gyrus, bilateral cerebellum and basal ganglia; 2 the functional connectivity of supplementary motor area with several frontal regions, anterior cingulate cortex and right angular gyrus was correlated to the produced interval during painful expression processing. Our data support the hypothesis that observing emotional expressions distorts subjective time perception through the interaction of the neural network subserving processing of facial expressions with the brain network involved in timing. Within this frame, middle temporal gyrus appears to be the key region of the interplay between the two neural systems.

  14. Effect of abacus training on executive function development and underlying neural correlates in Chinese children.

    Science.gov (United States)

    Wang, Chunjie; Weng, Jian; Yao, Yuan; Dong, Shanshan; Liu, Yuqiu; Chen, Feiyan

    2017-10-01

    Executive function (EF) refers to a set of cognitive abilities involved in self-regulated behavior. Given the critical role of EF in cognition, strategies for improving EF have attracted intensive attention in recent years. Previous studies have explored the effects of abacus-based mental calculation (AMC) training on several cognitive abilities. However, it remains unclear whether AMC training affects EF and its neural correlates. In this study, participants were randomly assigned to AMC or control groups upon starting primary school. The AMC group received 2 h AMC training every week, while the control group did not have any abacus experience. Neural activity during an EF task was examined using functional MRI for both groups in their 4 th and 6 th grades. Our results showed that the AMC group performed better and faster than the control group in both grades. They also had lower activation in the frontoparietal reigons than the control group in the 6 th grade. From the 4 th to the 6 th grade, the AMC group showed activation decreases in the frontoparietal regions, while the control group exhibited an opposite pattern. Furthermore, voxel-wise regression analyses revealed that better performance was associated with lower task-relevant brain activity in the AMC group but associated with greater task-relevant brain activity in the control group. These results suggest that long-term AMC training, with calculation ability as its original target, may improve EF and enhance neural efficiency of the frontoparietal regions during development. Hum Brain Mapp 38:5234-5249, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  15. Modulation of neural circuits underlying temporal production by facial expressions of pain

    Science.gov (United States)

    Lui, Fausta; Porro, Carlo Adolfo; Nichelli, Paolo Frigio; Benuzzi, Francesca

    2018-01-01

    According to the Scalar Expectancy Theory, humans are equipped with a biological internal clock, possibly modulated by attention and arousal. Both emotions and pain are arousing and can absorb attentional resources, thus causing distortions of temporal perception. The aims of the present single-event fMRI study were to investigate: a) whether observation of facial expressions of pain interferes with time production; and b) the neural network subserving this kind of temporal distortions. Thirty healthy volunteers took part in the study. Subjects were asked to perform a temporal production task and a concurrent gender discrimination task, while viewing faces of unknown people with either pain-related or neutral expressions. Behavioural data showed temporal underestimation (i.e., longer produced intervals) during implicit pain expression processing; this was accompanied by increased activity of right middle temporal gyrus, a region known to be active during the perception of emotional and painful faces. Psycho-Physiological Interaction analyses showed that: 1) the activity of middle temporal gyrus was positively related to that of areas previously reported to play a role in timing: left primary motor cortex, middle cingulate cortex, supplementary motor area, right anterior insula, inferior frontal gyrus, bilateral cerebellum and basal ganglia; 2) the functional connectivity of supplementary motor area with several frontal regions, anterior cingulate cortex and right angular gyrus was correlated to the produced interval during painful expression processing. Our data support the hypothesis that observing emotional expressions distorts subjective time perception through the interaction of the neural network subserving processing of facial expressions with the brain network involved in timing. Within this frame, middle temporal gyrus appears to be the key region of the interplay between the two neural systems. PMID:29447256

  16. Factors limiting the operation of structures under high gradient

    International Nuclear Information System (INIS)

    Schriber, S.O.

    1986-01-01

    Factors limiting the operation of rf structures under high-gradient conditions are described. Included are recent rf measurements at laboratories in Europe, Asia, and North America and how these measurements relate to earlier data as exemplified by the use of the Kilpatrick criterion (Kp). Operation limitations will cover mechanical, geometry, thermal, and surface constraints and the associated impact on structure design, fabrication, and material selection. Generally, structures operating continuous wave (100% duty factor) appear to be limited to peak surface fields at about twice the Kilpatrick limit, whereas pulsed structures operating with pulse lengths less than a millisecond can attain peak surface fields five times the Kilpatrick limit

  17. Violence exposure and neural systems underlying working memory for emotional stimuli in youth.

    Science.gov (United States)

    Jenness, Jessica L; Rosen, Maya L; Sambrook, Kelly A; Dennison, Meg J; Lambert, Hilary K; Sheridan, Margaret A; McLaughlin, Katie A

    2017-11-16

    Violence exposure during childhood is common and associated with poor cognitive and academic functioning. However, little is known about how violence exposure influences cognitive processes that might contribute to these disparities, such as working memory, or their neural underpinnings, particularly for cognitive processes that occur in emotionally salient contexts. We address this gap in a sample of 54 participants aged 8 to 19 years (50% female), half with exposure to interpersonal violence. Participants completed a delayed match to sample task for emotional faces while undergoing functional magnetic resonance imaging scanning. Violence-exposed youth performed worse than controls on happy and neutral, but not angry, trials. In whole-brain analysis, violence-exposed youth had reduced activation in the left middle frontal gyrus and right intraparietal sulcus during encoding and the left superior temporal sulcus and temporal-parietal junction during retrieval compared to control youth. Reduced activation in the left middle frontal gyrus during encoding and the left superior temporal sulcus during retrieval mediated the association between violence exposure and task performance. Violence exposure influences the frontoparietal network that supports working memory as well as regions involved in facial processing during working memory for emotional stimuli. Reduced neural recruitment in these regions may explain atypical patterns of cognitive processing seen among violence-exposed youth, particularly within emotional contexts.

  18. Identifying temporal and causal contributions of neural processes underlying the Implicit Association Test (IAT

    Directory of Open Access Journals (Sweden)

    Chad Edward Forbes

    2012-11-01

    Full Text Available The Implicit Association Test (IAT is a popular behavioral measure that assesses the associative strength between outgroup members and stereotypical and counterstereotypical traits. Less is known, however, about the degree to which the IAT reflects automatic processing. Two studies examined automatic processing contributions to a gender-IAT using a data driven, social neuroscience approach. Performance on congruent (e.g., categorizing male names with synonyms of strength and incongruent (e.g., categorizing female names with synonyms of strength IAT blocks were separately analyzed using EEG (event-related potentials, or ERPs, and coherence; Study 1 and lesion (Study 2 methodologies. Compared to incongruent blocks, performance on congruent IAT blocks was associated with more positive ERPs that manifested in frontal and occipital regions at automatic processing speeds, occipital regions at more controlled processing speeds and was compromised by volume loss in the anterior temporal lobe, insula and medial PFC. Performance on incongruent blocks was associated with volume loss in supplementary motor areas, cingulate gyrus and a region in medial PFC similar to that found for congruent blocks. Greater coherence was found between frontal and occipital regions to the extent individuals exhibited more bias. This suggests there are separable neural contributions to congruent and incongruent blocks of the IAT but there is also a surprising amount of overlap. Given the temporal and regional neural distinctions, these results provide converging evidence that stereotypic associative strength assessed by the IAT indexes automatic processing to a degree.

  19. Neural mechanisms underlying contextual dependency of subjective values: converging evidence from monkeys and humans.

    Science.gov (United States)

    Abitbol, Raphaëlle; Lebreton, Maël; Hollard, Guillaume; Richmond, Barry J; Bouret, Sébastien; Pessiglione, Mathias

    2015-02-04

    A major challenge for decision theory is to account for the instability of expressed preferences across time and context. Such variability could arise from specific properties of the brain system used to assign subjective values. Growing evidence has identified the ventromedial prefrontal cortex (VMPFC) as a key node of the human brain valuation system. Here, we first replicate this observation with an fMRI study in humans showing that subjective values of painting pictures, as expressed in explicit pleasantness ratings, are specifically encoded in the VMPFC. We then establish a bridge with monkey electrophysiology, by comparing single-unit activity evoked by visual cues between the VMPFC and the orbitofrontal cortex. At the neural population level, expected reward magnitude was only encoded in the VMPFC, which also reflected subjective cue values, as expressed in Pavlovian appetitive responses. In addition, we demonstrate in both species that the additive effect of prestimulus activity on evoked activity has a significant impact on subjective values. In monkeys, the factor dominating prestimulus VMPFC activity was trial number, which likely indexed variations in internal dispositions related to fatigue or satiety. In humans, prestimulus VMPFC activity was externally manipulated through changes in the musical context, which induced a systematic bias in subjective values. Thus, the apparent stochasticity of preferences might relate to the VMPFC automatically aggregating the values of contextual features, which would bias subsequent valuation because of temporal autocorrelation in neural activity. Copyright © 2015 the authors 0270-6474/15/352308-13$15.00/0.

  20. Tuning to the significant: neural and genetic processes underlying affective enhancement of visual perception and memory.

    Science.gov (United States)

    Markovic, Jelena; Anderson, Adam K; Todd, Rebecca M

    2014-02-01

    Emotionally arousing events reach awareness more easily and evoke greater visual cortex activation than more mundane events. Recent studies have shown that they are also perceived more vividly and that emotionally enhanced perceptual vividness predicts memory vividness. We propose that affect-biased attention (ABA) - selective attention to emotionally salient events - is an endogenous attentional system tuned by an individual's history of reward and punishment. We present the Biased Attention via Norepinephrine (BANE) model, which unifies genetic, neuromodulatory, neural and behavioural evidence to account for ABA. We review evidence supporting BANE's proposal that a key mechanism of ABA is locus coeruleus-norepinephrine (LC-NE) activity, which interacts with activity in hubs of affective salience networks to modulate visual cortex activation and heighten the subjective vividness of emotionally salient stimuli. We further review literature on biased competition and look at initial evidence for its potential as a neural mechanism behind ABA. We also review evidence supporting the role of the LC-NE system as a driving force of ABA. Finally, we review individual differences in ABA and memory including differences in sensitivity to stimulus category and valence. We focus on differences arising from a variant of the ADRA2b gene, which codes for the alpha2b adrenoreceptor as a way of investigating influences of NE availability on ABA in humans. Copyright © 2013 Elsevier B.V. All rights reserved.

  1. Anything goes? Regulation of the neural processes underlying response inhibition in TBI patients.

    Science.gov (United States)

    Moreno-López, Laura; Manktelow, Anne E; Sahakian, Barbara J; Menon, David K; Stamatakis, Emmanuel A

    2017-02-01

    Despite evidence for beneficial use of methylphenidate in response inhibition, no studies so far have investigated the effects of this drug in the neurobiology of inhibitory control in traumatic brain injury (TBI), even though impulsive behaviours are frequently reported in this patient group. We investigated the neural basis of response inhibition in a group of TBI patients using functional magnetic resonance imaging and a stop-signal paradigm. In a randomised double-blinded crossover study, the patients received either a single 30mg dose of methylphenidate or placebo and performed the stop-signal task. Activation in the right inferior frontal gyrus (RIFG), an area associated with response inhibition, was significantly lower in patients compared to healthy controls. Poor response inhibition in this group was associated with greater connectivity between the RIFG and a set of regions considered to be part of the default mode network (DMN), a finding that suggests the interplay between DMN and frontal executive networks maybe compromised. A single dose of methylphenidate rendered activity and connectivity profiles of the patients RIFG near normal. The results of this study indicate that the neural circuitry involved in response inhibition in TBI patients may be partially restored with methylphenidate. Given the known mechanisms of action of methylphenidate, the effect we observed may be due to increased dopamine and noradrenaline levels. Copyright © 2016 Elsevier B.V. and ECNP. All rights reserved.

  2. Bad and worse: neural systems underlying reappraisal of high- and low-intensity negative emotions.

    Science.gov (United States)

    Silvers, Jennifer A; Weber, Jochen; Wager, Tor D; Ochsner, Kevin N

    2015-02-01

    One of the most effective strategies for regulating emotional responses is cognitive reappraisal. While prior work has made great strides in characterizing reappraisal's neural mechanisms and behavioral outcomes, the key issue of how regulation varies as a function of emotional intensity remains unaddressed. We compared the behavioral and neural correlates of reappraisal of high- and low-intensity emotional responses using functional magnetic resonance imaging (fMRI). We found that successful reappraisal of both high- and low-intensity emotions depends upon recruitment of dorsomedial (dmPFC) as well as left dorsolateral (dlPFC) and ventrolateral (vlPFC) prefrontal cortex. However, reappraisal of high-intensity emotions more strongly activated left dlPFC, and in addition, activated right lateral and dorsomedial PFC regions not recruited by low-intensity reappraisal. No brain regions were more strongly recruited during reappraisal of low when compared with high-intensity emotions. Taken together, these results suggest that reappraisal of high-intensity emotion requires greater cognitive resources as evidenced by quantitative and qualitative differences in prefrontal recruitment. These data have implications for understanding how and when specific PFC systems are needed to regulate different types of emotional responses. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  3. Gender Differences in Behavioral and Neural Responses to Unfairness Under Social Pressure.

    Science.gov (United States)

    Zheng, Li; Ning, Reipeng; Li, Lin; Wei, Chunli; Cheng, Xuemei; Zhou, Chu; Guo, Xiuyan

    2017-10-18

    Numerous studies have revealed the key role of social pressure on individuals' decision-making processes. However, the impact of social pressure on unfairness-related decision-making processes remains unclear. In the present study, we investigated how social pressure modulated men's and women's responses in an ultimatum game. Twenty women and eighteen men played the ultimatum game as responders in the scanner, where fair and unfair offers were tendered by proposers acting alone (low pressure) or by proposers endorsed by three supporters (high pressure). Results showed that men rejected more, whereas women accepted more unfair offers in the high versus low pressure context. Neurally, pregenual anterior cingulate cortex activation in women positively predicted their acceptance rate difference between contexts. In men, stronger right anterior insula activation and increased connectivity between right anterior insula and dorsal anterior cingulate cortex were observed when they receiving unfair offers in the high than low pressure context. Furthermore, more bilateral anterior insula and left dorsolateral prefrontal cortex activations were found when men rejected (relative to accepted) unfair offers in the high than low pressure context. These findings highlighted gender differences in the modulation of behavioral and neural responses to unfairness by social pressure.

  4. Neural dynamics underlying attentional orienting to auditory representations in short-term memory.

    Science.gov (United States)

    Backer, Kristina C; Binns, Malcolm A; Alain, Claude

    2015-01-21

    Sounds are ephemeral. Thus, coherent auditory perception depends on "hearing" back in time: retrospectively attending that which was lost externally but preserved in short-term memory (STM). Current theories of auditory attention assume that sound features are integrated into a perceptual object, that multiple objects can coexist in STM, and that attention can be deployed to an object in STM. Recording electroencephalography from humans, we tested these assumptions, elucidating feature-general and feature-specific neural correlates of auditory attention to STM. Alpha/beta oscillations and frontal and posterior event-related potentials indexed feature-general top-down attentional control to one of several coexisting auditory representations in STM. Particularly, task performance during attentional orienting was correlated with alpha/low-beta desynchronization (i.e., power suppression). However, attention to one feature could occur without simultaneous processing of the second feature of the representation. Therefore, auditory attention to memory relies on both feature-specific and feature-general neural dynamics. Copyright © 2015 the authors 0270-6474/15/351307-12$15.00/0.

  5. The necessity of connection structures in neural models of variable binding.

    Science.gov (United States)

    van der Velde, Frank; de Kamps, Marc

    2015-08-01

    In his review of neural binding problems, Feldman (Cogn Neurodyn 7:1-11, 2013) addressed two types of models as solutions of (novel) variable binding. The one type uses labels such as phase synchrony of activation. The other ('connectivity based') type uses dedicated connections structures to achieve novel variable binding. Feldman argued that label (synchrony) based models are the only possible candidates to handle novel variable binding, whereas connectivity based models lack the flexibility required for that. We argue and illustrate that Feldman's analysis is incorrect. Contrary to his conclusion, connectivity based models are the only viable candidates for models of novel variable binding because they are the only type of models that can produce behavior. We will show that the label (synchrony) based models analyzed by Feldman are in fact examples of connectivity based models. Feldman's analysis that novel variable binding can be achieved without existing connection structures seems to result from analyzing the binding problem in a wrong frame of reference, in particular in an outside instead of the required inside frame of reference. Connectivity based models can be models of novel variable binding when they possess a connection structure that resembles a small-world network, as found in the brain. We will illustrate binding with this type of model with episode binding and the binding of words, including novel words, in sentence structures.

  6. Language Learning Enhanced by Massive Multiple Online Role-Playing Games (MMORPGs) and the Underlying Behavioral and Neural Mechanisms.

    Science.gov (United States)

    Zhang, Yongjun; Song, Hongwen; Liu, Xiaoming; Tang, Dinghong; Chen, Yue-E; Zhang, Xiaochu

    2017-01-01

    Massive Multiple Online Role-Playing Games (MMORPGs) have increased in popularity among children, juveniles, and adults since MMORPGs' appearance in this digital age. MMORPGs can be applied to enhancing language learning, which is drawing researchers' attention from different fields and many studies have validated MMORPGs' positive effect on language learning. However, there are few studies on the underlying behavioral or neural mechanism of such effect. This paper reviews the educational application of the MMORPGs based on relevant macroscopic and microscopic studies, showing that gamers' overall language proficiency or some specific language skills can be enhanced by real-time online interaction with peers and game narratives or instructions embedded in the MMORPGs. Mechanisms underlying the educational assistant role of MMORPGs in second language learning are discussed from both behavioral and neural perspectives. We suggest that attentional bias makes gamers/learners allocate more cognitive resources toward task-related stimuli in a controlled or an automatic way. Moreover, with a moderating role played by activation of reward circuit, playing the MMORPGs may strengthen or increase functional connectivity from seed regions such as left anterior insular/frontal operculum (AI/FO) and visual word form area to other language-related brain areas.

  7. Language Learning Enhanced by Massive Multiple Online Role-Playing Games (MMORPGs) and the Underlying Behavioral and Neural Mechanisms

    Science.gov (United States)

    Zhang, Yongjun; Song, Hongwen; Liu, Xiaoming; Tang, Dinghong; Chen, Yue-e; Zhang, Xiaochu

    2017-01-01

    Massive Multiple Online Role-Playing Games (MMORPGs) have increased in popularity among children, juveniles, and adults since MMORPGs’ appearance in this digital age. MMORPGs can be applied to enhancing language learning, which is drawing researchers’ attention from different fields and many studies have validated MMORPGs’ positive effect on language learning. However, there are few studies on the underlying behavioral or neural mechanism of such effect. This paper reviews the educational application of the MMORPGs based on relevant macroscopic and microscopic studies, showing that gamers’ overall language proficiency or some specific language skills can be enhanced by real-time online interaction with peers and game narratives or instructions embedded in the MMORPGs. Mechanisms underlying the educational assistant role of MMORPGs in second language learning are discussed from both behavioral and neural perspectives. We suggest that attentional bias makes gamers/learners allocate more cognitive resources toward task-related stimuli in a controlled or an automatic way. Moreover, with a moderating role played by activation of reward circuit, playing the MMORPGs may strengthen or increase functional connectivity from seed regions such as left anterior insular/frontal operculum (AI/FO) and visual word form area to other language-related brain areas. PMID:28303097

  8. Prediction of composite fatigue life under variable amplitude loading using artificial neural network trained by genetic algorithm

    Science.gov (United States)

    Rohman, Muhamad Nur; Hidayat, Mas Irfan P.; Purniawan, Agung

    2018-04-01

    Neural networks (NN) have been widely used in application of fatigue life prediction. In the use of fatigue life prediction for polymeric-base composite, development of NN model is necessary with respect to the limited fatigue data and applicable to be used to predict the fatigue life under varying stress amplitudes in the different stress ratios. In the present paper, Multilayer-Perceptrons (MLP) model of neural network is developed, and Genetic Algorithm was employed to optimize the respective weights of NN for prediction of polymeric-base composite materials under variable amplitude loading. From the simulation result obtained with two different composite systems, named E-glass fabrics/epoxy (layups [(±45)/(0)2]S), and E-glass/polyester (layups [90/0/±45/0]S), NN model were trained with fatigue data from two different stress ratios, which represent limited fatigue data, can be used to predict another four and seven stress ratios respectively, with high accuracy of fatigue life prediction. The accuracy of NN prediction were quantified with the small value of mean square error (MSE). When using 33% from the total fatigue data for training, the NN model able to produce high accuracy for all stress ratios. When using less fatigue data during training (22% from the total fatigue data), the NN model still able to produce high coefficient of determination between the prediction result compared with obtained by experiment.

  9. Numerical Analysis of Vibrations of Structures under Moving Inertial Load

    CERN Document Server

    Bajer, Czeslaw I

    2012-01-01

    Moving inertial loads are applied to structures in civil engineering, robotics, and mechanical engineering. Some fundamental books exist, as well as thousands of research papers. Well known is the book by L. Frýba, Vibrations of Solids and Structures Under Moving Loads, which describes almost all problems concerning non-inertial loads. This book presents broad description of numerical tools successfully applied to structural dynamic analysis. Physically we deal with non-conservative systems. The discrete approach formulated with the use of the classical finite element method results in elemental matrices, which can be directly added to global structure matrices. A more general approach is carried out with the space-time finite element method. In such a case, a trajectory of the moving concentrated parameter in space and time can be simply defined. We consider structures described by pure hyperbolic differential equations such as strings and structures described by hyperbolic-parabolic differential equations ...

  10. Evoked EMG-based torque prediction under muscle fatigue in implanted neural stimulation

    Science.gov (United States)

    Hayashibe, Mitsuhiro; Zhang, Qin; Guiraud, David; Fattal, Charles

    2011-10-01

    In patients with complete spinal cord injury, fatigue occurs rapidly and there is no proprioceptive feedback regarding the current muscle condition. Therefore, it is essential to monitor the muscle state and assess the expected muscle response to improve the current FES system toward adaptive force/torque control in the presence of muscle fatigue. Our team implanted neural and epimysial electrodes in a complete paraplegic patient in 1999. We carried out a case study, in the specific case of implanted stimulation, in order to verify the corresponding torque prediction based on stimulus evoked EMG (eEMG) when muscle fatigue is occurring during electrical stimulation. Indeed, in implanted stimulation, the relationship between stimulation parameters and output torques is more stable than external stimulation in which the electrode location strongly affects the quality of the recruitment. Thus, the assumption that changes in the stimulation-torque relationship would be mainly due to muscle fatigue can be made reasonably. The eEMG was proved to be correlated to the generated torque during the continuous stimulation while the frequency of eEMG also decreased during fatigue. The median frequency showed a similar variation trend to the mean absolute value of eEMG. Torque prediction during fatigue-inducing tests was performed based on eEMG in model cross-validation where the model was identified using recruitment test data. The torque prediction, apart from the potentiation period, showed acceptable tracking performances that would enable us to perform adaptive closed-loop control through implanted neural stimulation in the future.

  11. Estimation of lost circulation amount occurs during under balanced drilling using drilling data and neural network

    Directory of Open Access Journals (Sweden)

    Pouria Behnoud far

    2017-09-01

    Full Text Available Lost circulation can cause an increase in time and cost of operation. Pipe sticking, formation damage and uncontrolled flow of oil and gas may be consequences of lost circulation. Dealing with this problem is a key factor to conduct a successful drilling operation. Estimation of lost circulation amount is necessary to find a solution. Lost circulation is influenced by different parameters such as mud weight, pump pressure, depth etc. Mud weight, pump pressure and flow rate of mud should be designed to prevent induced fractures and have the least amount of lost circulation. Artificial neural network is useful to find the relations of parameters with lost circulation. Genetic algorithm is applied on the achieved relations to determine the optimum mud weight, pump pressure, and flow rate. In an Iranian oil field, daily drilling reports of wells which are drilled using UBD technique are studied. Asmari formation is the most important oil reservoir of the studied field and UBD is used only in this interval. Three wells with the most, moderate and without lost circulation are chosen. In this article, the effect of mud weight, depth, pump pressure and flow rate of pump on lost circulation in UBD of Asmari formation in one of the Southwest Iranian fields is studied using drilling data and artificial neural network. In addition, the amount of lost circulation is predicted precisely with respect to two of the studied parameters using the presented correlations and the optimum mud weight, pump pressure and flow rate are calculated to minimize the lost circulation amount.

  12. Adaptive Sliding Mode Control of Dynamic Systems Using Double Loop Recurrent Neural Network Structure.

    Science.gov (United States)

    Fei, Juntao; Lu, Cheng

    2018-04-01

    In this paper, an adaptive sliding mode control system using a double loop recurrent neural network (DLRNN) structure is proposed for a class of nonlinear dynamic systems. A new three-layer RNN is proposed to approximate unknown dynamics with two different kinds of feedback loops where the firing weights and output signal calculated in the last step are stored and used as the feedback signals in each feedback loop. Since the new structure has combined the advantages of internal feedback NN and external feedback NN, it can acquire the internal state information while the output signal is also captured, thus the new designed DLRNN can achieve better approximation performance compared with the regular NNs without feedback loops or the regular RNNs with a single feedback loop. The new proposed DLRNN structure is employed in an equivalent controller to approximate the unknown nonlinear system dynamics, and the parameters of the DLRNN are updated online by adaptive laws to get favorable approximation performance. To investigate the effectiveness of the proposed controller, the designed adaptive sliding mode controller with the DLRNN is applied to a -axis microelectromechanical system gyroscope to control the vibrating dynamics of the proof mass. Simulation results demonstrate that the proposed methodology can achieve good tracking property, and the comparisons of the approximation performance between radial basis function NN, RNN, and DLRNN show that the DLRNN can accurately estimate the unknown dynamics with a fast speed while the internal states of DLRNN are more stable.

  13. Nonlinear dynamic systems identification using recurrent interval type-2 TSK fuzzy neural network - A novel structure.

    Science.gov (United States)

    El-Nagar, Ahmad M

    2018-01-01

    In this study, a novel structure of a recurrent interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network (FNN) is introduced for nonlinear dynamic and time-varying systems identification. It combines the type-2 fuzzy sets (T2FSs) and a recurrent FNN to avoid the data uncertainties. The fuzzy firing strengths in the proposed structure are returned to the network input as internal variables. The interval type-2 fuzzy sets (IT2FSs) is used to describe the antecedent part for each rule while the consequent part is a TSK-type, which is a linear function of the internal variables and the external inputs with interval weights. All the type-2 fuzzy rules for the proposed RIT2TSKFNN are learned on-line based on structure and parameter learning, which are performed using the type-2 fuzzy clustering. The antecedent and consequent parameters of the proposed RIT2TSKFNN are updated based on the Lyapunov function to achieve network stability. The obtained results indicate that our proposed network has a small root mean square error (RMSE) and a small integral of square error (ISE) with a small number of rules and a small computation time compared with other type-2 FNNs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  14. Preschool externalizing behavior predicts gender-specific variation in adolescent neural structure.

    Directory of Open Access Journals (Sweden)

    Jessica Z K Caldwell

    Full Text Available Dysfunction in the prefrontal cortex, amygdala, and hippocampus is believed to underlie the development of much psychopathology. However, to date only limited longitudinal data relate early behavior with neural structure later in life. Our objective was to examine the relationship of early life externalizing behavior with adolescent brain structure. We report here the first longitudinal study linking externalizing behavior during preschool to brain structure during adolescence. We examined the relationship of preschool externalizing behavior with amygdala, hippocampus, and prefrontal cortex volumes at age 15 years in a community sample of 76 adolescents followed longitudinally since their mothers' pregnancy. A significant gender by externalizing behavior interaction revealed that males-but not females-with greater early childhood externalizing behavior had smaller amygdala volumes at adolescence (t = 2.33, p = .023. No significant results were found for the hippocampus or the prefrontal cortex. Greater early externalizing behavior also related to smaller volume of a cluster including the angular gyrus and tempoparietal junction across genders. Results were not attributable to the impact of preschool anxiety, preschool maternal stress, school-age internalizing or externalizing behaviors, or adolescent substance use. These findings demonstrate a novel, gender-specific relationship between early-childhood externalizing behavior and adolescent amygdala volume, as well as a cross-gender result for the angular gyrus and tempoparietal junction.

  15. The Effect of an Enrichment Reading Program on the Cognitive Processes and Neural Structures of Children Having Reading Difficulties

    Science.gov (United States)

    Kuruyer, Hayriye Gül; Akyol, Hayati; Karli Oguz, Kader; Has, Arzu Ceylan

    2017-01-01

    The main purpose of the current study is to explain the effect of an enrichment reading program on the cognitive processes and neural structures of children experiencing reading difficulties. The current study was carried out in line with a single-subject research method and the between-subjects multiple probe design belonging to this method. This…

  16. Characterizing Thematized Derivative Schema by the Underlying Emergent Structures

    Science.gov (United States)

    Garcia, Mercedes; Llinares, Salvador; Sanchez-Matamoros, Gloria

    2011-01-01

    This paper reports on different underlying structures of the derivative schema of three undergraduate students that were considered to be at the trans level of development of the derivative schema (action-process-object-schema). The derivative schema is characterized in terms of the students' ability to explicitly transfer the relationship between…

  17. Colloidal hard dumbbells under gravity: structure and crystallization

    NARCIS (Netherlands)

    Marechal, M.A.T.; Dijkstra, M.

    2011-01-01

    We study the structure and phase behavior of hard dumbbells under gravity. The fluid shows layering near the wall, where subsequent layers of dumbbells align alternatingly parallel or perpendicular to the wall. We observe coexistence of a fluid with a plastic crystal (PC) and an aligned crystal

  18. The Fatigue Behavior of Steel Structures under Random Loading

    DEFF Research Database (Denmark)

    Agerskov, Henning

    2009-01-01

    Fatigue damage accumulation in steel structures under random loading has been studied in a number of investigations at the Technical University of Denmark. The fatigue life of welded joints has been determined both experimentally and from a fracture mechanics analysis. In the experimental part...

  19. The Fatigue Behavior of Steel Structures under Random Loading

    DEFF Research Database (Denmark)

    Agerskov, Henning

    2008-01-01

    Fatigue damage accumulation in steel structures under random loading has been studied in a number of investigations at the Technical University of Denmark. The fatigue life of welded joints has been determined both experimentally and from a fracture mechanics analysis. In the experimental part...

  20. Structural composite panel performance under long-term load

    Science.gov (United States)

    Theodore L. Laufenberg

    1988-01-01

    Information on the performance of wood-based structural composite panels under long-term load is currently needed to permit their use in engineered assemblies and systems. A broad assessment of the time-dependent properties of panels is critical for creating databases and models of the creep-rupture phenomenon that lead to reliability-based design procedures. This...

  1. [Calculation of soil water erosion modulus based on RUSLE and its assessment under support of artificial neural network].

    Science.gov (United States)

    Li, Yuhuan; Wang, Jing; Zhang, Jixian

    2006-06-01

    With Hengshan County of Shanxi Province in the North Loess Plateau as an example, and by using ETM + and remote sensing data and RUSLE module, this paper quantitatively derived the soil and water loss in loess hilly region based on "3S" technology, and assessed the derivation results under the support of artificial neural network. The results showed that the annual average erosion modulus of Hengshan County was 103.23 t x hm(-2), and the gross erosion loss per year was 4. 38 x 10(7) t. The erosion was increased from northwest to southeast, and varied significantly with topographic position. A slight erosion or no erosion happened in walled basin, flat-headed mountain ridges and sandy area, which always suffered from dropping erosion, while strip erosion often happened on the upslope of mountain ridge and mountaintop flat. Moderate rill erosion always occurred on the middle and down slope of mountain ridge and mountaintop flat, and weighty rushing erosion occurred on the steep ravine and brink. The RUSLE model and artificial neural network technique were feasible and could be propagandized for drainage areas control and preserved practice.

  2. Neural correlates and network connectivity underlying narrative production and comprehension: a combined fMRI and PET study.

    Science.gov (United States)

    AbdulSabur, Nuria Y; Xu, Yisheng; Liu, Siyuan; Chow, Ho Ming; Baxter, Miranda; Carson, Jessica; Braun, Allen R

    2014-08-01

    The neural correlates of narrative production and comprehension remain poorly understood. Here, using positron emission tomography (PET), functional magnetic resonance imaging (fMRI), contrast and functional network connectivity analyses we comprehensively characterize the neural mechanisms underlying these complex behaviors. Eighteen healthy subjects told and listened to fictional stories during scanning. In addition to traditional language areas (e.g., left inferior frontal and posterior middle temporal gyri), both narrative production and comprehension engaged regions associated with mentalizing and situation model construction (e.g., dorsomedial prefrontal cortex, precuneus and inferior parietal lobules) as well as neocortical premotor areas, such as the pre-supplementary motor area and left dorsal premotor cortex. Narrative comprehension alone showed marked bilaterality, activating right hemisphere homologs of perisylvian language areas. Narrative production remained predominantly left lateralized, uniquely activating executive and motor-related regions essential to language formulation and articulation. Connectivity analyses revealed strong associations between language areas and the superior and middle temporal gyri during both tasks. However, only during storytelling were these same language-related regions connected to cortical and subcortical motor regions. In contrast, during story comprehension alone, they were strongly linked to regions supporting mentalizing. Thus, when employed in a more complex, ecologically-valid context, language production and comprehension show both overlapping and idiosyncratic patterns of activation and functional connectivity. Importantly, in each case the language system is integrated with regions that support other cognitive and sensorimotor domains. Copyright © 2014. Published by Elsevier Ltd.

  3. Design of mild steel structures under unequal cyclic loads

    International Nuclear Information System (INIS)

    In this paper a method is proposed to investigate the behavior and life of structural components under unequal cyclic loading conditions. Appropriate cyclic moment-curvature relations and life information, in the form of life versus extreme fiber strain, are developed from tests on beams under pure bending conditions. Theoretical predictions of behavior are based on structural geometry and the cyclic moment-curvature relations used in association with the simple curvature-area method. Structural life is also predicted using the life information developed and the theoretical strain history at the critical section in conjunction with a linear damage summation criterion. Theoretical predictions of behavior and life compare reasonably well with the experiments. Based on this study, a design procedure is proposed for mild steel components subjected to unequal cyclic loading conditions. The loads on the tested components were such that they failed due to low cyclic fatigue (i.e., at less than 10 5 cycles)

  4. What ethologically based models have taught us about the neural systems underlying fear and anxiety

    Directory of Open Access Journals (Sweden)

    N.S. Canteras

    2012-04-01

    Full Text Available Classical Pavlovian fear conditioning to painful stimuli has provided the generally accepted view of a core system centered in the central amygdala to organize fear responses. Ethologically based models using other sources of threat likely to be expected in a natural environment, such as predators or aggressive dominant conspecifics, have challenged this concept of a unitary core circuit for fear processing. We discuss here what the ethologically based models have told us about the neural systems organizing fear responses. We explored the concept that parallel paths process different classes of threats, and that these different paths influence distinct regions in the periaqueductal gray - a critical element for the organization of all kinds of fear responses. Despite this parallel processing of different kinds of threats, we have discussed an interesting emerging view that common cortical-hippocampal-amygdalar paths seem to be engaged in fear conditioning to painful stimuli, to predators and, perhaps, to aggressive dominant conspecifics as well. Overall, the aim of this review is to bring into focus a more global and comprehensive view of the systems organizing fear responses.

  5. Engagement of neural circuits underlying 2D spatial navigation in a rodent virtual reality system

    Science.gov (United States)

    Aronov, Dmitriy; Tank, David W.

    2015-01-01

    SUMMARY Virtual reality (VR) enables precise control of an animal’s environment and otherwise impossible experimental manipulations. Neural activity in navigating rodents has been studied on virtual linear tracks. However, the spatial navigation system’s engagement in complete two-dimensional environments has not been shown. We describe a VR setup for rats, including control software and a large-scale electrophysiology system, which supports 2D navigation by allowing animals to rotate and walk in any direction. The entorhinal-hippocampal circuit, including place cells, grid cells, head direction cells and border cells, showed 2D activity patterns in VR similar to those in the real world. Hippocampal neurons exhibited various remapping responses to changes in the appearance or the shape of the virtual environment, including a novel form in which a VR-induced cue conflict caused remapping to lock to geometry rather than salient cues. These results suggest a general-purpose tool for novel types of experimental manipulations in navigating rats. PMID:25374363

  6. Artificial neural networks based estimation of optical parameters by diffuse reflectance imaging under in vitro conditions

    Directory of Open Access Journals (Sweden)

    Mahmut Ozan Gökkan

    2017-01-01

    Full Text Available Optical parameters (properties of tissue-mimicking phantoms are determined through noninvasive optical imaging. Objective of this study is to decompose obtained diffuse reflectance into these optical properties such as absorption and scattering coefficients. To do so, transmission spectroscopy is firstly used to measure the coefficients via an experimental setup. Next, the optical properties of each characterized phantom are input for Monte Carlo (MC simulations to get diffuse reflectance. Also, a surface image for each single phantom with its known optical properties is obliquely captured due to reflectance-based geometrical setup using CMOS camera that is positioned at 5∘ angle to the phantoms. For the illumination of light, a laser light source at 633nm wavelength is preferred, because optical properties of different components in a biological tissue on that wavelength are nonoverlapped. During in vitro measurements, we prepared 30 different mixture samples adding clinoleic intravenous lipid emulsion (CILE and evans blue (EB dye into a distilled water. Finally, all obtained diffuse reflectance values are used to estimate the optical coefficients by artificial neural networks (ANNs in inverse modeling. For a biological tissue it is found that the simulated and measured values in our results are in good agreement.

  7. Neural oscillatory mechanisms during novel grammar learning underlying language analytical abilities.

    Science.gov (United States)

    Kepinska, Olga; Pereda, Ernesto; Caspers, Johanneke; Schiller, Niels O

    2017-12-01

    The goal of the present study was to investigate the initial phases of novel grammar learning on a neural level, concentrating on mechanisms responsible for individual variability between learners. Two groups of participants, one with high and one with average language analytical abilities, performed an Artificial Grammar Learning (AGL) task consisting of learning and test phases. During the task, EEG signals from 32 cap-mounted electrodes were recorded and epochs corresponding to the learning phases were analysed. We investigated spectral power modulations over time, and functional connectivity patterns by means of a bivariate, frequency-specific index of phase synchronization termed Phase Locking Value (PLV). Behavioural data showed learning effects in both groups, with a steeper learning curve and higher ultimate attainment for the highly skilled learners. Moreover, we established that cortical connectivity patterns and profiles of spectral power modulations over time differentiated L2 learners with various levels of language analytical abilities. Over the course of the task, the learning process seemed to be driven by whole-brain functional connectivity between neuronal assemblies achieved by means of communication in the beta band frequency. On a shorter time-scale, increasing proficiency on the AGL task appeared to be supported by stronger local synchronisation within the right hemisphere regions. Finally, we observed that the highly skilled learners might have exerted less mental effort, or reduced attention for the task at hand once the learning was achieved, as evidenced by the higher alpha band power. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. Neural computations underlying arbitration between model-based and model-free learning

    Science.gov (United States)

    Lee, Sang Wan; Shimojo, Shinsuke; O’Doherty, John P.

    2014-01-01

    SUMMARY There is accumulating neural evidence to support the existence of two distinct systems for guiding action-selection in the brain, a deliberative “model-based” and a reflexive “model-free” system. However, little is known about how the brain determines which of these systems controls behavior at one moment in time. We provide evidence for an arbitration mechanism that allocates the degree of control over behavior by model-based and model-free systems as a function of the reliability of their respective predictions. We show that inferior lateral prefrontal and frontopolar cortex encode both reliability signals and the output of a comparison between those signals, implicating these regions in the arbitration process. Moreover, connectivity between these regions and model-free valuation areas is negatively modulated by the degree of model-based control in the arbitrator, suggesting that arbitration may work through modulation of the model-free valuation system when the arbitrator deems that the model-based system should drive behavior. PMID:24507199

  9. Neural correlates of exemplar novelty processing under different spatial attention conditions.

    Science.gov (United States)

    Stoppel, Christian Michael; Boehler, Carsten Nicolas; Strumpf, Hendrik; Heinze, Hans-Jochen; Hopf, Jens Max; Düzel, Emrah; Schoenfeld, Mircea Ariel

    2009-11-01

    The detection of novel events and their identification is a basic prerequisite in a rapidly changing environment. Recently, the processing of novelty has been shown to rely on the hippocampus and to be associated with activity in reward-related areas. The present study investigated the influence of spatial attention on neural processing of novel relative to frequently presented standard and target stimuli. Never-before-seen Mandelbrot-fractals absent of semantic content were employed as stimulus material. Consistent with current theories, novelty activated a widespread network of brain areas including the hippocampus. No activity, however, could be observed in reward-related areas with the novel stimuli absent of a semantic meaning employed here. In the perceptual part of the novelty-processing network a region in the lingual gyrus was found to specifically process novel events when they occurred outside the focus of spatial attention. These findings indicate that the initial detection of unexpected novel events generally occurs in specialized perceptual areas within the ventral visual stream, whereas activation of reward-related areas appears to be restricted to events that do possess a semantic content indicative of the biological relevance of the stimulus.

  10. Reduced Fidelity of Neural Representation Underlies Episodic Memory Decline in Normal Aging.

    Science.gov (United States)

    Zheng, Li; Gao, Zhiyao; Xiao, Xiaoqian; Ye, Zhifang; Chen, Chuansheng; Xue, Gui

    2017-06-07

    Emerging studies have emphasized the importance of the fidelity of cortical representation in forming enduring episodic memory. No study, however, has examined whether there are age-related reductions in representation fidelity that can explain memory declines in normal aging. Using functional MRI and multivariate pattern analysis, we found that older adults showed reduced representation fidelity in the visual cortex, which accounted for their decreased memory performance even after controlling for the contribution of reduced activation level. This reduced fidelity was specifically due to older adults' poorer item-specific representation, not due to their lower activation level and variance, greater variability in neuro-vascular coupling, or decreased selectivity of categorical representation (i.e., dedifferentiation). Older adults also showed an enhanced subsequent memory effect in the prefrontal cortex based on activation level, and their prefrontal activation was associated with greater fidelity of representation in the visual cortex and better memory performance. The fidelity of cortical representation thus may serve as a promising neural index for better mechanistic understanding of the memory declines and its compensation in normal aging. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  11. Demystifying Multitask Deep Neural Networks for Quantitative Structure-Activity Relationships.

    Science.gov (United States)

    Xu, Yuting; Ma, Junshui; Liaw, Andy; Sheridan, Robert P; Svetnik, Vladimir

    2017-10-23

    Deep neural networks (DNNs) are complex computational models that have found great success in many artificial intelligence applications, such as computer vision1,2 and natural language processing.3,4 In the past four years, DNNs have also generated promising results for quantitative structure-activity relationship (QSAR) tasks.5,6 Previous work showed that DNNs can routinely make better predictions than traditional methods, such as random forests, on a diverse collection of QSAR data sets. It was also found that multitask DNN models-those trained on and predicting multiple QSAR properties simultaneously-outperform DNNs trained separately on the individual data sets in many, but not all, tasks. To date there has been no satisfactory explanation of why the QSAR of one task embedded in a multitask DNN can borrow information from other unrelated QSAR tasks. Thus, using multitask DNNs in a way that consistently provides a predictive advantage becomes a challenge. In this work, we explored why multitask DNNs make a difference in predictive performance. Our results show that during prediction a multitask DNN does borrow "signal" from molecules with similar structures in the training sets of the other tasks. However, whether this borrowing leads to better or worse predictive performance depends on whether the activities are correlated. On the basis of this, we have developed a strategy to use multitask DNNs that incorporate prior domain knowledge to select training sets with correlated activities, and we demonstrate its effectiveness on several examples.

  12. Acoustic leak detection at complicated geometrical structures using fuzzy logic and neural networks

    International Nuclear Information System (INIS)

    Hessel, G.; Schmitt, W.; Weiss, F.P.

    1993-10-01

    An acoustic method based on pattern recognition is being developed. During the learning phase, the localization classifier is trained with sound patterns that are generated with simulated leaks at all locations endangered by leak. The patterns are extracted from the signals of an appropriate sensor array. After training unknown leak positions can be recognized through comparison with the training patterns. The experimental part is performed at an acoustic 1:3 model of the reactor vessel and head and at an original VVER-440 reactor in the former NPP Greifswald. The leaks were simulated at the vessel head using mobile sound sources driven either by compressed air, a piezoelectric transmitter or by a thin metal blade excited through a jet of compressed air. The sound patterns of the simulated leaks are simultaneously detected with an AE-sensor array and with high frequency microphones measuring structure-borne sound and airborne sound, respectively. Pattern classifiers based on Fuzzy Pattern Classification (FPC) and Artificial Neural Networks (ANN) are currently tested for validation of the acoustic emission-sensor array (FPC), leak localization via structure-borne sound (FPC) and the leak localization using microphones (ANN). The initial results show the used classifiers principally to be capable of detecting and locating leaks, but they also show that further investigations are necessary to develop a reliable method applicable at NPPs. (orig./HP)

  13. BrainSegNet: a convolutional neural network architecture for automated segmentation of human brain structures.

    Science.gov (United States)

    Mehta, Raghav; Majumdar, Aabhas; Sivaswamy, Jayanthi

    2017-04-01

    Automated segmentation of cortical and noncortical human brain structures has been hitherto approached using nonrigid registration followed by label fusion. We propose an alternative approach for this using a convolutional neural network (CNN) which classifies a voxel into one of many structures. Four different kinds of two-dimensional and three-dimensional intensity patches are extracted for each voxel, providing local and global (context) information to the CNN. The proposed approach is evaluated on five different publicly available datasets which differ in the number of labels per volume. The obtained mean Dice coefficient varied according to the number of labels, for example, it is [Formula: see text] and [Formula: see text] for datasets with the least (32) and the most (134) number of labels, respectively. These figures are marginally better or on par with those obtained with the current state-of-the-art methods on nearly all datasets, at a reduced computational time. The consistently good performance of the proposed method across datasets and no requirement for registration make it attractive for many applications where reduced computational time is necessary.

  14. A Damage Prognosis Method of Girder Structures Based on Wavelet Neural Networks

    Directory of Open Access Journals (Sweden)

    Rumian Zhong

    2014-01-01

    Full Text Available Based on the basic theory of wavelet neural networks and finite element model updating method, a basic framework of damage prognosis method is proposed in this paper. Firstly, a damaged I-steel beam model testing is used to verify the feasibility and effectiveness of the proposed damage prognosis method. The results show that the predicted results of the damage prognosis method and the measured results are very well consistent, and the maximum error is less than 5%. Furthermore, Xinyihe Bridge in the Beijing-Shanghai Highway is selected as the engineering background, and the damage prognosis is conducted based on the data from the structural health monitoring system. The results show that the traffic volume will increase and seasonal differences will decrease in the next year and a half. The displacement has a slight increase and seasonal characters in the critical section of mid span, but the strain will increase distinctly. The analysis results indicate that the proposed method can be applied to the damage prognosis of girder bridge structures and has the potential for the bridge health monitoring and safety prognosis.

  15. Prediction of municipal solid waste generation using artificial neural network approach enhanced by structural break analysis.

    Science.gov (United States)

    Adamović, Vladimir M; Antanasijević, Davor Z; Ristić, Mirjana Đ; Perić-Grujić, Aleksandra A; Pocajt, Viktor V

    2017-01-01

    This paper presents the development of a general regression neural network (GRNN) model for the prediction of annual municipal solid waste (MSW) generation at the national level for 44 countries of different size, population and economic development level. Proper modelling of MSW generation is essential for the planning of MSW management system as well as for the simulation of various environmental impact scenarios. The main objective of this work was to examine the potential influence of economy crisis (global or local) on the forecast of MSW generation obtained by the GRNN model. The existence of the so-called structural breaks that occur because of the economic crisis in the studied period (2000-2012) for each country was determined and confirmed using the Chow test and Quandt-Andrews test. Two GRNN models, one which did not take into account the influence of the economic crisis (GRNN) and another one which did (SB-GRNN), were developed. The novelty of the applied method is that it uses broadly available social, economic and demographic indicators and indicators of sustainability, together with GRNN and structural break testing for the prediction of MSW generation at the national level. The obtained results demonstrate that the SB-GRNN model provide more accurate predictions than the model which neglected structural breaks, with a mean absolute percentage error (MAPE) of 4.0 % compared to 6.7 % generated by the GRNN model. The proposed model enhanced with structural breaks can be a viable alternative for a more accurate prediction of MSW generation at the national level, especially for developing countries for which a lack of MSW data is notable.

  16. Inverse analysis of aerodynamic loads from strain information using structural models and neural networks

    Science.gov (United States)

    Wada, Daichi; Sugimoto, Yohei

    2017-04-01

    Aerodynamic loads on aircraft wings are one of the key parameters to be monitored for reliable and effective aircraft operations and management. Flight data of the aerodynamic loads would be used onboard to control the aircraft and accumulated data would be used for the condition-based maintenance and the feedback for the fatigue and critical load modeling. The effective sensing techniques such as fiber optic distributed sensing have been developed and demonstrated promising capability of monitoring structural responses, i.e., strains on the surface of the aircraft wings. By using the developed techniques, load identification methods for structural health monitoring are expected to be established. The typical inverse analysis for load identification using strains calculates the loads in a discrete form of concentrated forces, however, the distributed form of the loads is essential for the accurate and reliable estimation of the critical stress at structural parts. In this study, we demonstrate an inverse analysis to identify the distributed loads from measured strain information. The introduced inverse analysis technique calculates aerodynamic loads not in a discrete but in a distributed manner based on a finite element model. In order to verify the technique through numerical simulations, we apply static aerodynamic loads on a flat panel model, and conduct the inverse identification of the load distributions. We take two approaches to build the inverse system between loads and strains. The first one uses structural models and the second one uses neural networks. We compare the performance of the two approaches, and discuss the effect of the amount of the strain sensing information.

  17. Modeling root length density of field grown potatoes under different irrigation strategies and soil textures using artificial neural networks

    DEFF Research Database (Denmark)

    Ahmadi, Seyed Hamid; Sepaskhah, Ali Reza; Andersen, Mathias Neumann

    2014-01-01

    Root length density (RLD) is a highly wanted parameter for use in crop growth modeling but difficult to measure under field conditions. Therefore, artificial neural networks (ANNs) were implemented to predict the RLD of field grown potatoes that were subject to three irrigation strategies and three...... soil textures with different soil water status and soil densities. The objectives of the study were to test whether soil textural information, soil water status, and soil density might be used by ANN to simulate RLD at harvest. In the study 63 data pairs were divided into data sets of training (80......) of the eight input variables: soil layer intervals (D), percentages of sand (Sa), silt (Si), and clay (Cl), bulk density of soil layers (Bd), weighted soil moisture deficit during the irrigation strategies period (SMD), geometric mean particle size diameter (dg), and geometric standard deviation (σg...

  18. Modulation of endothelial glycocalyx structure under inflammatory conditions.

    Science.gov (United States)

    Kolářová, Hana; Ambrůzová, Barbora; Svihálková Šindlerová, Lenka; Klinke, Anna; Kubala, Lukáš

    2014-01-01

    The glycocalyx of the endothelium is an intravascular compartment that creates a barrier between circulating blood and the vessel wall. The glycocalyx is suggested to play an important role in numerous physiological processes including the regulation of vascular permeability, the prevention of the margination of blood cells to the vessel wall, and the transmission of shear stress. Various theoretical models and experimental approaches provide data about changes to the structure and functions of the glycocalyx under various types of inflammatory conditions. These alterations are suggested to promote inflammatory processes in vessels and contribute to the pathogenesis of number of diseases. In this review we summarize current knowledge about the modulation of the glycocalyx under inflammatory conditions and the consequences for the course of inflammation in vessels. The structure and functions of endothelial glycocalyx are briefly discussed in the context of methodological approaches regarding the determination of endothelial glycocalyx and the uncertainty and challenges involved in glycocalyx structure determination. In addition, the modulation of glycocalyx structure under inflammatory conditions and the possible consequences for pathogenesis of selected diseases and medical conditions (in particular, diabetes, atherosclerosis, ischemia/reperfusion, and sepsis) are summarized. Finally, therapeutic strategies to ameliorate glycocalyx dysfunction suggested by various authors are discussed.

  19. Modulation of Endothelial Glycocalyx Structure under Inflammatory Conditions

    Directory of Open Access Journals (Sweden)

    Hana Kolářová

    2014-01-01

    Full Text Available The glycocalyx of the endothelium is an intravascular compartment that creates a barrier between circulating blood and the vessel wall. The glycocalyx is suggested to play an important role in numerous physiological processes including the regulation of vascular permeability, the prevention of the margination of blood cells to the vessel wall, and the transmission of shear stress. Various theoretical models and experimental approaches provide data about changes to the structure and functions of the glycocalyx under various types of inflammatory conditions. These alterations are suggested to promote inflammatory processes in vessels and contribute to the pathogenesis of number of diseases. In this review we summarize current knowledge about the modulation of the glycocalyx under inflammatory conditions and the consequences for the course of inflammation in vessels. The structure and functions of endothelial glycocalyx are briefly discussed in the context of methodological approaches regarding the determination of endothelial glycocalyx and the uncertainty and challenges involved in glycocalyx structure determination. In addition, the modulation of glycocalyx structure under inflammatory conditions and the possible consequences for pathogenesis of selected diseases and medical conditions (in particular, diabetes, atherosclerosis, ischemia/reperfusion, and sepsis are summarized. Finally, therapeutic strategies to ameliorate glycocalyx dysfunction suggested by various authors are discussed.

  20. Chaotic diagonal recurrent neural network

    International Nuclear Information System (INIS)

    Wang Xing-Yuan; Zhang Yi

    2012-01-01

    We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning algorithm are designed. The multilayer feedforward neural network, diagonal recurrent neural network, and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map. The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks. (interdisciplinary physics and related areas of science and technology)

  1. Neural mechanisms underlying catastrophic failure in human-machine interaction during aerial navigation

    Science.gov (United States)

    Saproo, Sameer; Shih, Victor; Jangraw, David C.; Sajda, Paul

    2016-12-01

    Objective. We investigated the neural correlates of workload buildup in a fine visuomotor task called the boundary avoidance task (BAT). The BAT has been known to induce naturally occurring failures of human-machine coupling in high performance aircraft that can potentially lead to a crash—these failures are termed pilot induced oscillations (PIOs). Approach. We recorded EEG and pupillometry data from human subjects engaged in a flight BAT simulated within a virtual 3D environment. Main results. We find that workload buildup in a BAT can be successfully decoded from oscillatory features in the electroencephalogram (EEG). Information in delta, theta, alpha, beta, and gamma spectral bands of the EEG all contribute to successful decoding, however gamma band activity with a lateralized somatosensory topography has the highest contribution, while theta band activity with a fronto-central topography has the most robust contribution in terms of real-world usability. We show that the output of the spectral decoder can be used to predict PIO susceptibility. We also find that workload buildup in the task induces pupil dilation, the magnitude of which is significantly correlated with the magnitude of the decoded EEG signals. These results suggest that PIOs may result from the dysregulation of cortical networks such as the locus coeruleus (LC)—anterior cingulate cortex (ACC) circuit. Significance. Our findings may generalize to similar control failures in other cases of tight man-machine coupling where gains and latencies in the control system must be inferred and compensated for by the human operators. A closed-loop intervention using neurophysiological decoding of workload buildup that targets the LC-ACC circuit may positively impact operator performance in such situations.

  2. Neural substrates of cognitive control under the belief of getting neurofeedback training

    Directory of Open Access Journals (Sweden)

    Manuel eNinaus

    2013-12-01

    Full Text Available Learning to modulate one’s own brain activity is the fundament of neurofeedback (NF applications. Besides the neural networks directly involved in the generation and modulation of the neurophysiological parameter being specifically trained, more general determinants of NF efficacy such as self-referential processes and cognitive control have been frequently disregarded. Nonetheless, deeper insight into these cognitive mechanisms and their neuronal underpinnings sheds light on various open NF related questions concerning individual differences, brain-computer interface (BCI illiteracy as well as a more general model of NF learning. In this context, we investigated the neuronal substrate of these more general regulatory mechanisms that are engaged when participants believe that they are receiving NF. Twenty healthy participants (40-63 years, 10 female performed a sham NF paradigm during fMRI scanning. All participants were novices to NF-experiments and were instructed to voluntarily modulate their own brain activity based on a visual display of moving color bars. However, the bar depicted a recording and not the actual brain activity of participants. Reports collected at the end of the experiment indicate that participants were unaware of the sham feedback. In comparison to a passive watching condition, bilateral insula, anterior cingulate cortex and supplementary motor and dorsomedial and lateral prefrontal area were activated when participants actively tried to control the bar. In contrast, when merely watching moving bars, increased activation in the left angular gyrus was observed. These results show that the intention to control a moving bar is sufficient to engage a broad frontoparietal and cingulo-opercular network involved in cognitive control. The results of the present study indicate that tasks such as those generally employed in NF training recruit the neuronal correlates of cognitive control even when only sham NF is presented.

  3. Neural substrates of cognitive control under the belief of getting neurofeedback training.

    Science.gov (United States)

    Ninaus, Manuel; Kober, Silvia E; Witte, Matthias; Koschutnig, Karl; Stangl, Matthias; Neuper, Christa; Wood, Guilherme

    2013-01-01

    Learning to modulate one's own brain activity is the fundament of neurofeedback (NF) applications. Besides the neural networks directly involved in the generation and modulation of the neurophysiological parameter being specifically trained, more general determinants of NF efficacy such as self-referential processes and cognitive control have been frequently disregarded. Nonetheless, deeper insight into these cognitive mechanisms and their neuronal underpinnings sheds light on various open NF related questions concerning individual differences, brain-computer interface (BCI) illiteracy as well as a more general model of NF learning. In this context, we investigated the neuronal substrate of these more general regulatory mechanisms that are engaged when participants believe that they are receiving NF. Twenty healthy participants (40-63 years, 10 female) performed a sham NF paradigm during fMRI scanning. All participants were novices to NF-experiments and were instructed to voluntarily modulate their own brain activity based on a visual display of moving color bars. However, the bar depicted a recording and not the actual brain activity of participants. Reports collected at the end of the experiment indicate that participants were unaware of the sham feedback. In comparison to a passive watching condition, bilateral insula, anterior cingulate cortex and supplementary motor and dorsomedial and lateral prefrontal areas were activated when participants actively tried to control the bar. In contrast, when merely watching moving bars, increased activation in the left angular gyrus was observed. These results show that the intention to control a moving bar is sufficient to engage a broad frontoparietal and cingulo-opercular network involved in cognitive control. The results of the present study indicate that tasks such as those generally employed in NF training recruit the neuronal correlates of cognitive control even when only sham NF is presented.

  4. Safety margins of containment structures under impulsive loading

    International Nuclear Information System (INIS)

    Lu, S.C.H.

    1978-01-01

    Containment structures for nuclear power plants are designed to a large extent to satisfy the various stress limits specified by ASME Boiler and Pressure Vessel Code. For short-duration impulsive loads, the common practice of meeting the Code stress limits based on a quasi-static approach is a poor measure of the reserve load-carrying capacity of a structure and always results in a conservative design with a greater than desired margin of safety. There are situations, however, where one might wish to quantify this additional conservatism to avoid excessive or unnecessary field modification. Typical examples were found in re-evaluation studies of MARK I Boiling Water Reactor containment structures under the hydrodynamic loads expected during a postulated loss-of-coolant accident. The paper is based on the results of a plane strain, large displacement, elastic-plastic, finite-element analysis of a thin cylindrical shell subjected to external pressure pulses. An analytical procedure is presented for estimating the ultimate load capacity of the thin shell structure and, subsequently, for quantifying the design margins of safety for the type of loads under consideration. For defining failure of structures, a finite strain failure criterion is derived that accounts for multiaxiality effects

  5. Effect of support conditions on structural response under dynamic loading

    International Nuclear Information System (INIS)

    Akram, T.; Memon, S.A.

    2008-01-01

    In design practice, dynamic structural analysis is carried out with base of structure considered as fixed; this means that foundation is placed on rock like soil material. While conducting this type of analyses the role of foundation and soil behaviour is totally neglected. The actions in members and loads transferred at foundation level obtained in this manner do not depict the true structural behaviour. FEM (Finite Element Methods) analysis where both superstructure and foundation soil are coupled together is quite complicated and expensive for design environments. A simplified model is required to depict dynamic response of structures with foundations based on flexible soils. The primary purpose of this research is to compare the superstructure dynamic responses of structural systems with fixed base to that of simple soil model base. The selected simple soil model is to be suitable for use in a design environment to give more realistic results. For this purpose building models are idealized with various heights and structural systems in both 2D (Two Dimensional) and 3D (Three Dimensional) space. These models are then provided with visco-elastic supports representing three soil bearing capacities and the analysis results are compared to that of fixed supports models. The results indicate that fixed support system underestimates natural time period of the structures. Dynamic behavior and force response of visco-elastic support is different from fixed support model. Fixed support models result in over designed base columns and under designed beams. (author)

  6. Generalized Minimum Variance Control for MDOF Structures under Earthquake Excitation

    Directory of Open Access Journals (Sweden)

    Lakhdar Guenfaf

    2016-01-01

    Full Text Available Control of a multi-degree-of-freedom structural system under earthquake excitation is investigated in this paper. The control approach based on the Generalized Minimum Variance (GMV algorithm is developed and presented. Our approach is a generalization to multivariable systems of the GMV strategy designed initially for single-input-single-output (SISO systems. Kanai-Tajimi and Clough-Penzien models are used to generate the seismic excitations. Those models are calculated using the specific soil parameters. Simulation tests using a 3DOF structure are performed and show the effectiveness of the control method.

  7. Structural Evaluation on HIC Transport Packaging under Accident Conditions

    International Nuclear Information System (INIS)

    Chung, Sung Hwan; Kim, Duck Hoi; Jung, Jin Se; Yang, Ke Hyung; Lee, Heung Young

    2005-01-01

    HIC transport packaging to transport a high integrity container(HIC) containing dry spent resin generated from nuclear power plants is to comply with the regulatory requirements of Korea and IAEA for Type B packaging due to the high radioactivity of the content, and to maintain the structural integrity under normal and accident conditions. It must withstand 9 m free drop impact onto an unyielding surface and 1 m drop impact onto a mild steel bar in a position causing maximum damage. For the conceptual design of a cylindrical HIC transport package, three dimensional dynamic structural analysis to ensure that the integrity of the package is maintained under all credible loads for 9 m free drop and 1 m puncture conditions were carried out using ABAQUS code.

  8. Bearing Fault Diagnosis under Variable Speed Using Convolutional Neural Networks and the Stochastic Diagonal Levenberg-Marquardt Algorithm

    Directory of Open Access Journals (Sweden)

    Viet Tra

    2017-12-01

    Full Text Available This paper presents a novel method for diagnosing incipient bearing defects under variable operating speeds using convolutional neural networks (CNNs trained via the stochastic diagonal Levenberg-Marquardt (S-DLM algorithm. The CNNs utilize the spectral energy maps (SEMs of the acoustic emission (AE signals as inputs and automatically learn the optimal features, which yield the best discriminative models for diagnosing incipient bearing defects under variable operating speeds. The SEMs are two-dimensional maps that show the distribution of energy across different bands of the AE spectrum. It is hypothesized that the variation of a bearing’s speed would not alter the overall shape of the AE spectrum rather, it may only scale and translate it. Thus, at different speeds, the same defect would yield SEMs that are scaled and shifted versions of each other. This hypothesis is confirmed by the experimental results, where CNNs trained using the S-DLM algorithm yield significantly better diagnostic performance under variable operating speeds compared to existing methods. In this work, the performance of different training algorithms is also evaluated to select the best training algorithm for the CNNs. The proposed method is used to diagnose both single and compound defects at six different operating speeds.

  9. What's the gist? The influence of schemas on the neural correlates underlying true and false memories.

    Science.gov (United States)

    Webb, Christina E; Turney, Indira C; Dennis, Nancy A

    2016-12-01

    The current study used a novel scene paradigm to investigate the role of encoding schemas on memory. Specifically, the study examined the influence of a strong encoding schema on retrieval of both schematic and non-schematic information, as well as false memories for information associated with the schema. Additionally, the separate roles of recollection and familiarity in both veridical and false memory retrieval were examined. The study identified several novel results. First, while many common neural regions mediated both schematic and non-schematic retrieval success, schematic recollection exhibited greater activation in visual cortex and hippocampus, regions commonly shown to mediate detailed retrieval. More effortful cognitive control regions in the prefrontal and parietal cortices, on the other hand, supported non-schematic recollection, while lateral temporal cortices supported familiarity-based retrieval of non-schematic items. Second, both true and false recollection, as well as familiarity, were mediated by activity in left middle temporal gyrus, a region associated with semantic processing and retrieval of schematic gist. Moreover, activity in this region was greater for both false recollection and false familiarity, suggesting a greater reliance on lateral temporal cortices for retrieval of illusory memories, irrespective of memory strength. Consistent with previous false memory studies, visual cortex showed increased activity for true compared to false recollection, suggesting that visual cortices are critical for distinguishing between previously viewed targets and related lures at retrieval. Additionally, the absence of common visual activity between true and false retrieval suggests that, unlike previous studies utilizing visual stimuli, when false memories are predicated on schematic gist and not perceptual overlap, there is little reliance on visual processes during false memory retrieval. Finally, the medial temporal lobe exhibited an

  10. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  11. Development of relative humidity models by using optimized neural network structures

    Energy Technology Data Exchange (ETDEWEB)

    Martinez-romero, A.; Ortega, J. F.; Juan, J. A.; Tarjuelo, J. M.; Moreno, M. A.

    2010-07-01

    Climate has always had a very important role in life on earth, as well as human activity and health. The influence of relative humidity (RH) in controlled environments (e.g. industrial processes in agro-food processing, cold storage of foods such as fruits, vegetables and meat, or controls in greenhouses) is very important. Relative humidity is a main factor in agricultural production and crop yield (due to the influence on crop water demand or the development and distribution of pests and diseases, for example). The main objective of this paper is to estimate RH [maximum (RHmax), average (RHave), and minimum (RHmin)] data in a specific area, being applied to the Region of Castilla-La Mancha (C-LM) in this case, from available data at thermo-pluviometric weather stations. In this paper Artificial neural networks (ANN) are used to generate RH considering maximum and minimum temperatures and extraterrestrial solar radiation data. Model validation and generation is based on data from the years 2000 to 2008 from 44 complete agroclimatic weather stations. Relative errors are estimated as 1) spatial errors of 11.30%, 6.80% and 10.27% and 2) temporal errors of 10.34%, 6.59% and 9.77% for RHmin, RHmax and RHave, respectively. The use of ANNs is interesting in generating climate parameters from available climate data. For determining optimal ANN structure in estimating RH values, model calibration and validation is necessary, considering spatial and temporal variability. (Author) 44 refs.

  12. Feature Selection Combined with Neural Network Structure Optimization for HIV-1 Protease Cleavage Site Prediction

    Directory of Open Access Journals (Sweden)

    Hui Liu

    2015-01-01

    Full Text Available It is crucial to understand the specificity of HIV-1 protease for designing HIV-1 protease inhibitors. In this paper, a new feature selection method combined with neural network structure optimization is proposed to analyze the specificity of HIV-1 protease and find the important positions in an octapeptide that determined its cleavability. Two kinds of newly proposed features based on Amino Acid Index database plus traditional orthogonal encoding features are used in this paper, taking both physiochemical and sequence information into consideration. Results of feature selection prove that p2, p1, p1′, and p2′ are the most important positions. Two feature fusion methods are used in this paper: combination fusion and decision fusion aiming to get comprehensive feature representation and improve prediction performance. Decision fusion of subsets that getting after feature selection obtains excellent prediction performance, which proves feature selection combined with decision fusion is an effective and useful method for the task of HIV-1 protease cleavage site prediction. The results and analysis in this paper can provide useful instruction and help designing HIV-1 protease inhibitor in the future.

  13. A low-profile three-dimensional neural probe array using a silicon lead transfer structure

    Science.gov (United States)

    Cheng, Ming-Yuan; Je, Minkyu; Tan, Kwan Ling; Lim Tan, Ee; Lim, Ruiqi; Yao, Lei; Li, Peng; Park, Woo-Tae; Phua, Eric Jian Rong; Lip Gan, Chee; Yu, Aibin

    2013-09-01

    This paper presents a microassembly method for low-profile three-dimensional probe arrays for neural prosthesis and neuroscience applications. A silicon (Si) lead transfer structure, Si interposer, is employed to form electrical connections between two orthogonal planes—the two dimensional probes and the dummy application-specific integrated circuit (ASIC) chip. In order to hold the probe array and facilitate the alignment of probes during assembly, a Si platform is designed to have through-substrate slots for the insertion of probes and cavities for holding the Si interposers. The electrical interconnections between the probes and the dummy ASIC chip are formed by solder reflow, resulting in greatly improved throughput in the proposed assembly method. Moreover, since the backbone of the probe can be embedded inside the cavity of the Si platform, the profile of the probe array above the cortical surface can be controlled within 750 µm. This low-profile allows the probe array not to touch the skull after it is implanted on the brain. The impedance of the assembled probe is also measured and discussed.

  14. Neural Mechanisms Underlying Social Intelligence and Their Relationship with the Performance of Sales Managers

    NARCIS (Netherlands)

    R.C. Dietvorst (Roeland)

    2010-01-01

    textabstractIdentifying the drivers of salespeople’s performance, strategies and moral behavior have been under the scrutiny of marketing scholars for many years. The functioning of the drivers of salespeople’s behaviors rests on processes going on in the minds of salespeople. However, research to

  15. Portfolio optimization with structured products under return constraint

    Directory of Open Access Journals (Sweden)

    Baweja Meena

    2015-01-01

    Full Text Available A new approach for optimizing risk in a portfolio of financial instruments involving structured products is presented. This paper deals with a portfolio selection model which uses optimization methodology to minimize conditional Value-at-Risk (CVaR under return constraint. It focuses on minimizing CVaR rather than on minimizing value-at-Risk VaR, as portfolios with low CVaR necessarily have low VaR as well. We consider a simple investment problem where besides stocks and bonds, the investor can also include structured products into the investment portfolio. Due to possible intermediate payments from structured product, we have to deal with a re-investment problem modeled as a linear optimization problem.

  16. Structural pounding of concrete frame structure with masonry infill wall under seismic loading

    Science.gov (United States)

    Ismail, Rozaina; Hasnan, Mohd Hafizudin; Shamsudin, Nurhanis

    2017-10-01

    Structural pounding is additional problem than the other harmful damage that may occurs due to the earthquake vibrations. A lot of study has been made by past researcher but most of them did not include the walls. The infill masonry walls are rarely involved analysis of structural systems but it does contribute to earthquake response of the structures. In this research, a comparison between adjacent building of 10-storey and 7-storey concrete frame structure without of masonry infill walls and the same dynamic properties of buildings. The diagonal strut approach is adopted for modeling masonry infill walls. This research also focused on finding critical building separation in order to prevent the adjacent structures from pounding. LUSAS FEA v14.03 software has been used for modeling analyzing the behavior of structures due to seismic loading and the displacement each floor of the building has been taken in order to determine the critical separation distance between the buildings. From the analysis that has been done, it is found that masonry infill walls do affect the structures behavior under seismic load. Structures without masonry infill walls needs more distance between the structures to prevent structural pounding due to higher displacement of the buildings when it sways under seismic load compared to structures with masonry infill walls. This shows that contribution of masonry infill walls to the analysis of structures cannot be neglected.

  17. Bayesian inversion of surface wave data for discontinuities and velocity structure in the upper mantle using Neural Networks. Geologica Ultraiectina (287)

    NARCIS (Netherlands)

    Meier, U.

    2008-01-01

    We present a neural network approach to invert surface wave data for discontinuities and velocity structure in the upper mantle. We show how such a neural network can be trained on a set of random samples to give a continuous approximation to the inverse relation in a compact and computationally

  18. Optimal Tuned Mass Damper for Nonlinear Structure under Different Earthquakes

    Directory of Open Access Journals (Sweden)

    K. Shakeri

    2015-07-01

    Full Text Available Since there is no closed-form formula for designing TMD (Tuned Mass Damper for nonlinear structures, some researchers have proposed numerical optimization procedures such as a genetic algorithm to obtain the optimal values of TMD parameters for nonlinear structures. These methods are based on determining the optimal values of TMD parameters to minimize the maximum response (e.g. inter story drift of the controlled structure subjected to a specific earthquake record. Therefore, the performance of TMD that has been designed using a specific record strongly depends on the characteristics of the earthquake record. By changing the characteristics of the input earthquake record, the efficiency of TMD is changed and in some cases, it is possible that the response of the controlled structure is increased. To overcome the shortcomings of the previous researches, in this paper, an efficient method for designing optimal TMD on nonlinear structures is proposed, in which the effect of different ground motion records is considered in the design procedure. In the proposed method, the optimal value of the TMD parameters are determined so that the average maximum response (e.g. inter story drift resulting from different records in the controlled structure is minimized. To illustrate the procedure of the propose method, the method is used to design optimal TMD for a sample structure. The results of numerical simulations show that the average maximum response of controlled structure resulting from different records is reduced significantly. Hence, it can be concluded that the proposed method for designing optimal TMD under different earthquakes is effective.

  19. Crystalline structures of poly(L-lactide) formed under pressure and structure transitions with heating

    DEFF Research Database (Denmark)

    Huang, Shaoyong; Li, Hongfei; Yu, Donghong

    2013-01-01

    The isothermally crystallized poly(L-lactide) (PLLA) samples were obtained at 135 °C under pressures (Pc) ranging from 1 bar to 2.5 kbar. The crystalline structures, the structure transition, and thermal properties of the prepared samples were investigated by wide-angle X-ray diffraction (WAXD...

  20. Artificial neural network for prediction of the area under the disease progress curve of tomato late blight

    Directory of Open Access Journals (Sweden)

    Daniel Pedrosa Alves

    Full Text Available ABSTRACT: Artificial neural networks (ANN are computational models inspired by the neural systems of living beings capable of learning from examples and using them to solve problems such as non-linear prediction, and pattern recognition, in addition to several other applications. In this study, ANN were used to predict the value of the area under the disease progress curve (AUDPC for the tomato late blight pathosystem. The AUDPC is widely used by epidemiologic studies of polycyclic diseases, especially those regarding quantitative resistance of genotypes. However, a series of six evaluations over time is necessary to obtain the final area value for this pathosystem. This study aimed to investigate the utilization of ANN to construct an AUDPC in the tomato late blight pathosystem, using a reduced number of severity evaluations. For this, four independent experiments were performed giving a total of 1836 plants infected with Phytophthora infestans pathogen. They were assessed every three days, comprised six opportunities and AUDPC calculations were performed by the conventional method. After the ANN were created it was possible to predict the AUDPC with correlations of 0.97 and 0.84 when compared to conventional methods, using 50 % and 67 % of the genotype evaluations, respectively. When using the ANN created in an experiment to predict the AUDPC of the other experiments the average correlation was 0.94, with two evaluations, 0.96, with three evaluations, between the predicted values of the ANN and they were observed in six evaluations. We present in this study a new paradigm for the use of AUDPC information in tomato experiments faced with P. infestans. This new proposed paradigm might be adapted to different pathosystems.

  1. Do neural tube defects lead to structural alterations in the human bladder?

    Science.gov (United States)

    Pazos, Helena M F; Lobo, Márcio Luiz de P; Costa, Waldemar S; Sampaio, Francisco J B; Cardoso, Luis Eduardo M; Favorito, Luciano Alves

    2011-05-01

    Anencephaly is the most severe neural tube defect in human fetuses. The objective of this paper is to analyze the structure of the bladder in anencephalic human fetuses. We studied 40 bladders of normal human fetuses (20 male and 20 female, aged 14 to 23 WPC) and 12 bladders of anencephalic fetuses (5 male and 7 female, aged 18 to 22 WPC). The bladders were removed and processed by routine histological techniques. Stereological analysis of collagen, elastic system fibers and smooth muscle was performed in sections. Data were expressed as volumetric density (Vv-%). The images were captured with Olympus BX51 microscopy and Olympus DP70 camera. The stereological analysis was done using the software Image Pro and Image J. For biochemical analysis, samples were fixed in acetone, and collagen concentrations were expressed as micrograms of hydroxyproline per mg of dry tissue. Means were statistically compared using the unpaired t-test (p<0.05). We observed a significant increase (p<0.0001) in the Vv of collagen in the bladders of anencephalic fetuses (69.71%) when compared to normal fetuses (52.74%), and a significant decrease (p<0.0001) in the Vv of smooth muscle cells in the bladders of anencephalic fetuses (23.96%) when compared to normal fetuses (38.35%). The biochemical analyses showed a higher concentration of total collagen in the bladders of anencephalic fetuses (37354 µg/mg) when compared to normal fetuses (48117 µg/mg, p<0.02). The structural alterations of the bladder found in this study may suggest the existence of functional alterations in the bladder of anencephalic human fetuses.

  2. Modulating Conscious Movement Intention by Noninvasive Brain Stimulation and the Underlying Neural Mechanisms

    OpenAIRE

    Douglas, Zachary H.; Maniscalco, Brian; Hallett, Mark; Wassermann, Eric M.; He, Biyu J.

    2015-01-01

    Conscious intention is a fundamental aspect of the human experience. Despite long-standing interest in the basis and implications of intention, its underlying neurobiological mechanisms remain poorly understood. Using high-definition transcranial DC stimulation (tDCS), we observed that enhancing spontaneous neuronal excitability in both the angular gyrus and the primary motor cortex caused the reported time of conscious movement intention to be ∼60–70 ms earlier. Slow brain waves recorded ∼2–...

  3. Predicting Dynamic Response of Structures under Earthquake Loads Using Logical Analysis of Data

    Directory of Open Access Journals (Sweden)

    Ayman Abd-Elhamed

    2018-04-01

    Full Text Available In this paper, logical analysis of data (LAD is used to predict the seismic response of building structures employing the captured dynamic responses. In order to prepare the data, computational simulations using a single degree of freedom (SDOF building model under different ground motion records are carried out. The selected excitation records are real and of different peak ground accelerations (PGA. The sensitivity of the seismic response in terms of displacements of floors to the variation in earthquake characteristics, such as soil class, characteristic period, and time step of records, peak ground displacement, and peak ground velocity, have also been considered. The dynamic equation of motion describing the building model and the applied earthquake load are presented and solved incrementally using the Runge-Kutta method. LAD then finds the characteristic patterns which lead to forecast the seismic response of building structures. The accuracy of LAD is compared to that of an artificial neural network (ANN, since the latter is the most known machine learning technique. Based on the conducted study, the proposed LAD model has been proven to be an efficient technique to learn, simulate, and blindly predict the dynamic response behaviour of building structures subjected to earthquake loads.

  4. Modular Adaptive System Based on a Multi-Stage Neural Structure for Recognition of 2D Objects of Discontinuous Production

    Directory of Open Access Journals (Sweden)

    I. Topalova

    2005-03-01

    Full Text Available This is a presentation of a new system for invariant recognition of 2D objects with overlapping classes, that can not be effectively recognized with the traditional methods. The translation, scale and partial rotation invariant contour object description is transformed in a DCT spectrum space. The obtained frequency spectrums are decomposed into frequency bands in order to feed different BPG neural nets (NNs. The NNs are structured in three stages - filtering and full rotation invariance; partial recognition; general classification. The designed multi-stage BPG Neural Structure shows very good accuracy and flexibility when tested with 2D objects used in the discontinuous production. The reached speed and the opportunuty for an easy restructuring and reprogramming of the system makes it suitable for application in different applied systems for real time work.

  5. Parallel Evolution of Chromatin Structure Underlying Metabolic Adaptation.

    Science.gov (United States)

    Cheng, Jian; Guo, Xiaoxian; Cai, Pengli; Cheng, Xiaozhi; Piškur, Jure; Ma, Yanhe; Jiang, Huifeng; Gu, Zhenglong

    2017-11-01

    Parallel evolution occurs when a similar trait emerges in independent evolutionary lineages. Although changes in protein coding and gene transcription have been investigated as underlying mechanisms for parallel evolution, parallel changes in chromatin structure have never been reported. Here, Saccharomyces cerevisiae and a distantly related yeast species, Dekkera bruxellensis, are investigated because both species have independently evolved the capacity of aerobic fermentation. By profiling and comparing genome sequences, transcriptomic landscapes, and chromatin structures, we revealed that parallel changes in nucleosome occupancy in the promoter regions of mitochondria-localized genes led to concerted suppression of mitochondrial functions by glucose, which can explain the metabolic convergence in these two independent yeast species. Further investigation indicated that similar mutational processes in the promoter regions of these genes in the two independent evolutionary lineages underlay the parallel changes in chromatin structure. Our results indicate that, despite several hundred million years of separation, parallel changes in chromatin structure, can be an important adaptation mechanism for different organisms. Due to the important role of chromatin structure changes in regulating gene expression and organism phenotypes, the novel mechanism revealed in this study could be a general phenomenon contributing to parallel adaptation in nature. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  6. Fatigue life prediction of mechanical structures under stochastic loading

    Directory of Open Access Journals (Sweden)

    Leitner Bohuš

    2018-01-01

    Full Text Available Problems of fatigue life prediction of materials and structures are discussed in the paper. Service loading is assumed as a continuous loading process with possible discontinuous events, which are caused by various operating conditions. The damage in a material is due to a cumulative degradation process. The damaging process is then represented either by rain-flow matrices or by a fatigue damage function which is derived using some hypothesis of a fatigue failure criterion. Presented theoretical procedure enables a very effective estimation of a service life and/or reliable evaluation of residual life of any structures under various types of loading and environmental conditions. This approach creates a good basis for powerful expert systems in structural and mechanical engineering. The aim of the paper is to present briefly some results of analysis of load-bearing steel structure loads of special railway crane PKP 25/20i which was utilized in some specific ad relatively hard operating conditions. Virtual models of the structure were being used in an analysis of acting working dynamics loads influence to be able to forecast fatigue life of load-bearing of the crane jib.

  7. Artificial neural networks for control of a grid-connected rectifier/inverter under disturbance, dynamic and power converter switching conditions.

    Science.gov (United States)

    Li, Shuhui; Fairbank, Michael; Johnson, Cameron; Wunsch, Donald C; Alonso, Eduardo; Proaño, Julio L

    2014-04-01

    Three-phase grid-connected converters are widely used in renewable and electric power system applications. Traditionally, grid-connected converters are controlled with standard decoupled d-q vector control mechanisms. However, recent studies indicate that such mechanisms show limitations in their applicability to dynamic systems. This paper investigates how to mitigate such restrictions using a neural network to control a grid-connected rectifier/inverter. The neural network implements a dynamic programming algorithm and is trained by using back-propagation through time. To enhance performance and stability under disturbance, additional strategies are adopted, including the use of integrals of error signals to the network inputs and the introduction of grid disturbance voltage to the outputs of a well-trained network. The performance of the neural-network controller is studied under typical vector control conditions and compared against conventional vector control methods, which demonstrates that the neural vector control strategy proposed in this paper is effective. Even in dynamic and power converter switching environments, the neural vector controller shows strong ability to trace rapidly changing reference commands, tolerate system disturbances, and satisfy control requirements for a faulted power system.

  8. The neural correlates of subjective utility of monetary outcome and probability weight in economic and in motor decision under risk

    Science.gov (United States)

    Wu, Shih-Wei; Delgado, Mauricio R.; Maloney, Laurence T.

    2011-01-01

    In decision under risk, people choose between lotteries that contain a list of potential outcomes paired with their probabilities of occurrence. We previously developed a method for translating such lotteries to mathematically equivalent motor lotteries. The probability of each outcome in a motor lottery is determined by the subject’s noise in executing a movement. In this study, we used functional magnetic resonance imaging in humans to compare the neural correlates of monetary outcome and probability in classical lottery tasks where information about probability was explicitly communicated to the subjects and in mathematically equivalent motor lottery tasks where probability was implicit in the subjects’ own motor noise. We found that activity in the medial prefrontal cortex (mPFC) and the posterior cingulate cortex (PCC) quantitatively represent the subjective utility of monetary outcome in both tasks. For probability, we found that the mPFC significantly tracked the distortion of such information in both tasks. Specifically, activity in mPFC represents probability information but not the physical properties of the stimuli correlated with this information. Together, the results demonstrate that mPFC represents probability from two distinct forms of decision under risk. PMID:21677166

  9. Decision making under uncertainty in a spiking neural network model of the basal ganglia.

    Science.gov (United States)

    Héricé, Charlotte; Khalil, Radwa; Moftah, Marie; Boraud, Thomas; Guthrie, Martin; Garenne, André

    2016-12-01

    The mechanisms of decision-making and action selection are generally thought to be under the control of parallel cortico-subcortical loops connecting back to distinct areas of cortex through the basal ganglia and processing motor, cognitive and limbic modalities of decision-making. We have used these properties to develop and extend a connectionist model at a spiking neuron level based on a previous rate model approach. This model is demonstrated on decision-making tasks that have been studied in primates and the electrophysiology interpreted to show that the decision is made in two steps. To model this, we have used two parallel loops, each of which performs decision-making based on interactions between positive and negative feedback pathways. This model is able to perform two-level decision-making as in primates. We show here that, before learning, synaptic noise is sufficient to drive the decision-making process and that, after learning, the decision is based on the choice that has proven most likely to be rewarded. The model is then submitted to lesion tests, reversal learning and extinction protocols. We show that, under these conditions, it behaves in a consistent manner and provides predictions in accordance with observed experimental data.

  10. The Effect of an Enrichment Reading Program on the Cognitive Processes and Neural Structures of Children Having Reading Difficulties

    Directory of Open Access Journals (Sweden)

    Hayriye Gül KURUYER

    2017-06-01

    Full Text Available The main purpose of the current study is to explain the effect of an enrichment reading program on the cognitive processes and neural structures of children experiencing reading difficulties. The current study was carried out in line with a single-subject research method and the between-subjects multiple probe design belonging to this method. This research focuses on a group of eight students with reading difficulties. Within the context of the study, memory capacities, attention spans, reading-related activation and white matter pathways of the students were determined before and after the application of the enrichment reading program. This determination process was carried out in two stages. Neuro-imaging was performed in the first stage and in the second stage the students’ cognitive processes and neural structures were investigated in terms of focusing attention and memory capacities by using the following tools: Stroop Test TBAG Form, Auditory Verbal Digit Span Test-Form B, Cancellation Test and Number Order Learning Test. The results obtained show that the enrichment reading program resulted in an improvement in the reading profiles of the students having reading difficulties in terms of their cognitive processes and neural structures.

  11. The effect of an enrichment reading program on the cognitive processes and neural structures of children having reading difficulties

    Directory of Open Access Journals (Sweden)

    Hayriye Gül Kuruyer

    2017-06-01

    Full Text Available The main purpose of the current study is to explain the effect of an enrichment reading program on the cognitive processes and neural structures of children experiencing reading difficulties. The current study was carried out in line with a single-subject research method and the between-subjects multiple probe design belonging to this method. This research focuses on a group of eight students with reading difficulties. Within the context of the study, memory capacities, attention spans, reading-related activation and white matter pathways of the students were determined before and after the application of the enrichment reading program. This determination process was carried out in two stages. Neuro-imaging was performed in the first stage and in the second stage the students’ cognitive processes and neural structures were investigated in terms of focusing attention and memory capacities by using the following tools: Stroop Test TBAG Form, Auditory Verbal Digit Span Test-Form B, Cancellation Test and Number Order Learning Test. The results obtained show that the enrichment reading program resulted in an improvement in the reading profiles of the students having reading difficulties in terms of their cognitive processes and neural structures.

  12. Neural Changes Underlying the Development of Episodic Memory During Middle Childhood

    Science.gov (United States)

    Ghetti, Simona; Bunge, Silvia A.

    2012-01-01

    Episodic memory is central to the human experience. In typically developing children, episodic memory improves rapidly during middle childhood. While the developmental cognitive neuroscience of episodic memory remains largely uncharted, recent research has begun to provide important insights. It has long been assumed that hippocampus-dependent binding mechanisms are in place by early childhood, and that improvements in episodic memory observed during middle childhood result from the protracted development of the prefrontal cortex. We revisit the notion that binding mechanisms are age-invariant, and propose that changes in the hippocampus and its projections to cortical regions also contribute to the development of episodic memory. We further review the role of developmental changes in lateral prefrontal and parietal cortices in this development. Finally, we discuss changes in white matter tracts connecting brain regions that are critical for episodic memory. Overall, we argue that changes in episodic memory emerge from the concerted effort of a network of relevant brain structures. PMID:22770728

  13. CARIBBEAN OFFSHORE CORPORATE STRUCTURES UNDER A SWOT ANALYSIS

    Directory of Open Access Journals (Sweden)

    Ana-Maria GEAMÃNU

    2015-04-01

    Full Text Available Tax havens have long been under the attention of numerous Governments and International Organizations which triggered the concern of an uneven playing field in the taxation area. As a result numerous amendments have been made to both their commercial and tax legislations in order to be in line with the internationally agreed tax standards. The aim of this article is to conduct a SWOT analysis on the offshore corporate structures found in the Caribbean landscape. Based on a selection process of the most commonly recognized tax havens in the Caribbean region and an analysis of their offshore companies at the level of incorporation, administration, activities conducted and costs, a set of frequently met characteristics have been identified which stand at the basis of the SWOT analysis. The results stand to present a comprehensive four dimension framework of the offshore corporate structures in regards to their strengths, weaknesses, opportunities and threats.

  14. Capital Structure Arbitrage under a Risk-Neutral Calibration

    Directory of Open Access Journals (Sweden)

    Peter J. Zeitsch

    2017-01-01

    Full Text Available By reinterpreting the calibration of structural models, a reassessment of the importance of the input variables is undertaken. The analysis shows that volatility is the key parameter to any calibration exercise, by several orders of magnitude. To maximize the sensitivity to volatility, a simple formulation of Merton’s model is proposed that employs deep out-of-the-money option implied volatilities. The methodology also eliminates the use of historic data to specify the default barrier, thereby leading to a full risk-neutral calibration. Subsequently, a new technique for identifying and hedging capital structure arbitrage opportunities is illustrated. The approach seeks to hedge the volatility risk, or vega, as opposed to the exposure from the underlying equity itself, or delta. The results question the efficacy of the common arbitrage strategy of only executing the delta hedge.

  15. Structural characterization of lipidic systems under nonequilibrium conditions

    DEFF Research Database (Denmark)

    Yaghmur, Anan; Rappolt, Michael

    2012-01-01

    manipulation techniques including, for instance, stop-flow mixing or rapid temperature-jump perturbation is given. Second, our recent synchrotron SAXS findings on the dynamic structural response of gold nanoparticle-loaded vesicles upon exposure to an ultraviolet light source, the impact of rapidly mixing...... and the possible formation of intermediate states in the millisecond to second range. The need for investigating self-assembled systems, mainly stimuli-responsive drug nanocarriers, under nonequilibrium conditions is discussed. For pharmaceutically relevant applications, it is essential to combine...

  16. Structural Behavior of SC and RC Panels under Impact Loading

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Hyuk-Kee; Kim, Seung-Eock [Sejong University, Seoul (Korea, Republic of)

    2015-05-15

    NPP structures have been generally constructed using reinforced concrete (RC) structures. In recent studies, however, it has been confirmed that a steel-plate concrete (SC) structures has a much better impact resistance than an RC structure. In this paper, the impact resistance of SC and RC panels is evaluated using the commercial software LS-DYNA. To verify finite element (FE) models, the analysis results for SC and half steel-plate concrete panels under impact loading are compared with the test results conducted in other research. The impact analysis according to the different steel ratios with four different concrete thicknesses is performed in order to compare the impact resistance of SC and RC panels. To compare the impact resistance of SC and RC panels, the impact analysis was performed according to the different steel ratios with four different concrete thicknesses. Based on this study, the following conclusions have been obtained: (1) The rear face steel plate of SC panel plays more important role than the rear rebar of RC panel in preventing perforation. (2) When the perforation failure occurs, RC panel is more effective than SC panel to reduce the velocity of the missile.

  17. On structural design optimization under uncertainty and risk

    International Nuclear Information System (INIS)

    Teofilo Beck, Andre; Santana Gomes, Wellison Jose de

    2010-01-01

    In this paper, the effects of uncertainty and risk on structural design optimization are investigated, by comparing results of Deterministic Design Optimization (DDO), Reliability-based Design Optimization (RBDO) and Reliability-based Risk Optimization (RBRO). DDO yields a structural topology (or shape) which is optimum in terms of mechanics, but does not explicitly address parameter uncertainties and their effects on structural safety. RBDO properly models safety-under-uncertainty, allowing the optimum structure to maintain an acceptable level of safety. Results, however, are dependent on the failure probability used as constraint. Risk optimization (RBRO) increases the scope of the problem, by addressing the compromising goals of economy and safety. This is accomplished by quantifying the costs associated to construction, operation and maintenance, as well as the monetary consequences of failure. RBRO yields the optimum topology and the optimum point of balance between economy and safety. Results are compared for some example problems. The broader RBRO solution is found first, and optimum results are used as constraints in DDO and RBDO. Results show that even when the optimum safety coefficients are used as constraint in DDO, the formulation leads to optimum configurations which respect these design constraints, reduce manufacturing costs but increase total expected costs (including expected cost of failure). If the (optimum) system failure probability is used as constraint in RBDO, the optimum solution reduces manufacturing costs, but by increasing total expected costs. This happens when the costs associated to different failure modes are distinct.

  18. Topological spin-singlet superconductors with underlying sublattice structure

    Science.gov (United States)

    Dutreix, C.

    2017-07-01

    Majorana boundary quasiparticles may naturally emerge in a spin-singlet superconductor with Rashba spin-orbit interactions when a Zeeman magnetic field breaks time-reversal symmetry. Their existence and robustness against adiabatic changes is deeply related, via a bulk-edge correspondence, to topological properties of the band structure. The present paper shows that the spin-orbit may be responsible for topological transitions when the superconducting system has an underlying sublattice structure, as it appears in a dimerized Peierls chain, graphene, and phosphorene. These systems, which belong to the Bogoliubov-de Gennes class D, are found to have an extra symmetry that plays the role of the parity. It enables the characterization of the topology of the particle-hole symmetric band structure in terms of band inversions. The topological phase diagrams this leads to are then obtained analytically and exactly. They reveal that, because of the underlying sublattice structure, the existence of topological superconducting phases requires a minimum doping fixed by the strength of the Rashba spin orbit. Majorana boundary quasiparticles are finally predicted to emerge when the Fermi level lies in the vicinity of the bottom (top) of the conduction (valence) band in semiconductors such as the dimerized Peierls chain and phosphorene. In a two-dimensional topological superconductor based on (stretched) graphene, which is semimetallic, Majorana quasiparticles cannot emerge at zero and low doping, that is, when the Fermi level is close to the Dirac points. Nevertheless, they are likely to appear in the vicinity of the van Hove singularities.

  19. A View of the Neural Representation of Second Language Syntax through Artificial Language Learning under Implicit Contexts of Exposure

    Science.gov (United States)

    Morgan-Short, Kara; Deng, ZhiZhou; Brill-Schuetz, Katherine A.; Faretta- Stutenberg, Mandy; Wong, Patrick C. M.; Wong, Francis C. K.

    2015-01-01

    The current study aims to make an initial neuroimaging contribution to central implicit-explicit issues in second language (L2) acquisition by considering how implicit and explicit contexts mediate the neural representation of L2. Focusing on implicit contexts, the study employs a longitudinal design to examine the neural representation of L2…

  20. Neural and psychophysiological correlates of human performance under stress and high mental workload.

    Science.gov (United States)

    Mandrick, Kevin; Peysakhovich, Vsevolod; Rémy, Florence; Lepron, Evelyne; Causse, Mickaël

    2016-12-01

    In our anxiogenic and stressful world, the maintenance of an optimal cognitive performance is a constant challenge. It is particularly true in complex working environments (e.g. flight deck, air traffic control tower), where individuals have sometimes to cope with a high mental workload and stressful situations. Several models (i.e. processing efficiency theory, cognitive-energetical framework) have attempted to provide a conceptual basis on how human performance is modulated by high workload and stress/anxiety. These models predict that stress can reduce human cognitive efficiency, even in the absence of a visible impact on the task performance. Performance may be protected under stress thanks to compensatory effort, but only at the expense of a cognitive cost. Yet, the psychophysiological cost of this regulation remains unclear. We designed two experiments involving pupil diameter, cardiovascular and prefrontal oxygenation measurements. Participants performed the Toulouse N-back Task that intensively engaged both working memory and mental calculation processes under the threat (or not) of unpredictable aversive sounds. The results revealed that higher task difficulty (higher n level) degraded the performance and induced an increased tonic pupil diameter, heart rate and activity in the lateral prefrontal cortex, and a decreased phasic pupil response and heart rate variability. Importantly, the condition of stress did not impact the performance, but at the expense of a psychophysiological cost as demonstrated by lower phasic pupil response, and greater heart rate and prefrontal activity. Prefrontal cortex seems to be a central region for mitigating the influence of stress because it subserves crucial functions (e.g. inhibition, working memory) that can promote the engagement of coping strategies. Overall, findings confirmed the psychophysiological cost of both mental effort and stress. Stress likely triggered increased motivation and the recruitment of additional

  1. The Burden of Binge and Heavy Drinking on the Brain: Effects on Adolescent and Young Adult Neural Structure and Function

    Directory of Open Access Journals (Sweden)

    Anita Cservenka

    2017-06-01

    Full Text Available Introduction: Adolescence and young adulthood are periods of continued biological and psychosocial maturation. Thus, there may be deleterious effects of consuming large quantities of alcohol on neural development and associated cognition during this time. The purpose of this mini review is to highlight neuroimaging research that has specifically examined the effects of binge and heavy drinking on adolescent and young adult brain structure and function.Methods: We review cross-sectional and longitudinal studies of young binge and heavy drinkers that have examined brain structure (e.g., gray and white matter volume, cortical thickness, white matter microstructure and investigated brain response using functional magnetic resonance imaging (fMRI.Results: Binge and heavy-drinking adolescents and young adults have systematically thinner and lower volume in prefrontal cortex and cerebellar regions, and attenuated white matter development. They also show elevated brain activity in fronto-parietal regions during working memory, verbal learning, and inhibitory control tasks. In response to alcohol cues, relative to controls or light-drinking individuals, binge and heavy drinkers show increased neural response mainly in mesocorticolimbic regions, including the striatum, anterior cingulate cortex (ACC, hippocampus, and amygdala. Mixed findings are present in risky decision-making tasks, which could be due to large variation in task design and analysis.Conclusions: These findings suggest altered neural structure and activity in binge and heavy-drinking youth may be related to the neurotoxic effects of consuming alcohol in large quantities during a highly plastic neurodevelopmental period, which could result in neural reorganization, and increased risk for developing an alcohol use disorder (AUD.

  2. IGF-1 Signaling Plays an Important Role in the Formation of Three-Dimensional Laminated Neural Retina and Other Ocular Structures From Human Embryonic Stem Cells.

    Science.gov (United States)

    Mellough, Carla B; Collin, Joseph; Khazim, Mahmoud; White, Kathryn; Sernagor, Evelyne; Steel, David H W; Lako, Majlinda

    2015-08-01

    We and others have previously demonstrated that retinal cells can be derived from human embryonic stem cells (hESCs) and induced pluripotent stem cells under defined culture conditions. While both cell types can give rise to retinal derivatives in the absence of inductive cues, this requires extended culture periods and gives lower overall yield. Further understanding of this innate differentiation ability, the identification of key factors that drive the differentiation process, and the development of clinically compatible culture conditions to reproducibly generate functional neural retina is an important goal for clinical cell based therapies. We now report that insulin-like growth factor 1 (IGF-1) can orchestrate the formation of three-dimensional ocular-like structures from hESCs which, in addition to retinal pigmented epithelium and neural retina, also contain primitive lens and corneal-like structures. Inhibition of IGF-1 receptor signaling significantly reduces the formation of optic vesicle and optic cups, while exogenous IGF-1 treatment enhances the formation of correctly laminated retinal tissue composed of multiple retinal phenotypes that is reminiscent of the developing vertebrate retina. Most importantly, hESC-derived photoreceptors exhibit advanced maturation features such as the presence of primitive rod- and cone-like photoreceptor inner and outer segments and phototransduction-related functional responses as early as 6.5 weeks of differentiation, making these derivatives promising candidates for cell replacement studies and in vitro disease modeling. © 2015 AlphaMed Press.

  3. Neural stem cells and neuro/gliogenesis in the central nervous system: understanding the structural and functional plasticity of the developing, mature, and diseased brain.

    Science.gov (United States)

    Yamaguchi, Masahiro; Seki, Tatsunori; Imayoshi, Itaru; Tamamaki, Nobuaki; Hayashi, Yoshitaka; Tatebayashi, Yoshitaka; Hitoshi, Seiji

    2016-05-01

    Neurons and glia in the central nervous system (CNS) originate from neural stem cells (NSCs). Knowledge of the mechanisms of neuro/gliogenesis from NSCs is fundamental to our understanding of how complex brain architecture and function develop. NSCs are present not only in the developing brain but also in the mature brain in adults. Adult neurogenesis likely provides remarkable plasticity to the mature brain. In addition, recent progress in basic research in mental disorders suggests an etiological link with impaired neuro/gliogenesis in particular brain regions. Here, we review the recent progress and discuss future directions in stem cell and neuro/gliogenesis biology by introducing several topics presented at a joint meeting of the Japanese Association of Anatomists and the Physiological Society of Japan in 2015. Collectively, these topics indicated that neuro/gliogenesis from NSCs is a common event occurring in many brain regions at various ages in animals. Given that significant structural and functional changes in cells and neural networks are accompanied by neuro/gliogenesis from NSCs and the integration of newly generated cells into the network, stem cell and neuro/gliogenesis biology provides a good platform from which to develop an integrated understanding of the structural and functional plasticity that underlies the development of the CNS, its remodeling in adulthood, and the recovery from diseases that affect it.

  4. Do horizontal saccadic eye movements increase interhemispheric coherence? Investigation of a hypothesized neural mechanism underlying EMDR

    Directory of Open Access Journals (Sweden)

    Zoe eSamara

    2011-03-01

    Full Text Available Series of horizontal saccadic eye movements (EMs are known to improve episodic memory retrieval in healthy adults and to facilitate the processing of traumatic memories in eye-movement desensitization and reprocessing (EMDR therapy. Several authors have proposed that EMs achieve these effects by increasing the functional connectivity of the two brain hemispheres, but direct evidence for this proposal is lacking. The aim of this study was to investigate whether memory enhancement following bilateral EMs is associated with increased interhemispheric coherence in the electroencephalogram (EEG. Fourteen healthy young adults were asked to freely recall lists of studied neutral and emotional words after a series of bilateral EMs and a control procedure. Baseline EEG activity was recorded before and after the EM and control procedures. Phase and amplitude coherence between bilaterally homologous brain areas were calculated for six frequency bands and electrode pairs across the entire scalp. Behavioral analyses showed that participants recalled more emotional (but not neutral words following the EM procedure than following the control procedure. However, the EEG analyses indicated no evidence that the EMs altered participants’ interhemispheric coherence or that improvements in recall were correlated with such changes in coherence. These findings cast doubt on the interhemispheric interaction hypothesis, and therefore may have important implications for future research on the neurobiological mechanism underlying EMDR.

  5. Revealing the Neural Mechanisms Underlying the Beneficial Effects of Tai Chi: A Neuroimaging Perspective.

    Science.gov (United States)

    Yu, Angus P; Tam, Bjorn T; Lai, Christopher W; Yu, Doris S; Woo, Jean; Chung, Ka-Fai; Hui, Stanley S; Liu, Justina Y; Wei, Gao X; Siu, Parco M

    2018-01-01

    Tai Chi Chuan (TCC), a traditional Chinese martial art, is well-documented to result in beneficial consequences in physical and mental health. TCC is regarded as a mind-body exercise that is comprised of physical exercise and meditation. Favorable effects of TCC on body balance, gait, bone mineral density, metabolic parameters, anxiety, depression, cognitive function, and sleep have been previously reported. However, the underlying mechanisms explaining the effects of TCC remain largely unclear. Recently, advances in neuroimaging technology have offered new investigative opportunities to reveal the effects of TCC on anatomical morphologies and neurological activities in different regions of the brain. These neuroimaging findings have provided new clues for revealing the mechanisms behind the observed effects of TCC. In this review paper, we discussed the possible effects of TCC-induced modulation of brain morphology, functional homogeneity and connectivity, regional activity and macro-scale network activity on health. Moreover, we identified possible links between the alterations in brain and beneficial effects of TCC, such as improved motor functions, pain perception, metabolic profile, cognitive functions, mental health and sleep quality. This paper aimed to stimulate further mechanistic neuroimaging studies in TCC and its effects on brain morphology, functional homogeneity and connectivity, regional activity and macro-scale network activity, which ultimately lead to a better understanding of the mechanisms responsible for the beneficial effects of TCC on human health.

  6. Neural changes underlying the development of episodic memory during middle childhood.

    Science.gov (United States)

    Ghetti, Simona; Bunge, Silvia A

    2012-10-01

    Episodic memory is central to the human experience. In typically developing children, episodic memory improves rapidly during middle childhood. While the developmental cognitive neuroscience of episodic memory remains largely uncharted, recent research has begun to provide important insights. It has long been assumed that hippocampus-dependent binding mechanisms are in place by early childhood, and that improvements in episodic memory observed during middle childhood result from the protracted development of the prefrontal cortex. We revisit the notion that binding mechanisms are age-invariant, and propose that changes in the hippocampus and its projections to cortical regions also contribute to the development of episodic memory. We further review the role of developmental changes in lateral prefrontal and parietal cortices in this development. Finally, we discuss changes in white matter tracts connecting brain regions that are critical for episodic memory. Overall, we argue that changes in episodic memory emerge from the concerted effort of a network of relevant brain structures. Copyright © 2012 Elsevier Ltd. All rights reserved.

  7. Stability assessment of structures under earthquake hazard through GRID technology

    Science.gov (United States)

    Prieto Castrillo, F.; Boton Fernandez, M.

    2009-04-01

    This work presents a GRID framework to estimate the vulnerability of structures under earthquake hazard. The tool has been designed to cover the needs of a typical earthquake engineering stability analysis; preparation of input data (pre-processing), response computation and stability analysis (post-processing). In order to validate the application over GRID, a simplified model of structure under artificially generated earthquake records has been implemented. To achieve this goal, the proposed scheme exploits the GRID technology and its main advantages (parallel intensive computing, huge storage capacity and collaboration analysis among institutions) through intensive interaction among the GRID elements (Computing Element, Storage Element, LHC File Catalogue, federated database etc.) The dynamical model is described by a set of ordinary differential equations (ODE's) and by a set of parameters. Both elements, along with the integration engine, are encapsulated into Java classes. With this high level design, subsequent improvements/changes of the model can be addressed with little effort. In the procedure, an earthquake record database is prepared and stored (pre-processing) in the GRID Storage Element (SE). The Metadata of these records is also stored in the GRID federated database. This Metadata contains both relevant information about the earthquake (as it is usual in a seismic repository) and also the Logical File Name (LFN) of the record for its later retrieval. Then, from the available set of accelerograms in the SE, the user can specify a range of earthquake parameters to carry out a dynamic analysis. This way, a GRID job is created for each selected accelerogram in the database. At the GRID Computing Element (CE), displacements are then obtained by numerical integration of the ODE's over time. The resulting response for that configuration is stored in the GRID Storage Element (SE) and the maximum structure displacement is computed. Then, the corresponding

  8. Structural Health Monitoring under Nonlinear Environmental or Operational Influences

    Directory of Open Access Journals (Sweden)

    Jyrki Kullaa

    2014-01-01

    Full Text Available Vibration-based structural health monitoring is based on detecting changes in the dynamic characteristics of the structure. It is well known that environmental or operational variations can also have an influence on the vibration properties. If these effects are not taken into account, they can result in false indications of damage. If the environmental or operational variations cause nonlinear effects, they can be compensated using a Gaussian mixture model (GMM without the measurement of the underlying variables. The number of Gaussian components can also be estimated. For the local linear components, minimum mean square error (MMSE estimation is applied to eliminate the environmental or operational influences. Damage is detected from the residuals after applying principal component analysis (PCA. Control charts are used for novelty detection. The proposed approach is validated using simulated data and the identified lowest natural frequencies of the Z24 Bridge under temperature variation. Nonlinear models are most effective if the data dimensionality is low. On the other hand, linear models often outperform nonlinear models for high-dimensional data.

  9. Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

    International Nuclear Information System (INIS)

    Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin

    2015-01-01

    In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing

  10. Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule.

    Science.gov (United States)

    Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin

    2015-11-01

    In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.

  11. Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin, E-mail: xmli@cqu.edu.cn [Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044 (China); College of Automation, Chongqing University, Chongqing 400044 (China)

    2015-11-15

    In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.

  12. An application of neural network for Structural Health Monitoring of an adaptive wing with an array of FBG sensors

    International Nuclear Information System (INIS)

    Mieloszyk, Magdalena; Skarbek, Lukasz; Ostachowicz, Wieslaw; Krawczuk, Marek

    2011-01-01

    This paper presents an application of neural networks to determinate the level of activation of shape memory alloy actuators of an adaptive wing. In this concept the shape of the wing can be controlled and altered thanks to the wing design and the use of integrated shape memory alloy actuators. The wing is assumed as assembled from a number of wing sections that relative positions can be controlled independently by thermal activation of shape memory actuators. The investigated wing is employed with an array of Fibre Bragg Grating sensors. The Fibre Bragg Grating sensors with combination of a neural network have been used to Structural Health Monitoring of the wing condition. The FBG sensors are a great tool to control the condition of composite structures due to their immunity to electromagnetic fields as well as their small size and weight. They can be mounted onto the surface or embedded into the wing composite material without any significant influence on the wing strength. The paper concentrates on analysis of the determination of the twisting moment produced by an activated shape memory alloy actuator. This has been analysed both numerically using the finite element method by a commercial code ABAQUS (registered) and experimentally using Fibre Bragg Grating sensor measurements. The results of the analysis have been then used by a neural network to determine twisting moments produced by each shape memory alloy actuator.

  13. Neural-network-based depth-resolved multiscale structural optimization using density functional theory and electron diffraction data

    Science.gov (United States)

    Pennington, Robert S.; Coll, Catalina; Estradé, Sònia; Peiró, Francesca; Koch, Christoph T.

    2018-01-01

    Iterative neural-network-based three-dimensional structural optimization of atomic positions over tens of nanometers is performed using transmission electron microscope (TEM) diffraction data simulated from density functional theory (DFT) all-electron densities, thus retrieving parameter variations along the beam direction. We first use experimental data to show that the GPAW DFT code's all-electron densities are considerably more accurate for electron diffraction calculations compared to conventional isolated-atom scattering factors, and they also compare well to Wien2K DFT simulations. This DFT-TEM combination is then integrated into an iterative neural-network-optimization-based algorithm (PRIMES, parameter retrieval and inversion from multiple electron scattering) to retrieve nanometer-scale ferroelectric polarization domains and strain in theoretical bulklike specimens from TEM data. DFT and isolated-atom methods produce substantially different diffraction patterns and retrieved polarization domain parameters, and DFT is sufficient to retrieve strain properties from a silicon specimen simulated using experimentally derived structure factors. Thus, we show that the improved accuracy, fast computation, and intuitive integration make the GPAW DFT code well suited for three-dimensional materials characterization and demonstrate this using an iterative neural-network algorithm that is verifiable on the mesoscale and, with DFT integration, self-consistent on the nanoscale.

  14. Mixed Stimulus-Induced Mode Selection in Neural Activity Driven by High and Low Frequency Current under Electromagnetic Radiation

    Directory of Open Access Journals (Sweden)

    Lulu Lu

    2017-01-01

    Full Text Available The electrical activities of neurons are dependent on the complex electrophysiological condition in neuronal system, the three-variable Hindmarsh-Rose (HR neuron model is improved to describe the dynamical behaviors of neuronal activities with electromagnetic induction being considered, and the mode transition of electrical activities in neuron is detected when external electromagnetic radiation is imposed on the neuron. In this paper, different types of electrical stimulus impended with a high-low frequency current are imposed on new HR neuron model, and mixed stimulus-induced mode selection in neural activity is discussed in detail. It is found that mode selection of electrical activities stimulated by high-low frequency current, which also changes the excitability of neuron, can be triggered owing to adding the Gaussian white noise. Meanwhile, the mode selection of the neuron electrical activity is much dependent on the amplitude B of the high frequency current under the same noise intensity, and the high frequency response is selected preferentially by applying appropriate parameters and noise intensity. Our results provide insights into the transmission of complex signals in nerve system, which is valuable in engineering prospective applications such as information encoding.

  15. 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. PMID:22837740

  16. Modelling the release of volatile fission product cesium from CANDU fuel under severe accident conditions using artificial neural networks

    International Nuclear Information System (INIS)

    Andrews, W.S.; Lewis, B.J.; Cox, D.S.

    1997-01-01

    An artificial neural network (ANN) model has been developed to predict the release of volatile fission products from CANDU fuel under severe accident conditions. The model was based on data for the release Of 134 Cs measured during three annealing experiments (Hot Cell Experiments 1 and 2, or HCE- 1, HCE-2 and Metallurgical Cell Experiment 1, or MCE- 1) at Chalk River Laboratories. These experiments were comprised of a total of 30 separate tests. The ANN established a correlation among 14 separate input variables and predicted the cumulative fractional release for a set of 386 data points drawn from 29 tests to a normalized error, E n , of 0.104 and an average absolute error, E abs , of 0.064. Predictions for a blind validation set (test HCE2-CM6) had an E n of 0.064 and an E abs of 0.054. A methodology is presented for deploying the ANN model by providing the connection weights. Finally, the performance of an ANN model was compared to a fuel oxidation model developed by Lewis et al. and to the U.S. Nuclear Regulatory Commission's CORSOR-M. (author)

  17. Mechanisms Underlying the Antiproliferative and Prodifferentiative Effects of Psoralen on Adult Neural Stem Cells via DNA Microarray

    Directory of Open Access Journals (Sweden)

    You Ning

    2013-01-01

    Full Text Available Adult neural stem cells (NSCs persist throughout life to replace mature cells that are lost during turnover, disease, or injury. The investigation of NSC creates novel treatments for central nervous system (CNS injuries and neurodegenerative disorders. The plasticity and reparative potential of NSC are regulated by different factors, which are critical for neurological regenerative medicine research. We investigated the effects of Psoralen, which is the mature fruit of Psoralea corylifolia L., on NSC behaviors and the underlying mechanisms. The self-renewal and proliferation of NSC were examined. We detected neuron- and/or astrocyte-specific markers using immunofluorescence and Western blotting, which could evaluate NSC differentiation. Psoralen treatment significantly inhibited neurosphere formation in a dose-dependent manner. Psoralen treatment increased the expression of the astrocyte-specific marker but decreased neuron-specific marker expression. These results suggested that Psoralen was a differentiation inducer in astrocyte. Differential gene expression following Psoralen treatment was screened using DNA microarray and confirmed by quantitative real-time PCR. Our microarray study demonstrated that Psoralen could effectively regulate the specific gene expression profile of NSC. The genes involved in the classification of cellular differentiation, proliferation, and metabolism, the transcription factors belonging to Ets family, and the hedgehog pathway may be closely related to the regulation.

  18. Anatomy of the soul as reflected in the cerebral hemispheres: neural circuits underlying voluntary control of basic motivated behaviors.

    Science.gov (United States)

    Swanson, Larry W

    2005-12-05

    Understanding the principles of cerebral hemisphere neural network organization is essential for understanding the biological foundations of cognition and affect-thinking and feeling. A tripartite model of cerebral structure-function organization is reviewed, with attention focused on a behavior control system differentiation that mediates voluntary influences on three fundamental classes of goal-oriented behavior common to all animals. The model postulates just three cerebral divisions, one cortical and two nuclear (lateral or striatal, and medial or pallidal), that together generate a triple descending projection to the brainstem/cord motor system. This minimal circuit element is topographically organized and regionally differentiated, with the map of cortical areas serving as a basic starting point. Virtually all of the cerebral hemisphere projects on the upper brainstem behavior control column, atop the motor system hierarchy. The latter's rostral segment helps control ingestive (eating and drinking), defensive (fight or flight), and reproductive (sexual and parental) motivated behaviors, whereas its caudal segment helps control foraging or exploratory behavior to obtain or avoid specific goal objects associated with all classes of motivated behavior. (c) 2005 Wiley-Liss, Inc.

  19. Anatomical position of the asterion and its underlying structure.

    Science.gov (United States)

    Sripairojkul, B; Adultrakoon, A

    2000-09-01

    Surface anatomy is important for surgical planning. The asterion has been believed and used for locating the underlying posterior fossa dura. To prove whether this landmark is reliable or not, forty-three fixed heads of cadaver were dissected. A burr hole was made on the asterion and its underlying structure was examined. Seventy-four point four per cent (74.4%) of the asterion on the right side were adjacent to the transverse-sigmoid sinus complex when compared to 58.1 per cent on the left. Twenty-three point three per cent (23.3%) of the asterion on the right side were found over the infratentorial dura while that on the left side were 32.6 per cent. Two point three per cent (2.3%) of the asterion were located over the supratentorial dura on the right and 9.3 per cent on the left side. It is concluded, therefore, that the asterion is not an appropriate landmark to locate the underlying posterior fossa dura.

  20. Size-dependent structure of silver nanoparticles under high pressure

    Energy Technology Data Exchange (ETDEWEB)

    Koski, Kristie Jo [Univ. of California, Berkeley, CA (United States)

    2008-12-31

    Silver noble metal nanoparticles that are<10 nm often possess multiply twinned grains allowing them to adopt shapes and atomic structures not observed in bulk materials. The properties exhibited by particles with multiply twinned polycrystalline structures are often far different from those of single-crystalline particles and from the bulk. I will present experimental evidence that silver nanoparticles<10 nm undergo a reversible structural transformation under hydrostatic pressures up to 10 GPa. Results for nanoparticles in the intermediate size range of 5 to 10 nm suggest a reversible linear pressure-dependent rhombohedral distortion which has not been previously observed in bulk silver. I propose a mechanism for this transitiion that considers the bond-length distribution in idealized multiply twinned icosahedral particles. Results for nanoparticles of 3.9 nm suggest a reversible linear pressure-dependent orthorhombic distortion. This distortion is interpreted in the context of idealized decahedral particles. In addition, given these size-dependent measurements of silver nanoparticle compression with pressure, we have constructed a pressure calibration curve. Encapsulating these silver nanoparticles in hollow metal oxide nanospheres then allows us to measure the pressure inside a nanoshell using x-ray diffraction. We demonstrate the measurement of pressure gradients across nanoshells and show that these nanoshells have maximum resolved shear strengths on the order of 500 MPa to IGPa.

  1. Comparison of Neural Network Error Measures for Simulation of Slender Marine Structures

    DEFF Research Database (Denmark)

    Christiansen, Niels H.; Voie, Per Erlend Torbergsen; Winther, Ole

    2014-01-01

    Training of an artificial neural network (ANN) adjusts the internal weights of the network in order to minimize a predefined error measure. This error measure is given by an error function. Several different error functions are suggested in the literature. However, the far most common measure...... for regression is the mean square error. This paper looks into the possibility of improving the performance of neural networks by selecting or defining error functions that are tailor-made for a specific objective. A neural network trained to simulate tension forces in an anchor chain on a floating offshore...... platform is designed and tested. The purpose of setting up the network is to reduce calculation time in a fatigue life analysis. Therefore, the networks trained on different error functions are compared with respect to accuracy of rain flow counts of stress cycles over a number of time series simulations...

  2. The artist emerges: visual art learning alters neural structure and function.

    Science.gov (United States)

    Schlegel, Alexander; Alexander, Prescott; Fogelson, Sergey V; Li, Xueting; Lu, Zhengang; Kohler, Peter J; Riley, Enrico; Tse, Peter U; Meng, Ming

    2015-01-15

    How does the brain mediate visual artistic creativity? Here we studied behavioral and neural changes in drawing and painting students compared to students who did not study art. We investigated three aspects of cognition vital to many visual artists: creative cognition, perception, and perception-to-action. We found that the art students became more creative via the reorganization of prefrontal white matter but did not find any significant changes in perceptual ability or related neural activity in the art students relative to the control group. Moreover, the art students improved in their ability to sketch human figures from observation, and multivariate patterns of cortical and cerebellar activity evoked by this drawing task became increasingly separable between art and non-art students. Our findings suggest that the emergence of visual artistic skills is supported by plasticity in neural pathways that enable creative cognition and mediate perceptuomotor integration. Copyright © 2014 Elsevier Inc. All rights reserved.

  3. Dissecting CNBP, a zinc-finger protein required for neural crest development, in its structural and functional domains.

    Science.gov (United States)

    Armas, Pablo; Agüero, Tristán H; Borgognone, Mariana; Aybar, Manuel J; Calcaterra, Nora B

    2008-10-17

    Cellular nucleic-acid-binding protein (CNBP) plays an essential role in forebrain and craniofacial development by controlling cell proliferation and survival to mediate neural crest expansion. CNBP binds to single-stranded nucleic acids and displays nucleic acid chaperone activity in vitro. The CNBP family shows a conserved modular organization of seven Zn knuckles and an arginine-glycine-glycine (RGG) box between the first and second Zn knuckles. The participation of these structural motifs in CNBP biochemical activities has still not been addressed. Here, we describe the generation of CNBP mutants that dissect the protein into regions with structurally and functionally distinct properties. Mutagenesis approaches were followed to generate: (i) an amino acid replacement that disrupted the fifth Zn knuckle; (ii) N-terminal deletions that removed the first Zn knuckle and the RGG box, or the RGG box alone; and (iii) a C-terminal deletion that eliminated the three last Zn knuckles. Mutant proteins were overexpressed in Escherichia coli, purified, and used to analyze their biochemical features in vitro, or overexpressed in Xenopus laevis embryos to study their function in vivo during neural crest cell development. We found that the Zn knuckles are required, but not individually essential, for CNBP biochemical activities, whereas the RGG box is essential for RNA-protein binding and nucleic acid chaperone activity. Removal of the RGG box allowed CNBP to preserve a weak single-stranded-DNA-binding capability. A mutant mimicking the natural N-terminal proteolytic CNBP form behaved as the RGG-deleted mutant. By gain-of-function and loss-of-function experiments in Xenopus embryos, we confirmed the participation of CNBP in neural crest development, and we demonstrated that the CNBP mutants lacking the N-terminal region or the RGG box alone may act as dominant negatives in vivo. Based on these data, we speculate about the existence of a specific proteolytic mechanism for the

  4. Structure-from-motion: dissociating perception, neural persistence, and sensory memory of illusory depth and illusory rotation.

    Science.gov (United States)

    Pastukhov, Alexander; Braun, Jochen

    2013-02-01

    In the structure-from-motion paradigm, physical motion on a screen produces the vivid illusion of an object rotating in depth. Here, we show how to dissociate illusory depth and illusory rotation in a structure-from-motion stimulus using a rotationally asymmetric shape and reversals of physical motion. Reversals of physical motion create a conflict between the original illusory states and the new physical motion: Either illusory depth remains constant and illusory rotation reverses, or illusory rotation stays the same and illusory depth reverses. When physical motion reverses after the interruption in presentation, we find that illusory rotation tends to remain constant for long blank durations (T (blank) ≥ 0.5 s), but illusory depth is stabilized if interruptions are short (T (blank) ≤ 0.1 s). The stability of illusory depth over brief interruptions is consistent with the effect of neural persistence. When this is curtailed using a mask, stability of ambiguous vision (for either illusory depth or illusory rotation) is disrupted. We also examined the selectivity of the neural persistence of illusory depth. We found that it relies on a static representation of an interpolated illusory object, since changes to low-level display properties had little detrimental effect. We discuss our findings with respect to other types of history dependence in multistable displays (sensory stabilization memory, neural fatigue, etc.). Our results suggest that when brief interruptions are used during the presentation of multistable displays, switches in perception are likely to rely on the same neural mechanisms as spontaneous switches, rather than switches due to the initial percept choice at the stimulus onset.

  5. AcconPred: Predicting Solvent Accessibility and Contact Number Simultaneously by a Multitask Learning Framework under the Conditional Neural Fields Model

    Directory of Open Access Journals (Sweden)

    Jianzhu Ma

    2015-01-01

    Full Text Available Motivation. The solvent accessibility of protein residues is one of the driving forces of protein folding, while the contact number of protein residues limits the possibilities of protein conformations. The de novo prediction of these properties from protein sequence is important for the study of protein structure and function. Although these two properties are certainly related with each other, it is challenging to exploit this dependency for the prediction. Method. We present a method AcconPred for predicting solvent accessibility and contact number simultaneously, which is based on a shared weight multitask learning framework under the CNF (conditional neural fields model. The multitask learning framework on a collection of related tasks provides more accurate prediction than the framework trained only on a single task. The CNF method not only models the complex relationship between the input features and the predicted labels, but also exploits the interdependency among adjacent labels. Results. Trained on 5729 monomeric soluble globular protein datasets, AcconPred could reach 0.68 three-state accuracy for solvent accessibility and 0.75 correlation for contact number. Tested on the 105 CASP11 domain datasets for solvent accessibility, AcconPred could reach 0.64 accuracy, which outperforms existing methods.

  6. Finding Risk Groups by Optimizing Artificial Neural Networks on the Area under the Survival Curve Using Genetic Algorithms.

    Directory of Open Access Journals (Sweden)

    Jonas Kalderstam

    Full Text Available We investigate a new method to place patients into risk groups in censored survival data. Properties such as median survival time, and end survival rate, are implicitly improved by optimizing the area under the survival curve. Artificial neural networks (ANN are trained to either maximize or minimize this area using a genetic algorithm, and combined into an ensemble to predict one of low, intermediate, or high risk groups. Estimated patient risk can influence treatment choices, and is important for study stratification. A common approach is to sort the patients according to a prognostic index and then group them along the quartile limits. The Cox proportional hazards model (Cox is one example of this approach. Another method of doing risk grouping is recursive partitioning (Rpart, which constructs a decision tree where each branch point maximizes the statistical separation between the groups. ANN, Cox, and Rpart are compared on five publicly available data sets with varying properties. Cross-validation, as well as separate test sets, are used to validate the models. Results on the test sets show comparable performance, except for the smallest data set where Rpart's predicted risk groups turn out to be inverted, an example of crossing survival curves. Cross-validation shows that all three models exhibit crossing of some survival curves on this small data set but that the ANN model manages the best separation of groups in terms of median survival time before such crossings. The conclusion is that optimizing the area under the survival curve is a viable approach to identify risk groups. Training ANNs to optimize this area combines two key strengths from both prognostic indices and Rpart. First, a desired minimum group size can be specified, as for a prognostic index. Second, the ability to utilize non-linear effects among the covariates, which Rpart is also able to do.

  7. Instrumental variables estimation under a structural Cox model

    DEFF Research Database (Denmark)

    Martinussen, Torben; Nørbo Sørensen, Ditte; Vansteelandt, Stijn

    2017-01-01

    Instrumental variable (IV) analysis is an increasingly popular tool for inferring the effect of an exposure on an outcome, as witnessed by the growing number of IV applications in epidemiology, for instance. The majority of IV analyses of time-to-event endpoints are, however, dominated by heuristic...... and instruments. We propose a novel class of estimators and derive their asymptotic properties. The methodology is illustrated using two real data applications, and using simulated data....... approaches. More rigorous proposals have either sidestepped the Cox model, or considered it within a restrictive context with dichotomous exposure and instrument, amongst other limitations. The aim of this article is to reconsider IV estimation under a structural Cox model, allowing for arbitrary exposure...

  8. Graded Geometric Structures Underlying F-Theory Related Defect Theories

    Science.gov (United States)

    Oikonomou, V. K.

    2013-08-01

    In the context of F-theory, we study the related eight-dimensional super-Yang-Mills theory and reveal the underlying supersymmetric quantum mechanics algebra that the fermionic fields localized on the corresponding defect theory are related to. Particularly, the localized fermionic fields constitute a graded vector space, and in turn this graded space enriches the geometric structures that can be built on the initial eight-dimensional space. We construct the implied composite fiber bundles, which include the graded affine vector space and demonstrate that the composite sections of this fiber bundle are in one-to-one correspondence to the sections of the square root of the canonical bundle corresponding to the submanifold on which the zero modes are localized.

  9. Mechanisms underlying metabolic and neural defects in zebrafish and human multiple acyl-CoA dehydrogenase deficiency (MADD.

    Directory of Open Access Journals (Sweden)

    Yuanquan Song

    2009-12-01

    Full Text Available In humans, mutations in electron transfer flavoprotein (ETF or electron transfer flavoprotein dehydrogenase (ETFDH lead to MADD/glutaric aciduria type II, an autosomal recessively inherited disorder characterized by a broad spectrum of devastating neurological, systemic and metabolic symptoms. We show that a zebrafish mutant in ETFDH, xavier, and fibroblast cells from MADD patients demonstrate similar mitochondrial and metabolic abnormalities, including reduced oxidative phosphorylation, increased aerobic glycolysis, and upregulation of the PPARG-ERK pathway. This metabolic dysfunction is associated with aberrant neural proliferation in xav, in addition to other neural phenotypes and paralysis. Strikingly, a PPARG antagonist attenuates aberrant neural proliferation and alleviates paralysis in xav, while PPARG agonists increase neural proliferation in wild type embryos. These results show that mitochondrial dysfunction, leading to an increase in aerobic glycolysis, affects neurogenesis through the PPARG-ERK pathway, a potential target for therapeutic intervention.

  10. Neural networks in signal processing

    International Nuclear Information System (INIS)

    Govil, R.

    2000-01-01

    Nuclear Engineering has matured during the last decade. In research and design, control, supervision, maintenance and production, mathematical models and theories are used extensively. In all such applications signal processing is embedded in the process. Artificial Neural Networks (ANN), because of their nonlinear, adaptive nature are well suited to such applications where the classical assumptions of linearity and second order Gaussian noise statistics cannot be made. ANN's can be treated as nonparametric techniques, which can model an underlying process from example data. They can also adopt their model parameters to statistical change with time. Algorithms in the framework of Neural Networks in Signal processing have found new applications potentials in the field of Nuclear Engineering. This paper reviews the fundamentals of Neural Networks in signal processing and their applications in tasks such as recognition/identification and control. The topics covered include dynamic modeling, model based ANN's, statistical learning, eigen structure based processing and generalization structures. (orig.)

  11. Travelling waves in models of neural tissue: from localised structures to periodic waves

    NARCIS (Netherlands)

    Meijer, Hil Gaétan Ellart; Coombes, Stephen

    2014-01-01

    We consider travelling waves (fronts, pulses and periodics) in spatially extended one dimensional neural field models. We demonstrate for an excitatory field with linear adaptation that, in addition to an expected stable pulse solution, a stable anti-pulse can exist. Varying the adaptation strength

  12. Behavior of grid-stiffened composite structures under transverse loading

    Science.gov (United States)

    Gan, Changsheng

    The energy absorption characteristics and failure modes of grid-stiffened composite plates under transverse load were studied in detail. Several laboratory scale composite grid plates were fabricated by using co-mingled E-glass fiber/polypropylene matrix and carbon/nylon composites in a thermoplastic stamping process. Both experimental and finite element approaches were used to evaluate and understand the role of major failure modes on the performance of damaged grid-stiffened composite plates under transverse load. The load-deflection responses of grid-stiffened composite plates were determined and compared with those of sandwich composite plates of the same size. The failure modes of grid-stiffened composite plates under different load conditions were investigated and used as the basis for FEA models. The intrinsic strength properties of constituent composite materials were measured by using either three point bending or tensile test and were used as input data to the FEA models. Several FEA models including the major failure modes based on the experimental results were built to simulate the damage processes of grid-stiffened composite plates under transverse load. A FORTRAN subroutine was implemented within the ABAQUS code to incorporate the material failure models. Effects of damage on the modal frequencies and loss factors of grid-stiffened composite plates were also investigated experimentally. Experimental and simulation results showed that sandwich composite specimens failed catastrophically with the load dropping sharply at the displacement corresponding to initial and final failure. However, grid-stiffened composite specimens failed in a more gradual and forgiving way in a sequence of relatively small load drops. No catastrophic load drops were observed in the grid structures over the range of displacements investigated here. The SEA values of the grid composite specimens are typically higher than those of the sandwich specimens with the same boundary

  13. Structural evolution of zirconium carbide under ion irradiation

    Energy Technology Data Exchange (ETDEWEB)

    Gosset, D. [CEA Saclay, DEN/DMN/SRMA, F-91191 Gif/Yvette cedex (France)], E-mail: dominique.gosset@cea.fr; Dolle, M. [CEMES-CNRS (UPR 8011), BP 94347, F-31055 Toulouse cedex 4 (France); Simeone, D. [CEA Saclay, DEN/DMN/SRMA, F-91191 Gif/Yvette cedex (France); Baldinozzi, G. [SPMS, Ecole Centrale Paris, F-92295 Chatenay-Malabry cedex (France); Thome, L. [CSNSM, bat. 108, F-91405 Orsay (France)

    2008-02-15

    Zirconium carbide is one of the candidate materials to be used for some fuel components of the high temperature nuclear reactors planned in the frame of the Gen-IV project. Few data exist regarding its behaviour under irradiation. We have irradiated ZrC samples at room temperature with slow heavy ions (4 MeV Au, fluence from 10{sup 11} to 5 x 10{sup 15} cm{sup -2}) in order to simulate neutron irradiations. Grazing incidence X-Ray diffraction (GIXRD) and transmission electron microscopy (TEM) analysis have been performed in order to study the microstructural evolution of the material versus ion fluence. A high sensitivity to oxidation is observed with the formation of zirconia precipitates during the ion irradiations. Three damage stages are observed. At low fluence (<10{sup 12} cm{sup -2}), low modifications are observed. At intermediate fluence, high micro-strains appear together with small faulted dislocation loops. At the highest fluence (>10{sup 14} cm{sup -2}), the micro-strains saturate and the loops coalesce to form a dense dislocation network. No other structural modification is observed. The material shows a moderate cell parameter increase, corresponding to a 0.6 vol.% swelling, which saturates around 10{sup 14} ions/cm{sup 2}, i.e., a few Zr dpa. As a result, in spite of a strong covalent bonding component, ZrC seems to have a behaviour under irradiation close to cubic metals.

  14. Analysis of Dynamic Properties of Piezoelectric Structure under Impact Load

    Directory of Open Access Journals (Sweden)

    Taotao Zhang

    2015-10-01

    Full Text Available An analytical model of the dynamic properties is established for a piezoelectric structure under impact load, without considering noise and perturbations in this paper. Based on the general theory of piezo-elasticity and impact mechanics, the theoretical solutions of the mechanical and electrical fields of the smart structure are obtained with the standing and traveling wave methods, respectively. The comparisons between the two methods have shown that the standing wave method is better for studying long-time response after an impact load. In addition, good agreements are found between the theoretical and the numerical results. To simulate the impact load, both triangle and step pulse loads are used and comparisons are given. Furthermore, the influence of several parameters is discussed so as to provide some advices for practical use. It can be seen that the proposed analytical model would benefit, to some extent, the design and application (especially the airport runway of the related smart devices by taking into account their impact load performance.

  15. BACKSTEPPING ALGORITHM FOR LINEAR SISO PLANTS UNDER STRUCTURAL UNCERTAINTIES

    Directory of Open Access Journals (Sweden)

    I. B. Furtat

    2016-01-01

    Full Text Available The robust algorithm is proposed for parametric and structurally uncertain linear plants under external bounded disturbances. The structural uncertainty is an unknown dynamic order of the model of plants. The developed algorithm provides plant output tracking for a smooth bounded reference signal with a required accuracy at a finite time. It is assumed that only scalar input and output of the plants are available for measurement, but not their derivatives. For the synthesis of the control algorithm we use a modified backstepping algorithm. The synthesis of control algorithm is separated into rsteps, where ris an upper bound of the relative degree of control plant model. At each step we synthesize auxiliary controls that stabilize each subsystem about a zero. At the last step we synthesize a basic control law, which provides output tracking for smooth reference signal. It is shown that for the implementation of the algorithm we need to use only one filter of the control signal and the simplified control laws obtained by application of the real derivative elements. It allows simplifying significantly the calculation and implementation of the control system. Numerical examples and results of computer simulation are given, illustrating the operation of the proposed scheme.

  16. On the underlying gauge group structure of D=11 supergravity

    International Nuclear Information System (INIS)

    Bandos, I.A.; Azcarraga, J.A. de; Izquierdo, J.M.; Picon, M.; Varela, O.

    2004-01-01

    The underlying gauge group structure of D=11 supergravity is revisited. It may be described by a one-parametric family of Lie supergroups Σ-bar (s)x-bar SO(1,10), s 0. The family of superalgebras E-bar (s) associated to Σ-bar (s) is given by a family of extensions of the M-algebra {Pa,Qα,Zab,Za1...a5} by an additional fermionic central charge Qα'. The Chevalley-Eilenberg four-cocycle ω4∼Πα-bar Πβ-bar Πa-bar ΠbΓabαβ on the standard D=11 supersymmetry algebra may be trivialized on E-bar (s), and this implies that the three-form field A3 of D=11 supergravity may be expressed as a composite of the Σ-bar (s) one-form gauge fields ea, ψα, Bab, Ba1...a5 and ηα. Two superalgebras of E-bar (s) recover the two earlier D'Auria and Fre decompositions of A3. Another member of E-bar (s) allows for a simpler composite structure for A3 that does not involve the Ba1...a5 field. Σ-bar (s) is a deformation of Σ-bar (0), which is singularized by having an enhanced Sp(32) (rather than just SO(1,10)) automorphism symmetry and by being an expansion of OSp(1 vertical bar 32)

  17. A critical appraisal of neuroimaging studies of bipolar disorder: toward a new conceptualization of underlying neural circuitry and roadmap for future research

    Science.gov (United States)

    Phillips, Mary L; Swartz, Holly A.

    2014-01-01

    Objective This critical review appraises neuroimaging findings in bipolar disorder in emotion processing, emotion regulation, and reward processing neural circuitry, to synthesize current knowledge of the neural underpinnings of bipolar disorder, and provide a neuroimaging research “roadmap” for future studies. Method We examined findings from all major studies in bipolar disorder that used fMRI, volumetric analyses, diffusion imaging, and resting state techniques, to inform current conceptual models of larger-scale neural circuitry abnormalities in bipolar disorder Results Bipolar disorder can be conceptualized in neural circuitry terms as parallel dysfunction in bilateral prefrontal cortical (especially ventrolateral prefrontal cortical)-hippocampal-amygdala emotion processing and emotion regulation neural circuitries, together with an “overactive” left-sided ventral striatal-ventrolateral and orbitofrontal cortical reward processing circuitry, that result in characteristic behavioral abnormalities associated with bipolar disorder: emotional lability, emotional dysregulation and heightened reward sensitivity. A potential structural basis for these functional abnormalities are gray matter decreases in prefrontal and temporal cortices, amygdala and hippocampus, and fractional anisotropy decreases in white matter tracts connecting prefrontal and subcortical regions. Conclusion Neuroimaging studies of bipolar disorder clearly demonstrate abnormalities in neural circuitries supporting emotion processing, emotion regulation and reward processing, although there are several limitations to these studies. Future neuroimaging research in bipolar disorder should include studies adopting dimensional approaches; larger studies examining neurodevelopmental trajectories in bipolar disorder and at-risk youth; multimodal neuroimaging studies using integrated systems approaches; and studies using pattern recognition approaches to provide clinically useful, individual

  18. Essential role of structural integrity and firm attachment of surface-anchored epidermal growth factor in adherent culture of neural stem cells.

    Science.gov (United States)

    Nakaji-Hirabayashi, Tadashi; Kato, Koichi; Iwata, Hiroo

    2008-11-01

    Surface immobilization of proteins provides various biomaterials that permit the control of cellular functions through protein-protein interactions. Our previous study demonstrated that human epidermal growth factor carrying a hexahistidine sequence at the C-terminus (hEGF-His) could be anchored to the Ni-chelated surface by coordination, providing the versatile substrate for the selective proliferation of neural stem cells. The present study was undertaken to gain deeper insights into the basis for such an outstanding property of the surface with coordinated hEGF-His. For this purpose, the structure of the coordinated hEGF-His was analyzed by multiple internal reflection-infrared absorption spectroscopy. In addition, stability of coordinate bonds was assessed under cell culture conditions using a spatially-restricted anchoring technique. These data were compared to the results obtained from surfaces with covalently immobilized and physically adsorbed hEGF-His. The results presented here demonstrate that coordinated hEGF-His remains its intact conformation and is firmly anchored to the surface during cell culture. These attributes are both crucial for establishing the adherent culture and hence selective expansion of neural stem cells.

  19. Artificial Neural Network Modelling of Photodegradation in Suspension of Manganese Doped Zinc Oxide Nanoparticles under Visible-Light Irradiation

    Directory of Open Access Journals (Sweden)

    Yadollah Abdollahi

    2014-01-01

    Full Text Available The artificial neural network (ANN modeling of m-cresol photodegradation was carried out for determination of the optimum and importance values of the effective variables to achieve the maximum efficiency. The photodegradation was carried out in the suspension of synthesized manganese doped ZnO nanoparticles under visible-light irradiation. The input considered effective variables of the photodegradation were irradiation time, pH, photocatalyst amount, and concentration of m-cresol while the efficiency was the only response as output. The performed experiments were designed into three data sets such as training, testing, and validation that were randomly splitted by the software’s option. To obtain the optimum topologies, ANN was trained by quick propagation (QP, Incremental Back Propagation (IBP, Batch Back Propagation (BBP, and Levenberg-Marquardt (LM algorithms for testing data set. The topologies were determined by the indicator of minimized root mean squared error (RMSE for each algorithm. According to the indicator, the QP-4-8-1, IBP-4-15-1, BBP-4-6-1, and LM-4-10-1 were selected as the optimized topologies. Among the topologies, QP-4-8-1 has presented the minimum RMSE and absolute average deviation as well as maximum R-squared. Therefore, QP-4-8-1 was selected as final model for validation test and navigation of the process. The model was used for determination of the optimum values of the effective variables by a few three-dimensional plots. The optimum points of the variables were confirmed by further validated experiments. Moreover, the model predicted the relative importance of the variables which showed none of them was neglectable in this work.

  20. Neural substrates underlying reconcentration for the preparation of an appropriate cognitive state to prevent future mistakes: a functional magnetic resonance imaging study

    Science.gov (United States)

    Miura, Naoki; Nozawa, Takayuki; Takahashi, Makoto; Yokoyama, Ryoichi; Sasaki, Yukako; Sakaki, Kohei; Kawashima, Ryuta

    2015-01-01

    The ability to reconcentrate on the present situation by recognizing one’s own recent errors is a cognitive mechanism that is crucial for safe and appropriate behavior in a particular situation. However, an individual may not be able to adequately perform a subsequent task even if he/she recognize his/her own error; thus, it is hypothesized that the neural mechanisms underlying the reconcentration process are different from the neural substrates supporting error recognition. The present study performed a functional magnetic resonance imaging (fMRI) analysis to explore the neural substrates associated with reconcentration related to achieving an appropriate cognitive state, and to dissociate these brain regions from the neural substrates involved in recognizing one’s own mistake. This study included 44 healthy volunteers who completed an experimental procedure that was based on the Eriksen flanker task and included feedback regarding the results of the current trial. The hemodynamic response induced by each instance of feedback was modeled using a combination of the successes and failures of the current and subsequent trials in order to identify the neural substrates underlying the ability to reconcentrate for the next situation and to dissociate them from those involved in recognizing current errors. The fMRI findings revealed significant and specific activation in the dorsal aspect of the medial prefrontal cortex (MFC) when participants successfully reconcentrated on the task after recognizing their own error based on feedback. Additionally, this specific activation was clearly dissociated from the activation foci that occurred during error recognition. These findings indicate that the dorsal aspect of the MFC may be a distinct functional region that specifically supports the reconcentration process and that is associated with the prevention of successive errors when a human subject recognizes his/her own mistake. Furthermore, it is likely that this

  1. Unraveling the Molecular Mechanisms Underlying the Nasopharyngeal Bacterial Community Structure

    Directory of Open Access Journals (Sweden)

    Wouter A. A. de Steenhuijsen Piters

    2016-03-01

    Full Text Available The upper respiratory tract is colonized by a diverse array of commensal bacteria that harbor potential pathogens, such as Streptococcus pneumoniae. As long as the local microbial ecosystem—also called “microbiome”—is in balance, these potentially pathogenic bacterial residents cause no harm to the host. However, similar to macrobiological ecosystems, when the bacterial community structure gets perturbed, potential pathogens can overtake the niche and cause mild to severe infections. Recent studies using next-generation sequencing show that S. pneumoniae, as well as other potential pathogens, might be kept at bay by certain commensal bacteria, including Corynebacterium and Dolosigranulum spp. Bomar and colleagues are the first to explore a specific biological mechanism contributing to the antagonistic interaction between Corynebacterium accolens and S. pneumoniae in vitro [L. Bomar, S. D. Brugger, B. H. Yost, S. S. Davies, K. P. Lemon, mBio 7(1:e01725-15, 2016, doi:10.1128/mBio.01725-15]. The authors comprehensively show that C. accolens is capable of hydrolyzing host triacylglycerols into free fatty acids, which display antipneumococcal properties, suggesting that these bacteria might contribute to the containment of pneumococcus. This work exemplifies how molecular epidemiological findings can lay the foundation for mechanistic studies to elucidate the host-microbe and microbial interspecies interactions underlying the bacterial community structure. Next, translation of these results to an in vivo setting seems necessary to unveil the magnitude and importance of the observed effect in its natural, polymicrobial setting.

  2. Modeling Distillation Column Using ARX Model Structure and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Reza Pirmoradi

    2012-04-01

    Full Text Available Distillation is a complex and highly nonlinear industrial process. In general it is not always possible to obtain accurate first principles models for high-purity distillation columns. On the other hand the development of first principles models is usually time consuming and expensive. To overcome these problems, empirical models such as neural networks can be used. One major drawback of empirical models is that the prediction is valid only inside the data domain that is sufficiently covered by measurement data. Modeling distillation columns by means of neural networks is reported in literature by using recursive networks. The recursive networks are proper for modeling purpose, but such models have the problems of high complexity and high computational cost. The objective of this paper is to propose a simple and reliable model for distillation column. The proposed model uses feed forward neural networks which results in a simple model with less parameters and faster training time. Simulation results demonstrate that predictions of the proposed model in all regions are close to outputs of the dynamic model and the error in negligible. This implies that the model is reliable in all regions.

  3. The Network Structure Underlying the Earth Observation Assessment

    Science.gov (United States)

    Vitkin, S.; Doane, W. E. J.; Mary, J. C.

    2017-12-01

    The Earth Observations Assessment (EOA 2016) is a multiyear project designed to assess the effectiveness of civil earth observation data sources (instruments, sensors, models, etc.) on societal benefit areas (SBAs) for the United States. Subject matter experts (SMEs) provided input and scored how data sources inform products, product groups, key objectives, SBA sub-areas, and SBAs in an attempt to quantify the relationships between data sources and SBAs. The resulting data were processed by Integrated Applications Incorporated (IAI) using MITRE's PALMA software to create normalized relative impact scores for each of these relationships. However, PALMA processing obscures the natural network representation of the data. Any network analysis that might identify patterns of interaction among data sources, products, and SBAs is therefore impossible. Collaborating with IAI, we cleaned and recreated a network from the original dataset. Using R and Python we explore the underlying structure of the network and apply frequent itemset mining algorithms to identify groups of data sources and products that interact. We reveal interesting patterns and relationships in the EOA dataset that were not immediately observable from the EOA 2016 report and provide a basis for further exploration of the EOA network dataset.

  4. Structure activity relationships to assess new chemicals under TSCA

    Energy Technology Data Exchange (ETDEWEB)

    Auletta, A.E. [Environmental Protection Agency, Washington, DC (United States)

    1990-12-31

    Under Section 5 of the Toxic Substances Control Act (TSCA), manufacturers must notify the US Environmental Protection Agency (EPA) 90 days before manufacturing, processing, or importing a new chemical substance. This is referred to as a premanufacture notice (PMN). The PMN must contain certain information including chemical identity, production volume, proposed uses, estimates of exposure and release, and any health or environmental test data that are available to the submitter. Because there is no explicit statutory authority that requires testing of new chemicals prior to their entry into the market, most PMNs are submitted with little or no data. As a result, EPA has developed special techniques for hazard assessment of PMN chemicals. These include (1) evaluation of available data on the chemical itself, (2) evaluation of data on analogues of the PMN, or evaluation of data on metabolites or analogues of metabolites of the PMN, (3) use of quantitative structure activity relationships (QSARs), and (4) knowledge and judgement of scientific assessors in the interpretation and integration of the information developed in the course of the assessment. This approach to evaluating potential hazards of new chemicals is used to identify those that are most in need of addition review of further testing. It should not be viewed as a replacement for testing. 4 tabs.

  5. Interevent relationships and judgment under uncertainty: structure determines strategy.

    Science.gov (United States)

    Sanfey, Alan G; Hastie, Reid

    2002-09-01

    A fundamental empirical question regarding judgments about events is whether experienced absolute frequencies or relative frequencies are relied on when the likelihood of a particular occurrence is judged. The present research explicates the conditions under which people rely on remembered raw absolute frequencies versus on inferred relative frequencies or proportions when making predictions. Participants saw opinion poll results for candidates prior to an election and, on the basis of these, made judgments concerning the likelihood of each candidate's winning this election. Certain candidates demonstrated a high absolute frequency of winning in the polls, whereas other candidates had high relative win frequencies. The results indicated that adults are cognitively flexible with regard to the inputs used in this judgment. Certain stimulus event configurations induced reasoning by way of absolute frequencies, whereas other configurations elicited judgments based on relative frequencies. More specifically, as the relational complexity of the event structure increased and more inferences were required to make predictions, the tendency to rely on absolute, as opposed to relative, frequencies also increased.

  6. Sub-fragmentation of structural reactive material casings under explosion

    Science.gov (United States)

    Zhang, Fan; Gauthier, Maxime; Cojocaru, Cristian

    2017-01-01

    A concept of reactive hot spots intruded in a thick, structural reactive material casing was investigated to generate fine fragments for efficient energy release from casing material under explosive loading. This was achieved through distributing micro MoO3 particles into a granular Al casing, made by hot isostatic pressing, in a fuel-rich ratio of 10Al+MoO3. Reaction of Al and MoO3 during casing primary or secondary fragmentation creates heat and gas products to form micro-scale hot spots, whose expansion initiates local fractures leading to fine fragments of the rest of Al. Explosion experiments, using a 4.4 cm diameter cased charge with a casing-to-explosive mass ratio of 1.78 in a 2.1 m3 cylindrical chamber, demonstrated the presence of fine fragments and more efficient fragment combustion to augment air blast, as compared to a baseline pure Al-cased charge, thus indicating the feasibility of the concept.

  7. Quantitative Live Imaging of Human Embryonic Stem Cell Derived Neural Rosettes Reveals Structure-Function Dynamics Coupled to Cortical Development.

    Science.gov (United States)

    Ziv, Omer; Zaritsky, Assaf; Yaffe, Yakey; Mutukula, Naresh; Edri, Reuven; Elkabetz, Yechiel

    2015-10-01

    Neural stem cells (NSCs) are progenitor cells for brain development, where cellular spatial composition (cytoarchitecture) and dynamics are hypothesized to be linked to critical NSC capabilities. However, understanding cytoarchitectural dynamics of this process has been limited by the difficulty to quantitatively image brain development in vivo. Here, we study NSC dynamics within Neural Rosettes--highly organized multicellular structures derived from human pluripotent stem cells. Neural rosettes contain NSCs with strong epithelial polarity and are expected to perform apical-basal interkinetic nuclear migration (INM)--a hallmark of cortical radial glial cell development. We developed a quantitative live imaging framework to characterize INM dynamics within rosettes. We first show that the tendency of cells to follow the INM orientation--a phenomenon we referred to as radial organization, is associated with rosette size, presumably via mechanical constraints of the confining structure. Second, early forming rosettes, which are abundant with founder NSCs and correspond to the early proliferative developing cortex, show fast motions and enhanced radial organization. In contrast, later derived rosettes, which are characterized by reduced NSC capacity and elevated numbers of differentiated neurons, and thus correspond to neurogenesis mode in the developing cortex, exhibit slower motions and decreased radial organization. Third, later derived rosettes are characterized by temporal instability in INM measures, in agreement with progressive loss in rosette integrity at later developmental stages. Finally, molecular perturbations of INM by inhibition of actin or non-muscle myosin-II (NMII) reduced INM measures. Our framework enables quantification of cytoarchitecture NSC dynamics and may have implications in functional molecular studies, drug screening, and iPS cell-based platforms for disease modeling.

  8. A Feedback Model of Attention Explains the Diverse Effects of Attention on Neural Firing Rates and Receptive Field Structure.

    Directory of Open Access Journals (Sweden)

    Thomas Miconi

    2016-02-01

    Full Text Available Visual attention has many effects on neural responses, producing complex changes in firing rates, as well as modifying the structure and size of receptive fields, both in topological and feature space. Several existing models of attention suggest that these effects arise from selective modulation of neural inputs. However, anatomical and physiological observations suggest that attentional modulation targets higher levels of the visual system (such as V4 or MT rather than input areas (such as V1. Here we propose a simple mechanism that explains how a top-down attentional modulation, falling on higher visual areas, can produce the observed effects of attention on neural responses. Our model requires only the existence of modulatory feedback connections between areas, and short-range lateral inhibition within each area. Feedback connections redistribute the top-down modulation to lower areas, which in turn alters the inputs of other higher-area cells, including those that did not receive the initial modulation. This produces firing rate modulations and receptive field shifts. Simultaneously, short-range lateral inhibition between neighboring cells produce competitive effects that are automatically scaled to receptive field size in any given area. Our model reproduces the observed attentional effects on response rates (response gain, input gain, biased competition automatically scaled to receptive field size and receptive field structure (shifts and resizing of receptive fields both spatially and in complex feature space, without modifying model parameters. Our model also makes the novel prediction that attentional effects on response curves should shift from response gain to contrast gain as the spatial focus of attention drifts away from the studied cell.

  9. A Feedback Model of Attention Explains the Diverse Effects of Attention on Neural Firing Rates and Receptive Field Structure.

    Science.gov (United States)

    Miconi, Thomas; VanRullen, Rufin

    2016-02-01

    Visual attention has many effects on neural responses, producing complex changes in firing rates, as well as modifying the structure and size of receptive fields, both in topological and feature space. Several existing models of attention suggest that these effects arise from selective modulation of neural inputs. However, anatomical and physiological observations suggest that attentional modulation targets higher levels of the visual system (such as V4 or MT) rather than input areas (such as V1). Here we propose a simple mechanism that explains how a top-down attentional modulation, falling on higher visual areas, can produce the observed effects of attention on neural responses. Our model requires only the existence of modulatory feedback connections between areas, and short-range lateral inhibition within each area. Feedback connections redistribute the top-down modulation to lower areas, which in turn alters the inputs of other higher-area cells, including those that did not receive the initial modulation. This produces firing rate modulations and receptive field shifts. Simultaneously, short-range lateral inhibition between neighboring cells produce competitive effects that are automatically scaled to receptive field size in any given area. Our model reproduces the observed attentional effects on response rates (response gain, input gain, biased competition automatically scaled to receptive field size) and receptive field structure (shifts and resizing of receptive fields both spatially and in complex feature space), without modifying model parameters. Our model also makes the novel prediction that attentional effects on response curves should shift from response gain to contrast gain as the spatial focus of attention drifts away from the studied cell.

  10. Impaired Neural Structure and Function Contributing to Autonomic Symptoms in Congenital Central Hypoventilation Syndrome

    Directory of Open Access Journals (Sweden)

    Ronald M Harper

    2015-10-01

    Full Text Available Congenital central hypoventilation syndrome (CCHS patients show major autonomic alterations in addition to their better-known breathing deficiencies. The processes underlying CCHS, mutations in the PHOX2B gene, target autonomic neuronal development, with frame shift extent contributing to symptom severity. Many autonomic characteristics, such as impaired pupillary constriction and poor temperature regulation, reflect parasympathetic alterations, and can include disturbed alimentary processes, with malabsorption and intestinal motility dyscontrol. The sympathetic nervous system changes can exert life-threatening outcomes, with dysregulation of sympathetic outflow leading to high blood pressure, time-altered and dampened heart rate and breathing responses to challenges, cardiac arrhythmia, profuse sweating, and poor fluid regulation. The central mechanisms contributing to failed autonomic processes are readily apparent from structural and functional magnetic resonance imaging studies, which reveal substantial cortical thinning, tissue injury, and disrupted functional responses in hypothalamic, hippocampal, posterior thalamic, and basal ganglia sites and their descending projections, as well as insular, cingulate, and medial frontal cortices, which influence subcortical autonomic structures. Midbrain structures are also compromised, including the raphe system and its projections to cerebellar and medullary sites, the locus coeruleus, and medullary reflex integrating sites, including the dorsal and ventrolateral medullary nuclei. The damage to rostral autonomic sites overlaps metabolic, affective and cognitive regulatory regions, leading to hormonal disruption, anxiety, depression, behavioral control, and sudden death concerns. The injuries suggest that interventions for mitigating hypoxic exposure and nutrient loss may provide cellular protection, in the same fashion as interventions in other conditions with similar malabsorption, fluid turnover

  11. Input-based structure-specific proficiency predicts the neural mechanism of adult L2 syntactic processing.

    Science.gov (United States)

    Deng, Taiping; Zhou, Huixia; Bi, Hong-Yan; Chen, Baoguo

    2015-06-12

    This study used Event-Related Potentials (ERPs) to explore the role of input-based structure-specific proficiency in L2 syntactic processing, using English subject-verb agreement structures as the stimuli. A pre-test/trainings/post-test paradigm of experimental and control groups was employed, and Chinese speakers who learned English as a second language (L2) participated in the experiment. At pre-test, no ERP component related to the subject-verb agreement structures violations was observed in either group. At training session, the experimental group learned the subject-verb agreement structures, while the control group learned other syntactic structures. After two continuously intensive input trainings, at post-test, a significant P600 component related to the subject-verb agreement structures violations was elicited in the experimental group, but not in the control group. These findings suggest that input training improves structure-specific proficiency, which is reflected in the neural mechanism of L2 syntactic processing. Copyright © 2015 Elsevier B.V. All rights reserved.

  12. Development of a signal-analysis algorithm for the ZEUS transition-radiation detector under application of a neural network

    International Nuclear Information System (INIS)

    Wollschlaeger, U.

    1992-07-01

    The aim of this thesis consisted in the development of a procedure for the analysis of the data of the transition-radiation detector at ZEUS. For this a neural network was applied and first studied, which results concerning the separation power between electron an pions can be reached by this procedure. It was shown that neural nets yield within the error limits as well results as standard algorithms (total charge, cluster analysis). At an electron efficiency of 90% pion contaminations in the range 1%-2% were reached. Furthermore it could be confirmed that neural networks can be considered for the here present application field as robust in relatively insensitive against external perturbations. For the application in the experiment beside the separation power also the time-behaviour is of importance. The requirement to keep dead-times small didn't allow the application of standard method. By a simulation the time availabel for the signal analysis was estimated. For the testing of the processing time in a neural network subsequently the corresponding algorithm was implemented into an assembler code for the digital signal processor DSP56001. (orig./HSI) [de

  13. Untangling the neurobiology of coping styles in rodents : Towards neural mechanisms underlying individual differences in disease susceptibility

    NARCIS (Netherlands)

    de Boer, Sietse F; Buwalda, Bauke; Koolhaas, Jaap M.

    Considerable individual differences exist in trait-like patterns of behavioral and physiological responses to salient environmental challenges. This individual variation in stress coping styles has an important functional role in terms of health and fitness. Hence, understanding the neural embedding

  14. Normative data on development of neural and behavioral mechanisms underlying attention orienting toward social-emotional stimuli: an exploratory study.

    Science.gov (United States)

    Lindstrom, Kara M; Guyer, Amanda E; Mogg, Karin; Bradley, Brendan P; Fox, Nathan A; Ernst, Monique; Nelson, Eric E; Leibenluft, Ellen; Britton, Jennifer C; Monk, Christopher S; Pine, Daniel S; Bar-Haim, Yair

    2009-10-06

    The ability of positive and negative facial signals to influence attention orienting is crucial to social functioning. Given the dramatic developmental change in neural architecture supporting social function, positive and negative facial cues may influence attention orienting differently in relatively young or old individuals. However, virtually no research examines such age-related differences in the neural circuitry supporting attention orienting to emotional faces. We examined age-related correlations in attention-orienting biases to positive and negative face emotions in a healthy sample (N=37; 9-40 years old) using functional magnetic resonance imaging and a dot-probe task. The dot-probe task in an fMRI setting yields both behavioral and neural indices of attention biases towards or away from an emotional cue (happy or angry face). In the full sample, angry-face attention bias scores did not correlate with age, and age did not correlate with brain activation to angry faces. However, age did positively correlate with attention bias towards happy faces; age also negatively correlated with left cuneus and left caudate activation to a happy bias fMRI contrast. Secondary analyses suggested age-related changes in attention bias to happy faces. The tendency in younger children to direct attention away from happy faces (relative to neutral faces) was diminished in the older age groups, in tandem with increasing neural deactivation. Implications for future work on developmental changes in attention-emotion processing are discussed.

  15. Neural plasticity: the biological substrate for neurorehabilitation.

    Science.gov (United States)

    Warraich, Zuha; Kleim, Jeffrey A

    2010-12-01

    Decades of basic science have clearly demonstrated the capacity of the central nervous system (CNS) to structurally and functionally adapt in response to experience. The field of neurorehabilitation has begun to use this body of work to develop neurobiologically informed therapies that harness the key behavioral and neural signals that drive neural plasticity. The present review describes how neural plasticity supports both learning in the intact CNS and functional improvement in the damaged or diseased CNS. A pragmatic, interdisciplinary definition of neural plasticity is presented that may be used by both clinical and basic scientists studying neurorehabilitation. Furthermore, a description of how neural plasticity may act to drive different neural strategies underlying functional improvement after CNS injury or disease is provided. The understanding of the relationship between these different neural strategies, mechanisms of neural plasticity, and changes in behavior may facilitate the development of novel, more effective rehabilitation interventions. Copyright © 2010 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

  16. Neural plasticity expressed in central auditory structures with and without tinnitus

    Directory of Open Access Journals (Sweden)

    Larry E Roberts

    2012-05-01

    Full Text Available Sensory training therapies for tinnitus are based on the assumption that, notwithstanding neural changes related to tinnitus, auditory training can alter the response properties of neurons in auditory pathways. To address this question, we investigated whether brain changes induced by sensory training in tinnitus sufferers and measured by EEG are similar to those induced in age and hearing loss matched individuals without tinnitus trained on the same auditory task. Auditory training was given using a 5 kHz 40-Hz amplitude-modulated sound that was in the tinnitus frequency region of the tinnitus subjects and enabled extraction of the 40-Hz auditory steady-state response (ASSR and P2 transient response known to localize to primary and nonprimary auditory cortex, respectively. P2 amplitude increased with training equally in participants with tinnitus and in control subjects, suggesting normal remodeling of nonprimary auditory regions in tinnitus. However, training-induced changes in the ASSR differed between the tinnitus and control groups. In controls ASSR phase advanced toward the stimulus waveform by about ten degrees over training, in agreement with previous results obtained in young normal hearing individuals. However, ASSR phase did not change significantly with training in the tinnitus group, although some participants showed phase shifts resembling controls. On the other hand, ASSR amplitude increased with training in the tinnitus group, whereas in controls this response (which is difficult to remodel in young normal hearing subjects did not change with training. These results suggest that neural changes related to tinnitus altered how neural plasticity was expressed in the region of primary but not nonprimary auditory cortex. Auditory training did not reduce tinnitus loudness although a small effect on the tinnitus spectrum was detected.

  17. Studies on Pounding Response Considering Structure-Soil-Structure Interaction under Seismic Loads

    Directory of Open Access Journals (Sweden)

    Peizhen Li

    2017-12-01

    Full Text Available Pounding phenomena considering structure–soil–structure interaction (SSSI under seismic loads are investigated in this paper. Based on a practical engineering project, this work presents a three-dimensional finite element numerical simulation method using ANSYS software. According to Chinese design code, the models of adjacent shear wall structures on Shanghai soft soil with the rigid foundation, box foundation and pile foundation are built respectively. In the simulation, the Davidenkov model of the soil skeleton curve is assumed for soil behavior, and the contact elements with Kelvin model are adopted to simulate pounding phenomena between adjacent structures. Finally, the dynamic responses of adjacent structures considering the pounding and SSSI effects are analyzed. The results show that pounding phenomena may occur, indicating that the seismic separation requirement for adjacent buildings of Chinese design code may not be enough to avoid pounding effect. Pounding and SSSI effects worsen the adjacent buildings’ conditions because their acceleration and shear responses are amplified after pounding considering SSSI. These results are significant for studying the effect of pounding and SSSI phenomena on seismic responses of structures and national sustainable development, especially in earthquake prevention and disaster reduction.

  18. Reconstruction of ancestral RNA sequences under multiple structural constraints

    OpenAIRE

    Tremblay-Savard, Olivier; Reinharz, Vladimir; Waldisp?hl, J?r?me

    2016-01-01

    Background Secondary structures form the scaffold of multiple sequence alignment of non-coding RNA (ncRNA) families. An accurate reconstruction of ancestral ncRNAs must use this structural signal. However, the inference of ancestors of a single ncRNA family with a single consensus structure may bias the results towards sequences with high affinity to this structure, which are far from the true ancestors. Methods In this paper, we introduce achARNement, a maximum parsimony approach that, given...

  19. Weighted spiking neural P systems with structural plasticity working in sequential mode based on maximum spike number

    Science.gov (United States)

    Sun, Mingming; Qu, Jianhua

    2017-10-01

    Spiking neural P systems (SNP systems, in short) are a group of parallel and distributed computing devices inspired by the function and structure of spiking neurons. Recently, a new variant of SNP systems, called SNP systems with structural plasticity (SNPSP systems, in short) was proposed. In SNPSP systems, neuron can use plasticity ru les to create and delete synapses. In this work, we consider many restrictions sequentiality on SNPSP systems: (1) neuron with the maximum number of spikes is chosen to fire; (2) we use the weighted synapses. Specifically, we investigate the computational power of weighted SNPSP systems working in the sequential mode based on maximum spike number (WSNPSPM systems, in short) and we proved that SNPSP systems with these new restrictions are universal as generating devices.

  20. Interface stability of granular filter structures under currents

    NARCIS (Netherlands)

    Verheij, H.J.; Hoffmans, G.; Dorst, K.; Van de Sande, S.

    2012-01-01

    Granular filters are used for protection of structures against scour and erosion. For a proper functioning it is necessary that the interfaces between the filter structure, the subsoil and the water flowing above the filter structure are stable. Stability means that there is no transport of subsoil

  1. Neural network AE source location apart from structure size and material

    Czech Academy of Sciences Publication Activity Database

    Chlada, Milan; Převorovský, Zdeněk; Blaháček, Michal

    2010-01-01

    Roč. 28, - (2010), s. 99-107 ISSN 0730-0050. [European Conference on Acoustic Emission Testing 2010 /29./. Vídeň, 08.09.2010-10.09.2010] R&D Projects: GA ČR(CZ) GAP104/10/1430; GA MPO(CZ) FR-TI1/274 Institutional research plan: CEZ:AV0Z20760514 Keywords : AE source location * artificial neural network * arrival time profiles Subject RIV: BI - Acoustics http://www.ndt.net/search/abstract.php3?AbsID=10828

  2. Structures based on semi-degradable biomaterials for neural regeneration in the central nervous system

    OpenAIRE

    Perez Garnes, Manuel

    2015-01-01

    Se pretende obtener un material semibiodegradable basado en ácido hialurónico químicamente enlazado a cadenas de polímeros acrílicos. Los hidrogeles de ácido hialurónico presentan en general buenas características para su utilización en regeneración del sistema nervioso central: es biodegradable, es un componente importante del tejido neural, sus propiedades mecánicas son semejantes a las del tejido cerebral, promueve la formación de nuevos capilares (angiogénesis), y limita la inflamación. C...

  3. Classification of Forest Vertical Structure in South Korea from Aerial Orthophoto and Lidar Data Using an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Soo-Kyung Kwon

    2017-10-01

    Full Text Available Every vegetation colony has its own vertical structure. Forest vertical structure is considered as an important indicator of a forest’s diversity and vitality. The vertical structure of a forest has typically been investigated by field survey, which is the traditional method of forest inventory. However, this method is very time- and cost-consuming due to poor accessibility. Remote sensing data such as satellite imagery, aerial photography, and lidar data can be a viable alternative to the traditional field-based forestry survey. In this study, we classified forest vertical structures from red-green-blue (RGB aerial orthophotos and lidar data using an artificial neural network (ANN, which is a powerful machine learning technique. The test site was Gongju province in South Korea, which contains single-, double-, and triple-layered forest structures. The performance of the proposed method was evaluated by comparing the results with field survey data. The overall accuracy achieved was about 70%. It means that the proposed approach can classify the forest vertical structures from the aerial orthophotos and lidar data.

  4. Spike Pattern Structure Influences Efficacy Variability under STDP and Synaptic Homeostasis

    OpenAIRE

    Bi, Zedong; Zhou, Changsong; Zhou, Hai-Jun

    2015-01-01

    In neural systems, synaptic plasticity is usually driven by spike trains. Due to the inherent noises of neurons, synapses and networks, spike trains typically exhibit externally uncontrollable variability such as spatial heterogeneity and temporal stochasticity, resulting in variability of synapses, which we call efficacy variability. Spike patterns with the same population rate but inducing different efficacy variability may result in neuronal networks with sharply different structures and f...

  5. Structured chaos shapes spike-response noise entropy in balanced neural networks

    Directory of Open Access Journals (Sweden)

    Guillaume eLajoie

    2014-10-01

    Full Text Available Large networks of sparsely coupled, excitatory and inhibitory cells occur throughout the brain. For many models of these networks, a striking feature is that their dynamics are chaotic and thus, are sensitive to small perturbations. How does this chaos manifest in the neural code? Specifically, how variable are the spike patterns that such a network produces in response to an input signal? To answer this, we derive a bound for a general measure of variability -- spike-train entropy. This leads to important insights on the variability of multi-cell spike pattern distributions in large recurrent networks of spiking neurons responding to fluctuating inputs. The analysis is based on results from random dynamical systems theory and is complemented by detailed numerical simulations. We find that the spike pattern entropy is an order of magnitude lower than what would be extrapolated from single cells. This holds despite the fact that network coupling becomes vanishingly sparse as network size grows -- a phenomenon that depends on ``extensive chaos, as previously discovered for balanced networks without stimulus drive. Moreover, we show how spike pattern entropy is controlled by temporal features of the inputs. Our findings provide insight into how neural networks may encode stimuli in the presence of inherently chaotic dynamics.

  6. Segmentation of Bone Structure in X-ray Images using Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    CERNAZANU-GLAVAN, C.

    2013-02-01

    Full Text Available The segmentation process represents a first step necessary for any automatic method of extracting information from an image. In the case of X-ray images, through segmentation we can differentiate the bone tissue from the rest of the image. There are nowadays several segmentation techniques, but in general, they all require the human intervention in the segmentation process. Consequently, this article proposes a new segmentation method for the X-ray images using a Convolutional Neural Network (CNN. In present, the convolutional networks are the best techniques for image segmentation. This fact is demonstrated by their wide usage in all the fields, including the medical one. As the X-ray images have large dimensions, for reducing the training time, the method proposed by the present article selects only certain areas (maximum interest areas from the entire image. The neural network is used as pixel classifier thus causing the label of each pixel (bone or none-bone from a raw pixel values in a square area. We will also present the method through which the network final configuration was chosen and we will make a comparative analysis with other 3 CNN configurations. The network chosen by us obtained the best results for all the evaluation metrics used, i.e. warping error, rand error and pixel error.

  7. Non-Invasive Imaging of Neuroanatomical Structures and Neural Activation with High-Resolution MRI

    Science.gov (United States)

    Herberholz, Jens; Mishra, Subrata H.; Uma, Divya; Germann, Markus W.; Edwards, Donald H.; Potter, Kimberlee

    2011-01-01

    Several years ago, manganese-enhanced magnetic resonance imaging (MEMRI) was introduced as a new powerful tool to image active brain areas and to identify neural connections in living, non-human animals. Primarily restricted to studies in rodents and later adapted for bird species, MEMRI has recently been discovered as a useful technique for neuroimaging of invertebrate animals. Using crayfish as a model system, we highlight the advantages of MEMRI over conventional techniques for imaging of small nervous systems. MEMRI can be applied to image invertebrate nervous systems at relatively high spatial resolution, and permits identification of stimulus-evoked neural activation non-invasively. Since the selection of specific imaging parameters is critical for successful in vivo micro-imaging, we present an overview of different experimental conditions that are best suited for invertebrates. We also compare the effects of hardware and software specifications on image quality, and provide detailed descriptions of the steps necessary to prepare animals for successful imaging sessions. Careful consideration of hardware, software, experiments, and specimen preparation will promote a better understanding of this novel technique and facilitate future MEMRI studies in other laboratories. PMID:21503138

  8. Optimization and anti-optimization of structures under uncertainty

    National Research Council Canada - National Science Library

    Elishakoff, Isaac; Ohsaki, Makoto

    2010-01-01

    The volume presents a collaboration between internationally recognized experts on anti-optimization and structural optimization, and summarizes various novel ideas, methodologies and results studied over 20 years...

  9. Peak earthquake response of structures under multi-component excitations

    Science.gov (United States)

    Song, Jianwei; Liang, Zach; Chu, Yi-Lun; Lee, George C.

    2007-12-01

    Accurate estimation of the peak seismic responses of structures is important in earthquake resistant design. The internal force distributions and the seismic responses of structures are quite complex, since ground motions are multi-directional. One key issue is the uncertainty of the incident angle between the directions of ground motion and the reference axes of the structure. Different assumed seismic incidences can result in different peak values within the scope of design spectrum analysis for a given structure and earthquake ground motion record combination. Using time history analysis to determine the maximum structural responses excited by a given earthquake record requires repetitive calculations to determine the critical incident angle. This paper presents a transformation approach for relatively accurate and rapid determination of the maximum peak responses of a linear structure subjected to three-dimensional excitations within all possible seismic incident angles. The responses can be deformations, internal forces, strains and so on. An irregular building structure model is established using SAP2000 program. Several typical earthquake records and an artificial white noise are applied to the structure model to illustrate the variation of the maximum structural responses for different incident angles. Numerical results show that for many structural parameters, the variation can be greater than 100%. This method can be directly applied to time history analysis of structures using existing computer software to determine the peak responses without carrying out the analyses for all possible incident angles. It can also be used to verify and/or modify aseismic designs by using response spectrum analysis.

  10. Structural convergence under reversible and irreversible monetary unification

    NARCIS (Netherlands)

    Beetsma, R.M.W.J.; Jensen, H.

    2003-01-01

    We explore endogenous monetary unification in the context of a model in which a country with serious structural distortions (and, hence, high inflation) is admitted into a monetary union once its economic structure has converged sufficiently towards that of the existing participants. If unification

  11. Structural convergence under reversible and irreversible monetary unification

    NARCIS (Netherlands)

    Beetsma, R.M.W.J.; Jensen, H.

    1999-01-01

    We explore endogenous monetary unification in the context of a model in which a country with serious structural distortions (and, hence, high inflation) is admitted into a monetary union once its economic structure has converged sufficiently towards that of the existing participants. If unification

  12. Analysis Of Masonry Infilled RC Frame Structures Under Lateral Loading

    Directory of Open Access Journals (Sweden)

    Barnaure Mircea

    2015-03-01

    Full Text Available Partition walls are often made of masonry in Romania. Although they are usually considered non-structural elements in the case of reinforced concrete framed structures, the infill panels contribute significantly to the seismic behaviour of the building. Their impact is difficult to assess, mainly because the interaction between the bounding frame and the infill is an intricate issue. This paper analyses the structural behaviour of a masonry infilled reinforced concrete frame system subjected to in - plane loading. Three numerical models are proposed and their results are compared in terms of stiffness and strength of the structure. The role of the openings in the infill panel on the behaviour is analysed and discussed. The effect of gaps between the frame and the infill on the structural behaviour is also investigated. Comparisons are made with the in-force Romanian and European regulations provisions.

  13. Generation of Regionally Specified Neural Progenitors and Functional Neurons from Human Embryonic Stem Cells under Defined Conditions

    Directory of Open Access Journals (Sweden)

    Agnete Kirkeby

    2012-06-01

    Full Text Available To model human neural-cell-fate specification and to provide cells for regenerative therapies, we have developed a method to generate human neural progenitors and neurons from human embryonic stem cells, which recapitulates human fetal brain development. Through the addition of a small molecule that activates canonical WNT signaling, we induced rapid and efficient dose-dependent specification of regionally defined neural progenitors ranging from telencephalic forebrain to posterior hindbrain fates. Ten days after initiation of differentiation, the progenitors could be transplanted to the adult rat striatum, where they formed neuron-rich and tumor-free grafts with maintained regional specification. Cells patterned toward a ventral midbrain (VM identity generated a high proportion of authentic dopaminergic neurons after transplantation. The dopamine neurons showed morphology, projection pattern, and protein expression identical to that of human fetal VM cells grafted in parallel. VM-patterned but not forebrain-patterned neurons released dopamine and reversed motor deficits in an animal model of Parkinson's disease.

  14. Neural network system and methods for analysis of organic materials and structures using spectral data

    Science.gov (United States)

    Meyer, Bernd J.; Sellers, Jeffrey P.; Thomsen, Jan U.

    1993-01-01

    Apparatus and processes for recognizing and identifying materials. Characteristic spectra are obtained for the materials via spectroscopy techniques including nuclear magnetic resonance spectroscopy, infrared absorption analysis, x-ray analysis, mass spectroscopy and gas chromatography. Desired portions of the spectra may be selected and then placed in proper form and format for presentation to a number of input layer neurons in an offline neural network. The network is first trained according to a predetermined training process; it may then be employed to identify particular materials. Such apparatus and processes are particularly useful for recognizing and identifying organic compounds such as complex carbohydrates, whose spectra conventionally require a high level of training and many hours of hard work to identify, and are frequently indistinguishable from one another by human interpretation.

  15. Neural network system and methods for analysis of organic materials and structures using spectral data

    Science.gov (United States)

    Meyer, B.J.; Sellers, J.P.; Thomsen, J.U.

    1993-06-08

    Apparatus and processes are described for recognizing and identifying materials. Characteristic spectra are obtained for the materials via spectroscopy techniques including nuclear magnetic resonance spectroscopy, infrared absorption analysis, x-ray analysis, mass spectroscopy and gas chromatography. Desired portions of the spectra may be selected and then placed in proper form and format for presentation to a number of input layer neurons in an offline neural network. The network is first trained according to a predetermined training process; it may then be employed to identify particular materials. Such apparatus and processes are particularly useful for recognizing and identifying organic compounds such as complex carbohydrates, whose spectra conventionally require a high level of training and many hours of hard work to identify, and are frequently indistinguishable from one another by human interpretation.

  16. Neural substrates of decision-making.

    Science.gov (United States)

    Broche-Pérez, Y; Herrera Jiménez, L F; Omar-Martínez, E

    2016-06-01

    Decision-making is the process of selecting a course of action from among 2 or more alternatives by considering the potential outcomes of selecting each option and estimating its consequences in the short, medium and long term. The prefrontal cortex (PFC) has traditionally been considered the key neural structure in decision-making process. However, new studies support the hypothesis that describes a complex neural network including both cortical and subcortical structures. The aim of this review is to summarise evidence on the anatomical structures underlying the decision-making process, considering new findings that support the existence of a complex neural network that gives rise to this complex neuropsychological process. Current evidence shows that the cortical structures involved in decision-making include the orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), and dorsolateral prefrontal cortex (DLPFC). This process is assisted by subcortical structures including the amygdala, thalamus, and cerebellum. Findings to date show that both cortical and subcortical brain regions contribute to the decision-making process. The neural basis of decision-making is a complex neural network of cortico-cortical and cortico-subcortical connections which includes subareas of the PFC, limbic structures, and the cerebellum. Copyright © 2014 Sociedad Española de Neurología. Published by Elsevier España, S.L.U. All rights reserved.

  17. Ground Liquefaction and Deformation Analysis of Breakwater Structures Under Earthquakes

    Directory of Open Access Journals (Sweden)

    Zhao Jie

    2016-10-01

    Full Text Available Ground liquefaction and deformation is one of the important causes that damage engineering structures. Chinese current code for seismic design of breakwater is based on the single-level seismic design method as well as code for port and water-way engineering. However, this code can not exactly reflect the seismic performance of breakwater structures which experience different seismic intensities. In this paper, the author used a finite difference software, namely, FLAC3D, to analyze the state and compute seismic responses of breakwater structure. The breakwater foundation’s pore pressure ratio and displacement due to different earthquake have been studied. And the result show that: Smaller earthquakes have little influence on serviceability of the foundation, and severe earthquakes can liquefy some parts of the foundation; In the latter case , obvious changes of pores and foundation displaces can be found. Particularly, when seismic peak acceleration reachs 0.2g, Liquefaction appears in the foundation and mainly concentrated in the upper right side of the structure. In addition, the survey of ultra-hole pressure and displacement values of sand layers of the breakwater, manifests when the ultra pore pressure near 1.0, displacement and overturning structure is relatively large, resulting in varying degrees of damage to the structure. This paper’s research can provide theoretical and designable reference for similar engineering structures

  18. Optimal Design of Composite Structures Under Manufacturing Constraints

    DEFF Research Database (Denmark)

    Marmaras, Konstantinos

    determination of the appropriate laminate thickness and the material choice in the structure. The optimal design problems that arise are stated as nonconvex mixed integer programming problems. We resort to different reformulation techniques to state the optimization problems as either linear or nonlinear convex....... The continuous relaxation of the mixed integer programming problems is being solved by an implementation of a primal–dual interior point method for nonlinear programming that updates the barrier parameter adaptively. The method is chosen for its excellent convergence properties and the ability of the method...... design phase results in structures with better structural performance reducing the need of manually post–processing the found designs....

  19. Application of LMS-Based NN Structure for Power Quality Enhancement in a Distribution Network Under Abnormal Conditions.

    Science.gov (United States)

    Agarwal, Rahul Kumar; Hussain, Ikhlaq; Singh, Bhim

    2017-03-16

    This paper proposes an application of a least mean-square (LMS)-based neural network (NN) structure for the power quality improvement of a three-phase power distribution network under abnormal conditions. It uses a single-layer neuron structure for the control in a distribution static compensator (DSTATCOM) to attenuate the harmonics such as noise, bias, notches, dc offset, and distortion, injected in the grid current due to connection of several nonlinear loads. This admittance LMS-based NN structure has a simple architecture which reduces the computational complexity and burden which makes it easy to implement. A DSTATCOM is a custom power device which performs various functionalities such as harmonics attenuation, reactive power compensation, load balancing, zero voltage regulation, and power factor correction. Other main contribution of this paper involves operation of the system under abnormal conditions of distribution network which means noise and distortion in voltage and imbalance in three-phase voltages at the point of interconnection. For substantiating and demonstrating the performance of proposed control approach, simulations are carried on MATLAB/Simulink software and corresponding experimental tests are conducted on a developed prototype in the laboratory.

  20. Harvesting Energy from Vibrations of the Underlying Structure

    DEFF Research Database (Denmark)

    Han, Bo; Vssilaras, S; Papadias, C.B.

    2013-01-01

    The use of wireless sensors for structural health monitoring offers several advantages such as small size, easy installation and minimal intervention on existing structures. However the most significant concern about such wireless sensors is the lifetime of the system, which depends heavily...... to the long-term structural health of a building or bridge, but at the same time they can be exploited as a power source to power the wireless sensors that are monitoring this structural health. This paper presents a new energy harvesting method based on a vibration driven electromagnetic harvester. By using...... on the type of power supply. No matter how energy efficient the operation of a battery operated sensor is, the energy of the battery will be exhausted at some point. In order to achieve a virtually unlimited lifetime, the sensor node should be able to recharge its battery in an easy way. Energy harvesting...

  1. Localized Damage Process in Metal Structures Under High Velocity Deformation

    National Research Council Canada - National Science Library

    Vodenicharov, Stefan

    1999-01-01

    The ASB initiation and growth in high strength steel are investigated. An integrated energy theoretical approach is suggested for modeling ASB development and identifying post critical structure state in the bands...

  2. Determining wildlife use of wildlife crossing structures under different scenarios.

    Science.gov (United States)

    2012-05-01

    This research evaluated Utahs wildlife crossing structures to help UDOT and the Utah Division of Wildlife Resources assess crossing efficacy. In this study, remote motion-sensed cameras were used at 14 designated wildlife crossing culverts and bri...

  3. Performance based investigations of structural systems under fire

    DEFF Research Database (Denmark)

    Gentili, Filippo; Crosti, Chiara; Giuliani, Luisa

    2010-01-01

    Prescriptive measures and procedures developed over the past here are mostly aimed at preventing structural failures of single elements for the time required for the evacuation. The response to fire and fire effects of the structural system as a whole remains often unknown and the survival of the...... structures are presented and discussed, with particular attention to methodological aspects. The effects of different assumptions in the modeling and in the definition of the collapse are highlighted, as critical aspects of a performance-based investigation....... these kinds of events, the mitigation of possible collapse induced by fire should be achieved. In this respect, a performance-based investigation of the structure aimed at highlight fire effects and fire-induced collapse mechanisms becomes of interest. In the paper collapse mechanisms of some simple...

  4. Structural analysis of reinforced concrete structures under monotonous and cyclic loadings: numerical aspects

    International Nuclear Information System (INIS)

    Lepretre, C.; Millard, A.; Nahas, G.

    1989-01-01

    The structural analysis of reinforced concrete structures is usually performed either by means of simplified methods of strength of materials type i.e. global methods, or by means of detailed methods of continuum mechanics type, i.e. local methods. For this second type, some constitutive models are available for concrete and rebars in a certain number of finite element systems. These models are often validated on simple homogeneous tests. Therefore, it is important to appraise the validity of the results when applying them to the analysis of a reinforced concrete structure, in order to be able to make correct predictions of the actual behaviour, under normal and faulty conditions. For this purpose, some tests have been performed at I.N.S.A. de Lyon on reinforced concrete beams, subjected to monotonous and cyclic loadings, in order to generate reference solutions to be compared with the numerical predictions given by two finite element systems: - CASTEM, developed by C.E.A./.D.E.M.T. - ELEFINI, developed by I.N.S.A. de Lyon

  5. Neural structures, functioning and connectivity in Generalized Anxiety Disorder and interaction with neuroendocrine systems: a systematic review.

    Science.gov (United States)

    Hilbert, Kevin; Lueken, Ulrike; Beesdo-Baum, Katja

    2014-04-01

    Research on the neurobiological basis of Generalized Anxiety Disorder (GAD) has considerably expanded in recent years. However, many studies investigated different domains and used different methods and paradigms. Therefore, this review aims to integrate the findings to date and to identify the core correlates of neurobiological underpinnings of GAD discovered so far. We conducted a systematic review of original papers investigating neural correlates, connectivity, or structural changes as well as reporting changes in the serotonergic system, noradrenergic system and cortisol levels in DSM-IV-defined GAD samples until December 2013. Studies have identified abnormal amygdala and prefrontal cortex activation in patients and decreased functional connectivity between these areas. Furthermore, studies showed increased gray matter volume and decreased structural connectivity between these structures. Neuroendocrine findings are less consistent, but increased reactivity of the noradrenergic system and perpetuations in the cortisol secretion have been reported. Only studies on DSM-IV defined Generalized Anxiety Disorder which employed a group comparison were included. Current research suggests a distinct set of neurobiological alterations in Generalized Anxiety Disorder. However, future research on the interaction between these structures and systems and on the specificity of these findings in relation to other mental disorders is urgently needed. Copyright © 2014 Elsevier B.V. All rights reserved.

  6. Grid synchronization structure for wind converters under grid fault conditions

    OpenAIRE

    Garcia, Jose Ignacio; Candela García, José Ignacio; Luna Alloza, Álvaro; Catalan, Pedro

    2016-01-01

    This paper presents a grid synchronization structure for three-phase electric power systems based on the use of a filtered quadrature signal generator (FQSG) and a phase-locked loop (PLL) structure, named Adaptive Vector Grid Synchronization system (AVGS). This system estimates the magnitude, frequency and phase of a signal, specially three-phase voltages and currents, and allows fast and accurate detection of the symmetrical components meet with the transient operating requirements imposed b...

  7. Behavior of auxetic structures under compression and impact forces

    Science.gov (United States)

    Yang, Chulho; Vora, Hitesh D.; Chang, Young

    2018-02-01

    In recent years, various auxetic material structures have been designed and fabricated for diverse applications that utilize normal materials that follow Hooke’s law but still show the properties of negative Poisson’s ratios (NPR). One potential application is body protection pads that are comfortable to wear and effective in protecting body parts by reducing impact force and preventing injuries in high-risk individuals such as elderly people, industrial workers, law enforcement and military personnel, and athletes. This paper reports an integrated theoretical, computational, and experimental investigation conducted for typical auxetic materials that exhibit NPR properties. Parametric 3D CAD models of auxetic structures such as re-entrant hexagonal cells and arrowheads were developed. Then, key structural characteristics of protection pads were evaluated through static analyses of FEA models. Finally, impact analyses were conducted through dynamic simulations of FEA models to validate the results obtained from the static analyses. Efforts were also made to relate the individual and/or combined effect of auxetic structures and materials to the overall stiffness and shock-absorption performance of the protection pads. An advanced additive manufacturing (3D printing) technique was used to build prototypes of the auxetic structures. Three different materials typically used for fused deposition modeling technology, namely polylactic acid (PLA) and thermoplastic polyurethane material (NinjaFlex® and SemiFlex®), were used for different stiffness and shock-absorption properties. The 3D printed prototypes were then tested and the results were compared to the computational predictions. The results showed that the auxetic material could be effective in reducing the shock forces. Each structure and material combination demonstrated unique structural properties such as stiffness, Poisson’s ratio, and efficiency in shock absorption. Auxetic structures showed better shock

  8. Oxide glass structure evolution under swift heavy ion irradiation

    International Nuclear Information System (INIS)

    Mendoza, C.; Peuget, S.; Charpentier, T.; Moskura, M.; Caraballo, R.; Bouty, O.; Mir, A.H.; Monnet, I.; Grygiel, C.; Jegou, C.

    2014-01-01

    Highlights: • Structure of SHI irradiated glass is similar to the one of a hyper quenched glass. • D2 Raman band associated to 3 members ring is only observed in irradiated glass. • Irradiated state seems slightly different to an equilibrated liquid quenched rapidly. - Abstract: The effects of ion tracks on the structure of oxide glasses were examined by irradiating a silica glass and two borosilicate glass specimens containing 3 and 6 oxides with krypton ions (74 MeV) and xenon ions (92 MeV). Structural changes in the glass were observed by Raman and nuclear magnetic resonance spectroscopy using a multinuclear approach ( 11 B, 23 Na, 27 Al and 29 Si). The structure of irradiated silica glass resembles a structure quenched at very high temperature. Both borosilicate glass specimens exhibited depolymerization of the borosilicate network, a lower boron coordination number, and a change in the role of a fraction of the sodium atoms after irradiation, suggesting that the final borosilicate glass structures were quenched from a high temperature state. In addition, a sharp increase in the concentration of three membered silica rings and the presence of large amounts of penta- and hexacoordinate aluminum in the irradiated 6-oxide glass suggest that the irradiated glass is different from a liquid quenched at equilibrium, but it is rather obtained from a nonequilibrium liquid that is partially relaxed by very rapid quenching within the ion tracks

  9. Reconstruction of ancestral RNA sequences under multiple structural constraints

    Directory of Open Access Journals (Sweden)

    Olivier Tremblay-Savard

    2016-11-01

    Full Text Available Abstract Background Secondary structures form the scaffold of multiple sequence alignment of non-coding RNA (ncRNA families. An accurate reconstruction of ancestral ncRNAs must use this structural signal. However, the inference of ancestors of a single ncRNA family with a single consensus structure may bias the results towards sequences with high affinity to this structure, which are far from the true ancestors. Methods In this paper, we introduce achARNement, a maximum parsimony approach that, given two alignments of homologous ncRNA families with consensus secondary structures and a phylogenetic tree, simultaneously calculates ancestral RNA sequences for these two families. Results We test our methodology on simulated data sets, and show that achARNement outperforms classical maximum parsimony approaches in terms of accuracy, but also reduces by several orders of magnitude the number of candidate sequences. To conclude this study, we apply our algorithms on the Glm clan and the FinP-traJ clan from the Rfam database. Conclusions Our results show that our methods reconstruct small sets of high-quality candidate ancestors with better agreement to the two target structures than with classical approaches. Our program is freely available at: http://csb.cs.mcgill.ca/acharnement .

  10. Structural integrity analysis of an INPP building under external loading

    International Nuclear Information System (INIS)

    Dundulis, G.; Karalevicius, R.; Uspuras, E.; Kulak, R.F.; Marchertas, A.

    2005-01-01

    After the terrorist attacks in New York and Washington D. C. using civil airplanes, the evaluation of civil airplane crashes into civil and NPP structures has become very important. The interceptions of many terrorists' communications reveal that the use of commandeered commercial aircraft is still a major part of their plans for destruction. Aircraft crash or other flying objects in the territory of the Ignalina Nuclear Power Plant (INPP) represents a concern to the plant. Aircraft traveling at high velocity have a destructive potential. The aircraft crash may damage the roof and walls of buildings, pipelines, electric motors, cases of power supplies, power cables of electricity transmission and other elements and systems, which are important for safety. Therefore, the evaluation of the structural response to an of aircraft crash is important and was selected for analysis. The structural integrity analysis due to the effects of an aircraft crash on an NPP building structure is the subject of this paper. The finite element method was used for the structural analysis of a typical Ignalina NPP building. The structural integrity analysis was performed for a portion of the ALS using the dynamic loading of an aircraft crash impact model. The computer code NEPTUNE was used for this analysis. The local effects caused by impact of the aircraft's engine on the building wall were evaluated independently by using an empirical formula. (authors)

  11. Training set optimization under population structure in genomic selection.

    Science.gov (United States)

    Isidro, Julio; Jannink, Jean-Luc; Akdemir, Deniz; Poland, Jesse; Heslot, Nicolas; Sorrells, Mark E

    2015-01-01

    Population structure must be evaluated before optimization of the training set population. Maximizing the phenotypic variance captured by the training set is important for optimal performance. The optimization of the training set (TRS) in genomic selection has received much interest in both animal and plant breeding, because it is critical to the accuracy of the prediction models. In this study, five different TRS sampling algorithms, stratified sampling, mean of the coefficient of determination (CDmean), mean of predictor error variance (PEVmean), stratified CDmean (StratCDmean) and random sampling, were evaluated for prediction accuracy in the presence of different levels of population structure. In the presence of population structure, the most phenotypic variation captured by a sampling method in the TRS is desirable. The wheat dataset showed mild population structure, and CDmean and stratified CDmean methods showed the highest accuracies for all the traits except for test weight and heading date. The rice dataset had strong population structure and the approach based on stratified sampling showed the highest accuracies for all traits. In general, CDmean minimized the relationship between genotypes in the TRS, maximizing the relationship between TRS and the test set. This makes it suitable as an optimization criterion for long-term selection. Our results indicated that the best selection criterion used to optimize the TRS seems to depend on the interaction of trait architecture and population structure.

  12. Reconstruction of ancestral RNA sequences under multiple structural constraints.

    Science.gov (United States)

    Tremblay-Savard, Olivier; Reinharz, Vladimir; Waldispühl, Jérôme

    2016-11-11

    Secondary structures form the scaffold of multiple sequence alignment of non-coding RNA (ncRNA) families. An accurate reconstruction of ancestral ncRNAs must use this structural signal. However, the inference of ancestors of a single ncRNA family with a single consensus structure may bias the results towards sequences with high affinity to this structure, which are far from the true ancestors. In this paper, we introduce achARNement, a maximum parsimony approach that, given two alignments of homologous ncRNA families with consensus secondary structures and a phylogenetic tree, simultaneously calculates ancestral RNA sequences for these two families. We test our methodology on simulated data sets, and show that achARNement outperforms classical maximum parsimony approaches in terms of accuracy, but also reduces by several orders of magnitude the number of candidate sequences. To conclude this study, we apply our algorithms on the Glm clan and the FinP-traJ clan from the Rfam database. Our results show that our methods reconstruct small sets of high-quality candidate ancestors with better agreement to the two target structures than with classical approaches. Our program is freely available at: http://csb.cs.mcgill.ca/acharnement .

  13. A mathematical analysis of the effects of Hebbian learning rules on the dynamics and structure of discrete-time random recurrent neural networks.

    Science.gov (United States)

    Siri, Benoît; Berry, Hugues; Cessac, Bruno; Delord, Bruno; Quoy, Mathias

    2008-12-01

    We present a mathematical analysis of the effects of Hebbian learning in random recurrent neural networks, with a generic Hebbian learning rule, including passive forgetting and different timescales, for neuronal activity and learning dynamics. Previous numerical work has reported that Hebbian learning drives the system from chaos to a steady state through a sequence of bifurcations. Here, we interpret these results mathematically and show that these effects, involving a complex coupling between neuronal dynamics and synaptic graph structure, can be analyzed using Jacobian matrices, which introduce both a structural and a dynamical point of view on neural network evolution. Furthermore, we show that sensitivity to a learned pattern is maximal when the largest Lyapunov exponent is close to 0. We discuss how neural networks may take advantage of this regime of high functional interest.

  14. Oxytocin receptor polymorphism and childhood social experiences shape adult personality, brain structure and neural correlates of mentalizing.

    Science.gov (United States)

    Schneider-Hassloff, H; Straube, B; Jansen, A; Nuscheler, B; Wemken, G; Witt, S H; Rietschel, M; Kircher, T

    2016-07-01

    The oxytocin system is involved in human social behavior and social cognition such as attachment, emotion recognition and mentalizing (i.e. the ability to represent mental states of oneself and others). It is shaped by social experiences in early life, especially by parent-infant interactions. The single nucleotid polymorphism rs53576 in the oxytocin receptor (OXTR) gene has been linked to social behavioral phenotypes. In 195 adult healthy subjects we investigated the interaction of OXTR rs53576 and childhood attachment security (CAS) on the personality traits "adult attachment style" and "alexithymia" (i.e. emotional self-awareness), on brain structure (voxel-based morphometry) and neural activation (fMRI) during an interactive mentalizing paradigm (prisoner's dilemma game; subgroup: n=163). We found that in GG-homozygotes, but not in A-allele carriers, insecure childhood attachment is - in adulthood - associated with a) higher attachment-related anxiety and alexithymia, b) higher brain gray matter volume of left amygdala and lower volumes in right superior parietal lobule (SPL), left temporal pole (TP), and bilateral frontal regions, and c) higher mentalizing-related neural activity in bilateral TP and precunei, and right middle and superior frontal gyri. Interaction effects of genotype and CAS on brain volume and/or function were associated with individual differences in alexithymia and attachment-related anxiety. Interactive effects were in part sexually dimorphic. The interaction of OXTR genotype and CAS modulates adult personality as well as brain structure and function of areas implicated in salience processing and mentalizing. Rs53576 GG-homozygotes are partially more susceptible to childhood attachment experiences than A-allele carriers. Copyright © 2016 Elsevier Inc. All rights reserved.

  15. Response of structural elements under non-uniformly distributed dynamic loads

    NARCIS (Netherlands)

    Westerhof, T.A.T.; Huebner, M.; Ferretti, D.L.; Doormaal, J.C.A.M. van; Gebbeken, N.

    2016-01-01

    Determination of the structural response of a structural element under blast loading is of interest to vulnerability / lethality (V/L) studies of military operations in urban terrain. These studies require a quick and easy to use method to simulate the structural response of e.g. a wall under

  16. Modeling of fracture of protective concrete structures under impact loads

    Science.gov (United States)

    Radchenko, P. A.; Batuev, S. P.; Radchenko, A. V.; Plevkov, V. S.

    2015-10-01

    This paper presents results of numerical simulation of interaction between a Boeing 747-400 aircraft and the protective shell of a nuclear power plant. The shell is presented as a complex multilayered cellular structure consisting of layers of concrete and fiber concrete bonded with steel trusses. Numerical simulation was performed three-dimensionally using the original algorithm and software taking into account algorithms for building grids of complex geometric objects and parallel computations. Dynamics of the stress-strain state and fracture of the structure were studied. Destruction is described using a two-stage model that allows taking into account anisotropy of elastic and strength properties of concrete and fiber concrete. It is shown that wave processes initiate destruction of the cellular shell structure; cells start to destruct in an unloading wave originating after the compression wave arrival at free cell surfaces.

  17. The cortical topography of tonal structures underlying Western music.

    Science.gov (United States)

    Janata, Petr; Birk, Jeffrey L; Van Horn, John D; Leman, Marc; Tillmann, Barbara; Bharucha, Jamshed J

    2002-12-13

    Western tonal music relies on a formal geometric structure that determines distance relationships within a harmonic or tonal space. In functional magnetic resonance imaging experiments, we identified an area in the rostromedial prefrontal cortex that tracks activation in tonal space. Different voxels in this area exhibited selectivity for different keys. Within the same set of consistently activated voxels, the topography of tonality selectivity rearranged itself across scanning sessions. The tonality structure was thus maintained as a dynamic topography in cortical areas known to be at a nexus of cognitive, affective, and mnemonic processing.

  18. Combining ground-based and airborne EM through Artificial Neural Networks for modelling glacial till under saline groundwater conditions

    DEFF Research Database (Denmark)

    Gunnink, J.L.; Bosch, A.; Siemon, B.

    2012-01-01

    Airborne electromagnetic (AEM) methods supply data over large areas in a cost-effective way. We used ArtificialNeural Networks (ANN) to classify the geophysical signal into a meaningful geological parameter. By using examples of known relations between ground-based geophysical data (in this case ...... is acting as a layer that inhibits groundwater flow, due to its high clay-content, and is therefore an important layer in hydrogeological modelling and for predicting the effects of climate change on groundwater quantity and quality....

  19. Neural Network Enhanced Structure Determination of Osteoporosis, Immune System, and Radiation Repair Proteins, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — We propose a dual objective innovation that has valuable NASA applicability and tremendous commercial potential. The first innovation is the structure determination...

  20. The role of polysialic acid and other carbohydrate polymers in neural structural plasticity.

    Science.gov (United States)

    Fryer, H J; Hockfield, S

    1996-02-01

    Polysialic acid (PSA) fulfills several criteria for a molecule involved in structural plasticity, including expression in regions capable of plasticity, re-expression in structures undergoing synaptic rearrangement in the adult, downregulation following innervation, and regulation by activity. In addition, removal of PSA reduces the capacity for structural plasticity. PSA may be paradigmatic for other large polymeric carbohydrates, such as glycosaminoglycans and proteoglycans, which also are highly charged and can be extensively hydrated. These carbohydrates may affect structural plasticity by altering cell-cell and/or cell-matrix interactions by increasing intermolecular spacing through hydration.

  1. Interevent relationships and judgment under uncertainty: Structure determines strategy

    NARCIS (Netherlands)

    Sanfey, A.G.; Hastie, R.

    2002-01-01

    A fundamental empirical question regarding judgments about events is whether experienced absolute frequencies or relative. frequencies are relied on when the likelihood of a particular occurrence is judged. The present research explicates the conditions under which people rely on remembered raw

  2. Influence of amendments on soil structure and soil loss under ...

    African Journals Online (AJOL)

    Macromolecule polymers are significant types of chemical amendments because of their special structure, useful functions and low cost. Macromolecule polymers as soil amendment provide new territory for studying China's agricultural practices and for soil and water conservation, because polymers have the ability to ...

  3. Structural performance of HEPA filters under simulated tornado conditions

    International Nuclear Information System (INIS)

    Horak, H.L.; Gregory, W.S.; Ricketts, C.I.; Smith, P.R.

    1982-02-01

    This report contains the results of structural tests to determine the response of High Efficiency Particulate Air filters to simulated tornado conditions. The data include the structural limits of the filters, their resistance at high flow rates, and the effects of filter design features and tornado parameters. Considering all the filters tested, the mean break pressure or structural limit was found to be 2.35 pse (16.2 kPa). The maximum value was 2.87 psi (19.8 kPa), and the low value found was 1.31 psi (9.0 kPa). The type of failure was usually a medium break of the downstream filter fold. The type of filters that were evaluated were nuclear grade with design flow rates of 1000 cfm (0.472 m 3 /s), standard separators, and folded medium design. The parameters evaluated that are characteristic of the filter included manufacturer, separator type, faceguards, pack tightness, and aerosol loading. Manufacturer and medium properties were found to have a large effect on the structural limits

  4. Structure Formation of Thermoresponsive Microgels Suspensions Under Shear Flow

    NARCIS (Netherlands)

    Stieger, M.A.; Lindner, P.; Richtering, W.

    2004-01-01

    Shear-induced structures of concentrated temperature-sensitive poly(N-isopropylacrylamide) (PNiPAM) microgel suspensions have been studied employing small angle neutron scattering (rheo-SANS). The interaction potential of swollen PNiPAM microgels could be varied from repulsive at temperatures below

  5. Sustainability assessment of concrete structure durability under reinforcement corrosion

    DEFF Research Database (Denmark)

    Thybo, Anna Emilie A.; Michel, Alexander; Stang, Henrik

    In the present paper a parametric study is conducted based on an existing finite element based model. The influence of cover layer, reinforcement diameter and water-to-cement ratio is compared to a possible scatter in the results due to insufficient knowledge about the distribution of the corrosi...... and predict the durability of a given structure....

  6. Optimization and anti-optimization of structures under uncertainty

    National Research Council Canada - National Science Library

    Elishakoff, Isaac; Ohsaki, Makoto

    2010-01-01

    ..., architecture, civil, mechanical or ocean engineering, invariably adopt the either/or style. Namely, they devote themselves either to linear or to nonlinear analysis of the structure they are dealing with, they are engaged in analyzing it either in the elastic or in the inelastic range; they deal either with its static or with its dynamic behavior. Al...

  7. Occupational structure in the Czech lands under the second serfdom

    Czech Academy of Sciences Publication Activity Database

    Klein, Alexander; Ogilvie, S.

    2016-01-01

    Roč. 69, č. 2 (2016), s. 493-521 ISSN 0013-0117 R&D Projects: GA ČR GA13-13848S Institutional support: RVO:67985998 Keywords : occupational structure * Czech lands * Bohemia Subject RIV: AH - Economics OBOR OECD: Applied Economics, Econometrics Impact factor: 1.233, year: 2016

  8. Spherical harmonics based descriptor for neural network potentials: Structure and dynamics of Au147 nanocluster

    Science.gov (United States)

    Jindal, Shweta; Chiriki, Siva; Bulusu, Satya S.

    2017-05-01

    We propose a highly efficient method for fitting the potential energy surface of a nanocluster using a spherical harmonics based descriptor integrated with an artificial neural network. Our method achieves the accuracy of quantum mechanics and speed of empirical potentials. For large sized gold clusters (Au147), the computational time for accurate calculation of energy and forces is about 1.7 s, which is faster by several orders of magnitude compared to density functional theory (DFT). This method is used to perform the global minimum optimizations and molecular dynamics simulations for Au147, and it is found that its global minimum is not an icosahedron. The isomer that can be regarded as the global minimum is found to be 4 eV lower in energy than the icosahedron and is confirmed from DFT. The geometry of the obtained global minimum contains 105 atoms on the surface and 42 atoms in the core. A brief study on the fluxionality in Au147 is performed, and it is concluded that Au147 has a dynamic surface, thus opening a new window for studying its reaction dynamics.

  9. Spherical harmonics based descriptor for neural network potentials: Structure and dynamics of Au147nanocluster.

    Science.gov (United States)

    Jindal, Shweta; Chiriki, Siva; Bulusu, Satya S

    2017-05-28

    We propose a highly efficient method for fitting the potential energy surface of a nanocluster using a spherical harmonics based descriptor integrated with an artificial neural network. Our method achieves the accuracy of quantum mechanics and speed of empirical potentials. For large sized gold clusters (Au 147 ), the computational time for accurate calculation of energy and forces is about 1.7 s, which is faster by several orders of magnitude compared to density functional theory (DFT). This method is used to perform the global minimum optimizations and molecular dynamics simulations for Au 147 , and it is found that its global minimum is not an icosahedron. The isomer that can be regarded as the global minimum is found to be 4 eV lower in energy than the icosahedron and is confirmed from DFT. The geometry of the obtained global minimum contains 105 atoms on the surface and 42 atoms in the core. A brief study on the fluxionality in Au 147 is performed, and it is concluded that Au 147 has a dynamic surface, thus opening a new window for studying its reaction dynamics.

  10. Adjoint Techniques for Topology Optimization of Structures Under Damage Conditions

    Science.gov (United States)

    Akgun, Mehmet A.; Haftka, Raphael T.

    2000-01-01

    The objective of this cooperative agreement was to seek computationally efficient ways to optimize aerospace structures subject to damage tolerance criteria. Optimization was to involve sizing as well as topology optimization. The work was done in collaboration with Steve Scotti, Chauncey Wu and Joanne Walsh at the NASA Langley Research Center. Computation of constraint sensitivity is normally the most time-consuming step of an optimization procedure. The cooperative work first focused on this issue and implemented the adjoint method of sensitivity computation (Haftka and Gurdal, 1992) in an optimization code (runstream) written in Engineering Analysis Language (EAL). The method was implemented both for bar and plate elements including buckling sensitivity for the latter. Lumping of constraints was investigated as a means to reduce the computational cost. Adjoint sensitivity computation was developed and implemented for lumped stress and buckling constraints. Cost of the direct method and the adjoint method was compared for various structures with and without lumping. The results were reported in two papers (Akgun et al., 1998a and 1999). It is desirable to optimize topology of an aerospace structure subject to a large number of damage scenarios so that a damage tolerant structure is obtained. Including damage scenarios in the design procedure is critical in order to avoid large mass penalties at later stages (Haftka et al., 1983). A common method for topology optimization is that of compliance minimization (Bendsoe, 1995) which has not been used for damage tolerant design. In the present work, topology optimization is treated as a conventional problem aiming to minimize the weight subject to stress constraints. Multiple damage configurations (scenarios) are considered. Each configuration has its own structural stiffness matrix and, normally, requires factoring of the matrix and solution of the system of equations. Damage that is expected to be tolerated is local

  11. The electronic structure of core states under extreme compressions

    International Nuclear Information System (INIS)

    Straub, G.K.

    1992-01-01

    At normal density and for modest compressions, the electronic structure of a metal can be accurately described by treating the conduction electrons and their interactions with the usual methods of band theory. The core electrons remain essentially the same as for an isolated free atom and do not participate in the bonding forces responsible for creating a condensed phase. As the density increases, the core electrons begin to ''see'' one another as the overlap of the tails of wave functions can no longer be neglected. The electronic structure of the core electrons is responsible for an effective repulsive interaction that eventually becomes free-electron-like at very high compressions. The electronic structure of the interacting core electrons may be treated in a simple manner using the Atomic Surface Method (ASM). The ASM is a first-principles treatment of the electronic structure involving a rigorous integration of the Schroedinger equation within the atomic-sphere approximation. Solid phase wave functions are constructed from isolated atom wave functions and the band width W l and the center of gravity of the band C l are obtained from simple formulas. The ASM can also utilize analytic forms of the atomic wave functions and thus provide direct functional dependence of various aspects of the electronic structure. Of particular use in understanding the behavior of the core electrons, the ASM provides the ability to analytically determine the density dependence of the band widths and positions. The process whereby core states interact with one another is best viewed as the formation of narrow electron bands formed from atomic states. As the core-core overlap increases, the bands increase in width and mean energy. In Sec.3 this picture is further developed and from the ASM one obtains the analytic dependence on density of the relative motion of the different bands. Also in Sec. 3 is a discussion of the transition to free electron bands

  12. Prediction of hydrogen concentration in nuclear power plant containment under severe accidents using cascaded fuzzy neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Choi, Geon Pil; Kim, Dong Yeong; Yoo, Kwae Hwan; Na, Man Gyun, E-mail: magyna@chosun.ac.kr

    2016-04-15

    Highlights: • We present a hydrogen-concentration prediction method in an NPP containment. • The cascaded fuzzy neural network (CFNN) is used in this prediction model. • The CFNN model is much better than the existing FNN model. • This prediction can help prevent severe accidents in NPP due to hydrogen explosion. - Abstract: Recently, severe accidents in nuclear power plants (NPPs) have attracted worldwide interest since the Fukushima accident. If the hydrogen concentration in an NPP containment is increased above 4% in atmospheric pressure, hydrogen combustion will likely occur. Therefore, the hydrogen concentration must be kept below 4%. This study presents the prediction of hydrogen concentration using cascaded fuzzy neural network (CFNN). The CFNN model repeatedly applies FNN modules that are serially connected. The CFNN model was developed using data on severe accidents in NPPs. The data were obtained by numerically simulating the accident scenarios using the MAAP4 code for optimized power reactor 1000 (OPR1000) because real severe accident data cannot be obtained from actual NPP accidents. The root-mean-square error level predicted by the CFNN model is below approximately 5%. It was confirmed that the CFNN model could accurately predict the hydrogen concentration in the containment. If NPP operators can predict the hydrogen concentration in the containment using the CFNN model, this prediction can assist them in preventing a hydrogen explosion.

  13. Untangling the neurobiology of coping styles in rodents: Towards neural mechanisms underlying individual differences in disease susceptibility.

    Science.gov (United States)

    de Boer, Sietse F; Buwalda, Bauke; Koolhaas, Jaap M

    2017-03-01

    Considerable individual differences exist in trait-like patterns of behavioral and physiological responses to salient environmental challenges. This individual variation in stress coping styles has an important functional role in terms of health and fitness. Hence, understanding the neural embedding of coping style variation is fundamental for biobehavioral neurosciences in probing individual disease susceptibility. This review outlines individual differences in trait-aggressiveness as an adaptive component of the natural sociobiology of rats and mice, and highlights that these reflect the general style of coping that varies from proactive (aggressive) to reactive (docile). We propose that this qualitative coping style can be disentangled into multiple quantitative behavioral domains, e.g., flexibility/impulse control, emotional reactivity and harm avoidance/reward processing, that each are encoded into selective neural circuitries. Since functioning of all these brain circuitries rely on fine-tuned serotonin signaling, autoinhibitory control mechanisms of serotonergic neuron (re)activity are crucial in orchestrating general coping style. Untangling the precise neuromolecular mechanisms of different coping styles will provide a roadmap for developing better therapeutic strategies of stress-related diseases. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. Structural stability and theoretical strength of Cu crystal under equal ...

    Indian Academy of Sciences (India)

    The results indicate that, under sufficient tension, there exists a stress-free BCC phase which is unstable and slips spontaneously to a stress-free metastable BCT phase by consuming internal energy. The stable region ranges from −15.131 GPa to 2.803 GPa in the theoretical strength or from −5.801% to 4.972% in the strain ...

  15. Optimal Design of Composite Structures Under Manufacturing Constraints

    DEFF Research Database (Denmark)

    Marmaras, Konstantinos

    sequence of well–posed optimization problems. They provide us with a discrete feasible solution or correctly determine problem infeasibility. Our aim is to solve the considered problems to proven global optimality. We propose a combination of the convergent Outer Approximation and Local Branching......This thesis considers discrete multi material and thickness optimization of laminated composite structures including local failure criteria and manufacturing constraints. Our models closely follow an immediate extension of the Discrete Material Optimization scheme, which allows simultaneous...... determination of the appropriate laminate thickness and the material choice in the structure. The optimal design problems that arise are stated as nonconvex mixed integer programming problems. We resort to different reformulation techniques to state the optimization problems as either linear or nonlinear convex...

  16. Analysis of ADU structure obtained under different precipitation conditions

    International Nuclear Information System (INIS)

    Ramella, Jose L.; Esteban, Adolfo; Mendez De Leo, Lucia P.; Sassone, Ariel; Novara, Oscar E.; Boero, Norma L.; Leyva, Ana G.

    1999-01-01

    ADU is the nominal name for ammonium poly uranate. It is a very complex compound of polymeric structure, which may have, according to precipitation conditions, different chemical composition and crystallographic structure. ADU is used as uranium oxide precursor in the manufacture of fuel elements. In former papers it was proved that if ultrasound is applied during precipitation and digestion the characteristics of the final product (U 3 O 8 UO 2 ) improve. By studying ADU thermal decomposition obtained by ultrasonic application, it was intended to obtain its composition. Therefore, differential thermal gravimetric and differential thermal analyses were performed. Samples were taken from special points and analyzed by X-ray diffraction, infra-red spectroscopy and scanning. An experiment was also designed to identify the products released during heating. Results and conclusions obtained are presented in this work. (author)

  17. Fiscal reaction under endogenous structural changes in Brazil

    Directory of Open Access Journals (Sweden)

    Andrei G. Simonassi

    2014-01-01

    Full Text Available Regarding the importance of fiscal policy in smoothing the impact of shocks such as the international financial and economic crises, the paper analyzes the sustainability of the Brazilian fiscal policy by taking into consideration the possibility of multiple endogenous structural breaks on the coefficients of government reaction function. From monthly data in the period 1991–2008, tests on the reliable estimates dictate the occurrence of structural change in May 1994, and another in February 2003. There has been a situation of fiscal solvency in Brazil, but only from May 1994 the hitherto innocuous actions of government to formulate policies on public debt turn out to be significant, as it rose twofold after February 2003. This reinforces the existence of a more flexible alternative to implement strategic policy in Brazil, if an eventual alternative for increasing public spending is a way of hindering the effects of international financial crises without compromising the fiscal targets.

  18. Structural optimization under overhang constraints imposed by additive manufacturing technologies

    Science.gov (United States)

    Allaire, G.; Dapogny, C.; Estevez, R.; Faure, A.; Michailidis, G.

    2017-12-01

    This article addresses one of the major constraints imposed by additive manufacturing processes on shape optimization problems - that of overhangs, i.e. large regions hanging over void without sufficient support from the lower structure. After revisiting the 'classical' geometric criteria used in the literature, based on the angle between the structural boundary and the build direction, we propose a new mechanical constraint functional, which mimics the layer by layer construction process featured by additive manufacturing technologies, and thereby appeals to the physical origin of the difficulties caused by overhangs. This constraint, as well as some variants, is precisely defined; their shape derivatives are computed in the sense of Hadamard's method, and numerical strategies are extensively discussed, in two and three space dimensions, to efficiently deal with the appearance of overhang features in the course of shape optimization processes.

  19. Structure and morphology of mythimna pupa under diffraction enhanced imaging

    International Nuclear Information System (INIS)

    Huang Wanxia; Yuan Qingxi; Zhu Peiping; Wang Junyue; Liu Yijin; Chen Bo; Shu Hang; Hu Tiandou; Wu Ziyu; Ge Siqin

    2007-01-01

    As a technique of X-ray phase contrast imaging, the diffraction enhanced imaging (DEI) attracts much interest due to its high resolution and contrast. The top images of DEI were used to study the growth of a complete metamorphic mythimna in the period of pupa. Clear images about the pupa structure were obtained. The entire growth process of the pupa was observed, including the evolvement of part of organs and tissues from larva to imago. (authors)

  20. Structural performance of HEPA filters under simulated tornado conditions

    Science.gov (United States)

    Horak, H. L.; Gregory, W. S.; Ricketts, C. I.; Smith, P. R.

    1982-02-01

    The response of high efficiency particulate air filters to simulated tornado conditions was determined. The data include the structural limits of the filters, their resistance at high flow rates, and the effects of filter design features and tornado parameters. Considering all the filters tested, the mean break pressure or structural limit was found to be 2.35 pse (16.2 kPa). The maximum value was 2.87 psi (19.8 kPa), and the low value found was 1.31 psi (9.0 kPa). The type of failure was usually a medium break of the downstream filter fold. The types of filters that were evaluated were nuclear grade with design flow rates of 1000 cfm (0.472 cu m/s), standard separators, and folded medium design. The parameters evaluated that are characteristic of the filter included manufacturer, separator type, face-guards, pack tightness, and aerosol loading. Manufacturer and medium properties were found to have a large effect on the structural limits.

  1. Disrupted white matter structure underlies cognitive deficit in hypertensive patients

    International Nuclear Information System (INIS)

    Li, Xin; Ma, Chao; Zhang, Junying; Chen, Yaojing; Zhang, Zhanjun; Sun, Xuan; Chen, Kewei

    2016-01-01

    Hypertension is considered a risk factor of cognitive impairments and could result in white matter changes. Current studies on hypertension-related white matter (WM) changes focus only on regional changes, and the information about global changes in WM structure network is limited. We assessed the cognitive function in 39 hypertensive patients and 37 healthy controls with a battery of neuropsychological tests. The WM structural networks were constructed by utilizing diffusion tensor tractography and calculated topological properties of the networks using a graph theoretical method. The direct and indirect correlations among cognitive impairments, brain WM network disruptions and hypertension were analyzed with structural equation modelling (SEM). Hypertensive patients showed deficits in executive function, memory and attention compared with controls. An aberrant connectivity of WM networks was found in the hypertensive patients (P Eglob = 0.005, P Lp = 0.005), especially in the frontal and parietal regions. Importantly, SEM analysis showed that the decline of executive function resulted from aberrant WM networks in hypertensive patients (p = 0.3788, CFI = 0.99). These results suggest that the cognitive decline in hypertensive patients was due to frontal and parietal WM disconnections. Our findings highlight the importance of brain protection in hypertension patients. (orig.)

  2. The Response of Simple Polymer Structures Under Dynamic Loading

    Science.gov (United States)

    Proud, William; Ellison, Kay; Yapp, Su; Cole, Cloe; Galimberti, Stefano; Institute of Shock Physics Team

    2017-06-01

    The dynamic response of polymeric materials has been widely studied with the effects of degree of crystallinity, strain rate, temperature and sample size being commonly reported. This study uses a simple PMMA structure, a right cylindrical sample, with structural features such as holes. The features are added an varied in a systematic fashion. Samples were dynamically loaded using a Split Hopkinson Pressure Bar up to failure. The resulting stress-strain curves are presented showing the change in sample response. The strain to failure is shown to increase initially with the presence of holes, while failure stress is relatively unaffected. The fracture patterns seen in the failed samples change, with tensile cracks, Hertzian cones, shear effects being dominant for different holes sizes and geometries. The sample were prepared by laser cutting and checked for residual stress before experiment. The data is used to validate predictive model predictions where material, structure and damage are included.. The Institute of Shock Physics acknowledges the support of Imperial College London and the Atomic Weapons Establishment.

  3. THE RELATIONSHIP BETWEEN INDUSTRIAL PRODUCTION AND EMPLOYMENT UNDER STRUCTURAL BREAK

    Directory of Open Access Journals (Sweden)

    Umut HALAÇ

    2017-12-01

    Full Text Available For the economies which aim for the sustainable economic growth, one of the most important topic is industrialization. It is thought that it effects employability positively, by increasing the manufacturing. This study investigates the long-term relationship between industrial production and total employment, industrial employment and youth employment in Turkey using monthly data for the period from 2005:01 to 2017:06. Since the period involving structural changes, the stability of series was tested by standart Augmented Dickey Fuller unit root test and Zivot Andrews unit root test with structural breaks. Estimates of the cointegrating relation are obtained using Engle-Granger test procedure and Gregory Hansen test procedure taking structural breaks into account. The results of cointegration tests show that there is no long run relationship among the variables. The findings of the study indicate that the connections between industrial production and employment have been disappeared, during the time period examined for Turkey. This also suggests that the rise in the industrial production is still far from creating employability.

  4. Term Structure of Credit Spreads of A Firm When Its Underlying Assets are Discontinuous

    Directory of Open Access Journals (Sweden)

    Budhi Arta Surya

    2012-01-01

    Full Text Available We revisit the previous works of Leland [12], Leland and Toft [11] andHilberink and Rogers [7] on optimal capital structure and show that thecredit spreads of short-maturity corporate bonds can have nonzero valueswhen the underlying of the firm’s assets value has downward jumps. We givean analytical treatment of this fact under a general Levy process and discusssome numerical examples under pure jump processes.Keywords: Optimal capital structure, credit risk, term structure of creditspread

  5. Human neural progenitor cells decrease photoreceptor degeneration, normalize opsin distribution and support synapse structure in cultured porcine retina.

    Science.gov (United States)

    Mollick, Tanzina; Mohlin, Camilla; Johansson, Kjell

    2016-09-01

    Retinal neurodegenerative disorders like retinitis pigmentosa, age-related macular degeneration, diabetic retinopathy and retinal detachment decrease retinal functionality leading to visual impairment. The pathological events are characterized by photoreceptor degeneration, synaptic disassembly, remodeling of postsynaptic neurons and activation of glial cells. Despite intense research, no effective treatment has been found for these disorders. The current study explores the potential of human neural progenitor cell (hNPC) derived factors to slow the degenerative processes in adult porcine retinal explants. Retinas were cultured for 3 days with or without hNPCs as a feeder layer and investigated by terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL), immunohistochemical, western blot and quantitative real time-polymerase chain reaction (qRT-PCR) techniques. TUNEL showed that hNPCs had the capacity to limit photoreceptor cell death. Among cone photoreceptors, hNPC coculture resulted in better maintenance of cone outer segments and reduced opsin mislocalization. Additionally, maintained synaptic structural integrity and preservation of second order calbindin positive horizontal cells was also observed. However, Müller cell gliosis only seemed to be alleviated in terms of reduced Müller cell density. Our observations indicate that at 3 days of coculture, hNPC derived factors had the capacity to protect photoreceptors, maintain synaptic integrity and support horizontal cell survival. Human neural progenitor cell applied treatment modalities may be an effective strategy to help maintain retinal functionality in neurodegenerative pathologies. Whether hNPCs can independently hinder Müller cell gliosis by utilizing higher concentrations or by combination with other pharmacological agents still needs to be determined. Copyright © 2016 Elsevier B.V. All rights reserved.

  6. Learning the Relationship between the Primary Structure of HIV Envelope Glycoproteins and Neutralization Activity of Particular Antibodies by Using Artificial Neural Networks

    Science.gov (United States)

    Buiu, Cătălin; Putz, Mihai V.; Avram, Speranta

    2016-01-01

    The dependency between the primary structure of HIV envelope glycoproteins (ENV) and the neutralization data for given antibodies is very complicated and depends on a large number of factors, such as the binding affinity of a given antibody for a given ENV protein, and the intrinsic infection kinetics of the viral strain. This paper presents a first approach to learning these dependencies using an artificial feedforward neural network which is trained to learn from experimental data. The results presented here demonstrate that the trained neural network is able to generalize on new viral strains and to predict reliable values of neutralizing activities of given antibodies against HIV-1. PMID:27727189

  7. Learning the Relationship between the Primary Structure of HIV Envelope Glycoproteins and Neutralization Activity of Particular Antibodies by Using Artificial Neural Networks.

    Science.gov (United States)

    Buiu, Cătălin; Putz, Mihai V; Avram, Speranta

    2016-10-11

    The dependency between the primary structure of HIV envelope glycoproteins (ENV) and the neutralization data for given antibodies is very complicated and depends on a large number of factors, such as the binding affinity of a given antibody for a given ENV protein, and the intrinsic infection kinetics of the viral strain. This paper presents a first approach to learning these dependencies using an artificial feedforward neural network which is trained to learn from experimental data. The results presented here demonstrate that the trained neural network is able to generalize on new viral strains and to predict reliable values of neutralizing activities of given antibodies against HIV-1.

  8. What Is Neural Plasticity?

    Science.gov (United States)

    von Bernhardi, Rommy; Bernhardi, Laura Eugenín-von; Eugenín, Jaime

    2017-01-01

    "Neural plasticity" refers to the capacity of the nervous system to modify itself, functionally and structurally, in response to experience and injury. As the various chapters in this volume show, plasticity is a key component of neural development and normal functioning of the nervous system, as well as a response to the changing environment, aging, or pathological insult. This chapter discusses how plasticity is necessary not only for neural networks to acquire new functional properties, but also for them to remain robust and stable. The article also reviews the seminal proposals developed over the years that have driven experiments and strongly influenced concepts of neural plasticity.

  9. Structural modification of aluminium oxynitride phases under stresses at high temperatures, high pressures and under irradiation by fast neutrons

    International Nuclear Information System (INIS)

    Labbe, J.C.; Jeanne, A.; Roult, G.

    1990-01-01

    The structural modifications of the aluminium oxynitride phases under stresses are studied by the time of flight neutron diffraction method, at high temperatures (up to 1375degC), at high pressures (up to 2.4 GPa), and under irradiation by fast neutrons (up to 3.2 X 10 20 n/cm 2 ). In each case the evolutions of cell parameter, interatomic bond angles, bond lengths and atomic positions are given. (orig.)

  10. Correlation of HIV protease structure with Indinavir resistance: a data mining and neural networks approach

    Science.gov (United States)

    Draghici, Sorin; Cumberland, Lonnie T., Jr.; Kovari, Ladislau C.

    2000-04-01

    This paper presents some results of data mining HIV genotypic and structural data. Our aim is to try to relate structural features of HIV enzymes essential to its reproductive abilities to the drug resistance phenomenon. This paper concentrates on the HIV protease enzyme and Indinavir which is one of the FDA approved protease inhibitors. Our starting point was the current list of HIV mutations related to drug resistance. We used the fact that some molecular structures determined through high resolution X-ray crystallography were available for the protease-Indinavir complex. Starting with these structures and the known mutations, we modelled the mutant proteases and studied the pattern of atomic contacts between the protease and the drug. After suitable pre- processing, these patterns have been used as the input of our data mining process. We have used both supervised and unsupervised learning techniques with the aim of understanding the relationship between structural features at a molecular level and resistance to Indinavir. The supervised learning was aimed at predicting IC90 values for arbitrary mutants. The SOFM was aimed at identifying those structural features that are important for drug resistance and discovering a classifier based on such features. We have used validation and cross validation to test the generalization abilities of the learning paradigm we have designed. The straightforward supervised learning was able to learn very successfully but validation results are less than satisfactory. This is due to the insufficient number of patterns in the training set which in turn is due to the scarcity of the available data. The data mining using SOFM was very successful. We have managed to distinguish between resistant and non-resistant mutants using structural features. We have been able to divide all reported HIV mutants into several categories based on their 3- dimensional molecular structures and the pattern of contacts between the mutant protease and

  11. Structural behavior of human lumbar intervertebral disc under direct shear.

    Science.gov (United States)

    Schmidt, Hendrik; Häussler, Kim; Wilke, Hans-Joachim; Wolfram, Uwe

    2015-03-18

    The intervertebral disc (IVD) is a complex, flexible joint between adjacent vertebral bodies that provides load transmission while permitting movements of the spinal column. Finite element models can be used to help clarify why and how IVDs fail or degenerate. To do so, it is of importance to validate those models against controllable experiments. Due to missing experimental data, shear properties are not used thus far in validating finite element models. This study aimed to investigate the structural shear properties of human lumbar IVDs in posteroanterior (PA) and laterolateral (LL) loading directions. Fourteen lumbar IVDs (median age: 49 years) underwent direct shear in PA and LL loading directions. A custom-build shear device was used in combination with a materials testing machine to load the specimens until failure. Shear stiffness, ultimate shear force and displacement, and work to failure were determined. Each specimen was tested until complete or partial disruption. Median stiffness in PA direction was 490 N/mm and in LL direction 568 N/mm. Median ultimate shear force in the PA direction was 2,877 N and in the LL direction 3,199 N. Work to failure was 12 Nm in the PA and 9 Nm in the LL direction. This study was an experiment to subject IVDs to direct shear. The results could help us to understand the structure and function of IVDs with regard to mechanical spinal stability, and they can be used to validate finite element models of the IVD.

  12. Structure Crack Identification Based on Surface-mounted Active Sensor Network with Time-Domain Feature Extraction and Neural Network

    Directory of Open Access Journals (Sweden)

    Chunling DU

    2012-03-01

    Full Text Available In this work the condition of metallic structures are classified based on the acquired sensor data from a surface-mounted piezoelectric sensor/actuator network. The structures are aluminum plates with riveted holes and possible crack damage at these holes. A 400 kHz sine wave burst is used as diagnostic signals. The combination of time-domain S0 waves from received sensor signals is directly used as features and preprocessing is not needed for the dam age detection. Since the time sequence of the extracted S0 has a high dimension, principal component estimation is applied to reduce its dimension before entering NN (neural network training for classification. An LVQ (learning vector quantization NN is used to classify the conditions as healthy or damaged. A number of FEM (finite element modeling results are taken as inputs to the NN for training, since the simulated S0 waves agree well with the experimental results on real plates. The performance of the classification is then validated by using these testing results.

  13. Structure and Mutagenesis of Neural Cell Adhesion Molecule Domains Evidence for Flexibility in the Placement of Polysialic Acid Attachment Sites

    Energy Technology Data Exchange (ETDEWEB)

    Foley, Deirdre A.; Swartzentruber, Kristin G.; Lavie, Arnon; Colley, Karen J. (UICM)

    2010-11-09

    The addition of {alpha}2,8-polysialic acid to the N-glycans of the neural cell adhesion molecule, NCAM, is critical for brain development and plays roles in synaptic plasticity, learning and memory, neuronal regeneration, and the growth and invasiveness of cancer cells. Our previous work indicates that the polysialylation of two N-glycans located on the fifth immunoglobulin domain (Ig5) of NCAM requires the presence of specific sequences in the adjacent fibronectin type III repeat (FN1). To understand the relationship of these two domains, we have solved the crystal structure of the NCAM Ig5-FN1 tandem. Unexpectedly, the structure reveals that the sites of Ig5 polysialylation are on the opposite face from the FN1 residues previously found to be critical for N-glycan polysialylation, suggesting that the Ig5-FN1 domain relationship may be flexible and/or that there is flexibility in the placement of Ig5 glycosylation sites for polysialylation. To test the latter possibility, new Ig5 glycosylation sites were engineered and their polysialylation tested. We observed some flexibility in glycosylation site location for polysialylation and demonstrate that the lack of polysialylation of a glycan attached to Asn-423 may be in part related to a lack of terminal processing. The data also suggest that, although the polysialyltransferases do not require the Ig5 domain for NCAM recognition, their ability to engage with this domain is necessary for polysialylation to occur on Ig5 N-glycans.

  14. Learning control of inverted pendulum system by neural network driven fuzzy reasoning: The learning function of NN-driven fuzzy reasoning under changes of reasoning environment

    Science.gov (United States)

    Hayashi, Isao; Nomura, Hiroyoshi; Wakami, Noboru

    1991-01-01

    Whereas conventional fuzzy reasonings are associated with tuning problems, which are lack of membership functions and inference rule designs, a neural network driven fuzzy reasoning (NDF) capable of determining membership functions by neural network is formulated. In the antecedent parts of the neural network driven fuzzy reasoning, the optimum membership function is determined by a neural network, while in the consequent parts, an amount of control for each rule is determined by other plural neural networks. By introducing an algorithm of neural network driven fuzzy reasoning, inference rules for making a pendulum stand up from its lowest suspended point are determined for verifying the usefulness of the algorithm.

  15. Structural evaluation of electrosleeved tubes under severe accident transients

    International Nuclear Information System (INIS)

    Majumdar, S.

    1999-01-01

    A flow stress model was developed for predicting failure of Electrosleeved PWR steam generator tubing under severe accident transients. The Electrosleeve, which is nanocrystalline pure nickel, loses its strength at temperatures greater than 400 C during severe accidents because of grain growth. A grain growth model and the Hall-Petch relationship were used to calculate the loss of flow stress as a function of time and temperature during the accident. Available tensile test data as well as high temperature failure tests on notched Electrosleeved tube specimens were used to derive the basic parameters of the failure model. The model was used to predict the failure temperatures of Electrosleeved tubes with axial cracks in the parent tube during postulated severe accident transients

  16. Nonequilibrium structure of colloidal dumbbells under oscillatory shear.

    Science.gov (United States)

    Heptner, Nils; Chu, Fangfang; Lu, Yan; Lindner, Peter; Ballauff, Matthias; Dzubiella, Joachim

    2015-11-01

    We investigate the nonequilibrium behavior of dense, plastic-crystalline suspensions of mildly anisotropic colloidal hard dumbbells under the action of an oscillatory shear field by employing Brownian dynamics computer simulations. In particular, we extend previous investigations, where we uncovered nonequilibrium phase transitions, to other aspect ratios and to a larger nonequilibrium parameter space, that is, a wider range of strains and shear frequencies. We compare and discuss selected results in the context of scattering and rheological experiments. Both simulations and experiments demonstrate that the previously found transitions from the plastic crystal phase with increasing shear strain also occur at other aspect ratios. We explore the transition behavior in the strain-frequency phase and summarize it in a nonequilibrium phase diagram. Additionally, the experimental rheology results hint at a slowing down of the colloidal dynamics with higher aspect ratio.

  17. Finite element modeling of Balsa wood structures under severe loadings

    International Nuclear Information System (INIS)

    Toson, B.; Pesque, J.J.; Viot, P.

    2014-01-01

    In order to compute, in various situations, the requirements for transporting packages using Balsa wood as an energy absorber, a constitutive model is needed that takes into account all of the specific characteristics of the wood, such as its anisotropy, compressibility, softening, densification, and strain rate dependence. Such a model must also include the treatment of rupture of the wood when it is in traction. The complete description of wood behavior is not sufficient: robustness is also necessary because this model has to work in presence of large deformations and of many other external nonlinear phenomena in the surrounding structures. We propose such a constitutive model that we have developed using the commercial finite element package ABAQUS. The necessary data were acquired through an extensive compilation of the existing literature with the augmentation of personal measurements. Numerous validation tests are presented that represent different impact situations that a transportation cask might endure. (authors)

  18. Structural attributes of stand overstory and light under the canopy

    Directory of Open Access Journals (Sweden)

    Alice Angelini

    2015-02-01

    Full Text Available  This paper reviews the literature relating to the relationship between light availability in the understory and the main qualitative and quantitative attributes of stand overstory usually considered in forest management and planning (species composition, density, tree sizes, etc. as well as their changes as consequences of harvesting. The paper is divided in two sections: the first one reviews studies which investigated the influence of species composition on understory light conditions; the second part examines research on the relationships among stand parameters determined from dendrometric field data and the radiation on understory layer. The objective was to highlight which are the most significant stand traits and management features to build more practical models for predicting light regimes in any forest stand and, in more general terms, to support forest managers in planning and designing silvicultural treatments that retain structure in different way in order to meet different objectives.

  19. Durability reliability analysis for corroding concrete structures under uncertainty

    Science.gov (United States)

    Zhang, Hao

    2018-02-01

    This paper presents a durability reliability analysis of reinforced concrete structures subject to the action of marine chloride. The focus is to provide insight into the role of epistemic uncertainties on durability reliability. The corrosion model involves a number of variables whose probabilistic characteristics cannot be fully determined due to the limited availability of supporting data. All sources of uncertainty, both aleatory and epistemic, should be included in the reliability analysis. Two methods are available to formulate the epistemic uncertainty: the imprecise probability-based method and the purely probabilistic method in which the epistemic uncertainties are modeled as random variables. The paper illustrates how the epistemic uncertainties are modeled and propagated in the two methods, and shows how epistemic uncertainties govern the durability reliability.

  20. Chemical Structures of Novel Maillard Reaction Products under Hyperglycemic Conditions.

    Science.gov (United States)

    Imahori, Daisuke; Matsumoto, Takahiro; Kojima, Naoto; Hasei, Tomohiro; Sumii, Megumi; Sumida, Taishi; Yamashita, Masayuki; Watanabe, Tetsushi

    2018-01-01

    Two novel and two known compounds, 4-quinolylaldoxime and indole-3-aldehyde, were isolated from a reaction mixture consisting of D-glucose and L-tryptophan at physiological temperature and pH. The chemical structures of the two novel compounds were elucidated by spectroscopic analysis such as X-ray crystallography. One of the novel compound and the indole-3-aldehyde showed mutagenicity toward Salmonella typhimurium YG1024 with S9 mix. Furthermore, 4-quinolylaldoxime was detected from streptozotocin-induced diabetic rat plasma by LC-MS/MS analysis; however, the isolated compounds were not detected in rat diet extracts. To our knowledge, this is the first report in which 4-quinolylaldoxime was detected in rat plasma. These results suggest that amino-carbonyl reaction products may be formed in diabetic condition and induce genetic damage.

  1. Reliability prediction for structures under cyclic loads and recurring inspections

    Directory of Open Access Journals (Sweden)

    Alberto W. S. Mello Jr

    2009-06-01

    Full Text Available This work presents a methodology for determining the reliability of fracture control plans for structures subjected to cyclic loads. It considers the variability of the parameters involved in the problem, such as initial flaw and crack growth curve. The probability of detection (POD curve of the field non-destructive inspection method and the condition/environment are used as important factors for structural confidence. According to classical damage tolerance analysis (DTA, inspection intervals are based on detectable crack size and crack growth rate. However, all variables have uncertainties, which makes the final result totally stochastic. The material properties, flight loads, engineering tools and even the reliability of inspection methods are subject to uncertainties which can affect significantly the final maintenance schedule. The present methodology incorporates all the uncertainties in a simulation process, such as Monte Carlo, and establishes a relationship between the reliability of the overall maintenance program and the proposed inspection interval, forming a “cascade” chart. Due to the scatter, it also defines the confidence level of the “acceptable” risk. As an example, the damage tolerance analysis (DTA results are presented for the upper cockpit longeron splice bolt of the BAF upgraded F-5EM. In this case, two possibilities of inspection intervals were found: one that can be characterized as remote risk, with a probability of failure (integrity nonsuccess of 1 in 10 million, per flight hour; and other as extremely improbable, with a probability of nonsuccess of 1 in 1 billion, per flight hour, according to aviation standards. These two results are compared with the classical military airplane damage tolerance requirements.

  2. Data-mining assisted structural optimization using the evolutionary algorithm and neural network

    Science.gov (United States)

    Chen, Ting-Yu; Cheng, Yi-Liang

    2010-03-01

    The use of evolutionary algorithms for global optimization has increased rapidly during the past several years. But evolutionary computations have a common drawback: they need a huge number of function evaluations. This makes them inadequate for structural optimization. To overcome this difficulty, the authors propose a method that integrates the evolutionary algorithm with data mining and approximate analysis to find the optimal solution in structural optimization. The approximate analysis is used to replace exact finite element analyses and the data mining is employed to identify feasible solutions. These combined efforts can reduce the computational time and search the feasible region intensively. As a result, the efficiency and quality of structural optimization using evolutionary algorithms will be increased. Some test problems show that the proposed method not only finds the global solution but is also less computationally demanding.

  3. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

    The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

  4. The neural correlates of morphological complexity processing: Detecting structure in pseudowords.

    Science.gov (United States)

    Schuster, Swetlana; Scharinger, Mathias; Brooks, Colin; Lahiri, Aditi; Hartwigsen, Gesa

    2018-03-02

    Morphological complexity is a highly debated issue in visual word recognition. Previous neuroimaging studies have shown that speakers are sensitive to degrees of morphological complexity. Two-step derived complex words (bridging through bridge N  > bridge V  > bridging) led to more enhanced activation in the left inferior frontal gyrus than their 1-step derived counterparts (running through run V  > running). However, it remains unclear whether sensitivity to degrees of morphological complexity extends to pseudowords. If this were the case, it would indicate that abstract knowledge of morphological structure is independent of lexicality. We addressed this question by investigating the processing of two sets of pseudowords in German. Both sets contained morphologically viable two-step derived pseudowords differing in the number of derivational steps required to access an existing lexical representation and therefore the degree of structural analysis expected during processing. Using a 2 × 2 factorial design, we found lexicality effects to be distinct from processing signatures relating to structural analysis in pseudowords. Semantically-driven processes such as lexical search showed a more frontal distribution while combinatorial processes related to structural analysis engaged more parietal parts of the network. Specifically, more complex pseudowords showed increased activation in parietal regions (right superior parietal lobe and left precuneus) relative to pseudowords that required less structural analysis to arrive at an existing lexical representation. As the two sets were matched on cohort size and surface form, these results highlight the role of internal levels of morphological structure even in forms that do not possess a lexical representation. © 2018 Wiley Periodicals, Inc.

  5. Revisiting Earth's radial seismic structure using a Bayesian neural network approach

    NARCIS (Netherlands)

    de Wit, R.W.L.

    2015-01-01

    The gross features of seismic observations can be explained by relatively simple spherically symmetric (1-D) models of wave velocities, density and attenuation, which describe the Earth's average(radial) structure. 1-D earth models are often used as a reference for studies on Earth's thermo-chemical

  6. Learning to Perceive Structure from Motion and Neural Plasticity in Patients with Alzheimer's Disease

    Science.gov (United States)

    Kim, Nam-Gyoon; Park, Jong-Hee

    2010-01-01

    Recent research has demonstrated that Alzheimer's disease (AD) affects the visual sensory pathways, producing a variety of visual deficits, including the capacity to perceive structure-from-motion (SFM). Because the sensory areas of the adult brain are known to retain a large degree of plasticity, the present study was conducted to explore whether…

  7. Neural correlates of emotional personality: a structural and functional magnetic resonance imaging study.

    Science.gov (United States)

    Koelsch, Stefan; Skouras, Stavros; Jentschke, Sebastian

    2013-01-01

    Studies addressing brain correlates of emotional personality have remained sparse, despite the involvement of emotional personality in health and well-being. This study investigates structural and functional brain correlates of psychological and physiological measures related to emotional personality. Psychological measures included neuroticism, extraversion, and agreeableness scores, as assessed using a standard personality questionnaire. As a physiological measure we used a cardiac amplitude signature, the so-called E κ value (computed from the electrocardiogram) which has previously been related to tender emotionality. Questionnaire scores and E κ values were related to both functional (eigenvector centrality mapping, ECM) and structural (voxel-based morphometry, VBM) neuroimaging data. Functional magnetic resonance imaging (fMRI) data were obtained from 22 individuals (12 females) while listening to music (joy, fear, or neutral music). ECM results showed that agreeableness scores correlated with centrality values in the dorsolateral prefrontal cortex, the anterior cingulate cortex, and the ventral striatum (nucleus accumbens). Individuals with higher E κ values (indexing higher tender emotionality) showed higher centrality values in the subiculum of the right hippocampal formation. Structural MRI data from an independent sample of 59 individuals (34 females) showed that neuroticism scores correlated with volume of the left amygdaloid complex. In addition, individuals with higher E κ showed larger gray matter volume in the same portion of the subiculum in which individuals with higher E κ showed higher centrality values. Our results highlight a role of the amygdala in neuroticism. Moreover, they indicate that a cardiac signature related to emotionality (E κ) correlates with both function (increased network centrality) and structure (grey matter volume) of the subiculum of the hippocampal formation, suggesting a role of the hippocampal formation for

  8. Neural correlates of emotional personality: a structural and functional magnetic resonance imaging study.

    Directory of Open Access Journals (Sweden)

    Stefan Koelsch

    Full Text Available Studies addressing brain correlates of emotional personality have remained sparse, despite the involvement of emotional personality in health and well-being. This study investigates structural and functional brain correlates of psychological and physiological measures related to emotional personality. Psychological measures included neuroticism, extraversion, and agreeableness scores, as assessed using a standard personality questionnaire. As a physiological measure we used a cardiac amplitude signature, the so-called E κ value (computed from the electrocardiogram which has previously been related to tender emotionality. Questionnaire scores and E κ values were related to both functional (eigenvector centrality mapping, ECM and structural (voxel-based morphometry, VBM neuroimaging data. Functional magnetic resonance imaging (fMRI data were obtained from 22 individuals (12 females while listening to music (joy, fear, or neutral music. ECM results showed that agreeableness scores correlated with centrality values in the dorsolateral prefrontal cortex, the anterior cingulate cortex, and the ventral striatum (nucleus accumbens. Individuals with higher E κ values (indexing higher tender emotionality showed higher centrality values in the subiculum of the right hippocampal formation. Structural MRI data from an independent sample of 59 individuals (34 females showed that neuroticism scores correlated with volume of the left amygdaloid complex. In addition, individuals with higher E κ showed larger gray matter volume in the same portion of the subiculum in which individuals with higher E κ showed higher centrality values. Our results highlight a role of the amygdala in neuroticism. Moreover, they indicate that a cardiac signature related to emotionality (E κ correlates with both function (increased network centrality and structure (grey matter volume of the subiculum of the hippocampal formation, suggesting a role of the hippocampal formation for

  9. Statistical structure of intrinsic climate variability under global warming

    Science.gov (United States)

    Zhu, Xiuhua; Bye, John; Fraedrich, Klaus

    2017-04-01

    Climate variability is often studied in terms of fluctuations with respect to the mean state, whereas the dependence between the mean and variability is rarely discussed. We propose a new climate metric to measure the relationship between means and standard deviations of annual surface temperature computed over non-overlapping 100-year segments. This metric is analyzed based on equilibrium simulations of the Max Planck Institute-Earth System Model (MPI-ESM): the last millennium climate (800-1799), the future climate projection following the A1B scenario (2100-2199), and the 3100-year unforced control simulation. A linear relationship is globally observed in the control simulation and thus termed intrinsic climate variability, which is most pronounced in the tropical region with negative regression slopes over the Pacific warm pool and positive slopes in the eastern tropical Pacific. It relates to asymmetric changes in temperature extremes and associates fluctuating climate means with increase or decrease in intensity and occurrence of both El Niño and La Niña events. In the future scenario period, the linear regression slopes largely retain their spatial structure with appreciable changes in intensity and geographical locations. Since intrinsic climate variability describes the internal rhythm of the climate system, it may serve as guidance for interpreting climate variability and climate change signals in the past and the future.

  10. Structural changes in elastically stressed crystallites under irradiation

    International Nuclear Information System (INIS)

    Zolnikov, K.P.; Korchuganov, A.V.; Kryzhevich, D.S.; Chernov, V.M.; Psakhie, S.G.

    2015-01-01

    The response of elastically stressed iron and vanadium crystallites to atomic displacement cascades was investigated by molecular dynamics simulation. Interatomic interaction in vanadium was described by a many-body potential calculated in the Finnis–Sinclair approximation of the embedded atom method. Interatomic interaction in iron was described by a many-body potential constructed in the approximation of valence-electron gas. The crystallite temperature in the calculations was varied from 100 to 600 K. The elastically stressed state in the crystallites was formed through uniaxial tension by 4–8% such that their volume remained unchanged. The energy of a primary knock-on atom was varied from 0.5 to 50 keV. It is shown that the lower the temperature and the higher the strain degree of an initial crystallite, the lower the threshold primary knock-on atom energy for plastic deformation generation in the crystallite. The structural rearrangements induced in the crystallites by an atomic displacement cascade are similar to those induced by mechanical loading. It is found that the rearrangements are realized through twinning

  11. Lifetime Reliability Prediction of Ceramic Structures Under Transient Thermomechanical Loads

    Science.gov (United States)

    Nemeth, Noel N.; Jadaan, Osama J.; Gyekenyesi, John P.

    2005-01-01

    An analytical methodology is developed to predict the probability of survival (reliability) of ceramic components subjected to harsh thermomechanical loads that can vary with time (transient reliability analysis). This capability enables more accurate prediction of ceramic component integrity against fracture in situations such as turbine startup and shutdown, operational vibrations, atmospheric reentry, or other rapid heating or cooling situations (thermal shock). The transient reliability analysis methodology developed herein incorporates the following features: fast-fracture transient analysis (reliability analysis without slow crack growth, SCG); transient analysis with SCG (reliability analysis with time-dependent damage due to SCG); a computationally efficient algorithm to compute the reliability for components subjected to repeated transient loading (block loading); cyclic fatigue modeling using a combined SCG and Walker fatigue law; proof testing for transient loads; and Weibull and fatigue parameters that are allowed to vary with temperature or time. Component-to-component variation in strength (stochastic strength response) is accounted for with the Weibull distribution, and either the principle of independent action or the Batdorf theory is used to predict the effect of multiaxial stresses on reliability. The reliability analysis can be performed either as a function of the component surface (for surface-distributed flaws) or component volume (for volume-distributed flaws). The transient reliability analysis capability has been added to the NASA CARES/ Life (Ceramic Analysis and Reliability Evaluation of Structures/Life) code. CARES/Life was also updated to interface with commercially available finite element analysis software, such as ANSYS, when used to model the effects of transient load histories. Examples are provided to demonstrate the features of the methodology as implemented in the CARES/Life program.

  12. Effects of turbidity on the neural structures of two closely related ...

    African Journals Online (AJOL)

    Table 3 Means and standard errors (SE) for the deli- nitive brain morphology of P. afer and P. asper. All measurements are in millimetres (L ~ length, W = width;. Lx W (mm')) i\\eural structure. OlfaClOI)' bulb W. (S.E.). OlfaClory bulb L. (5.£.) Olfactory bulb L X W. (SE.) Telencephalon W. (S.E.). Telencephalon L. (5E).

  13. The neural processing of hierarchical structure in music and speech at different timescales

    Directory of Open Access Journals (Sweden)

    Morwaread Mary Farbood

    2015-05-01

    Full Text Available Music, like speech, is a complex auditory signal that contains structures at multiple timescales, and as such a potentially powerful entry point into the question of how the brain integrates complex streams of information. Using an experimental design modeled after previous studies that used scrambled versions of a spoken story (Lerner, Honey, Silbert, & Hasson, 2011 and a silent movie (Hasson, Yang, Vallines, Heeger, & Rubin, 2008, we investigate whether listeners perceive hierarchical structure in music beyond short (~6 sec time windows and whether there is cortical overlap between music and language processing at multiple timescales. Experienced pianists were presented with an extended musical excerpt scrambled at multiple timescales––by measure, phrase, and section––while measuring brain activity with functional magnetic resonance imaging (fMRI. The reliability of evoked activity, as quantified by inter-subject correlation of the fMRI responses was measured. We found that response reliability depended systematically on musical structural coherence, revealing a topographically organized hierarchy of processing timescales. Early auditory areas (at the bottom of the hierarchy responded reliably in all conditions. For brain areas at the top of the hierarchy, the original (unscrambled excerpt evoked more reliable responses than any of the scrambled excerpts, indicating that these brain areas process long-timescale musical structures, on the order of minutes. The topography of processing timescales was analogous with that reported previously for speech, but the timescale gradients for music and speech overlapped with one another only partially, suggesting that temporally analogous structures––words/measures, sentences/musical phrases, paragraph/sections––are processed separately.

  14. Characterization of sputtered iridium oxide thin films on planar and laser micro-structured platinum thin film surfaces for neural stimulation applications

    Science.gov (United States)

    Thanawala, Sachin

    Electrical stimulation of neurons provides promising results for treatment of a number of diseases and for restoration of lost function. Clinical examples include retinal stimulation for treatment of blindness and cochlear implants for deafness and deep brain stimulation for treatment of Parkinsons disease. A wide variety of materials have been tested for fabrication of electrodes for neural stimulation applications, some of which are platinum and its alloys, titanium nitride, and iridium oxide. In this study iridium oxide thin films were sputtered onto laser micro-structured platinum thin films by pulsed-DC reactive sputtering of iridium metal in oxygen-containing atmosphere, to obtain high charge capacity coatings for neural stimulation applications. The micro-structuring of platinum films was achieved by a pulsed-laser-based technique (KrF excimer laser emitting at lambda=248nm). The surface morphology of the micro-structured films was studied using different surface characterization techniques. In-vitro biocompatibility of these laser micro-structured films coated with iridium oxide thin films was evaluated using cortical neurons isolated from rat embryo brain. Characterization of these laser micro-structured films coated with iridium oxide, by cyclic voltammetry and impedance spectroscopy has revealed a considerable decrease in impedance and increase in charge capacity. A comparison between amorphous and crystalline iridium oxide thin films as electrode materials indicated that amorphous iridium oxide has significantly higher charge capacity and lower impedance making it preferable material for neural stimulation application. Our biocompatibility studies show that neural cells can grow and differentiate successfully on our laser micro-structured films coated with iridium oxide. This indicates that reactively sputtered iridium oxide (SIROF) is biocompatible.

  15. Risk-taking and social exclusion in adolescence: Neural mechanisms underlying peer influences on decision-making

    Science.gov (United States)

    Peake, Shannon J.; Dishion, Thomas J.; Stormshak, Elizabeth A.; Moore, William E.; Pfeifer, Jennifer H.

    2013-01-01

    Social exclusion and risk-taking are both common experiences of concern in adolescence, yet little is known about how the two may be related at behavioral or neural levels. In this fMRI study, adolescents (N=27, 14 male, 14–17 years-old) completed a series of tasks in the scanner assessing risky decision-making before and after an episode of social exclusion. In this particular context, exclusion was associated with greater behavioral risk-taking among adolescents with low self-reported resistance to peer influence (RPI). When making risky decisions after social exclusion, adolescents who had lower RPI exhibited higher levels of activity in right temporoparietal junction (rTPJ), and this response in rTPJ was a significant mediator of the relationship between RPI and greater risk-taking after social exclusion. Lower RPI was also associated with lower levels of activity in lPFC during crashes following social exclusion, but unlike rTPJ this response in lPFC was not a significant mediator of the relationship between RPI and greater risk-taking after social exclusion. The results suggest that mentalizing and/or attentional mechanisms have a unique direct effect on adolescents’ vulnerability to peer influence on risk-taking. PMID:23707590

  16. Activity-dependent neural plasticity from bench to bedside.

    Science.gov (United States)

    Ganguly, Karunesh; Poo, Mu-Ming

    2013-10-30

    Much progress has been made in understanding how behavioral experience and neural activity can modify the structure and function of neural circuits during development and in the adult brain. Studies of physiological and molecular mechanisms underlying activity-dependent plasticity in animal models have suggested potential therapeutic approaches for a wide range of brain disorders in humans. Physiological and electrical stimulations as well as plasticity-modifying molecular agents may facilitate functional recovery by selectively enhancing existing neural circuits or promoting the formation of new functional circuits. Here, we review the advances in basic studies of neural plasticity mechanisms in developing and adult nervous systems and current clinical treatments that harness neural plasticity, and we offer perspectives on future development of plasticity-based therapy. Copyright © 2013 Elsevier Inc. All rights reserved.

  17. Eigenvalue perturbation theory of structured matrices under generic structured rank one perturbations: Symplectic, orthogonal, and unitary matrices.

    NARCIS (Netherlands)

    Ran, A.C.M.; Mehl, Chr.; Mehrmann, V.; Rodman, L.

    2014-01-01

    We study the perturbation theory of structured matrices under structured rank one perturbations, with emphasis on matrices that are unitary, orthogonal, or symplectic with respect to an indefinite inner product. The rank one perturbations are not necessarily of arbitrary small size (in the sense of

  18. Sequential neural models with stochastic layers

    DEFF Research Database (Denmark)

    Fraccaro, Marco; Sønderby, Søren Kaae; Paquet, Ulrich

    2016-01-01

    How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks? This paper introduces stochastic recurrent neural networks which glue a deterministic recurrent neural network and a state space model together to form a stochastic and sequential neural...... generative model. The clear separation of deterministic and stochastic layers allows a structured variational inference network to track the factorization of the model's posterior distribution. By retaining both the nonlinear recursive structure of a recurrent neural network and averaging over...

  19. Neural responses to nostalgia-evoking music modeled by elements of dynamic musical structure and individual differences in affective traits.

    Science.gov (United States)

    Barrett, Frederick S; Janata, Petr

    2016-10-01

    Nostalgia is an emotion that is most commonly associated with personally and socially relevant memories. It is primarily positive in valence and is readily evoked by music. It is also an idiosyncratic experience that varies between individuals based on affective traits. We identified frontal, limbic, paralimbic, and midbrain brain regions in which the strength of the relationship between ratings of nostalgia evoked by music and blood-oxygen-level-dependent (BOLD) signal was predicted by affective personality measures (nostalgia proneness and the sadness scale of the Affective Neuroscience Personality Scales) that are known to modulate the strength of nostalgic experiences. We also identified brain areas including the inferior frontal gyrus, substantia nigra, cerebellum, and insula in which time-varying BOLD activity correlated more strongly with the time-varying tonal structure of nostalgia-evoking music than with music that evoked no or little nostalgia. These findings illustrate one way in which the reward and emotion regulation networks of the brain are recruited during the experiencing of complex emotional experiences triggered by music. These findings also highlight the importance of considering individual differences when examining the neural responses to strong and idiosyncratic emotional experiences. Finally, these findings provide a further demonstration of the use of time-varying stimulus-specific information in the investigation of music-evoked experiences. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. Developing and Multi-Objective Optimization of a Combined Energy Absorber Structure Using Polynomial Neural Networks and Evolutionary Algorithms

    Directory of Open Access Journals (Sweden)

    Amir Najibi

    Full Text Available Abstract In this study a newly developed thin-walled structure with the combination of circular and square sections is investigated in term of crashworthiness. The results of the experimental tests are utilized to validate the Abaqus/ExplicitTM finite element simulations and analysis of the crush phenomenon. Three polynomial meta-models based on the evolved group method of data handling (GMDH neural networks are employed to simply represent the specific energy absorption (SEA, the initial peak crushing load (P1 and the secondary peak crushing load (P2 with respect to the geometrical variables. The training and testing data are extracted from the finite element analysis. The modified genetic algorithm NSGA-II, is used in multi-objective optimisation of the specific energy absorption, primary and secondary peak crushing load according to the geometrical variables. Finally, in each optimisation process, the optimal section energy absorptions are compared with the results of the finite element analysis. The nearest to ideal point and TOPSIS optimisation methods are applied to choose the optimal points.

  1. Tinnitus Neural Mechanisms and Structural Changes in the Brain: The Contribution of Neuroimaging Research

    Directory of Open Access Journals (Sweden)

    Simonetti, Patricia

    2015-03-01

    Full Text Available Introduction Tinnitus is an abnormal perception of sound in the absence of an external stimulus. Chronic tinnitus usually has a high impact in many aspects of patients' lives, such as emotional stress, sleep disturbance, concentration difficulties, and so on. These strong reactions are usually attributed to central nervous system involvement. Neuroimaging has revealed the implication of brain structures in the auditory system. Objective This systematic review points out neuroimaging studies that contribute to identifying the structures involved in the pathophysiological mechanism of generation and persistence of various forms of tinnitus. Data Synthesis Functional imaging research reveals that tinnitus perception is associated with the involvement of the nonauditory brain areas, including the front parietal area; the limbic system, which consists of the anterior cingulate cortex, anterior insula, and amygdala; and the hippocampal and parahippocampal area. Conclusion The neuroimaging research confirms the involvement of the mechanisms of memory and cognition in the persistence of perception, anxiety, distress, and suffering associated with tinnitus.

  2. Prediction of protein structural features by use of artificial neural networks

    DEFF Research Database (Denmark)

    Petersen, Bent

    like for example GenBank are increasing considerably in size and GenBank currently contains more than 132 million sequences. Similar the Protein Data Bank currently contains more than 71,000 experimentally determined structures of nucleic acids, proteins and nucleic acid/protein complexes...... and accuracy is an important factor, hence the developed tools are expected to return a result in a rapid and efficient manner. Our way of solving that problem was to pre calculate protein sequence data. Currently, more than 500,000 protein sequences are in the local cache. In relation to surface exposure......In the past decades we have seen an exponential growth of biological sequence data. The cost for DNA sequencing has dropped significantly since the announcement of the first sequenced genome and newly sequenced genomes are published almost every week. Publicly available genetic sequence databases...

  3. Neural coding of binary mixtures in a structurally related odorant pair

    Science.gov (United States)

    Cruz, Georgina; Lowe, Graeme

    2013-01-01

    The encoding of odorant mixtures by olfactory sensory neurons depends on molecular interactions at peripheral receptors. However, the pharmacological basis of these interactions is not well defined. Both competitive and noncompetitive mechanisms of receptor binding and activation, or suppression, could contribute to coding. We studied this by analyzing responses of olfactory bulb glomeruli evoked by a pair of structurally related odorants, eugenol (EG) and methyl isoeugenol (MIEG). Fluorescence imaging in synaptopHluorin (spH) mice revealed that EG and MIEG evoked highly overlapped glomerular inputs, increasing the likelihood of mixture interactions. Glomerular responses to binary mixtures of EG and MIEG mostly showed hypoadditive interactions at intermediate and high odorant concentrations, with a few near threshold responses showing hyperadditivity. Dose-response profiles were well fitted by a model of two odorants competitively binding and activating a shared receptor linked to a non-linear transduction cascade. We saw no evidence of non-competitive mechanisms. PMID:23386975

  4. Effects of Neuromodulation on Excitatory-Inhibitory Neural Network Dynamics Depend on Network Connectivity Structure

    Science.gov (United States)

    Rich, Scott; Zochowski, Michal; Booth, Victoria

    2018-01-01

    Acetylcholine (ACh), one of the brain's most potent neuromodulators, can affect intrinsic neuron properties through blockade of an M-type potassium current. The effect of ACh on excitatory and inhibitory cells with this potassium channel modulates their membrane excitability, which in turn affects their tendency to synchronize in networks. Here, we study the resulting changes in dynamics in networks with inter-connected excitatory and inhibitory populations (E-I networks), which are ubiquitous in the brain. Utilizing biophysical models of E-I networks, we analyze how the network connectivity structure in terms of synaptic connectivity alters the influence of ACh on the generation of synchronous excitatory bursting. We investigate networks containing all combinations of excitatory and inhibitory cells with high (Type I properties) or low (Type II properties) modulatory tone. To vary network connectivity structure, we focus on the effects of the strengths of inter-connections between excitatory and inhibitory cells (E-I synapses and I-E synapses), and the strengths of intra-connections among excitatory cells (E-E synapses) and among inhibitory cells (I-I synapses). We show that the presence of ACh may or may not affect the generation of network synchrony depending on the network connectivity. Specifically, strong network inter-connectivity induces synchronous excitatory bursting regardless of the cellular propensity for synchronization, which aligns with predictions of the PING model. However, when a network's intra-connectivity dominates its inter-connectivity, the propensity for synchrony of either inhibitory or excitatory cells can determine the generation of network-wide bursting.

  5. The neural cell adhesion molecule

    DEFF Research Database (Denmark)

    Berezin, V; Bock, E; Poulsen, F M

    2000-01-01

    During the past year, the understanding of the structure and function of neural cell adhesion has advanced considerably. The three-dimensional structures of several of the individual modules of the neural cell adhesion molecule (NCAM) have been determined, as well as the structure of the complex...

  6. Self vs. other: neural correlates underlying agent identification based on unimodal auditory information as revealed by electrotomography (sLORETA).

    Science.gov (United States)

    Justen, C; Herbert, C; Werner, K; Raab, M

    2014-02-14

    Recent neuroscientific studies have identified activity changes in an extensive cerebral network consisting of medial prefrontal cortex, precuneus, temporo-parietal junction, and temporal pole during the perception and identification of self- and other-generated stimuli. Because this network is supposed to be engaged in tasks which require agent identification, it has been labeled the evaluation network (e-network). The present study used self- versus other-generated movement sounds (long jumps) and electroencephalography (EEG) in order to unravel the neural dynamics of agent identification for complex auditory information. Participants (N=14) performed an auditory self-other identification task with EEG. Data was then subjected to a subsequent standardized low-resolution brain electromagnetic tomography (sLORETA) analysis (source localization analysis). Differences between conditions were assessed using t-statistics (corrected for multiple testing) on the normalized and log-transformed current density values of the sLORETA images. Three-dimensional sLORETA source localization analysis revealed cortical activations in brain regions mostly associated with the e-network, especially in the medial prefrontal cortex (bilaterally in the alpha-1-band and right-lateralized in the gamma-band) and the temporo-parietal junction (right hemisphere in the alpha-1-band). Taken together, the findings are partly consistent with previous functional neuroimaging studies investigating unimodal visual or multimodal agent identification tasks (cf. e-network) and extent them to the auditory domain. Cortical activations in brain regions of the e-network seem to have functional relevance, especially the significantly higher cortical activation in the right medial prefrontal cortex. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

  7. Modified feed-forward neural network structures and combined-function-derivative approximations incorporating exchange symmetry for potential energy surface fitting.

    Science.gov (United States)

    Nguyen, Hieu T T; Le, Hung M

    2012-05-10

    The classical interchange (permutation) of atoms of similar identity does not have an effect on the overall potential energy. In this study, we present feed-forward neural network structures that provide permutation symmetry to the potential energy surfaces of molecules. The new feed-forward neural network structures are employed to fit the potential energy surfaces for two illustrative molecules, which are H(2)O and ClOOCl. Modifications are made to describe the symmetric interchange (permutation) of atoms of similar identity (or mathematically, the permutation of symmetric input parameters). The combined-function-derivative approximation algorithm (J. Chem. Phys. 2009, 130, 134101) is also implemented to fit the neural-network potential energy surfaces accurately. The combination of our symmetric neural networks and the function-derivative fitting effectively produces PES fits using fewer numbers of training data points. For H(2)O, only 282 configurations are employed as the training set; the testing root-mean-squared and mean-absolute energy errors are respectively reported as 0.0103 eV (0.236 kcal/mol) and 0.0078 eV (0.179 kcal/mol). In the ClOOCl case, 1693 configurations are required to construct the training set; the root-mean-squared and mean-absolute energy errors for the ClOOCl testing set are 0.0409 eV (0.943 kcal/mol) and 0.0269 eV (0.620 kcal/mol), respectively. Overall, we find good agreements between ab initio and NN prediction in term of energy and gradient errors, and conclude that the new feed-forward neural-network models advantageously describe the molecules with excellent accuracy.

  8. Conserved structural domains in FoxD4L1, a neural forkhead box transcription factor, are required to repress or activate target genes.

    Directory of Open Access Journals (Sweden)

    Steven L Klein

    Full Text Available FoxD4L1 is a forkhead transcription factor that expands the neural ectoderm by down-regulating genes that promote the onset of neural differentiation and up-regulating genes that maintain proliferative neural precursors in an immature state. We previously demonstrated that binding of Grg4 to an Eh-1 motif enhances the ability of FoxD4L1 to down-regulate target neural genes but does not account for all of its repressive activity. Herein we analyzed the protein sequence for additional interaction motifs and secondary structure. Eight conserved motifs were identified in the C-terminal region of fish and frog proteins. Extending the analysis to mammals identified a high scoring motif downstream of the Eh-1 domain that contains a tryptophan residue implicated in protein-protein interactions. In addition, secondary structure prediction programs predicted an α-helical structure overlapping with amphibian-specific Motif 6 in Xenopus, and similarly located α-helical structures in other vertebrate FoxD proteins. We tested functionality of this site by inducing a glutamine-to-proline substitution expected to break the predicted α-helical structure; this significantly reduced FoxD4L1's ability to repress zic3 and irx1. Because this mutation does not interfere with Grg4 binding, these results demonstrate that at least two regions, the Eh-1 motif and a more C-terminal predicted α-helical/Motif 6 site, additively contribute to repression. In the N-terminal region we previously identified a 14 amino acid motif that is required for the up-regulation of target genes. Secondary structure prediction programs predicted a short β-strand separating two acidic domains. Mutant constructs show that the β-strand itself is not required for transcriptional activation. Instead, activation depends upon a glycine residue that is predicted to provide sufficient flexibility to bring the two acidic domains into close proximity. These results identify conserved predicted

  9. Body Position Influences Which Neural Structures Are Recruited by Lumbar Transcutaneous Spinal Cord Stimulation.

    Directory of Open Access Journals (Sweden)

    Simon M Danner

    Full Text Available Transcutaneous stimulation of the human lumbosacral spinal cord is used to evoke spinal reflexes and to neuromodulate altered sensorimotor function following spinal cord injury. Both applications require the reliable stimulation of afferent posterior root fibers. Yet under certain circumstances, efferent anterior root fibers can be co-activated. We hypothesized that body position influences the preferential stimulation of sensory or motor fibers. Stimulus-triggered responses to transcutaneous spinal cord stimulation were recorded using surface-electromyography from quadriceps, hamstrings, tibialis anterior, and triceps surae muscles in 10 individuals with intact nervous systems in the supine, standing and prone positions. Single and paired (30-ms inter-stimulus intervals biphasic stimulation pulses were applied through surface electrodes placed on the skin between the T11 and T12 inter-spinous processes referenced to electrodes on the abdomen. The paired stimulation was applied to evaluate the origin of the evoked electromyographic response; trans-synaptic responses would be suppressed whereas direct efferent responses would almost retain their amplitude. We found that responses to the second stimulus were decreased to 14%±5% of the amplitude of the response to the initial pulse in the supine position across muscles, to 30%±5% in the standing, and to only 80%±5% in the prone position. Response thresholds were lowest during standing and highest in the prone position and response amplitudes were largest in the supine and smallest in the prone position. The responses obtained in the supine and standing positions likely resulted from selective stimulation of sensory fibers while concomitant motor-fiber stimulation occurred in the prone position. We assume that changes of root-fiber paths within the generated electric field when in the prone position increase the stimulation thresholds of posterior above those of anterior root fibers. Thus, we

  10. Surgery and subsequent rehabilitation for cervical spine tumours compressing neural structures.

    Science.gov (United States)

    Kiwerski, Jerzy E

    2008-01-01

    Bone malignancies account for merely about 1.5% of all cancers, with a small percentage of these tumours developing in the cervical spine. However, the cervical spine is also the site of benign tumours and neoplasms involving not only bony tissue. Benign tumours do not metastasize but pose a threat to the spinal cord when located intrathecally. Even though such tumours do not represent malignancy, they are considered to be locally malignant. The most common cervical spine neoplasms are intradural tumours, usually extramedullary: neurofibromas, meningiomas or gliomas.Indications for surgery depend of the nature and location of the tumour and the consequences of tumour growth. Surgery is obviously necessary for intrathecal tumours compressing the spinal cord. The choice of surgical approach and manner of stabilisation depend primarily on the location of the lesion and the presence of spinal cord compression.Rehabilitation is indicated in all patients, but is particularly important, and at the same time difficult, when the growth of the tumour has resulted in neurological disturbances. The task is all the more difficult when in the presence of a massive and high spinal cord damage. Rehabilitation programmes should be designed individually for each patient and should account for the degree of paresis, stage of the underlying malignant disease, survival prognosis, disturbances in the function of other systems, apart from musculoskeletal apparatus, age of the patient, his or her commitment to treatment and other factors.The treatment of malignant neoplasms is usually associated with an unfavourable outcome. However, combination drug treatments, radiation therapy and surgery with subsequent rehabilitation will often prolong survival, ameliorate suffering and improve patients' quality of life.

  11. Wettability, soil organic matter and structure-properties of typical chernozems under the forest and under the arable land

    Science.gov (United States)

    Bykova, Galina; Umarova, Aminat; Tyugai, Zemfira; Milanovskiy, Evgeny; Shein, Evgeny

    2017-04-01

    Intensive tillage affects the properties of soil: decrease in content of soil organic matter and in hydrophobicity of the soil's solid phase, the reduction of amount of water stable aggregates - all this leads to deterioration of the structure of the soil and affects the process of movement of moisture in the soil profile. One of the hypotheses of soil's structure formation ascribes the formation of water stable aggregates with the presence of hydrophobic organic substances on the surface of the soil's solid phase. The aim of this work is to study the effect of tillage on properties of typical chernozems (pachic Voronic Chernozems, Haplic Chernozems) (Russia, Kursk region), located under the forest and under the arable land. The determination of soil-water contact angle was performed by a Drop Shape Analyzer DSA100 (Krüss GmbH, Germany) by the static sessile drop method. For all samples the content of total and organic carbon by dry combustion in oxygen flow and the particle size distribution by the laser diffraction method on the device Analysette 22 comfort, FRITCH, Germany were determined. The estimation of aggregate composition was performed by dry sieving (AS 200, Retsch, Germany), the content of water stable aggregates was estimated by the Savvinov method. There was a positive correlation between the content of organic matter and soil's wettability in studied soils, a growth of contact angle with the increasing the content of organic matter. Under the forest the content of soil organic matter was changed from 6,41% on the surface up to 1,9% at the depth of 100 cm. In the Chernozem under the arable land the organic carbon content in arable horizon is almost two times less. The maximum of hydrophobicity (78.1o) was observed at the depth of 5 cm under the forest. In the profile under the arable land the contact angle value at the same depth was 50o. The results of the structure analysis has shown a decrease in the content of agronomically valuable and water

  12. What’s the Gist? The influence of schemas on the neural correlates underlying true and false memories

    Science.gov (United States)

    Webb, Christina E.; Turney, Indira C.; Dennis, Nancy A.

    2017-01-01

    The current study used a novel scene paradigm to investigate the role of encoding schemas on memory. Specifically, the study examined the influence of a strong encoding schema on retrieval of both schematic and non-schematic information, as well as false memories for information associated with the schema. Additionally, the separate roles of recollection and familiarity in both veridical and false memory retrieval were examined. The study identified several novel results. First, while many common neural regions mediated both schematic and non-schematic retrieval success, schematic recollection exhibited greater activation in visual cortex and hippocampus, regions commonly shown to mediate detailed retrieval. More effortful cognitive control regions in the prefrontal and parietal cortices, on the other hand, supported non-schematic recollection, while lateral temporal cortices supported familiarity-based retrieval of non-schematic items. Second, both true and false recollection, as well as familiarity, were mediated by activity in left middle temporal gyrus, a region associated with semantic processing and retrieval of schematic gist. Moreover, activity in this region was greater for both false recollection and false familiarity, suggesting a greater reliance on lateral temporal cortices for retrieval of illusory memories, irrespective of memory strength. Consistent with previous false memory studies, visual cortex showed increased activity for true compared to false recollection, suggesting that visual cortices are critical for distinguishing between previously viewed targets and related lures at retrieval. Additionally, the absence of common visual activity between true and false retrieval suggests that, unlike previous studies utilizing visual stimuli, when false memories are predicated on schematic gist and not perceptual overlap, there is little reliance on visual processes during false memory retrieval. Finally, the medial temporal lobe exhibited an

  13. Sensory Entrainment Mechanisms in Auditory Perception: Neural Synchronization Cortico-Striatal Activation

    Science.gov (United States)

    Sameiro-Barbosa, Catia M.; Geiser, Eveline

    2016-01-01

    The auditory system displays modulations in sensitivity that can align with the temporal structure of the acoustic environment. This sensory entrainment can facilitate sensory perception and is particularly relevant for audition. Systems neuroscience is slowly uncovering the neural mechanisms underlying the behaviorally observed sensory entrainment effects in the human sensory system. The present article summarizes the prominent behavioral effects of sensory entrainment and reviews our current understanding of the neural basis of sensory entrainment, such as synchronized neural oscillations, and potentially, neural activation in the cortico-striatal system. PMID:27559306

  14. YB-1 gene expression is kept constant during myocyte differentiation through replacement of different transcription factors and then falls gradually under the control of neural activity.

    Science.gov (United States)

    Kobayashi, Shunsuke; Tanaka, Toru; Moue, Masamitsu; Ohashi, Sachiyo; Nishikawa, Taishi

    2015-11-01

    We have previously reported that translation of acetylcholine receptor α-subunit (AChR α) mRNA in skeletal muscle cells is regulated by Y-box binding protein 1 (YB-1) in response to neural activity, and that in the postnatal mouse developmental changes in the amount of YB-1 mRNA are similar to those of AChR α mRNA, which is known to be regulated by myogenic transcription factors. Here, we examined transcriptional regulation of the YB-1 gene in mouse skeletal muscle and differentiating C2C12 myocytes. Although neither YB-1 nor AChR α was detected at either the mRNA or protein level in adult hind limb muscle, YB-1 expression was transiently activated in response to denervation of the sciatic nerve and completely paralleled that of AChR α, suggesting that these genes are regulated by the same transcription factors. However, during differentiation of C2C12 cells to myotubes, the level of YB-1 remained constant even though the level of AChR α increased markedly. Reporter gene, gel mobility shift and ChIP assays revealed that in the initial stage of myocyte differentiation, transcription of the YB-1 gene was regulated by E2F1 and Sp1, and was then gradually replaced under the control of both MyoD and myogenin through an E-box sequence in the proximal region of the YB-1 gene promoter. These results suggest that transcription factors for the YB-1 gene are exchanged during skeletal muscle cell differentiation, perhaps playing a role in translational control of mRNAs by YB-1 in both myotube formation and the response of skeletal muscle tissues to neural stimulation. Copyright © 2015 Elsevier Ltd. All rights reserved.

  15. Novel stable structure of Li3PS4 predicted by evolutionary algorithm under high-pressure

    Directory of Open Access Journals (Sweden)

    S. Iikubo

    2018-01-01

    Full Text Available By combining theoretical predictions and in-situ X-ray diffraction under high pressure, we found a novel stable crystal structure of Li3PS4 under high pressures. At ambient pressure, Li3PS4 shows successive structural transitions from γ-type to β-type and from β-type to α type with increasing temperature, as is well established. In this study, an evolutionary algorithm successfully predicted the γ-type crystal structure at ambient pressure and further predicted a possible stable δ-type crystal structures under high pressure. The stability of the obtained structures is examined in terms of both static and dynamic stability by first-principles calculations. In situ X-ray diffraction using a synchrotron radiation revealed that the high-pressure phase is the predicted δ-Li3PS4 phase.

  16. Review of parameters influencing the structural response of a submerged body under cavitation conditions

    OpenAIRE

    Escaler, X; De La Torre, O; Farhat, M

    2015-01-01

    Submerged structures that operate under extreme flows are prone to suffer large scale cavitation attached to thei r surfaces. Under such conditions the added mass effects differ from the expected ones in pure liquids. Moreover, the existence of small gaps between the structure and surrounding bodies filled with fluid also influence the dynamic response. A series of experiments and numerical simulations have been carried out with a truncated NACA0009 hydrofoil mounted as a cantilever beam at t...

  17. Investigating category- and shape-selective neural processing in ventral and dorsal visual stream under interocular suppression.

    Science.gov (United States)

    Ludwig, Karin; Kathmann, Norbert; Sterzer, Philipp; Hesselmann, Guido

    2015-01-01

    Recent behavioral and neuroimaging studies using continuous flash suppression (CFS) have suggested that action-related processing in the dorsal visual stream might be independent of perceptual awareness, in line with the "vision-for-perception" versus "vision-for-action" distinction of the influential dual-stream theory. It remains controversial if evidence suggesting exclusive dorsal stream processing of tool stimuli under CFS can be explained by their elongated shape alone or by action-relevant category representations in dorsal visual cortex. To approach this question, we investigated category- and shape-selective functional magnetic resonance imaging-blood-oxygen level-dependent responses in both visual streams using images of faces and tools. Multivariate pattern analysis showed enhanced decoding of elongated relative to non-elongated tools, both in the ventral and dorsal visual stream. The second aim of our study was to investigate whether the depth